diff --git a/litellm/__init__.py b/litellm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..837e5443420cefad92507e47846cd1d3c64cd076 --- /dev/null +++ b/litellm/__init__.py @@ -0,0 +1,557 @@ +### INIT VARIABLES ### +import threading, requests +from typing import Callable, List, Optional, Dict, Union, Any +from litellm.caching import Cache +from litellm._logging import set_verbose +from litellm.proxy._types import KeyManagementSystem +import httpx + +input_callback: List[Union[str, Callable]] = [] +success_callback: List[Union[str, Callable]] = [] +failure_callback: List[Union[str, Callable]] = [] +callbacks: List[Callable] = [] +_async_input_callback: List[ + Callable +] = [] # internal variable - async custom callbacks are routed here. +_async_success_callback: List[ + Union[str, Callable] +] = [] # internal variable - async custom callbacks are routed here. +_async_failure_callback: List[ + Callable +] = [] # internal variable - async custom callbacks are routed here. +pre_call_rules: List[Callable] = [] +post_call_rules: List[Callable] = [] +email: Optional[ + str +] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +token: Optional[ + str +] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +telemetry = True +max_tokens = 256 # OpenAI Defaults +drop_params = False +retry = True +api_key: Optional[str] = None +openai_key: Optional[str] = None +azure_key: Optional[str] = None +anthropic_key: Optional[str] = None +replicate_key: Optional[str] = None +cohere_key: Optional[str] = None +maritalk_key: Optional[str] = None +ai21_key: Optional[str] = None +openrouter_key: Optional[str] = None +huggingface_key: Optional[str] = None +vertex_project: Optional[str] = None +vertex_location: Optional[str] = None +togetherai_api_key: Optional[str] = None +cloudflare_api_key: Optional[str] = None +baseten_key: Optional[str] = None +aleph_alpha_key: Optional[str] = None +nlp_cloud_key: Optional[str] = None +use_client: bool = False +logging: bool = True +caching: bool = False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +caching_with_models: bool = False # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 +cache: Optional[ + Cache +] = None # cache object <- use this - https://docs.litellm.ai/docs/caching +model_alias_map: Dict[str, str] = {} +model_group_alias_map: Dict[str, str] = {} +max_budget: float = 0.0 # set the max budget across all providers +_openai_completion_params = [ + "functions", + "function_call", + "temperature", + "temperature", + "top_p", + "n", + "stream", + "stop", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "user", + "request_timeout", + "api_base", + "api_version", + "api_key", + "deployment_id", + "organization", + "base_url", + "default_headers", + "timeout", + "response_format", + "seed", + "tools", + "tool_choice", + "max_retries", +] +_litellm_completion_params = [ + "metadata", + "acompletion", + "caching", + "mock_response", + "api_key", + "api_version", + "api_base", + "force_timeout", + "logger_fn", + "verbose", + "custom_llm_provider", + "litellm_logging_obj", + "litellm_call_id", + "use_client", + "id", + "fallbacks", + "azure", + "headers", + "model_list", + "num_retries", + "context_window_fallback_dict", + "roles", + "final_prompt_value", + "bos_token", + "eos_token", + "request_timeout", + "complete_response", + "self", + "client", + "rpm", + "tpm", + "input_cost_per_token", + "output_cost_per_token", + "hf_model_name", + "model_info", + "proxy_server_request", + "preset_cache_key", +] +_current_cost = 0 # private variable, used if max budget is set +error_logs: Dict = {} +add_function_to_prompt: bool = False # if function calling not supported by api, append function call details to system prompt +client_session: Optional[httpx.Client] = None +aclient_session: Optional[httpx.AsyncClient] = None +model_fallbacks: Optional[List] = None # Deprecated for 'litellm.fallbacks' +model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" +suppress_debug_info = False +dynamodb_table_name: Optional[str] = None +s3_callback_params: Optional[Dict] = None +#### RELIABILITY #### +request_timeout: Optional[float] = 6000 +num_retries: Optional[int] = None # per model endpoint +fallbacks: Optional[List] = None +context_window_fallbacks: Optional[List] = None +allowed_fails: int = 0 +num_retries_per_request: Optional[ + int +] = None # for the request overall (incl. fallbacks + model retries) +####### SECRET MANAGERS ##################### +secret_manager_client: Optional[ + Any +] = None # list of instantiated key management clients - e.g. azure kv, infisical, etc. +_google_kms_resource_name: Optional[str] = None +_key_management_system: Optional[KeyManagementSystem] = None +############################################# + + +def get_model_cost_map(url: str): + try: + with requests.get( + url, timeout=5 + ) as response: # set a 5 second timeout for the get request + response.raise_for_status() # Raise an exception if the request is unsuccessful + content = response.json() + return content + except Exception as e: + import importlib.resources + import json + + with importlib.resources.open_text( + "litellm", "model_prices_and_context_window_backup.json" + ) as f: + content = json.load(f) + return content + + +model_cost = get_model_cost_map(url=model_cost_map_url) +custom_prompt_dict: Dict[str, dict] = {} + + +####### THREAD-SPECIFIC DATA ################### +class MyLocal(threading.local): + def __init__(self): + self.user = "Hello World" + + +_thread_context = MyLocal() + + +def identify(event_details): + # Store user in thread local data + if "user" in event_details: + _thread_context.user = event_details["user"] + + +####### ADDITIONAL PARAMS ################### configurable params if you use proxy models like Helicone, map spend to org id, etc. +api_base = None +headers = None +api_version = None +organization = None +config_path = None +####### COMPLETION MODELS ################### +open_ai_chat_completion_models: List = [] +open_ai_text_completion_models: List = [] +cohere_models: List = [] +anthropic_models: List = [] +openrouter_models: List = [] +vertex_language_models: List = [] +vertex_vision_models: List = [] +vertex_chat_models: List = [] +vertex_code_chat_models: List = [] +vertex_text_models: List = [] +vertex_code_text_models: List = [] +ai21_models: List = [] +nlp_cloud_models: List = [] +aleph_alpha_models: List = [] +bedrock_models: List = [] +deepinfra_models: List = [] +perplexity_models: List = [] +for key, value in model_cost.items(): + if value.get("litellm_provider") == "openai": + open_ai_chat_completion_models.append(key) + elif value.get("litellm_provider") == "text-completion-openai": + open_ai_text_completion_models.append(key) + elif value.get("litellm_provider") == "cohere": + cohere_models.append(key) + elif value.get("litellm_provider") == "anthropic": + anthropic_models.append(key) + elif value.get("litellm_provider") == "openrouter": + openrouter_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-text-models": + vertex_text_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-code-text-models": + vertex_code_text_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-language-models": + vertex_language_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-vision-models": + vertex_vision_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-chat-models": + vertex_chat_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-code-chat-models": + vertex_code_chat_models.append(key) + elif value.get("litellm_provider") == "ai21": + ai21_models.append(key) + elif value.get("litellm_provider") == "nlp_cloud": + nlp_cloud_models.append(key) + elif value.get("litellm_provider") == "aleph_alpha": + aleph_alpha_models.append(key) + elif value.get("litellm_provider") == "bedrock": + bedrock_models.append(key) + elif value.get("litellm_provider") == "deepinfra": + deepinfra_models.append(key) + elif value.get("litellm_provider") == "perplexity": + perplexity_models.append(key) + +# known openai compatible endpoints - we'll eventually move this list to the model_prices_and_context_window.json dictionary +openai_compatible_endpoints: List = [ + "api.perplexity.ai", + "api.endpoints.anyscale.com/v1", + "api.deepinfra.com/v1/openai", + "api.mistral.ai/v1", +] + +# this is maintained for Exception Mapping +openai_compatible_providers: List = [ + "anyscale", + "mistral", + "deepinfra", + "perplexity", + "xinference", +] + + +# well supported replicate llms +replicate_models: List = [ + # llama replicate supported LLMs + "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", + "a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52", + "meta/codellama-13b:1c914d844307b0588599b8393480a3ba917b660c7e9dfae681542b5325f228db", + # Vicuna + "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b", + "joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe", + # Flan T-5 + "daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f" + # Others + "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5", + "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad", +] + +huggingface_models: List = [ + "meta-llama/Llama-2-7b-hf", + "meta-llama/Llama-2-7b-chat-hf", + "meta-llama/Llama-2-13b-hf", + "meta-llama/Llama-2-13b-chat-hf", + "meta-llama/Llama-2-70b-hf", + "meta-llama/Llama-2-70b-chat-hf", + "meta-llama/Llama-2-7b", + "meta-llama/Llama-2-7b-chat", + "meta-llama/Llama-2-13b", + "meta-llama/Llama-2-13b-chat", + "meta-llama/Llama-2-70b", + "meta-llama/Llama-2-70b-chat", +] # these have been tested on extensively. But by default all text2text-generation and text-generation models are supported by liteLLM. - https://docs.litellm.ai/docs/providers + +together_ai_models: List = [ + # llama llms - chat + "togethercomputer/llama-2-70b-chat", + # llama llms - language / instruct + "togethercomputer/llama-2-70b", + "togethercomputer/LLaMA-2-7B-32K", + "togethercomputer/Llama-2-7B-32K-Instruct", + "togethercomputer/llama-2-7b", + # falcon llms + "togethercomputer/falcon-40b-instruct", + "togethercomputer/falcon-7b-instruct", + # alpaca + "togethercomputer/alpaca-7b", + # chat llms + "HuggingFaceH4/starchat-alpha", + # code llms + "togethercomputer/CodeLlama-34b", + "togethercomputer/CodeLlama-34b-Instruct", + "togethercomputer/CodeLlama-34b-Python", + "defog/sqlcoder", + "NumbersStation/nsql-llama-2-7B", + "WizardLM/WizardCoder-15B-V1.0", + "WizardLM/WizardCoder-Python-34B-V1.0", + # language llms + "NousResearch/Nous-Hermes-Llama2-13b", + "Austism/chronos-hermes-13b", + "upstage/SOLAR-0-70b-16bit", + "WizardLM/WizardLM-70B-V1.0", +] # supports all together ai models, just pass in the model id e.g. completion(model="together_computer/replit_code_3b",...) + + +baseten_models: List = [ + "qvv0xeq", + "q841o8w", + "31dxrj3", +] # FALCON 7B # WizardLM # Mosaic ML + + +# used for Cost Tracking & Token counting +# https://azure.microsoft.com/en-in/pricing/details/cognitive-services/openai-service/ +# Azure returns gpt-35-turbo in their responses, we need to map this to azure/gpt-3.5-turbo for token counting +azure_llms = { + "gpt-35-turbo": "azure/gpt-35-turbo", + "gpt-35-turbo-16k": "azure/gpt-35-turbo-16k", + "gpt-35-turbo-instruct": "azure/gpt-35-turbo-instruct", +} + +azure_embedding_models = { + "ada": "azure/ada", +} + +petals_models = [ + "petals-team/StableBeluga2", +] + +ollama_models = ["llama2"] + +maritalk_models = ["maritalk"] + +model_list = ( + open_ai_chat_completion_models + + open_ai_text_completion_models + + cohere_models + + anthropic_models + + replicate_models + + openrouter_models + + huggingface_models + + vertex_chat_models + + vertex_text_models + + ai21_models + + together_ai_models + + baseten_models + + aleph_alpha_models + + nlp_cloud_models + + ollama_models + + bedrock_models + + deepinfra_models + + perplexity_models + + maritalk_models +) + +provider_list: List = [ + "openai", + "custom_openai", + "text-completion-openai", + "cohere", + "anthropic", + "replicate", + "huggingface", + "together_ai", + "openrouter", + "vertex_ai", + "palm", + "gemini", + "ai21", + "baseten", + "azure", + "sagemaker", + "bedrock", + "vllm", + "nlp_cloud", + "petals", + "oobabooga", + "ollama", + "ollama_chat", + "deepinfra", + "perplexity", + "anyscale", + "mistral", + "maritalk", + "voyage", + "cloudflare", + "xinference", + "custom", # custom apis +] + +models_by_provider: dict = { + "openai": open_ai_chat_completion_models + open_ai_text_completion_models, + "cohere": cohere_models, + "anthropic": anthropic_models, + "replicate": replicate_models, + "huggingface": huggingface_models, + "together_ai": together_ai_models, + "baseten": baseten_models, + "openrouter": openrouter_models, + "vertex_ai": vertex_chat_models + vertex_text_models, + "ai21": ai21_models, + "bedrock": bedrock_models, + "petals": petals_models, + "ollama": ollama_models, + "deepinfra": deepinfra_models, + "perplexity": perplexity_models, + "maritalk": maritalk_models, +} + +# mapping for those models which have larger equivalents +longer_context_model_fallback_dict: dict = { + # openai chat completion models + "gpt-3.5-turbo": "gpt-3.5-turbo-16k", + "gpt-3.5-turbo-0301": "gpt-3.5-turbo-16k-0301", + "gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613", + "gpt-4": "gpt-4-32k", + "gpt-4-0314": "gpt-4-32k-0314", + "gpt-4-0613": "gpt-4-32k-0613", + # anthropic + "claude-instant-1": "claude-2", + "claude-instant-1.2": "claude-2", + # vertexai + "chat-bison": "chat-bison-32k", + "chat-bison@001": "chat-bison-32k", + "codechat-bison": "codechat-bison-32k", + "codechat-bison@001": "codechat-bison-32k", + # openrouter + "openrouter/openai/gpt-3.5-turbo": "openrouter/openai/gpt-3.5-turbo-16k", + "openrouter/anthropic/claude-instant-v1": "openrouter/anthropic/claude-2", +} + +####### EMBEDDING MODELS ################### +open_ai_embedding_models: List = ["text-embedding-ada-002"] +cohere_embedding_models: List = [ + "embed-english-v3.0", + "embed-english-light-v3.0", + "embed-multilingual-v3.0", + "embed-english-v2.0", + "embed-english-light-v2.0", + "embed-multilingual-v2.0", +] +bedrock_embedding_models: List = [ + "amazon.titan-embed-text-v1", + "cohere.embed-english-v3", + "cohere.embed-multilingual-v3", +] + +all_embedding_models = ( + open_ai_embedding_models + cohere_embedding_models + bedrock_embedding_models +) + +####### IMAGE GENERATION MODELS ################### +openai_image_generation_models = ["dall-e-2", "dall-e-3"] + + +from .timeout import timeout +from .utils import ( + client, + exception_type, + get_optional_params, + modify_integration, + token_counter, + cost_per_token, + completion_cost, + get_litellm_params, + Logging, + acreate, + get_model_list, + get_max_tokens, + get_model_info, + register_prompt_template, + validate_environment, + check_valid_key, + get_llm_provider, + register_model, + encode, + decode, + _calculate_retry_after, + _should_retry, + get_secret, +) +from .llms.huggingface_restapi import HuggingfaceConfig +from .llms.anthropic import AnthropicConfig +from .llms.replicate import ReplicateConfig +from .llms.cohere import CohereConfig +from .llms.ai21 import AI21Config +from .llms.together_ai import TogetherAIConfig +from .llms.cloudflare import CloudflareConfig +from .llms.palm import PalmConfig +from .llms.gemini import GeminiConfig +from .llms.nlp_cloud import NLPCloudConfig +from .llms.aleph_alpha import AlephAlphaConfig +from .llms.petals import PetalsConfig +from .llms.vertex_ai import VertexAIConfig +from .llms.sagemaker import SagemakerConfig +from .llms.ollama import OllamaConfig +from .llms.maritalk import MaritTalkConfig +from .llms.bedrock import ( + AmazonTitanConfig, + AmazonAI21Config, + AmazonAnthropicConfig, + AmazonCohereConfig, + AmazonLlamaConfig, +) +from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig +from .llms.azure import AzureOpenAIConfig, AzureOpenAIError +from .main import * # type: ignore +from .integrations import * +from .exceptions import ( + AuthenticationError, + InvalidRequestError, + BadRequestError, + NotFoundError, + RateLimitError, + ServiceUnavailableError, + OpenAIError, + ContextWindowExceededError, + ContentPolicyViolationError, + BudgetExceededError, + APIError, + Timeout, + APIConnectionError, + APIResponseValidationError, + UnprocessableEntityError, +) +from .budget_manager import BudgetManager +from .proxy.proxy_cli import run_server +from .router import Router diff --git a/litellm/_logging.py b/litellm/_logging.py new file mode 100644 index 0000000000000000000000000000000000000000..e3b54a012c57d1aa68f5bfbb63949df3b970f56b --- /dev/null +++ b/litellm/_logging.py @@ -0,0 +1,30 @@ +import logging + +set_verbose = False + +# Create a handler for the logger (you may need to adapt this based on your needs) +handler = logging.StreamHandler() +handler.setLevel(logging.DEBUG) + +# Create a formatter and set it for the handler + +formatter = logging.Formatter("\033[92m%(name)s - %(levelname)s\033[0m: %(message)s") + +handler.setFormatter(formatter) + + +def print_verbose(print_statement): + try: + if set_verbose: + print(print_statement) # noqa + except: + pass + + +verbose_proxy_logger = logging.getLogger("LiteLLM Proxy") +verbose_router_logger = logging.getLogger("LiteLLM Router") +verbose_logger = logging.getLogger("LiteLLM") + +# Add the handler to the logger +verbose_router_logger.addHandler(handler) +verbose_proxy_logger.addHandler(handler) diff --git a/litellm/_redis.py b/litellm/_redis.py new file mode 100644 index 0000000000000000000000000000000000000000..bee73f134934dbf766d9d18135f3e2112b7b2d8a --- /dev/null +++ b/litellm/_redis.py @@ -0,0 +1,93 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +# s/o [@Frank Colson](https://www.linkedin.com/in/frank-colson-422b9b183/) for this redis implementation +import os +import inspect +import redis, litellm +from typing import List, Optional + + +def _get_redis_kwargs(): + arg_spec = inspect.getfullargspec(redis.Redis) + + # Only allow primitive arguments + exclude_args = { + "self", + "connection_pool", + "retry", + } + + include_args = ["url"] + + available_args = [x for x in arg_spec.args if x not in exclude_args] + include_args + + return available_args + + +def _get_redis_env_kwarg_mapping(): + PREFIX = "REDIS_" + + return {f"{PREFIX}{x.upper()}": x for x in _get_redis_kwargs()} + + +def _redis_kwargs_from_environment(): + mapping = _get_redis_env_kwarg_mapping() + + return_dict = {} + for k, v in mapping.items(): + value = litellm.get_secret(k, default_value=None) # check os.environ/key vault + if value is not None: + return_dict[v] = value + return return_dict + + +def get_redis_url_from_environment(): + if "REDIS_URL" in os.environ: + return os.environ["REDIS_URL"] + + if "REDIS_HOST" not in os.environ or "REDIS_PORT" not in os.environ: + raise ValueError( + "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified for Redis." + ) + + if "REDIS_PASSWORD" in os.environ: + redis_password = f":{os.environ['REDIS_PASSWORD']}@" + else: + redis_password = "" + + return ( + f"redis://{redis_password}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" + ) + + +def get_redis_client(**env_overrides): + ### check if "os.environ/" passed in + for k, v in env_overrides.items(): + if isinstance(v, str) and v.startswith("os.environ/"): + v = v.replace("os.environ/", "") + value = litellm.get_secret(v) + env_overrides[k] = value + + redis_kwargs = { + **_redis_kwargs_from_environment(), + **env_overrides, + } + + if "url" in redis_kwargs and redis_kwargs["url"] is not None: + redis_kwargs.pop("host", None) + redis_kwargs.pop("port", None) + redis_kwargs.pop("db", None) + redis_kwargs.pop("password", None) + + return redis.Redis.from_url(**redis_kwargs) + elif "host" not in redis_kwargs or redis_kwargs["host"] is None: + raise ValueError("Either 'host' or 'url' must be specified for redis.") + litellm.print_verbose(f"redis_kwargs: {redis_kwargs}") + return redis.Redis(**redis_kwargs) diff --git a/litellm/_version.py b/litellm/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..7b38bae2c2188ae2850421feb6e6a63ef70431e3 --- /dev/null +++ b/litellm/_version.py @@ -0,0 +1,6 @@ +import importlib_metadata + +try: + version = importlib_metadata.version("litellm") +except: + pass diff --git a/litellm/budget_manager.py b/litellm/budget_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..4a3bb2cae2b4a9f1c245edbe8880b6dec3ab4fc6 --- /dev/null +++ b/litellm/budget_manager.py @@ -0,0 +1,206 @@ +import os, json, time +import litellm +from litellm.utils import ModelResponse +import requests, threading +from typing import Optional, Union, Literal + + +class BudgetManager: + def __init__( + self, + project_name: str, + client_type: str = "local", + api_base: Optional[str] = None, + ): + self.client_type = client_type + self.project_name = project_name + self.api_base = api_base or "https://api.litellm.ai" + ## load the data or init the initial dictionaries + self.load_data() + + def print_verbose(self, print_statement): + try: + if litellm.set_verbose: + import logging + + logging.info(print_statement) + except: + pass + + def load_data(self): + if self.client_type == "local": + # Check if user dict file exists + if os.path.isfile("user_cost.json"): + # Load the user dict + with open("user_cost.json", "r") as json_file: + self.user_dict = json.load(json_file) + else: + self.print_verbose("User Dictionary not found!") + self.user_dict = {} + self.print_verbose(f"user dict from local: {self.user_dict}") + elif self.client_type == "hosted": + # Load the user_dict from hosted db + url = self.api_base + "/get_budget" + headers = {"Content-Type": "application/json"} + data = {"project_name": self.project_name} + response = requests.post(url, headers=headers, json=data) + response = response.json() + if response["status"] == "error": + self.user_dict = ( + {} + ) # assume this means the user dict hasn't been stored yet + else: + self.user_dict = response["data"] + + def create_budget( + self, + total_budget: float, + user: str, + duration: Optional[Literal["daily", "weekly", "monthly", "yearly"]] = None, + created_at: float = time.time(), + ): + self.user_dict[user] = {"total_budget": total_budget} + if duration is None: + return self.user_dict[user] + + if duration == "daily": + duration_in_days = 1 + elif duration == "weekly": + duration_in_days = 7 + elif duration == "monthly": + duration_in_days = 28 + elif duration == "yearly": + duration_in_days = 365 + else: + raise ValueError( + """duration needs to be one of ["daily", "weekly", "monthly", "yearly"]""" + ) + self.user_dict[user] = { + "total_budget": total_budget, + "duration": duration_in_days, + "created_at": created_at, + "last_updated_at": created_at, + } + self._save_data_thread() # [Non-Blocking] Update persistent storage without blocking execution + return self.user_dict[user] + + def projected_cost(self, model: str, messages: list, user: str): + text = "".join(message["content"] for message in messages) + prompt_tokens = litellm.token_counter(model=model, text=text) + prompt_cost, _ = litellm.cost_per_token( + model=model, prompt_tokens=prompt_tokens, completion_tokens=0 + ) + current_cost = self.user_dict[user].get("current_cost", 0) + projected_cost = prompt_cost + current_cost + return projected_cost + + def get_total_budget(self, user: str): + return self.user_dict[user]["total_budget"] + + def update_cost( + self, + user: str, + completion_obj: Optional[ModelResponse] = None, + model: Optional[str] = None, + input_text: Optional[str] = None, + output_text: Optional[str] = None, + ): + if model and input_text and output_text: + prompt_tokens = litellm.token_counter( + model=model, messages=[{"role": "user", "content": input_text}] + ) + completion_tokens = litellm.token_counter( + model=model, messages=[{"role": "user", "content": output_text}] + ) + ( + prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar, + ) = litellm.cost_per_token( + model=model, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar + elif completion_obj: + cost = litellm.completion_cost(completion_response=completion_obj) + model = completion_obj[ + "model" + ] # if this throws an error try, model = completion_obj['model'] + else: + raise ValueError( + "Either a chat completion object or the text response needs to be passed in. Learn more - https://docs.litellm.ai/docs/budget_manager" + ) + + self.user_dict[user]["current_cost"] = cost + self.user_dict[user].get( + "current_cost", 0 + ) + if "model_cost" in self.user_dict[user]: + self.user_dict[user]["model_cost"][model] = cost + self.user_dict[user][ + "model_cost" + ].get(model, 0) + else: + self.user_dict[user]["model_cost"] = {model: cost} + + self._save_data_thread() # [Non-Blocking] Update persistent storage without blocking execution + return {"user": self.user_dict[user]} + + def get_current_cost(self, user): + return self.user_dict[user].get("current_cost", 0) + + def get_model_cost(self, user): + return self.user_dict[user].get("model_cost", 0) + + def is_valid_user(self, user: str) -> bool: + return user in self.user_dict + + def get_users(self): + return list(self.user_dict.keys()) + + def reset_cost(self, user): + self.user_dict[user]["current_cost"] = 0 + self.user_dict[user]["model_cost"] = {} + return {"user": self.user_dict[user]} + + def reset_on_duration(self, user: str): + # Get current and creation time + last_updated_at = self.user_dict[user]["last_updated_at"] + current_time = time.time() + + # Convert duration from days to seconds + duration_in_seconds = self.user_dict[user]["duration"] * 24 * 60 * 60 + + # Check if duration has elapsed + if current_time - last_updated_at >= duration_in_seconds: + # Reset cost if duration has elapsed and update the creation time + self.reset_cost(user) + self.user_dict[user]["last_updated_at"] = current_time + self._save_data_thread() # Save the data + + def update_budget_all_users(self): + for user in self.get_users(): + if "duration" in self.user_dict[user]: + self.reset_on_duration(user) + + def _save_data_thread(self): + thread = threading.Thread( + target=self.save_data + ) # [Non-Blocking]: saves data without blocking execution + thread.start() + + def save_data(self): + if self.client_type == "local": + import json + + # save the user dict + with open("user_cost.json", "w") as json_file: + json.dump( + self.user_dict, json_file, indent=4 + ) # Indent for pretty formatting + return {"status": "success"} + elif self.client_type == "hosted": + url = self.api_base + "/set_budget" + headers = {"Content-Type": "application/json"} + data = {"project_name": self.project_name, "user_dict": self.user_dict} + response = requests.post(url, headers=headers, json=data) + response = response.json() + return response diff --git a/litellm/caching.py b/litellm/caching.py new file mode 100644 index 0000000000000000000000000000000000000000..e1678a109f91ca4d29c1a655e46aeabada413987 --- /dev/null +++ b/litellm/caching.py @@ -0,0 +1,678 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +import litellm +import time, logging +import json, traceback, ast, hashlib +from typing import Optional, Literal, List, Union, Any +from openai._models import BaseModel as OpenAIObject + + +def print_verbose(print_statement): + try: + if litellm.set_verbose: + print(print_statement) # noqa + except: + pass + + +class BaseCache: + def set_cache(self, key, value, **kwargs): + raise NotImplementedError + + def get_cache(self, key, **kwargs): + raise NotImplementedError + + +class InMemoryCache(BaseCache): + def __init__(self): + # if users don't provider one, use the default litellm cache + self.cache_dict = {} + self.ttl_dict = {} + + def set_cache(self, key, value, **kwargs): + self.cache_dict[key] = value + if "ttl" in kwargs: + self.ttl_dict[key] = time.time() + kwargs["ttl"] + + def get_cache(self, key, **kwargs): + if key in self.cache_dict: + if key in self.ttl_dict: + if time.time() > self.ttl_dict[key]: + self.cache_dict.pop(key, None) + return None + original_cached_response = self.cache_dict[key] + try: + cached_response = json.loads(original_cached_response) + except: + cached_response = original_cached_response + return cached_response + return None + + def flush_cache(self): + self.cache_dict.clear() + self.ttl_dict.clear() + + +class RedisCache(BaseCache): + def __init__(self, host=None, port=None, password=None, **kwargs): + import redis + + # if users don't provider one, use the default litellm cache + from ._redis import get_redis_client + + redis_kwargs = {} + if host is not None: + redis_kwargs["host"] = host + if port is not None: + redis_kwargs["port"] = port + if password is not None: + redis_kwargs["password"] = password + + redis_kwargs.update(kwargs) + + self.redis_client = get_redis_client(**redis_kwargs) + + def set_cache(self, key, value, **kwargs): + ttl = kwargs.get("ttl", None) + print_verbose(f"Set Redis Cache: key: {key}\nValue {value}") + try: + self.redis_client.set(name=key, value=str(value), ex=ttl) + except Exception as e: + # NON blocking - notify users Redis is throwing an exception + logging.debug("LiteLLM Caching: set() - Got exception from REDIS : ", e) + + def get_cache(self, key, **kwargs): + try: + print_verbose(f"Get Redis Cache: key: {key}") + cached_response = self.redis_client.get(key) + print_verbose( + f"Got Redis Cache: key: {key}, cached_response {cached_response}" + ) + if cached_response != None: + # cached_response is in `b{} convert it to ModelResponse + cached_response = cached_response.decode( + "utf-8" + ) # Convert bytes to string + try: + cached_response = json.loads( + cached_response + ) # Convert string to dictionary + except: + cached_response = ast.literal_eval(cached_response) + return cached_response + except Exception as e: + # NON blocking - notify users Redis is throwing an exception + traceback.print_exc() + logging.debug("LiteLLM Caching: get() - Got exception from REDIS: ", e) + + def flush_cache(self): + self.redis_client.flushall() + + +class S3Cache(BaseCache): + def __init__( + self, + s3_bucket_name, + s3_region_name=None, + s3_api_version=None, + s3_use_ssl=True, + s3_verify=None, + s3_endpoint_url=None, + s3_aws_access_key_id=None, + s3_aws_secret_access_key=None, + s3_aws_session_token=None, + s3_config=None, + **kwargs, + ): + import boto3 + + self.bucket_name = s3_bucket_name + # Create an S3 client with custom endpoint URL + self.s3_client = boto3.client( + "s3", + region_name=s3_region_name, + endpoint_url=s3_endpoint_url, + api_version=s3_api_version, + use_ssl=s3_use_ssl, + verify=s3_verify, + aws_access_key_id=s3_aws_access_key_id, + aws_secret_access_key=s3_aws_secret_access_key, + aws_session_token=s3_aws_session_token, + config=s3_config, + **kwargs, + ) + + def set_cache(self, key, value, **kwargs): + try: + print_verbose(f"LiteLLM SET Cache - S3. Key={key}. Value={value}") + ttl = kwargs.get("ttl", None) + # Convert value to JSON before storing in S3 + serialized_value = json.dumps(value) + if ttl is not None: + cache_control = f"immutable, max-age={ttl}, s-maxage={ttl}" + import datetime + + # Calculate expiration time + expiration_time = datetime.datetime.now() + ttl + + # Upload the data to S3 with the calculated expiration time + self.s3_client.put_object( + Bucket=self.bucket_name, + Key=key, + Body=serialized_value, + Expires=expiration_time, + CacheControl=cache_control, + ContentType="application/json", + ContentLanguage="en", + ContentDisposition=f"inline; filename=\"{key}.json\"" + ) + else: + cache_control = "immutable, max-age=31536000, s-maxage=31536000" + # Upload the data to S3 without specifying Expires + self.s3_client.put_object( + Bucket=self.bucket_name, + Key=key, + Body=serialized_value, + CacheControl=cache_control, + ContentType="application/json", + ContentLanguage="en", + ContentDisposition=f"inline; filename=\"{key}.json\"" + ) + except Exception as e: + # NON blocking - notify users S3 is throwing an exception + print_verbose(f"S3 Caching: set_cache() - Got exception from S3: {e}") + + def get_cache(self, key, **kwargs): + import boto3, botocore + + try: + print_verbose(f"Get S3 Cache: key: {key}") + # Download the data from S3 + cached_response = self.s3_client.get_object( + Bucket=self.bucket_name, Key=key + ) + + if cached_response != None: + # cached_response is in `b{} convert it to ModelResponse + cached_response = ( + cached_response["Body"].read().decode("utf-8") + ) # Convert bytes to string + try: + cached_response = json.loads( + cached_response + ) # Convert string to dictionary + except Exception as e: + cached_response = ast.literal_eval(cached_response) + if type(cached_response) is not dict: + cached_response = dict(cached_response) + print_verbose( + f"Got S3 Cache: key: {key}, cached_response {cached_response}. Type Response {type(cached_response)}" + ) + + return cached_response + except botocore.exceptions.ClientError as e: + if e.response["Error"]["Code"] == "NoSuchKey": + print_verbose( + f"S3 Cache: The specified key '{key}' does not exist in the S3 bucket." + ) + return None + + except Exception as e: + # NON blocking - notify users S3 is throwing an exception + traceback.print_exc() + print_verbose(f"S3 Caching: get_cache() - Got exception from S3: {e}") + + def flush_cache(self): + pass + + +class DualCache(BaseCache): + """ + This updates both Redis and an in-memory cache simultaneously. + When data is updated or inserted, it is written to both the in-memory cache + Redis. + This ensures that even if Redis hasn't been updated yet, the in-memory cache reflects the most recent data. + """ + + def __init__( + self, + in_memory_cache: Optional[InMemoryCache] = None, + redis_cache: Optional[RedisCache] = None, + ) -> None: + super().__init__() + # If in_memory_cache is not provided, use the default InMemoryCache + self.in_memory_cache = in_memory_cache or InMemoryCache() + # If redis_cache is not provided, use the default RedisCache + self.redis_cache = redis_cache + + def set_cache(self, key, value, local_only: bool = False, **kwargs): + # Update both Redis and in-memory cache + try: + print_verbose(f"set cache: key: {key}; value: {value}") + if self.in_memory_cache is not None: + self.in_memory_cache.set_cache(key, value, **kwargs) + + if self.redis_cache is not None and local_only == False: + self.redis_cache.set_cache(key, value, **kwargs) + except Exception as e: + print_verbose(e) + + def get_cache(self, key, local_only: bool = False, **kwargs): + # Try to fetch from in-memory cache first + try: + print_verbose(f"get cache: cache key: {key}; local_only: {local_only}") + result = None + if self.in_memory_cache is not None: + in_memory_result = self.in_memory_cache.get_cache(key, **kwargs) + + print_verbose(f"in_memory_result: {in_memory_result}") + if in_memory_result is not None: + result = in_memory_result + + if result is None and self.redis_cache is not None and local_only == False: + # If not found in in-memory cache, try fetching from Redis + redis_result = self.redis_cache.get_cache(key, **kwargs) + + if redis_result is not None: + # Update in-memory cache with the value from Redis + self.in_memory_cache.set_cache(key, redis_result, **kwargs) + + result = redis_result + + print_verbose(f"get cache: cache result: {result}") + return result + except Exception as e: + traceback.print_exc() + + def flush_cache(self): + if self.in_memory_cache is not None: + self.in_memory_cache.flush_cache() + if self.redis_cache is not None: + self.redis_cache.flush_cache() + + +#### LiteLLM.Completion / Embedding Cache #### +class Cache: + def __init__( + self, + type: Optional[Literal["local", "redis", "s3"]] = "local", + host: Optional[str] = None, + port: Optional[str] = None, + password: Optional[str] = None, + supported_call_types: Optional[ + List[Literal["completion", "acompletion", "embedding", "aembedding"]] + ] = ["completion", "acompletion", "embedding", "aembedding"], + # s3 Bucket, boto3 configuration + s3_bucket_name: Optional[str] = None, + s3_region_name: Optional[str] = None, + s3_api_version: Optional[str] = None, + s3_use_ssl: Optional[bool] = True, + s3_verify: Optional[Union[bool, str]] = None, + s3_endpoint_url: Optional[str] = None, + s3_aws_access_key_id: Optional[str] = None, + s3_aws_secret_access_key: Optional[str] = None, + s3_aws_session_token: Optional[str] = None, + s3_config: Optional[Any] = None, + **kwargs, + ): + """ + Initializes the cache based on the given type. + + Args: + type (str, optional): The type of cache to initialize. Can be "local" or "redis". Defaults to "local". + host (str, optional): The host address for the Redis cache. Required if type is "redis". + port (int, optional): The port number for the Redis cache. Required if type is "redis". + password (str, optional): The password for the Redis cache. Required if type is "redis". + supported_call_types (list, optional): List of call types to cache for. Defaults to cache == on for all call types. + **kwargs: Additional keyword arguments for redis.Redis() cache + + Raises: + ValueError: If an invalid cache type is provided. + + Returns: + None. Cache is set as a litellm param + """ + if type == "redis": + self.cache: BaseCache = RedisCache(host, port, password, **kwargs) + if type == "local": + self.cache = InMemoryCache() + if type == "s3": + self.cache = S3Cache( + s3_bucket_name=s3_bucket_name, + s3_region_name=s3_region_name, + s3_api_version=s3_api_version, + s3_use_ssl=s3_use_ssl, + s3_verify=s3_verify, + s3_endpoint_url=s3_endpoint_url, + s3_aws_access_key_id=s3_aws_access_key_id, + s3_aws_secret_access_key=s3_aws_secret_access_key, + s3_aws_session_token=s3_aws_session_token, + s3_config=s3_config, + **kwargs, + ) + if "cache" not in litellm.input_callback: + litellm.input_callback.append("cache") + if "cache" not in litellm.success_callback: + litellm.success_callback.append("cache") + if "cache" not in litellm._async_success_callback: + litellm._async_success_callback.append("cache") + self.supported_call_types = supported_call_types # default to ["completion", "acompletion", "embedding", "aembedding"] + self.type = type + + def get_cache_key(self, *args, **kwargs): + """ + Get the cache key for the given arguments. + + Args: + *args: args to litellm.completion() or embedding() + **kwargs: kwargs to litellm.completion() or embedding() + + Returns: + str: The cache key generated from the arguments, or None if no cache key could be generated. + """ + cache_key = "" + print_verbose(f"\nGetting Cache key. Kwargs: {kwargs}") + + # for streaming, we use preset_cache_key. It's created in wrapper(), we do this because optional params like max_tokens, get transformed for bedrock -> max_new_tokens + if kwargs.get("litellm_params", {}).get("preset_cache_key", None) is not None: + print_verbose(f"\nReturning preset cache key: {cache_key}") + return kwargs.get("litellm_params", {}).get("preset_cache_key", None) + + # sort kwargs by keys, since model: [gpt-4, temperature: 0.2, max_tokens: 200] == [temperature: 0.2, max_tokens: 200, model: gpt-4] + completion_kwargs = [ + "model", + "messages", + "temperature", + "top_p", + "n", + "stop", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "user", + "response_format", + "seed", + "tools", + "tool_choice", + ] + embedding_only_kwargs = [ + "input", + "encoding_format", + ] # embedding kwargs = model, input, user, encoding_format. Model, user are checked in completion_kwargs + + # combined_kwargs - NEEDS to be ordered across get_cache_key(). Do not use a set() + combined_kwargs = completion_kwargs + embedding_only_kwargs + for param in combined_kwargs: + # ignore litellm params here + if param in kwargs: + # check if param == model and model_group is passed in, then override model with model_group + if param == "model": + model_group = None + caching_group = None + metadata = kwargs.get("metadata", None) + litellm_params = kwargs.get("litellm_params", {}) + if metadata is not None: + model_group = metadata.get("model_group") + model_group = metadata.get("model_group", None) + caching_groups = metadata.get("caching_groups", None) + if caching_groups: + for group in caching_groups: + if model_group in group: + caching_group = group + break + if litellm_params is not None: + metadata = litellm_params.get("metadata", None) + if metadata is not None: + model_group = metadata.get("model_group", None) + caching_groups = metadata.get("caching_groups", None) + if caching_groups: + for group in caching_groups: + if model_group in group: + caching_group = group + break + param_value = ( + caching_group or model_group or kwargs[param] + ) # use caching_group, if set then model_group if it exists, else use kwargs["model"] + else: + if kwargs[param] is None: + continue # ignore None params + param_value = kwargs[param] + cache_key += f"{str(param)}: {str(param_value)}" + print_verbose(f"\nCreated cache key: {cache_key}") + # Use hashlib to create a sha256 hash of the cache key + hash_object = hashlib.sha256(cache_key.encode()) + # Hexadecimal representation of the hash + hash_hex = hash_object.hexdigest() + print_verbose(f"Hashed cache key (SHA-256): {hash_hex}") + return hash_hex + + def generate_streaming_content(self, content): + chunk_size = 5 # Adjust the chunk size as needed + for i in range(0, len(content), chunk_size): + yield { + "choices": [ + { + "delta": { + "role": "assistant", + "content": content[i : i + chunk_size], + } + } + ] + } + time.sleep(0.02) + + def get_cache(self, *args, **kwargs): + """ + Retrieves the cached result for the given arguments. + + Args: + *args: args to litellm.completion() or embedding() + **kwargs: kwargs to litellm.completion() or embedding() + + Returns: + The cached result if it exists, otherwise None. + """ + try: # never block execution + if "cache_key" in kwargs: + cache_key = kwargs["cache_key"] + else: + cache_key = self.get_cache_key(*args, **kwargs) + if cache_key is not None: + cache_control_args = kwargs.get("cache", {}) + max_age = cache_control_args.get( + "s-max-age", cache_control_args.get("s-maxage", float("inf")) + ) + cached_result = self.cache.get_cache(cache_key) + # Check if a timestamp was stored with the cached response + if ( + cached_result is not None + and isinstance(cached_result, dict) + and "timestamp" in cached_result + and max_age is not None + ): + timestamp = cached_result["timestamp"] + current_time = time.time() + + # Calculate age of the cached response + response_age = current_time - timestamp + + # Check if the cached response is older than the max-age + if response_age > max_age: + print_verbose( + f"Cached response for key {cache_key} is too old. Max-age: {max_age}s, Age: {response_age}s" + ) + return None # Cached response is too old + + # If the response is fresh, or there's no max-age requirement, return the cached response + # cached_response is in `b{} convert it to ModelResponse + cached_response = cached_result.get("response") + try: + if isinstance(cached_response, dict): + pass + else: + cached_response = json.loads( + cached_response + ) # Convert string to dictionary + except: + cached_response = ast.literal_eval(cached_response) + return cached_response + return cached_result + except Exception as e: + print_verbose(f"An exception occurred: {traceback.format_exc()}") + return None + + def add_cache(self, result, *args, **kwargs): + """ + Adds a result to the cache. + + Args: + *args: args to litellm.completion() or embedding() + **kwargs: kwargs to litellm.completion() or embedding() + + Returns: + None + """ + try: + if "cache_key" in kwargs: + cache_key = kwargs["cache_key"] + else: + cache_key = self.get_cache_key(*args, **kwargs) + if cache_key is not None: + if isinstance(result, OpenAIObject): + result = result.model_dump_json() + + ## Get Cache-Controls ## + if kwargs.get("cache", None) is not None and isinstance( + kwargs.get("cache"), dict + ): + for k, v in kwargs.get("cache").items(): + if k == "ttl": + kwargs["ttl"] = v + cached_data = {"timestamp": time.time(), "response": result} + self.cache.set_cache(cache_key, cached_data, **kwargs) + except Exception as e: + print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}") + traceback.print_exc() + pass + + async def _async_add_cache(self, result, *args, **kwargs): + self.add_cache(result, *args, **kwargs) + + +def enable_cache( + type: Optional[Literal["local", "redis", "s3"]] = "local", + host: Optional[str] = None, + port: Optional[str] = None, + password: Optional[str] = None, + supported_call_types: Optional[ + List[Literal["completion", "acompletion", "embedding", "aembedding"]] + ] = ["completion", "acompletion", "embedding", "aembedding"], + **kwargs, +): + """ + Enable cache with the specified configuration. + + Args: + type (Optional[Literal["local", "redis"]]): The type of cache to enable. Defaults to "local". + host (Optional[str]): The host address of the cache server. Defaults to None. + port (Optional[str]): The port number of the cache server. Defaults to None. + password (Optional[str]): The password for the cache server. Defaults to None. + supported_call_types (Optional[List[Literal["completion", "acompletion", "embedding", "aembedding"]]]): + The supported call types for the cache. Defaults to ["completion", "acompletion", "embedding", "aembedding"]. + **kwargs: Additional keyword arguments. + + Returns: + None + + Raises: + None + """ + print_verbose("LiteLLM: Enabling Cache") + if "cache" not in litellm.input_callback: + litellm.input_callback.append("cache") + if "cache" not in litellm.success_callback: + litellm.success_callback.append("cache") + if "cache" not in litellm._async_success_callback: + litellm._async_success_callback.append("cache") + + if litellm.cache == None: + litellm.cache = Cache( + type=type, + host=host, + port=port, + password=password, + supported_call_types=supported_call_types, + **kwargs, + ) + print_verbose(f"LiteLLM: Cache enabled, litellm.cache={litellm.cache}") + print_verbose(f"LiteLLM Cache: {vars(litellm.cache)}") + + +def update_cache( + type: Optional[Literal["local", "redis"]] = "local", + host: Optional[str] = None, + port: Optional[str] = None, + password: Optional[str] = None, + supported_call_types: Optional[ + List[Literal["completion", "acompletion", "embedding", "aembedding"]] + ] = ["completion", "acompletion", "embedding", "aembedding"], + **kwargs, +): + """ + Update the cache for LiteLLM. + + Args: + type (Optional[Literal["local", "redis"]]): The type of cache. Defaults to "local". + host (Optional[str]): The host of the cache. Defaults to None. + port (Optional[str]): The port of the cache. Defaults to None. + password (Optional[str]): The password for the cache. Defaults to None. + supported_call_types (Optional[List[Literal["completion", "acompletion", "embedding", "aembedding"]]]): + The supported call types for the cache. Defaults to ["completion", "acompletion", "embedding", "aembedding"]. + **kwargs: Additional keyword arguments for the cache. + + Returns: + None + + """ + print_verbose("LiteLLM: Updating Cache") + litellm.cache = Cache( + type=type, + host=host, + port=port, + password=password, + supported_call_types=supported_call_types, + **kwargs, + ) + print_verbose(f"LiteLLM: Cache Updated, litellm.cache={litellm.cache}") + print_verbose(f"LiteLLM Cache: {vars(litellm.cache)}") + + +def disable_cache(): + """ + Disable the cache used by LiteLLM. + + This function disables the cache used by the LiteLLM module. It removes the cache-related callbacks from the input_callback, success_callback, and _async_success_callback lists. It also sets the litellm.cache attribute to None. + + Parameters: + None + + Returns: + None + """ + from contextlib import suppress + + print_verbose("LiteLLM: Disabling Cache") + with suppress(ValueError): + litellm.input_callback.remove("cache") + litellm.success_callback.remove("cache") + litellm._async_success_callback.remove("cache") + + litellm.cache = None + print_verbose(f"LiteLLM: Cache disabled, litellm.cache={litellm.cache}") diff --git a/litellm/cost.json b/litellm/cost.json new file mode 100644 index 0000000000000000000000000000000000000000..360f981e455d09ef175b351215fe5500fc96309b --- /dev/null +++ b/litellm/cost.json @@ -0,0 +1,5 @@ +{ + "gpt-3.5-turbo-0613": 0.00015000000000000001, + "claude-2": 0.00016454, + "gpt-4-0613": 0.015408 +} \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/.env.template b/litellm/deprecated_litellm_server/.env.template new file mode 100644 index 0000000000000000000000000000000000000000..a1c32a4549570b176a5e56a6aaa25da9b0b5b1c8 --- /dev/null +++ b/litellm/deprecated_litellm_server/.env.template @@ -0,0 +1,43 @@ +# # set AUTH STRATEGY FOR LLM APIs - Defaults to using Environment Variables +# AUTH_STRATEGY = "ENV" # ENV or DYNAMIC, ENV always reads from environment variables, DYNAMIC reads request headers to set LLM api keys + +# OPENAI_API_KEY = "" + +# HUGGINGFACE_API_KEY="" + +# TOGETHERAI_API_KEY="" + +# REPLICATE_API_KEY="" + +# ## bedrock / sagemaker +# AWS_ACCESS_KEY_ID = "" +# AWS_SECRET_ACCESS_KEY = "" + +# AZURE_API_KEY = "" +# AZURE_API_BASE = "" +# AZURE_API_VERSION = "" + +# ANTHROPIC_API_KEY = "" + +# COHERE_API_KEY = "" + +# ## CONFIG FILE ## +# # CONFIG_FILE_PATH = "" # uncomment to point to config file + +# ## LOGGING ## + +# SET_VERBOSE = "False" # set to 'True' to see detailed input/output logs + +# ### LANGFUSE +# LANGFUSE_PUBLIC_KEY = "" +# LANGFUSE_SECRET_KEY = "" +# # Optional, defaults to https://cloud.langfuse.com +# LANGFUSE_HOST = "" # optional + + +# ## CACHING ## + +# ### REDIS +# REDIS_HOST = "" +# REDIS_PORT = "" +# REDIS_PASSWORD = "" diff --git a/litellm/deprecated_litellm_server/Dockerfile b/litellm/deprecated_litellm_server/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..9b3b314c4b7aca217d00d26253c914c546dc0cda --- /dev/null +++ b/litellm/deprecated_litellm_server/Dockerfile @@ -0,0 +1,10 @@ +# FROM python:3.10 + +# ENV LITELLM_CONFIG_PATH="/litellm.secrets.toml" +# COPY . /app +# WORKDIR /app +# RUN pip install -r requirements.txt + +# EXPOSE $PORT + +# CMD exec uvicorn main:app --host 0.0.0.0 --port $PORT --workers 10 \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/README.md b/litellm/deprecated_litellm_server/README.md new file mode 100644 index 0000000000000000000000000000000000000000..142bad18503c6206eac9399d43b5cfceef3fcbe1 --- /dev/null +++ b/litellm/deprecated_litellm_server/README.md @@ -0,0 +1,3 @@ +# litellm-server [experimental] + +Deprecated. See litellm/proxy \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/__init__.py b/litellm/deprecated_litellm_server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..54b9216d944db91d11a7d6bfb9f26e4caa0001f6 --- /dev/null +++ b/litellm/deprecated_litellm_server/__init__.py @@ -0,0 +1,2 @@ +# from .main import * +# from .server_utils import * diff --git a/litellm/deprecated_litellm_server/main.py b/litellm/deprecated_litellm_server/main.py new file mode 100644 index 0000000000000000000000000000000000000000..966d2ed194fb2db9e3c9c494d0c18f0572a70822 --- /dev/null +++ b/litellm/deprecated_litellm_server/main.py @@ -0,0 +1,193 @@ +# import os, traceback +# from fastapi import FastAPI, Request, HTTPException +# from fastapi.routing import APIRouter +# from fastapi.responses import StreamingResponse, FileResponse +# from fastapi.middleware.cors import CORSMiddleware +# import json, sys +# from typing import Optional +# sys.path.insert( +# 0, os.path.abspath("../") +# ) # Adds the parent directory to the system path - for litellm local dev +# import litellm + +# try: +# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose +# except ImportError: +# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose +# import dotenv +# dotenv.load_dotenv() # load env variables + +# app = FastAPI(docs_url="/", title="LiteLLM API") +# router = APIRouter() +# origins = ["*"] + +# app.add_middleware( +# CORSMiddleware, +# allow_origins=origins, +# allow_credentials=True, +# allow_methods=["*"], +# allow_headers=["*"], +# ) +# #### GLOBAL VARIABLES #### +# llm_router: Optional[litellm.Router] = None +# llm_model_list: Optional[list] = None +# server_settings: Optional[dict] = None + +# set_callbacks() # sets litellm callbacks for logging if they exist in the environment + +# if "CONFIG_FILE_PATH" in os.environ: +# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router, config_file_path=os.getenv("CONFIG_FILE_PATH")) +# else: +# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router) +# #### API ENDPOINTS #### +# @router.get("/v1/models") +# @router.get("/models") # if project requires model list +# def model_list(): +# all_models = litellm.utils.get_valid_models() +# if llm_model_list: +# all_models += llm_model_list +# return dict( +# data=[ +# { +# "id": model, +# "object": "model", +# "created": 1677610602, +# "owned_by": "openai", +# } +# for model in all_models +# ], +# object="list", +# ) +# # for streaming +# def data_generator(response): + +# for chunk in response: + +# yield f"data: {json.dumps(chunk)}\n\n" + +# @router.post("/v1/completions") +# @router.post("/completions") +# async def completion(request: Request): +# data = await request.json() +# response = litellm.completion( +# **data +# ) +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response + +# @router.post("/v1/embeddings") +# @router.post("/embeddings") +# async def embedding(request: Request): +# try: +# data = await request.json() +# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers +# if os.getenv("AUTH_STRATEGY", None) == "DYNAMIC" and "authorization" in request.headers: # if users pass LLM api keys as part of header +# api_key = request.headers.get("authorization") +# api_key = api_key.replace("Bearer", "").strip() # type: ignore +# if len(api_key.strip()) > 0: +# api_key = api_key +# data["api_key"] = api_key +# response = litellm.embedding( +# **data +# ) +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() +# error_msg = f"{str(e)}\n\n{error_traceback}" +# return {"error": error_msg} + +# @router.post("/v1/chat/completions") +# @router.post("/chat/completions") +# @router.post("/openai/deployments/{model:path}/chat/completions") # azure compatible endpoint +# async def chat_completion(request: Request, model: Optional[str] = None): +# global llm_model_list, server_settings +# try: +# data = await request.json() +# server_model = server_settings.get("completion_model", None) if server_settings else None +# data["model"] = server_model or model or data["model"] +# ## CHECK KEYS ## +# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers +# # env_validation = litellm.validate_environment(model=data["model"]) +# # if (env_validation['keys_in_environment'] is False or os.getenv("AUTH_STRATEGY", None) == "DYNAMIC") and ("authorization" in request.headers or "api-key" in request.headers): # if users pass LLM api keys as part of header +# # if "authorization" in request.headers: +# # api_key = request.headers.get("authorization") +# # elif "api-key" in request.headers: +# # api_key = request.headers.get("api-key") +# # print(f"api_key in headers: {api_key}") +# # if " " in api_key: +# # api_key = api_key.split(" ")[1] +# # print(f"api_key split: {api_key}") +# # if len(api_key) > 0: +# # api_key = api_key +# # data["api_key"] = api_key +# # print(f"api_key in data: {api_key}") +# ## CHECK CONFIG ## +# if llm_model_list and data["model"] in [m["model_name"] for m in llm_model_list]: +# for m in llm_model_list: +# if data["model"] == m["model_name"]: +# for key, value in m["litellm_params"].items(): +# data[key] = value +# break +# response = litellm.completion( +# **data +# ) +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() + +# error_msg = f"{str(e)}\n\n{error_traceback}" +# # return {"error": error_msg} +# raise HTTPException(status_code=500, detail=error_msg) + +# @router.post("/router/completions") +# async def router_completion(request: Request): +# global llm_router +# try: +# data = await request.json() +# if "model_list" in data: +# llm_router = litellm.Router(model_list=data.pop("model_list")) +# if llm_router is None: +# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body") + +# # openai.ChatCompletion.create replacement +# response = await llm_router.acompletion(model="gpt-3.5-turbo", +# messages=[{"role": "user", "content": "Hey, how's it going?"}]) + +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() +# error_msg = f"{str(e)}\n\n{error_traceback}" +# return {"error": error_msg} + +# @router.post("/router/embedding") +# async def router_embedding(request: Request): +# global llm_router +# try: +# data = await request.json() +# if "model_list" in data: +# llm_router = litellm.Router(model_list=data.pop("model_list")) +# if llm_router is None: +# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body") + +# response = await llm_router.aembedding(model="gpt-3.5-turbo", # type: ignore +# messages=[{"role": "user", "content": "Hey, how's it going?"}]) + +# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses +# return StreamingResponse(data_generator(response), media_type='text/event-stream') +# return response +# except Exception as e: +# error_traceback = traceback.format_exc() +# error_msg = f"{str(e)}\n\n{error_traceback}" +# return {"error": error_msg} + +# @router.get("/") +# async def home(request: Request): +# return "LiteLLM: RUNNING" + + +# app.include_router(router) diff --git a/litellm/deprecated_litellm_server/requirements.txt b/litellm/deprecated_litellm_server/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..09f6dba5729f776d686405572476d48f1c408845 --- /dev/null +++ b/litellm/deprecated_litellm_server/requirements.txt @@ -0,0 +1,7 @@ +# openai +# fastapi +# uvicorn +# boto3 +# litellm +# python-dotenv +# redis \ No newline at end of file diff --git a/litellm/deprecated_litellm_server/server_utils.py b/litellm/deprecated_litellm_server/server_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..75e5aa4d73887692cfdd0b553af8fb1ae2840f18 --- /dev/null +++ b/litellm/deprecated_litellm_server/server_utils.py @@ -0,0 +1,85 @@ +# import os, litellm +# import pkg_resources +# import dotenv +# dotenv.load_dotenv() # load env variables + +# def print_verbose(print_statement): +# pass + +# def get_package_version(package_name): +# try: +# package = pkg_resources.get_distribution(package_name) +# return package.version +# except pkg_resources.DistributionNotFound: +# return None + +# # Usage example +# package_name = "litellm" +# version = get_package_version(package_name) +# if version: +# print_verbose(f"The version of {package_name} is {version}") +# else: +# print_verbose(f"{package_name} is not installed") +# import yaml +# import dotenv +# from typing import Optional +# dotenv.load_dotenv() # load env variables + +# def set_callbacks(): +# ## LOGGING +# if len(os.getenv("SET_VERBOSE", "")) > 0: +# if os.getenv("SET_VERBOSE") == "True": +# litellm.set_verbose = True +# print_verbose("\033[92mLiteLLM: Switched on verbose logging\033[0m") +# else: +# litellm.set_verbose = False + +# ### LANGFUSE +# if (len(os.getenv("LANGFUSE_PUBLIC_KEY", "")) > 0 and len(os.getenv("LANGFUSE_SECRET_KEY", ""))) > 0 or len(os.getenv("LANGFUSE_HOST", "")) > 0: +# litellm.success_callback = ["langfuse"] +# print_verbose("\033[92mLiteLLM: Switched on Langfuse feature\033[0m") + +# ## CACHING +# ### REDIS +# # if len(os.getenv("REDIS_HOST", "")) > 0 and len(os.getenv("REDIS_PORT", "")) > 0 and len(os.getenv("REDIS_PASSWORD", "")) > 0: +# # print(f"redis host: {os.getenv('REDIS_HOST')}; redis port: {os.getenv('REDIS_PORT')}; password: {os.getenv('REDIS_PASSWORD')}") +# # from litellm.caching import Cache +# # litellm.cache = Cache(type="redis", host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT"), password=os.getenv("REDIS_PASSWORD")) +# # print("\033[92mLiteLLM: Switched on Redis caching\033[0m") + + +# def load_router_config(router: Optional[litellm.Router], config_file_path: Optional[str]='/app/config.yaml'): +# config = {} +# server_settings = {} +# try: +# if os.path.exists(config_file_path): # type: ignore +# with open(config_file_path, 'r') as file: # type: ignore +# config = yaml.safe_load(file) +# else: +# pass +# except: +# pass + +# ## SERVER SETTINGS (e.g. default completion model = 'ollama/mistral') +# server_settings = config.get("server_settings", None) +# if server_settings: +# server_settings = server_settings + +# ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..) +# litellm_settings = config.get('litellm_settings', None) +# if litellm_settings: +# for key, value in litellm_settings.items(): +# setattr(litellm, key, value) + +# ## MODEL LIST +# model_list = config.get('model_list', None) +# if model_list: +# router = litellm.Router(model_list=model_list) + +# ## ENVIRONMENT VARIABLES +# environment_variables = config.get('environment_variables', None) +# if environment_variables: +# for key, value in environment_variables.items(): +# os.environ[key] = value + +# return router, model_list, server_settings diff --git a/litellm/exceptions.py b/litellm/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..4f9629e71c80c5f64802a1ae092d34f8158994d0 --- /dev/null +++ b/litellm/exceptions.py @@ -0,0 +1,200 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +## LiteLLM versions of the OpenAI Exception Types + +from openai import ( + AuthenticationError, + BadRequestError, + NotFoundError, + RateLimitError, + APIStatusError, + OpenAIError, + APIError, + APITimeoutError, + APIConnectionError, + APIResponseValidationError, + UnprocessableEntityError, +) +import httpx + + +class AuthenticationError(AuthenticationError): # type: ignore + def __init__(self, message, llm_provider, model, response: httpx.Response): + self.status_code = 401 + self.message = message + self.llm_provider = llm_provider + self.model = model + super().__init__( + self.message, response=response, body=None + ) # Call the base class constructor with the parameters it needs + + +# raise when invalid models passed, example gpt-8 +class NotFoundError(NotFoundError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 404 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, response=response, body=None + ) # Call the base class constructor with the parameters it needs + + +class BadRequestError(BadRequestError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, response=response, body=None + ) # Call the base class constructor with the parameters it needs + + +class UnprocessableEntityError(UnprocessableEntityError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 422 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, response=response, body=None + ) # Call the base class constructor with the parameters it needs + + +class Timeout(APITimeoutError): # type: ignore + def __init__(self, message, model, llm_provider): + self.status_code = 408 + self.message = message + self.model = model + self.llm_provider = llm_provider + request = httpx.Request(method="POST", url="https://api.openai.com/v1") + super().__init__( + request=request + ) # Call the base class constructor with the parameters it needs + + +class RateLimitError(RateLimitError): # type: ignore + def __init__(self, message, llm_provider, model, response: httpx.Response): + self.status_code = 429 + self.message = message + self.llm_provider = llm_provider + self.modle = model + super().__init__( + self.message, response=response, body=None + ) # Call the base class constructor with the parameters it needs + + +# sub class of rate limit error - meant to give more granularity for error handling context window exceeded errors +class ContextWindowExceededError(BadRequestError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + message=self.message, + model=self.model, # type: ignore + llm_provider=self.llm_provider, # type: ignore + response=response, + ) # Call the base class constructor with the parameters it needs + + +class ContentPolicyViolationError(BadRequestError): # type: ignore + # Error code: 400 - {'error': {'code': 'content_policy_violation', 'message': 'Your request was rejected as a result of our safety system. Image descriptions generated from your prompt may contain text that is not allowed by our safety system. If you believe this was done in error, your request may succeed if retried, or by adjusting your prompt.', 'param': None, 'type': 'invalid_request_error'}} + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + message=self.message, + model=self.model, # type: ignore + llm_provider=self.llm_provider, # type: ignore + response=response, + ) # Call the base class constructor with the parameters it needs + + +class ServiceUnavailableError(APIStatusError): # type: ignore + def __init__(self, message, llm_provider, model, response: httpx.Response): + self.status_code = 503 + self.message = message + self.llm_provider = llm_provider + self.model = model + super().__init__( + self.message, response=response, body=None + ) # Call the base class constructor with the parameters it needs + + +# raise this when the API returns an invalid response object - https://github.com/openai/openai-python/blob/1be14ee34a0f8e42d3f9aa5451aa4cb161f1781f/openai/api_requestor.py#L401 +class APIError(APIError): # type: ignore + def __init__( + self, status_code, message, llm_provider, model, request: httpx.Request + ): + self.status_code = status_code + self.message = message + self.llm_provider = llm_provider + self.model = model + super().__init__(self.message, request=request, body=None) # type: ignore + + +# raised if an invalid request (not get, delete, put, post) is made +class APIConnectionError(APIConnectionError): # type: ignore + def __init__(self, message, llm_provider, model, request: httpx.Request): + self.message = message + self.llm_provider = llm_provider + self.model = model + self.status_code = 500 + super().__init__(message=self.message, request=request) + + +# raised if an invalid request (not get, delete, put, post) is made +class APIResponseValidationError(APIResponseValidationError): # type: ignore + def __init__(self, message, llm_provider, model): + self.message = message + self.llm_provider = llm_provider + self.model = model + request = httpx.Request(method="POST", url="https://api.openai.com/v1") + response = httpx.Response(status_code=500, request=request) + super().__init__(response=response, body=None, message=message) + + +class OpenAIError(OpenAIError): # type: ignore + def __init__(self, original_exception): + self.status_code = original_exception.http_status + super().__init__( + http_body=original_exception.http_body, + http_status=original_exception.http_status, + json_body=original_exception.json_body, + headers=original_exception.headers, + code=original_exception.code, + ) + self.llm_provider = "openai" + + +class BudgetExceededError(Exception): + def __init__(self, current_cost, max_budget): + self.current_cost = current_cost + self.max_budget = max_budget + message = f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}" + super().__init__(message) + + +## DEPRECATED ## +class InvalidRequestError(BadRequestError): # type: ignore + def __init__(self, message, model, llm_provider): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, f"{self.model}" + ) # Call the base class constructor with the parameters it needs diff --git a/litellm/integrations/__init__.py b/litellm/integrations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834 --- /dev/null +++ b/litellm/integrations/__init__.py @@ -0,0 +1 @@ +from . import * diff --git a/litellm/integrations/aispend.py b/litellm/integrations/aispend.py new file mode 100644 index 0000000000000000000000000000000000000000..2015d45ddd6e22fb695b4d1183189db7d8a05e16 --- /dev/null +++ b/litellm/integrations/aispend.py @@ -0,0 +1,177 @@ +#### What this does #### +# On success + failure, log events to aispend.io +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime + +model_cost = { + "gpt-3.5-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-35-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, # azure model name + "gpt-3.5-turbo-0613": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-0301": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-35-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, # azure model name + "gpt-3.5-turbo-16k-0613": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-4": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-0613": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-32k": { + "max_tokens": 8000, + "input_cost_per_token": 0.00006, + "output_cost_per_token": 0.00012, + }, + "claude-instant-1": { + "max_tokens": 100000, + "input_cost_per_token": 0.00000163, + "output_cost_per_token": 0.00000551, + }, + "claude-2": { + "max_tokens": 100000, + "input_cost_per_token": 0.00001102, + "output_cost_per_token": 0.00003268, + }, + "text-bison-001": { + "max_tokens": 8192, + "input_cost_per_token": 0.000004, + "output_cost_per_token": 0.000004, + }, + "chat-bison-001": { + "max_tokens": 4096, + "input_cost_per_token": 0.000002, + "output_cost_per_token": 0.000002, + }, + "command-nightly": { + "max_tokens": 4096, + "input_cost_per_token": 0.000015, + "output_cost_per_token": 0.000015, + }, +} + + +class AISpendLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.account_id = os.getenv("AISPEND_ACCOUNT_ID") + self.api_key = os.getenv("AISPEND_API_KEY") + + def price_calculator(self, model, response_obj, start_time, end_time): + # try and find if the model is in the model_cost map + # else default to the average of the costs + prompt_tokens_cost_usd_dollar = 0 + completion_tokens_cost_usd_dollar = 0 + if model in model_cost: + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + elif "replicate" in model: + # replicate models are charged based on time + # llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat + model_run_time = end_time - start_time # assuming time in seconds + cost_usd_dollar = model_run_time * 0.0032 + prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2 + completion_tokens_cost_usd_dollar = cost_usd_dollar / 2 + else: + # calculate average input cost + input_cost_sum = 0 + output_cost_sum = 0 + for model in model_cost: + input_cost_sum += model_cost[model]["input_cost_per_token"] + output_cost_sum += model_cost[model]["output_cost_per_token"] + avg_input_cost = input_cost_sum / len(model_cost.keys()) + avg_output_cost = output_cost_sum / len(model_cost.keys()) + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + + def log_event(self, model, response_obj, start_time, end_time, print_verbose): + # Method definition + try: + print_verbose( + f"AISpend Logging - Enters logging function for model {model}" + ) + + url = f"https://aispend.io/api/v1/accounts/{self.account_id}/data" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + + response_timestamp = datetime.datetime.fromtimestamp( + int(response_obj["created"]) + ).strftime("%Y-%m-%d") + + ( + prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar, + ) = self.price_calculator(model, response_obj, start_time, end_time) + prompt_tokens_cost_usd_cent = prompt_tokens_cost_usd_dollar * 100 + completion_tokens_cost_usd_cent = completion_tokens_cost_usd_dollar * 100 + data = [ + { + "requests": 1, + "requests_context": 1, + "context_tokens": response_obj["usage"]["prompt_tokens"], + "requests_generated": 1, + "generated_tokens": response_obj["usage"]["completion_tokens"], + "recorded_date": response_timestamp, + "model_id": response_obj["model"], + "generated_tokens_cost_usd_cent": prompt_tokens_cost_usd_cent, + "context_tokens_cost_usd_cent": completion_tokens_cost_usd_cent, + } + ] + + print_verbose(f"AISpend Logging - final data object: {data}") + except: + # traceback.print_exc() + print_verbose(f"AISpend Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/berrispend.py b/litellm/integrations/berrispend.py new file mode 100644 index 0000000000000000000000000000000000000000..7d91ffca7f1bd17239c03332d4136537f778a2f3 --- /dev/null +++ b/litellm/integrations/berrispend.py @@ -0,0 +1,184 @@ +#### What this does #### +# On success + failure, log events to aispend.io +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime + +model_cost = { + "gpt-3.5-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-35-turbo": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, # azure model name + "gpt-3.5-turbo-0613": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-0301": { + "max_tokens": 4000, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + }, + "gpt-3.5-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-35-turbo-16k": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, # azure model name + "gpt-3.5-turbo-16k-0613": { + "max_tokens": 16000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + }, + "gpt-4": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-0613": { + "max_tokens": 8000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.00006, + }, + "gpt-4-32k": { + "max_tokens": 8000, + "input_cost_per_token": 0.00006, + "output_cost_per_token": 0.00012, + }, + "claude-instant-1": { + "max_tokens": 100000, + "input_cost_per_token": 0.00000163, + "output_cost_per_token": 0.00000551, + }, + "claude-2": { + "max_tokens": 100000, + "input_cost_per_token": 0.00001102, + "output_cost_per_token": 0.00003268, + }, + "text-bison-001": { + "max_tokens": 8192, + "input_cost_per_token": 0.000004, + "output_cost_per_token": 0.000004, + }, + "chat-bison-001": { + "max_tokens": 4096, + "input_cost_per_token": 0.000002, + "output_cost_per_token": 0.000002, + }, + "command-nightly": { + "max_tokens": 4096, + "input_cost_per_token": 0.000015, + "output_cost_per_token": 0.000015, + }, +} + + +class BerriSpendLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.account_id = os.getenv("BERRISPEND_ACCOUNT_ID") + + def price_calculator(self, model, response_obj, start_time, end_time): + # try and find if the model is in the model_cost map + # else default to the average of the costs + prompt_tokens_cost_usd_dollar = 0 + completion_tokens_cost_usd_dollar = 0 + if model in model_cost: + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + elif "replicate" in model: + # replicate models are charged based on time + # llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat + model_run_time = end_time - start_time # assuming time in seconds + cost_usd_dollar = model_run_time * 0.0032 + prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2 + completion_tokens_cost_usd_dollar = cost_usd_dollar / 2 + else: + # calculate average input cost + input_cost_sum = 0 + output_cost_sum = 0 + for model in model_cost: + input_cost_sum += model_cost[model]["input_cost_per_token"] + output_cost_sum += model_cost[model]["output_cost_per_token"] + avg_input_cost = input_cost_sum / len(model_cost.keys()) + avg_output_cost = output_cost_sum / len(model_cost.keys()) + prompt_tokens_cost_usd_dollar = ( + model_cost[model]["input_cost_per_token"] + * response_obj["usage"]["prompt_tokens"] + ) + completion_tokens_cost_usd_dollar = ( + model_cost[model]["output_cost_per_token"] + * response_obj["usage"]["completion_tokens"] + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + + def log_event( + self, model, messages, response_obj, start_time, end_time, print_verbose + ): + # Method definition + try: + print_verbose( + f"BerriSpend Logging - Enters logging function for model {model}" + ) + + url = f"https://berrispend.berri.ai/spend" + headers = {"Content-Type": "application/json"} + + ( + prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar, + ) = self.price_calculator(model, response_obj, start_time, end_time) + total_cost = ( + prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar + ) + + response_time = (end_time - start_time).total_seconds() + if "response" in response_obj: + data = [ + { + "response_time": response_time, + "model_id": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "response": response_obj["choices"][0]["message"]["content"], + "account_id": self.account_id, + } + ] + elif "error" in response_obj: + data = [ + { + "response_time": response_time, + "model_id": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "error": response_obj["error"], + "account_id": self.account_id, + } + ] + + print_verbose(f"BerriSpend Logging - final data object: {data}") + response = requests.post(url, headers=headers, json=data) + except: + # traceback.print_exc() + print_verbose(f"BerriSpend Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..316e48aedcaf2f087c658d7444287c1857556763 --- /dev/null +++ b/litellm/integrations/custom_logger.py @@ -0,0 +1,130 @@ +#### What this does #### +# On success, logs events to Promptlayer +import dotenv, os +import requests +from litellm.proxy._types import UserAPIKeyAuth +from litellm.caching import DualCache +from typing import Literal + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + # Class variables or attributes + def __init__(self): + pass + + def log_pre_api_call(self, model, messages, kwargs): + pass + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + pass + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + pass + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + pass + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + pass + + #### ASYNC #### + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + pass + + async def async_log_pre_api_call(self, model, messages, kwargs): + pass + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + pass + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + pass + + #### CALL HOOKS - proxy only #### + """ + Control the modify incoming / outgoung data before calling the model + """ + + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: Literal["completion", "embeddings"], + ): + pass + + async def async_post_call_failure_hook( + self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth + ): + pass + + #### SINGLE-USE #### - https://docs.litellm.ai/docs/observability/custom_callback#using-your-custom-callback-function + + def log_input_event(self, model, messages, kwargs, print_verbose, callback_func): + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["log_event_type"] = "pre_api_call" + callback_func( + kwargs, + ) + print_verbose(f"Custom Logger - model call details: {kwargs}") + except: + traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + + async def async_log_input_event( + self, model, messages, kwargs, print_verbose, callback_func + ): + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["log_event_type"] = "pre_api_call" + await callback_func( + kwargs, + ) + print_verbose(f"Custom Logger - model call details: {kwargs}") + except: + traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + + def log_event( + self, kwargs, response_obj, start_time, end_time, print_verbose, callback_func + ): + # Method definition + try: + kwargs["log_event_type"] = "post_api_call" + callback_func( + kwargs, # kwargs to func + response_obj, + start_time, + end_time, + ) + print_verbose(f"Custom Logger - final response object: {response_obj}") + except: + # traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + pass + + async def async_log_event( + self, kwargs, response_obj, start_time, end_time, print_verbose, callback_func + ): + # Method definition + try: + kwargs["log_event_type"] = "post_api_call" + await callback_func( + kwargs, # kwargs to func + response_obj, + start_time, + end_time, + ) + print_verbose(f"Custom Logger - final response object: {response_obj}") + except: + # traceback.print_exc() + print_verbose(f"Custom Logger Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/dynamodb.py b/litellm/integrations/dynamodb.py new file mode 100644 index 0000000000000000000000000000000000000000..2ed6c3f9f76fd9f6055d7498b6336d082c90d707 --- /dev/null +++ b/litellm/integrations/dynamodb.py @@ -0,0 +1,92 @@ +#### What this does #### +# On success + failure, log events to Supabase + +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime, subprocess, sys +import litellm, uuid +from litellm._logging import print_verbose + + +class DyanmoDBLogger: + # Class variables or attributes + + def __init__(self): + # Instance variables + import boto3 + + self.dynamodb = boto3.resource( + "dynamodb", region_name=os.environ["AWS_REGION_NAME"] + ) + if litellm.dynamodb_table_name is None: + raise ValueError( + "LiteLLM Error, trying to use DynamoDB but not table name passed. Create a table and set `litellm.dynamodb_table_name=`" + ) + self.table_name = litellm.dynamodb_table_name + + async def _async_log_event( + self, kwargs, response_obj, start_time, end_time, print_verbose + ): + self.log_event(kwargs, response_obj, start_time, end_time, print_verbose) + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + try: + print_verbose( + f"DynamoDB Logging - Enters logging function for model {kwargs}" + ) + + # construct payload to send to DynamoDB + # follows the same params as langfuse.py + litellm_params = kwargs.get("litellm_params", {}) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) # if litellm_params['metadata'] == None + messages = kwargs.get("messages") + optional_params = kwargs.get("optional_params", {}) + call_type = kwargs.get("call_type", "litellm.completion") + usage = response_obj["usage"] + id = response_obj.get("id", str(uuid.uuid4())) + + # Build the initial payload + payload = { + "id": id, + "call_type": call_type, + "startTime": start_time, + "endTime": end_time, + "model": kwargs.get("model", ""), + "user": kwargs.get("user", ""), + "modelParameters": optional_params, + "messages": messages, + "response": response_obj, + "usage": usage, + "metadata": metadata, + } + + # Ensure everything in the payload is converted to str + for key, value in payload.items(): + try: + payload[key] = str(value) + except: + # non blocking if it can't cast to a str + pass + + print_verbose(f"\nDynamoDB Logger - Logging payload = {payload}") + + # put data in dyanmo DB + table = self.dynamodb.Table(self.table_name) + # Assuming log_data is a dictionary with log information + response = table.put_item(Item=payload) + + print_verbose(f"Response from DynamoDB:{str(response)}") + + print_verbose( + f"DynamoDB Layer Logging - final response object: {response_obj}" + ) + return response + except: + traceback.print_exc() + print_verbose(f"DynamoDB Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py new file mode 100644 index 0000000000000000000000000000000000000000..f9dff85dbf3808f9f0411d26457965130cb3253f --- /dev/null +++ b/litellm/integrations/helicone.py @@ -0,0 +1,114 @@ +#### What this does #### +# On success, logs events to Helicone +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class HeliconeLogger: + # Class variables or attributes + helicone_model_list = ["gpt", "claude"] + + def __init__(self): + # Instance variables + self.provider_url = "https://api.openai.com/v1" + self.key = os.getenv("HELICONE_API_KEY") + + def claude_mapping(self, model, messages, response_obj): + from anthropic import HUMAN_PROMPT, AI_PROMPT + + prompt = f"{HUMAN_PROMPT}" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{HUMAN_PROMPT}{message['content']}" + else: + prompt += f"{AI_PROMPT}{message['content']}" + else: + prompt += f"{HUMAN_PROMPT}{message['content']}" + prompt += f"{AI_PROMPT}" + claude_provider_request = {"model": model, "prompt": prompt} + + claude_response_obj = { + "completion": response_obj["choices"][0]["message"]["content"], + "model": model, + "stop_reason": "stop_sequence", + } + + return claude_provider_request, claude_response_obj + + def log_success( + self, model, messages, response_obj, start_time, end_time, print_verbose + ): + # Method definition + try: + print_verbose( + f"Helicone Logging - Enters logging function for model {model}" + ) + model = ( + model + if any( + accepted_model in model + for accepted_model in self.helicone_model_list + ) + else "gpt-3.5-turbo" + ) + provider_request = {"model": model, "messages": messages} + + if "claude" in model: + provider_request, response_obj = self.claude_mapping( + model=model, messages=messages, response_obj=response_obj + ) + + providerResponse = { + "json": response_obj, + "headers": {"openai-version": "2020-10-01"}, + "status": 200, + } + + # Code to be executed + url = "https://api.hconeai.com/oai/v1/log" + headers = { + "Authorization": f"Bearer {self.key}", + "Content-Type": "application/json", + } + start_time_seconds = int(start_time.timestamp()) + start_time_milliseconds = int( + (start_time.timestamp() - start_time_seconds) * 1000 + ) + end_time_seconds = int(end_time.timestamp()) + end_time_milliseconds = int( + (end_time.timestamp() - end_time_seconds) * 1000 + ) + data = { + "providerRequest": { + "url": self.provider_url, + "json": provider_request, + "meta": {"Helicone-Auth": f"Bearer {self.key}"}, + }, + "providerResponse": providerResponse, + "timing": { + "startTime": { + "seconds": start_time_seconds, + "milliseconds": start_time_milliseconds, + }, + "endTime": { + "seconds": end_time_seconds, + "milliseconds": end_time_milliseconds, + }, + }, # {"seconds": .., "milliseconds": ..} + } + response = requests.post(url, headers=headers, json=data) + if response.status_code == 200: + print_verbose("Helicone Logging - Success!") + else: + print_verbose( + f"Helicone Logging - Error Request was not successful. Status Code: {response.status_code}" + ) + print_verbose(f"Helicone Logging - Error {response.text}") + except: + # traceback.print_exc() + print_verbose(f"Helicone Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py new file mode 100644 index 0000000000000000000000000000000000000000..db410d986f5a58e04a25fc47e18174a5e9cabd2f --- /dev/null +++ b/litellm/integrations/langfuse.py @@ -0,0 +1,191 @@ +#### What this does #### +# On success, logs events to Langfuse +import dotenv, os +import requests +import requests +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +from packaging.version import Version + + +class LangFuseLogger: + # Class variables or attributes + def __init__(self): + try: + from langfuse import Langfuse + except Exception as e: + raise Exception( + f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\033[0m" + ) + # Instance variables + self.secret_key = os.getenv("LANGFUSE_SECRET_KEY") + self.public_key = os.getenv("LANGFUSE_PUBLIC_KEY") + self.langfuse_host = os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com") + self.langfuse_release = os.getenv("LANGFUSE_RELEASE") + self.langfuse_debug = os.getenv("LANGFUSE_DEBUG") + self.Langfuse = Langfuse( + public_key=self.public_key, + secret_key=self.secret_key, + host=self.langfuse_host, + release=self.langfuse_release, + debug=self.langfuse_debug, + ) + + def log_event( + self, kwargs, response_obj, start_time, end_time, user_id, print_verbose + ): + # Method definition + + try: + print_verbose( + f"Langfuse Logging - Enters logging function for model {kwargs}" + ) + litellm_params = kwargs.get("litellm_params", {}) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) # if litellm_params['metadata'] == None + prompt = [kwargs.get("messages")] + optional_params = kwargs.get("optional_params", {}) + + optional_params.pop("functions", None) + optional_params.pop("tools", None) + + # langfuse only accepts str, int, bool, float for logging + for param, value in optional_params.items(): + if not isinstance(value, (str, int, bool, float)): + try: + optional_params[param] = str(value) + except: + # if casting value to str fails don't block logging + pass + + # end of processing langfuse ######################## + input = prompt + output = response_obj["choices"][0]["message"].json() + print_verbose( + f"OUTPUT IN LANGFUSE: {output}; original: {response_obj['choices'][0]['message']}" + ) + self._log_langfuse_v2( + user_id, + metadata, + output, + start_time, + end_time, + kwargs, + optional_params, + input, + response_obj, + ) if self._is_langfuse_v2() else self._log_langfuse_v1( + user_id, + metadata, + output, + start_time, + end_time, + kwargs, + optional_params, + input, + response_obj, + ) + + self.Langfuse.flush() + print_verbose( + f"Langfuse Layer Logging - final response object: {response_obj}" + ) + except: + traceback.print_exc() + print_verbose(f"Langfuse Layer Error - {traceback.format_exc()}") + pass + + async def _async_log_event( + self, kwargs, response_obj, start_time, end_time, user_id, print_verbose + ): + self.log_event( + kwargs, response_obj, start_time, end_time, user_id, print_verbose + ) + + def _is_langfuse_v2(self): + import langfuse + + return Version(langfuse.version.__version__) >= Version("2.0.0") + + def _log_langfuse_v1( + self, + user_id, + metadata, + output, + start_time, + end_time, + kwargs, + optional_params, + input, + response_obj, + ): + from langfuse.model import CreateTrace, CreateGeneration + + print( + "Please upgrade langfuse to v2.0.0 or higher: https://github.com/langfuse/langfuse-python/releases/tag/v2.0.1" + ) + + trace = self.Langfuse.trace( + CreateTrace( + name=metadata.get("generation_name", "litellm-completion"), + input=input, + output=output, + userId=user_id, + ) + ) + + trace.generation( + CreateGeneration( + name=metadata.get("generation_name", "litellm-completion"), + startTime=start_time, + endTime=end_time, + model=kwargs["model"], + modelParameters=optional_params, + input=input, + output=output, + usage={ + "prompt_tokens": response_obj["usage"]["prompt_tokens"], + "completion_tokens": response_obj["usage"]["completion_tokens"], + }, + metadata=metadata, + ) + ) + + def _log_langfuse_v2( + self, + user_id, + metadata, + output, + start_time, + end_time, + kwargs, + optional_params, + input, + response_obj, + ): + trace = self.Langfuse.trace( + name=metadata.get("generation_name", "litellm-completion"), + input=input, + output=output, + user_id=metadata.get("trace_user_id", user_id), + id=metadata.get("trace_id", None), + ) + + trace.generation( + name=metadata.get("generation_name", "litellm-completion"), + id=metadata.get("generation_id", None), + startTime=start_time, + endTime=end_time, + model=kwargs["model"], + modelParameters=optional_params, + input=input, + output=output, + usage={ + "prompt_tokens": response_obj["usage"]["prompt_tokens"], + "completion_tokens": response_obj["usage"]["completion_tokens"], + }, + metadata=metadata, + ) diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py new file mode 100644 index 0000000000000000000000000000000000000000..d951d69244a8a3ada01d3d4c3287996f0498e57e --- /dev/null +++ b/litellm/integrations/langsmith.py @@ -0,0 +1,75 @@ +#### What this does #### +# On success, logs events to Langsmith +import dotenv, os +import requests +import requests +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class LangsmithLogger: + # Class variables or attributes + def __init__(self): + self.langsmith_api_key = os.getenv("LANGSMITH_API_KEY") + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + # inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb + metadata = {} + if "litellm_params" in kwargs: + metadata = kwargs["litellm_params"].get("metadata", {}) + # set project name and run_name for langsmith logging + # users can pass project_name and run name to litellm.completion() + # Example: litellm.completion(model, messages, metadata={"project_name": "my-litellm-project", "run_name": "my-langsmith-run"}) + # if not set litellm will use default project_name = litellm-completion, run_name = LLMRun + project_name = metadata.get("project_name", "litellm-completion") + run_name = metadata.get("run_name", "LLMRun") + print_verbose( + f"Langsmith Logging - project_name: {project_name}, run_name {run_name}" + ) + try: + print_verbose( + f"Langsmith Logging - Enters logging function for model {kwargs}" + ) + import requests + import datetime + from datetime import timezone + + try: + start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat() + end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat() + except: + start_time = datetime.datetime.utcnow().isoformat() + end_time = datetime.datetime.utcnow().isoformat() + + # filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs + new_kwargs = {} + for key in kwargs: + value = kwargs[key] + if key == "start_time" or key == "end_time": + pass + elif type(value) != dict: + new_kwargs[key] = value + + requests.post( + "https://api.smith.langchain.com/runs", + json={ + "name": run_name, + "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain" + "inputs": {**new_kwargs}, + "outputs": response_obj.json(), + "session_name": project_name, + "start_time": start_time, + "end_time": end_time, + }, + headers={"x-api-key": self.langsmith_api_key}, + ) + print_verbose( + f"Langsmith Layer Logging - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/litedebugger.py b/litellm/integrations/litedebugger.py new file mode 100644 index 0000000000000000000000000000000000000000..0b4ef03ad237d37ad4053fd132fac9b1d4aa6358 --- /dev/null +++ b/litellm/integrations/litedebugger.py @@ -0,0 +1,262 @@ +import requests, traceback, json, os +import types + + +class LiteDebugger: + user_email = None + dashboard_url = None + + def __init__(self, email=None): + self.api_url = "https://api.litellm.ai/debugger" + self.validate_environment(email) + pass + + def validate_environment(self, email): + try: + self.user_email = ( + email or os.getenv("LITELLM_TOKEN") or os.getenv("LITELLM_EMAIL") + ) + if ( + self.user_email == None + ): # if users are trying to use_client=True but token not set + raise ValueError( + "litellm.use_client = True but no token or email passed. Please set it in litellm.token" + ) + self.dashboard_url = "https://admin.litellm.ai/" + self.user_email + try: + print( + f"\033[92mHere's your LiteLLM Dashboard 👉 \033[94m\033[4m{self.dashboard_url}\033[0m" + ) + except: + print(f"Here's your LiteLLM Dashboard 👉 {self.dashboard_url}") + if self.user_email == None: + raise ValueError( + "[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_TOKEN. Set it in your environment. Eg.: os.environ['LITELLM_TOKEN']= " + ) + except Exception as e: + raise ValueError( + "[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_TOKEN. Set it in your environment. Eg.: os.environ['LITELLM_TOKEN']= " + ) + + def input_log_event( + self, + model, + messages, + end_user, + litellm_call_id, + call_type, + print_verbose, + litellm_params, + optional_params, + ): + print_verbose( + f"LiteDebugger: Pre-API Call Logging for call id {litellm_call_id}" + ) + try: + print_verbose( + f"LiteLLMDebugger: Logging - Enters input logging function for model {model}" + ) + + def remove_key_value(dictionary, key): + new_dict = dictionary.copy() # Create a copy of the original dictionary + new_dict.pop(key) # Remove the specified key-value pair from the copy + return new_dict + + updated_litellm_params = remove_key_value(litellm_params, "logger_fn") + + if call_type == "embedding": + for ( + message + ) in ( + messages + ): # assuming the input is a list as required by the embedding function + litellm_data_obj = { + "model": model, + "messages": [{"role": "user", "content": message}], + "end_user": end_user, + "status": "initiated", + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + "litellm_params": updated_litellm_params, + "optional_params": optional_params, + } + print_verbose( + f"LiteLLMDebugger: Logging - logged data obj {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: embedding api response - {response.text}") + elif call_type == "completion": + litellm_data_obj = { + "model": model, + "messages": messages + if isinstance(messages, list) + else [{"role": "user", "content": messages}], + "end_user": end_user, + "status": "initiated", + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + "litellm_params": updated_litellm_params, + "optional_params": optional_params, + } + print_verbose( + f"LiteLLMDebugger: Logging - logged data obj {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose( + f"LiteDebugger: completion api response - {response.text}" + ) + except: + print_verbose( + f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}" + ) + pass + + def post_call_log_event( + self, original_response, litellm_call_id, print_verbose, call_type, stream + ): + print_verbose( + f"LiteDebugger: Post-API Call Logging for call id {litellm_call_id}" + ) + try: + if call_type == "embedding": + litellm_data_obj = { + "status": "received", + "additional_details": { + "original_response": str( + original_response["data"][0]["embedding"][:5] + ) + }, # don't store the entire vector + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + } + elif call_type == "completion" and not stream: + litellm_data_obj = { + "status": "received", + "additional_details": {"original_response": original_response}, + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + } + elif call_type == "completion" and stream: + litellm_data_obj = { + "status": "received", + "additional_details": { + "original_response": "Streamed response" + if isinstance(original_response, types.GeneratorType) + else original_response + }, + "litellm_call_id": litellm_call_id, + "user_email": self.user_email, + } + print_verbose(f"litedebugger post-call data object - {litellm_data_obj}") + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: api response - {response.text}") + except: + print_verbose( + f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}" + ) + + def log_event( + self, + end_user, + response_obj, + start_time, + end_time, + litellm_call_id, + print_verbose, + call_type, + stream=False, + ): + print_verbose( + f"LiteDebugger: Success/Failure Call Logging for call id {litellm_call_id}" + ) + try: + print_verbose( + f"LiteLLMDebugger: Success/Failure Logging - Enters handler logging function for function {call_type} and stream set to {stream} with response object {response_obj}" + ) + total_cost = 0 # [TODO] implement cost tracking + response_time = (end_time - start_time).total_seconds() + if call_type == "completion" and stream == False: + litellm_data_obj = { + "response_time": response_time, + "total_cost": total_cost, + "response": response_obj["choices"][0]["message"]["content"], + "litellm_call_id": litellm_call_id, + "status": "success", + } + print_verbose( + f"LiteDebugger: Logging - final data object: {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + elif call_type == "embedding": + litellm_data_obj = { + "response_time": response_time, + "total_cost": total_cost, + "response": str(response_obj["data"][0]["embedding"][:5]), + "litellm_call_id": litellm_call_id, + "status": "success", + } + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + elif call_type == "completion" and stream == True: + if len(response_obj["content"]) > 0: # don't log the empty strings + litellm_data_obj = { + "response_time": response_time, + "total_cost": total_cost, + "response": response_obj["content"], + "litellm_call_id": litellm_call_id, + "status": "success", + } + print_verbose( + f"LiteDebugger: Logging - final data object: {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + elif "error" in response_obj: + if "Unable to map your input to a model." in response_obj["error"]: + total_cost = 0 + litellm_data_obj = { + "response_time": response_time, + "model": response_obj["model"], + "total_cost": total_cost, + "error": response_obj["error"], + "end_user": end_user, + "litellm_call_id": litellm_call_id, + "status": "failure", + "user_email": self.user_email, + } + print_verbose( + f"LiteDebugger: Logging - final data object: {litellm_data_obj}" + ) + response = requests.post( + url=self.api_url, + headers={"content-type": "application/json"}, + data=json.dumps(litellm_data_obj), + ) + print_verbose(f"LiteDebugger: api response - {response.text}") + except: + print_verbose( + f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}" + ) + pass diff --git a/litellm/integrations/llmonitor.py b/litellm/integrations/llmonitor.py new file mode 100644 index 0000000000000000000000000000000000000000..ff4c3990f83f2f5ffe0ce8573ca5c19cdafea185 --- /dev/null +++ b/litellm/integrations/llmonitor.py @@ -0,0 +1,127 @@ +#### What this does #### +# On success + failure, log events to aispend.io +import datetime +import traceback +import dotenv +import os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv + + +# convert to {completion: xx, tokens: xx} +def parse_usage(usage): + return { + "completion": usage["completion_tokens"] if "completion_tokens" in usage else 0, + "prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0, + } + + +def parse_messages(input): + if input is None: + return None + + def clean_message(message): + # if is strin, return as is + if isinstance(message, str): + return message + + if "message" in message: + return clean_message(message["message"]) + text = message["content"] + if text == None: + text = message.get("function_call", None) + + return { + "role": message["role"], + "text": text, + } + + if isinstance(input, list): + if len(input) == 1: + return clean_message(input[0]) + else: + return [clean_message(msg) for msg in input] + else: + return clean_message(input) + + +class LLMonitorLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.api_url = os.getenv("LLMONITOR_API_URL") or "https://app.llmonitor.com" + self.app_id = os.getenv("LLMONITOR_APP_ID") + + def log_event( + self, + type, + event, + run_id, + model, + print_verbose, + input=None, + user_id=None, + response_obj=None, + start_time=datetime.datetime.now(), + end_time=datetime.datetime.now(), + error=None, + ): + # Method definition + try: + print_verbose(f"LLMonitor Logging - Logging request for model {model}") + + if response_obj: + usage = ( + parse_usage(response_obj["usage"]) + if "usage" in response_obj + else None + ) + output = response_obj["choices"] if "choices" in response_obj else None + else: + usage = None + output = None + + if error: + error_obj = {"stack": error} + + else: + error_obj = None + + data = [ + { + "type": type, + "name": model, + "runId": run_id, + "app": self.app_id, + "event": "start", + "timestamp": start_time.isoformat(), + "userId": user_id, + "input": parse_messages(input), + }, + { + "type": type, + "runId": run_id, + "app": self.app_id, + "event": event, + "error": error_obj, + "timestamp": end_time.isoformat(), + "userId": user_id, + "output": parse_messages(output), + "tokensUsage": usage, + }, + ] + + print_verbose(f"LLMonitor Logging - final data object: {data}") + + response = requests.post( + self.api_url + "/api/report", + headers={"Content-Type": "application/json"}, + json={"events": data}, + ) + + print_verbose(f"LLMonitor Logging - response: {response}") + except: + # traceback.print_exc() + print_verbose(f"LLMonitor Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/prompt_layer.py b/litellm/integrations/prompt_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..4bf2089de2b445b43fe60bbe3ea321e43a7cd1d7 --- /dev/null +++ b/litellm/integrations/prompt_layer.py @@ -0,0 +1,72 @@ +#### What this does #### +# On success, logs events to Promptlayer +import dotenv, os +import requests +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class PromptLayerLogger: + # Class variables or attributes + def __init__(self): + # Instance variables + self.key = os.getenv("PROMPTLAYER_API_KEY") + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + try: + new_kwargs = {} + new_kwargs["model"] = kwargs["model"] + new_kwargs["messages"] = kwargs["messages"] + + # add kwargs["optional_params"] to new_kwargs + for optional_param in kwargs["optional_params"]: + new_kwargs[optional_param] = kwargs["optional_params"][optional_param] + + print_verbose( + f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}" + ) + + request_response = requests.post( + "https://api.promptlayer.com/rest/track-request", + json={ + "function_name": "openai.ChatCompletion.create", + "kwargs": new_kwargs, + "tags": ["hello", "world"], + "request_response": dict(response_obj), + "request_start_time": int(start_time.timestamp()), + "request_end_time": int(end_time.timestamp()), + "api_key": self.key, + # Optional params for PromptLayer + # "prompt_id": "", + # "prompt_input_variables": "", + # "prompt_version":1, + }, + ) + print_verbose( + f"Prompt Layer Logging: success - final response object: {request_response.text}" + ) + response_json = request_response.json() + if "success" not in request_response.json(): + raise Exception("Promptlayer did not successfully log the response!") + + if "request_id" in response_json: + print(kwargs["litellm_params"]["metadata"]) + if kwargs["litellm_params"]["metadata"] is not None: + response = requests.post( + "https://api.promptlayer.com/rest/track-metadata", + json={ + "request_id": response_json["request_id"], + "api_key": self.key, + "metadata": kwargs["litellm_params"]["metadata"], + }, + ) + print_verbose( + f"Prompt Layer Logging: success - metadata post response object: {response.text}" + ) + + except: + print_verbose(f"error: Prompt Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/s3.py b/litellm/integrations/s3.py new file mode 100644 index 0000000000000000000000000000000000000000..0187d13d6a67a6c4125611d3bfc97926c86bb5f4 --- /dev/null +++ b/litellm/integrations/s3.py @@ -0,0 +1,150 @@ +#### What this does #### +# On success + failure, log events to Supabase + +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime, subprocess, sys +import litellm, uuid +from litellm._logging import print_verbose + + +class S3Logger: + # Class variables or attributes + def __init__( + self, + s3_bucket_name=None, + s3_region_name=None, + s3_api_version=None, + s3_use_ssl=True, + s3_verify=None, + s3_endpoint_url=None, + s3_aws_access_key_id=None, + s3_aws_secret_access_key=None, + s3_aws_session_token=None, + s3_config=None, + **kwargs, + ): + import boto3 + + try: + print_verbose("in init s3 logger") + + if litellm.s3_callback_params is not None: + # read in .env variables - example os.environ/AWS_BUCKET_NAME + for key, value in litellm.s3_callback_params.items(): + if type(value) is str and value.startswith("os.environ/"): + litellm.s3_callback_params[key] = litellm.get_secret(value) + # now set s3 params from litellm.s3_logger_params + s3_bucket_name = litellm.s3_callback_params.get("s3_bucket_name") + s3_region_name = litellm.s3_callback_params.get("s3_region_name") + s3_api_version = litellm.s3_callback_params.get("s3_api_version") + s3_use_ssl = litellm.s3_callback_params.get("s3_use_ssl") + s3_verify = litellm.s3_callback_params.get("s3_verify") + s3_endpoint_url = litellm.s3_callback_params.get("s3_endpoint_url") + s3_aws_access_key_id = litellm.s3_callback_params.get( + "s3_aws_access_key_id" + ) + s3_aws_secret_access_key = litellm.s3_callback_params.get( + "s3_aws_secret_access_key" + ) + s3_aws_session_token = litellm.s3_callback_params.get( + "s3_aws_session_token" + ) + s3_config = litellm.s3_callback_params.get("s3_config") + # done reading litellm.s3_callback_params + + self.bucket_name = s3_bucket_name + # Create an S3 client with custom endpoint URL + self.s3_client = boto3.client( + "s3", + region_name=s3_region_name, + endpoint_url=s3_endpoint_url, + api_version=s3_api_version, + use_ssl=s3_use_ssl, + verify=s3_verify, + aws_access_key_id=s3_aws_access_key_id, + aws_secret_access_key=s3_aws_secret_access_key, + aws_session_token=s3_aws_session_token, + config=s3_config, + **kwargs, + ) + except Exception as e: + print_verbose(f"Got exception on init s3 client {str(e)}") + raise e + + async def _async_log_event( + self, kwargs, response_obj, start_time, end_time, print_verbose + ): + self.log_event(kwargs, response_obj, start_time, end_time, print_verbose) + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + try: + print_verbose(f"s3 Logging - Enters logging function for model {kwargs}") + + # construct payload to send to s3 + # follows the same params as langfuse.py + litellm_params = kwargs.get("litellm_params", {}) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) # if litellm_params['metadata'] == None + messages = kwargs.get("messages") + optional_params = kwargs.get("optional_params", {}) + call_type = kwargs.get("call_type", "litellm.completion") + cache_hit = kwargs.get("cache_hit", False) + usage = response_obj["usage"] + id = response_obj.get("id", str(uuid.uuid4())) + + # Build the initial payload + payload = { + "id": id, + "call_type": call_type, + "cache_hit": cache_hit, + "startTime": start_time, + "endTime": end_time, + "model": kwargs.get("model", ""), + "user": kwargs.get("user", ""), + "modelParameters": optional_params, + "messages": messages, + "response": response_obj, + "usage": usage, + "metadata": metadata, + } + + # Ensure everything in the payload is converted to str + for key, value in payload.items(): + try: + payload[key] = str(value) + except: + # non blocking if it can't cast to a str + pass + + s3_object_key = ( + payload["id"] + "-time=" + str(start_time) + ) # we need the s3 key to include the time, so we log cache hits too + + import json + + payload = json.dumps(payload) + + print_verbose(f"\ns3 Logger - Logging payload = {payload}") + + response = self.s3_client.put_object( + Bucket=self.bucket_name, + Key=s3_object_key, + Body=payload, + ContentType="application/json", + ContentLanguage="en", + ContentDisposition=f'inline; filename="{key}.json"', + ) + + print_verbose(f"Response from s3:{str(response)}") + + print_verbose(f"s3 Layer Logging - final response object: {response_obj}") + return response + except Exception as e: + traceback.print_exc() + print_verbose(f"s3 Layer Error - {str(e)}\n{traceback.format_exc()}") + pass diff --git a/litellm/integrations/supabase.py b/litellm/integrations/supabase.py new file mode 100644 index 0000000000000000000000000000000000000000..a99e4abc4b6a6dd81de2bb136bf89c1569e622ac --- /dev/null +++ b/litellm/integrations/supabase.py @@ -0,0 +1,117 @@ +#### What this does #### +# On success + failure, log events to Supabase + +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime, subprocess, sys +import litellm + + +class Supabase: + # Class variables or attributes + supabase_table_name = "request_logs" + + def __init__(self): + # Instance variables + self.supabase_url = os.getenv("SUPABASE_URL") + self.supabase_key = os.getenv("SUPABASE_KEY") + try: + import supabase + except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", "supabase"]) + import supabase + self.supabase_client = supabase.create_client( + self.supabase_url, self.supabase_key + ) + + def input_log_event( + self, model, messages, end_user, litellm_call_id, print_verbose + ): + try: + print_verbose( + f"Supabase Logging - Enters input logging function for model {model}" + ) + supabase_data_obj = { + "model": model, + "messages": messages, + "end_user": end_user, + "status": "initiated", + "litellm_call_id": litellm_call_id, + } + data, count = ( + self.supabase_client.table(self.supabase_table_name) + .insert(supabase_data_obj) + .execute() + ) + print_verbose(f"data: {data}") + except: + print_verbose(f"Supabase Logging Error - {traceback.format_exc()}") + pass + + def log_event( + self, + model, + messages, + end_user, + response_obj, + start_time, + end_time, + litellm_call_id, + print_verbose, + ): + try: + print_verbose( + f"Supabase Logging - Enters logging function for model {model}, response_obj: {response_obj}" + ) + + total_cost = litellm.completion_cost(completion_response=response_obj) + + response_time = (end_time - start_time).total_seconds() + if "choices" in response_obj: + supabase_data_obj = { + "response_time": response_time, + "model": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "response": response_obj["choices"][0]["message"]["content"], + "end_user": end_user, + "litellm_call_id": litellm_call_id, + "status": "success", + } + print_verbose( + f"Supabase Logging - final data object: {supabase_data_obj}" + ) + data, count = ( + self.supabase_client.table(self.supabase_table_name) + .upsert(supabase_data_obj, on_conflict="litellm_call_id") + .execute() + ) + elif "error" in response_obj: + if "Unable to map your input to a model." in response_obj["error"]: + total_cost = 0 + supabase_data_obj = { + "response_time": response_time, + "model": response_obj["model"], + "total_cost": total_cost, + "messages": messages, + "error": response_obj["error"], + "end_user": end_user, + "litellm_call_id": litellm_call_id, + "status": "failure", + } + print_verbose( + f"Supabase Logging - final data object: {supabase_data_obj}" + ) + data, count = ( + self.supabase_client.table(self.supabase_table_name) + .upsert(supabase_data_obj, on_conflict="litellm_call_id") + .execute() + ) + + except: + # traceback.print_exc() + print_verbose(f"Supabase Logging Error - {traceback.format_exc()}") + pass diff --git a/litellm/integrations/traceloop.py b/litellm/integrations/traceloop.py new file mode 100644 index 0000000000000000000000000000000000000000..bbdb9a1b0aac1ecdf84b86fa066f9228c9e4a1b1 --- /dev/null +++ b/litellm/integrations/traceloop.py @@ -0,0 +1,114 @@ +class TraceloopLogger: + def __init__(self): + from traceloop.sdk.tracing.tracing import TracerWrapper + from traceloop.sdk import Traceloop + + Traceloop.init(app_name="Litellm-Server", disable_batch=True) + self.tracer_wrapper = TracerWrapper() + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + from opentelemetry.trace import SpanKind + from opentelemetry.semconv.ai import SpanAttributes + + try: + tracer = self.tracer_wrapper.get_tracer() + + model = kwargs.get("model") + + # LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK + if kwargs.get("litellm_params").get("custom_llm_provider") == "openai": + return + + optional_params = kwargs.get("optional_params", {}) + with tracer.start_as_current_span( + "litellm.completion", + kind=SpanKind.CLIENT, + ) as span: + if span.is_recording(): + span.set_attribute( + SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model") + ) + if "stop" in optional_params: + span.set_attribute( + SpanAttributes.LLM_CHAT_STOP_SEQUENCES, + optional_params.get("stop"), + ) + if "frequency_penalty" in optional_params: + span.set_attribute( + SpanAttributes.LLM_FREQUENCY_PENALTY, + optional_params.get("frequency_penalty"), + ) + if "presence_penalty" in optional_params: + span.set_attribute( + SpanAttributes.LLM_PRESENCE_PENALTY, + optional_params.get("presence_penalty"), + ) + if "top_p" in optional_params: + span.set_attribute( + SpanAttributes.LLM_TOP_P, optional_params.get("top_p") + ) + if "tools" in optional_params or "functions" in optional_params: + span.set_attribute( + SpanAttributes.LLM_REQUEST_FUNCTIONS, + optional_params.get( + "tools", optional_params.get("functions") + ), + ) + if "user" in optional_params: + span.set_attribute( + SpanAttributes.LLM_USER, optional_params.get("user") + ) + if "max_tokens" in optional_params: + span.set_attribute( + SpanAttributes.LLM_REQUEST_MAX_TOKENS, + kwargs.get("max_tokens"), + ) + if "temperature" in optional_params: + span.set_attribute( + SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature") + ) + + for idx, prompt in enumerate(kwargs.get("messages")): + span.set_attribute( + f"{SpanAttributes.LLM_PROMPTS}.{idx}.role", + prompt.get("role"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_PROMPTS}.{idx}.content", + prompt.get("content"), + ) + + span.set_attribute( + SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model") + ) + usage = response_obj.get("usage") + if usage: + span.set_attribute( + SpanAttributes.LLM_USAGE_TOTAL_TOKENS, + usage.get("total_tokens"), + ) + span.set_attribute( + SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, + usage.get("completion_tokens"), + ) + span.set_attribute( + SpanAttributes.LLM_USAGE_PROMPT_TOKENS, + usage.get("prompt_tokens"), + ) + + for idx, choice in enumerate(response_obj.get("choices")): + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason", + choice.get("finish_reason"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role", + choice.get("message").get("role"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content", + choice.get("message").get("content"), + ) + + except Exception as e: + print_verbose(f"Traceloop Layer Error - {e}") diff --git a/litellm/integrations/weights_biases.py b/litellm/integrations/weights_biases.py new file mode 100644 index 0000000000000000000000000000000000000000..53e6070a5bfabb388b1b8c3d5095ed3a4ac00c82 --- /dev/null +++ b/litellm/integrations/weights_biases.py @@ -0,0 +1,223 @@ +imported_openAIResponse = True +try: + import io + import logging + import sys + from typing import Any, Dict, List, Optional, TypeVar + + from wandb.sdk.data_types import trace_tree + + if sys.version_info >= (3, 8): + from typing import Literal, Protocol + else: + from typing_extensions import Literal, Protocol + + logger = logging.getLogger(__name__) + + K = TypeVar("K", bound=str) + V = TypeVar("V") + + class OpenAIResponse(Protocol[K, V]): # type: ignore + # contains a (known) object attribute + object: Literal["chat.completion", "edit", "text_completion"] + + def __getitem__(self, key: K) -> V: + ... # pragma: no cover + + def get(self, key: K, default: Optional[V] = None) -> Optional[V]: + ... # pragma: no cover + + class OpenAIRequestResponseResolver: + def __call__( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> Optional[trace_tree.WBTraceTree]: + try: + if response["object"] == "edit": + return self._resolve_edit(request, response, time_elapsed) + elif response["object"] == "text_completion": + return self._resolve_completion(request, response, time_elapsed) + elif response["object"] == "chat.completion": + return self._resolve_chat_completion( + request, response, time_elapsed + ) + else: + logger.info(f"Unknown OpenAI response object: {response['object']}") + except Exception as e: + logger.warning(f"Failed to resolve request/response: {e}") + return None + + @staticmethod + def results_to_trace_tree( + request: Dict[str, Any], + response: OpenAIResponse, + results: List[trace_tree.Result], + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Converts the request, response, and results into a trace tree. + + params: + request: The request dictionary + response: The response object + results: A list of results object + time_elapsed: The time elapsed in seconds + returns: + A wandb trace tree object. + """ + start_time_ms = int(round(response["created"] * 1000)) + end_time_ms = start_time_ms + int(round(time_elapsed * 1000)) + span = trace_tree.Span( + name=f"{response.get('model', 'openai')}_{response['object']}_{response.get('created')}", + attributes=dict(response), # type: ignore + start_time_ms=start_time_ms, + end_time_ms=end_time_ms, + span_kind=trace_tree.SpanKind.LLM, + results=results, + ) + model_obj = {"request": request, "response": response, "_kind": "openai"} + return trace_tree.WBTraceTree(root_span=span, model_dict=model_obj) + + def _resolve_edit( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Edit`.""" + request_str = ( + f"\n\n**Instruction**: {request['instruction']}\n\n" + f"**Input**: {request['input']}\n" + ) + choices = [ + f"\n\n**Edited**: {choice['text']}\n" for choice in response["choices"] + ] + + return self._request_response_result_to_trace( + request=request, + response=response, + request_str=request_str, + choices=choices, + time_elapsed=time_elapsed, + ) + + def _resolve_completion( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Completion`.""" + request_str = f"\n\n**Prompt**: {request['prompt']}\n" + choices = [ + f"\n\n**Completion**: {choice['text']}\n" + for choice in response["choices"] + ] + + return self._request_response_result_to_trace( + request=request, + response=response, + request_str=request_str, + choices=choices, + time_elapsed=time_elapsed, + ) + + def _resolve_chat_completion( + self, + request: Dict[str, Any], + response: OpenAIResponse, + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Completion`.""" + prompt = io.StringIO() + for message in request["messages"]: + prompt.write(f"\n\n**{message['role']}**: {message['content']}\n") + request_str = prompt.getvalue() + + choices = [ + f"\n\n**{choice['message']['role']}**: {choice['message']['content']}\n" + for choice in response["choices"] + ] + + return self._request_response_result_to_trace( + request=request, + response=response, + request_str=request_str, + choices=choices, + time_elapsed=time_elapsed, + ) + + def _request_response_result_to_trace( + self, + request: Dict[str, Any], + response: OpenAIResponse, + request_str: str, + choices: List[str], + time_elapsed: float, + ) -> trace_tree.WBTraceTree: + """Resolves the request and response objects for `openai.Completion`.""" + results = [ + trace_tree.Result( + inputs={"request": request_str}, + outputs={"response": choice}, + ) + for choice in choices + ] + trace = self.results_to_trace_tree(request, response, results, time_elapsed) + return trace + +except: + imported_openAIResponse = False + + +#### What this does #### +# On success, logs events to Langfuse +import dotenv, os +import requests +import requests +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback + + +class WeightsBiasesLogger: + # Class variables or attributes + def __init__(self): + try: + import wandb + except: + raise Exception( + "\033[91m wandb not installed, try running 'pip install wandb' to fix this error\033[0m" + ) + if imported_openAIResponse == False: + raise Exception( + "\033[91m wandb not installed, try running 'pip install wandb' to fix this error\033[0m" + ) + self.resolver = OpenAIRequestResponseResolver() + + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + # Method definition + import wandb + + try: + print_verbose(f"W&B Logging - Enters logging function for model {kwargs}") + run = wandb.init() + print_verbose(response_obj) + + trace = self.resolver( + kwargs, response_obj, (end_time - start_time).total_seconds() + ) + + if trace is not None: + run.log({"trace": trace}) + + run.finish() + print_verbose( + f"W&B Logging Logging - final response object: {response_obj}" + ) + except: + # traceback.print_exc() + print_verbose(f"W&B Logging Layer Error - {traceback.format_exc()}") + pass diff --git a/litellm/llms/__init__.py b/litellm/llms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834 --- /dev/null +++ b/litellm/llms/__init__.py @@ -0,0 +1 @@ +from . import * diff --git a/litellm/llms/ai21.py b/litellm/llms/ai21.py new file mode 100644 index 0000000000000000000000000000000000000000..73d5afebe89f3fc56c9c242a158b1a0dec43d972 --- /dev/null +++ b/litellm/llms/ai21.py @@ -0,0 +1,212 @@ +import os, types, traceback +import json +from enum import Enum +import requests +import time, httpx +from typing import Callable, Optional +from litellm.utils import ModelResponse, Choices, Message +import litellm + + +class AI21Error(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.ai21.com/studio/v1/" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class AI21Config: + """ + Reference: https://docs.ai21.com/reference/j2-complete-ref + + The class `AI21Config` provides configuration for the AI21's API interface. Below are the parameters: + + - `numResults` (int32): Number of completions to sample and return. Optional, default is 1. If the temperature is greater than 0 (non-greedy decoding), a value greater than 1 can be meaningful. + + - `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`. + + - `minTokens` (int32): The minimum number of tokens to generate per result. Optional, default is 0. If `stopSequences` are given, they are ignored until `minTokens` are generated. + + - `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding. + + - `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass. + + - `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional. + + - `topKReturn` (int32): Range between 0 to 10, including both. Optional, default is 0. Specifies the top-K alternative tokens to return. A non-zero value includes the string representations and log-probabilities for each of the top-K alternatives at each position. + + - `frequencyPenalty` (object): Placeholder for frequency penalty object. + + - `presencePenalty` (object): Placeholder for presence penalty object. + + - `countPenalty` (object): Placeholder for count penalty object. + """ + + numResults: Optional[int] = None + maxTokens: Optional[int] = None + minTokens: Optional[int] = None + temperature: Optional[float] = None + topP: Optional[float] = None + stopSequences: Optional[list] = None + topKReturn: Optional[int] = None + frequencePenalty: Optional[dict] = None + presencePenalty: Optional[dict] = None + countPenalty: Optional[dict] = None + + def __init__( + self, + numResults: Optional[int] = None, + maxTokens: Optional[int] = None, + minTokens: Optional[int] = None, + temperature: Optional[float] = None, + topP: Optional[float] = None, + stopSequences: Optional[list] = None, + topKReturn: Optional[int] = None, + frequencePenalty: Optional[dict] = None, + presencePenalty: Optional[dict] = None, + countPenalty: Optional[dict] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing AI21 API Key - A call is being made to ai21 but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "content-type": "application/json", + "Authorization": "Bearer " + api_key, + } + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + model = model + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + + ## Load Config + config = litellm.AI21Config.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > ai21_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "prompt": prompt, + # "instruction": prompt, # some baseten models require the prompt to be passed in via the 'instruction' kwarg + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + api_base + model + "/complete", headers=headers, data=json.dumps(data) + ) + if response.status_code != 200: + raise AI21Error(status_code=response.status_code, message=response.text) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + ## RESPONSE OBJECT + completion_response = response.json() + try: + choices_list = [] + for idx, item in enumerate(completion_response["completions"]): + if len(item["data"]["text"]) > 0: + message_obj = Message(content=item["data"]["text"]) + else: + message_obj = Message(content=None) + choice_obj = Choices( + finish_reason=item["finishReason"]["reason"], + index=idx + 1, + message=message_obj, + ) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + raise AI21Error( + message=traceback.format_exc(), status_code=response.status_code + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + model_response["usage"] = { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": prompt_tokens + completion_tokens, + } + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/aleph_alpha.py b/litellm/llms/aleph_alpha.py new file mode 100644 index 0000000000000000000000000000000000000000..7168e7369cccdf61e8536afcaaf53d15dd195aa5 --- /dev/null +++ b/litellm/llms/aleph_alpha.py @@ -0,0 +1,304 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, Choices, Message, Usage +import httpx + + +class AlephAlphaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.aleph-alpha.com/complete" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class AlephAlphaConfig: + """ + Reference: https://docs.aleph-alpha.com/api/complete/ + + The `AlephAlphaConfig` class represents the configuration for the Aleph Alpha API. Here are the properties: + + - `maximum_tokens` (integer, required): The maximum number of tokens to be generated by the completion. The sum of input tokens and maximum tokens may not exceed 2048. + + - `minimum_tokens` (integer, optional; default value: 0): Generate at least this number of tokens before an end-of-text token is generated. + + - `echo` (boolean, optional; default value: false): Whether to echo the prompt in the completion. + + - `temperature` (number, nullable; default value: 0): Adjusts how creatively the model generates outputs. Use combinations of temperature, top_k, and top_p sensibly. + + - `top_k` (integer, nullable; default value: 0): Introduces randomness into token generation by considering the top k most likely options. + + - `top_p` (number, nullable; default value: 0): Adds randomness by considering the smallest set of tokens whose cumulative probability exceeds top_p. + + - `presence_penalty`, `frequency_penalty`, `sequence_penalty` (number, nullable; default value: 0): Various penalties that can reduce repetition. + + - `sequence_penalty_min_length` (integer; default value: 2): Minimum number of tokens to be considered as a sequence. + + - `repetition_penalties_include_prompt`, `repetition_penalties_include_completion`, `use_multiplicative_presence_penalty`,`use_multiplicative_frequency_penalty`,`use_multiplicative_sequence_penalty` (boolean, nullable; default value: false): Various settings that adjust how the repetition penalties are applied. + + - `penalty_bias` (string, nullable): Text used in addition to the penalized tokens for repetition penalties. + + - `penalty_exceptions` (string[], nullable): Strings that may be generated without penalty. + + - `penalty_exceptions_include_stop_sequences` (boolean, nullable; default value: true): Include all stop_sequences in penalty_exceptions. + + - `best_of` (integer, nullable; default value: 1): The number of completions will be generated on the server side. + + - `n` (integer, nullable; default value: 1): The number of completions to return. + + - `logit_bias` (object, nullable): Adjust the logit scores before sampling. + + - `log_probs` (integer, nullable): Number of top log probabilities for each token generated. + + - `stop_sequences` (string[], nullable): List of strings that will stop generation if they're generated. + + - `tokens` (boolean, nullable; default value: false): Flag indicating whether individual tokens of the completion should be returned or not. + + - `raw_completion` (boolean; default value: false): if True, the raw completion of the model will be returned. + + - `disable_optimizations` (boolean, nullable; default value: false): Disables any applied optimizations to both your prompt and completion. + + - `completion_bias_inclusion`, `completion_bias_exclusion` (string[], default value: []): Set of strings to bias the generation of tokens. + + - `completion_bias_inclusion_first_token_only`, `completion_bias_exclusion_first_token_only` (boolean; default value: false): Consider only the first token for the completion_bias_inclusion/exclusion. + + - `contextual_control_threshold` (number, nullable): Control over how similar tokens are controlled. + + - `control_log_additive` (boolean; default value: true): Method of applying control to attention scores. + """ + + maximum_tokens: Optional[ + int + ] = litellm.max_tokens # aleph alpha requires max tokens + minimum_tokens: Optional[int] = None + echo: Optional[bool] = None + temperature: Optional[int] = None + top_k: Optional[int] = None + top_p: Optional[int] = None + presence_penalty: Optional[int] = None + frequency_penalty: Optional[int] = None + sequence_penalty: Optional[int] = None + sequence_penalty_min_length: Optional[int] = None + repetition_penalties_include_prompt: Optional[bool] = None + repetition_penalties_include_completion: Optional[bool] = None + use_multiplicative_presence_penalty: Optional[bool] = None + use_multiplicative_frequency_penalty: Optional[bool] = None + use_multiplicative_sequence_penalty: Optional[bool] = None + penalty_bias: Optional[str] = None + penalty_exceptions_include_stop_sequences: Optional[bool] = None + best_of: Optional[int] = None + n: Optional[int] = None + logit_bias: Optional[dict] = None + log_probs: Optional[int] = None + stop_sequences: Optional[list] = None + tokens: Optional[bool] = None + raw_completion: Optional[bool] = None + disable_optimizations: Optional[bool] = None + completion_bias_inclusion: Optional[list] = None + completion_bias_exclusion: Optional[list] = None + completion_bias_inclusion_first_token_only: Optional[bool] = None + completion_bias_exclusion_first_token_only: Optional[bool] = None + contextual_control_threshold: Optional[int] = None + control_log_additive: Optional[bool] = None + + def __init__( + self, + maximum_tokens: Optional[int] = None, + minimum_tokens: Optional[int] = None, + echo: Optional[bool] = None, + temperature: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[int] = None, + presence_penalty: Optional[int] = None, + frequency_penalty: Optional[int] = None, + sequence_penalty: Optional[int] = None, + sequence_penalty_min_length: Optional[int] = None, + repetition_penalties_include_prompt: Optional[bool] = None, + repetition_penalties_include_completion: Optional[bool] = None, + use_multiplicative_presence_penalty: Optional[bool] = None, + use_multiplicative_frequency_penalty: Optional[bool] = None, + use_multiplicative_sequence_penalty: Optional[bool] = None, + penalty_bias: Optional[str] = None, + penalty_exceptions_include_stop_sequences: Optional[bool] = None, + best_of: Optional[int] = None, + n: Optional[int] = None, + logit_bias: Optional[dict] = None, + log_probs: Optional[int] = None, + stop_sequences: Optional[list] = None, + tokens: Optional[bool] = None, + raw_completion: Optional[bool] = None, + disable_optimizations: Optional[bool] = None, + completion_bias_inclusion: Optional[list] = None, + completion_bias_exclusion: Optional[list] = None, + completion_bias_inclusion_first_token_only: Optional[bool] = None, + completion_bias_exclusion_first_token_only: Optional[bool] = None, + contextual_control_threshold: Optional[int] = None, + control_log_additive: Optional[bool] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, + default_max_tokens_to_sample=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.AlephAlphaConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > aleph_alpha_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + completion_url = api_base + model = model + prompt = "" + if "control" in model: # follow the ###Instruction / ###Response format + for idx, message in enumerate(messages): + if "role" in message: + if ( + idx == 0 + ): # set first message as instruction (required), let later user messages be input + prompt += f"###Instruction: {message['content']}" + else: + if message["role"] == "system": + prompt += f"###Instruction: {message['content']}" + elif message["role"] == "user": + prompt += f"###Input: {message['content']}" + else: + prompt += f"###Response: {message['content']}" + else: + prompt += f"{message['content']}" + else: + prompt = " ".join(message["content"] for message in messages) + data = { + "model": model, + "prompt": prompt, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"] if "stream" in optional_params else False, + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise AlephAlphaError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + choices_list = [] + for idx, item in enumerate(completion_response["completions"]): + if len(item["completion"]) > 0: + message_obj = Message(content=item["completion"]) + else: + message_obj = Message(content=None) + choice_obj = Choices( + finish_reason=item["finish_reason"], + index=idx + 1, + message=message_obj, + ) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except: + raise AlephAlphaError( + message=json.dumps(completion_response), + status_code=response.status_code, + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"]["content"]) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py new file mode 100644 index 0000000000000000000000000000000000000000..4df032ba00978518d7fc110076f12f95828e6c7b --- /dev/null +++ b/litellm/llms/anthropic.py @@ -0,0 +1,215 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage +import litellm +from .prompt_templates.factory import prompt_factory, custom_prompt +import httpx + + +class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + + +class AnthropicError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.anthropic.com/v1/complete" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class AnthropicConfig: + """ + Reference: https://docs.anthropic.com/claude/reference/complete_post + + to pass metadata to anthropic, it's {"user_id": "any-relevant-information"} + """ + + max_tokens_to_sample: Optional[ + int + ] = litellm.max_tokens # anthropic requires a default + stop_sequences: Optional[list] = None + temperature: Optional[int] = None + top_p: Optional[int] = None + top_k: Optional[int] = None + metadata: Optional[dict] = None + + def __init__( + self, + max_tokens_to_sample: Optional[int] = 256, # anthropic requires a default + stop_sequences: Optional[list] = None, + temperature: Optional[int] = None, + top_p: Optional[int] = None, + top_k: Optional[int] = None, + metadata: Optional[dict] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +# makes headers for API call +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "anthropic-version": "2023-06-01", + "content-type": "application/json", + "x-api-key": api_key, + } + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages, + ) + else: + prompt = prompt_factory( + model=model, messages=messages, custom_llm_provider="anthropic" + ) + + ## Load Config + config = litellm.AnthropicConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data, "api_base": api_base}, + ) + + ## COMPLETION CALL + if "stream" in optional_params and optional_params["stream"] == True: + response = requests.post( + api_base, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"], + ) + + if response.status_code != 200: + raise AnthropicError( + status_code=response.status_code, message=response.text + ) + + return response.iter_lines() + else: + response = requests.post(api_base, headers=headers, data=json.dumps(data)) + if response.status_code != 200: + raise AnthropicError( + status_code=response.status_code, message=response.text + ) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + try: + completion_response = response.json() + except: + raise AnthropicError( + message=response.text, status_code=response.status_code + ) + if "error" in completion_response: + raise AnthropicError( + message=str(completion_response["error"]), + status_code=response.status_code, + ) + else: + if len(completion_response["completion"]) > 0: + model_response["choices"][0]["message"][ + "content" + ] = completion_response["completion"] + model_response.choices[0].finish_reason = completion_response["stop_reason"] + + ## CALCULATING USAGE + prompt_tokens = len( + encoding.encode(prompt) + ) ##[TODO] use the anthropic tokenizer here + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the anthropic tokenizer here + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py new file mode 100644 index 0000000000000000000000000000000000000000..c2e1e510b6552c56eef9b9d5ec0f0199f7b126c7 --- /dev/null +++ b/litellm/llms/azure.py @@ -0,0 +1,799 @@ +from typing import Optional, Union, Any +import types, requests +from .base import BaseLLM +from litellm.utils import ( + ModelResponse, + Choices, + Message, + CustomStreamWrapper, + convert_to_model_response_object, +) +from typing import Callable, Optional +from litellm import OpenAIConfig +import litellm, json +import httpx +from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport +from openai import AzureOpenAI, AsyncAzureOpenAI + + +class AzureOpenAIError(Exception): + def __init__( + self, + status_code, + message, + request: Optional[httpx.Request] = None, + response: Optional[httpx.Response] = None, + ): + self.status_code = status_code + self.message = message + if request: + self.request = request + else: + self.request = httpx.Request(method="POST", url="https://api.openai.com/v1") + if response: + self.response = response + else: + self.response = httpx.Response( + status_code=status_code, request=self.request + ) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class AzureOpenAIConfig(OpenAIConfig): + """ + Reference: https://platform.openai.com/docs/api-reference/chat/create + + The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters:: + + - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition. + + - `function_call` (string or object): This optional parameter controls how the model calls functions. + + - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs. + + - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. + + - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. + + - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message. + + - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics. + + - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. + + - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. + + - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. + """ + + def __init__( + self, + frequency_penalty: Optional[int] = None, + function_call: Optional[Union[str, dict]] = None, + functions: Optional[list] = None, + logit_bias: Optional[dict] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[int] = None, + stop: Optional[Union[str, list]] = None, + temperature: Optional[int] = None, + top_p: Optional[int] = None, + ) -> None: + super().__init__( + frequency_penalty, + function_call, + functions, + logit_bias, + max_tokens, + n, + presence_penalty, + stop, + temperature, + top_p, + ) + + +class AzureChatCompletion(BaseLLM): + def __init__(self) -> None: + super().__init__() + + def validate_environment(self, api_key, azure_ad_token): + headers = { + "content-type": "application/json", + } + if api_key is not None: + headers["api-key"] = api_key + elif azure_ad_token is not None: + headers["Authorization"] = f"Bearer {azure_ad_token}" + return headers + + def completion( + self, + model: str, + messages: list, + model_response: ModelResponse, + api_key: str, + api_base: str, + api_version: str, + api_type: str, + azure_ad_token: str, + print_verbose: Callable, + timeout, + logging_obj, + optional_params, + litellm_params, + logger_fn, + acompletion: bool = False, + headers: Optional[dict] = None, + client=None, + ): + super().completion() + exception_mapping_worked = False + try: + if model is None or messages is None: + raise AzureOpenAIError( + status_code=422, message=f"Missing model or messages" + ) + + max_retries = optional_params.pop("max_retries", 2) + + ### CHECK IF CLOUDFLARE AI GATEWAY ### + ### if so - set the model as part of the base url + if "gateway.ai.cloudflare.com" in api_base: + ## build base url - assume api base includes resource name + if client is None: + if not api_base.endswith("/"): + api_base += "/" + api_base += f"{model}" + + azure_client_params = { + "api_version": api_version, + "base_url": f"{api_base}", + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + + if acompletion is True: + client = AsyncAzureOpenAI(**azure_client_params) + else: + client = AzureOpenAI(**azure_client_params) + + data = {"model": None, "messages": messages, **optional_params} + else: + data = { + "model": model, # type: ignore + "messages": messages, + **optional_params, + } + + if acompletion is True: + if optional_params.get("stream", False): + return self.async_streaming( + logging_obj=logging_obj, + api_base=api_base, + data=data, + model=model, + api_key=api_key, + api_version=api_version, + azure_ad_token=azure_ad_token, + timeout=timeout, + client=client, + ) + else: + return self.acompletion( + api_base=api_base, + data=data, + model_response=model_response, + api_key=api_key, + api_version=api_version, + model=model, + azure_ad_token=azure_ad_token, + timeout=timeout, + client=client, + logging_obj=logging_obj, + ) + elif "stream" in optional_params and optional_params["stream"] == True: + return self.streaming( + logging_obj=logging_obj, + api_base=api_base, + data=data, + model=model, + api_key=api_key, + api_version=api_version, + azure_ad_token=azure_ad_token, + timeout=timeout, + client=client, + ) + else: + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "headers": { + "api_key": api_key, + "azure_ad_token": azure_ad_token, + }, + "api_version": api_version, + "api_base": api_base, + "complete_input_dict": data, + }, + ) + if not isinstance(max_retries, int): + raise AzureOpenAIError( + status_code=422, message="max retries must be an int" + ) + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AzureOpenAI(**azure_client_params) + else: + azure_client = client + response = azure_client.chat.completions.create(**data, timeout=timeout) # type: ignore + stringified_response = response.model_dump() + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=stringified_response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + ) + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise AzureOpenAIError(status_code=e.status_code, message=str(e)) + else: + raise AzureOpenAIError(status_code=500, message=str(e)) + + async def acompletion( + self, + api_key: str, + api_version: str, + model: str, + api_base: str, + data: dict, + timeout: Any, + model_response: ModelResponse, + azure_ad_token: Optional[str] = None, + client=None, # this is the AsyncAzureOpenAI + logging_obj=None, + ): + response = None + try: + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError( + status_code=422, message="max retries must be an int" + ) + + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AsyncAzureOpenAI(**azure_client_params) + else: + azure_client = client + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=azure_client.api_key, + additional_args={ + "headers": {"Authorization": f"Bearer {azure_client.api_key}"}, + "api_base": azure_client._base_url._uri_reference, + "acompletion": True, + "complete_input_dict": data, + }, + ) + response = await azure_client.chat.completions.create( + **data, timeout=timeout + ) + return convert_to_model_response_object( + response_object=response.model_dump(), + model_response_object=model_response, + ) + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise e + else: + raise AzureOpenAIError(status_code=500, message=str(e)) + + def streaming( + self, + logging_obj, + api_base: str, + api_key: str, + api_version: str, + data: dict, + model: str, + timeout: Any, + azure_ad_token: Optional[str] = None, + client=None, + ): + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError( + status_code=422, message="max retries must be an int" + ) + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AzureOpenAI(**azure_client_params) + else: + azure_client = client + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=azure_client.api_key, + additional_args={ + "headers": {"Authorization": f"Bearer {azure_client.api_key}"}, + "api_base": azure_client._base_url._uri_reference, + "acompletion": True, + "complete_input_dict": data, + }, + ) + response = azure_client.chat.completions.create(**data, timeout=timeout) + streamwrapper = CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider="azure", + logging_obj=logging_obj, + ) + return streamwrapper + + async def async_streaming( + self, + logging_obj, + api_base: str, + api_key: str, + api_version: str, + data: dict, + model: str, + timeout: Any, + azure_ad_token: Optional[str] = None, + client=None, + ): + try: + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": data.pop("max_retries", 2), + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + if client is None: + azure_client = AsyncAzureOpenAI(**azure_client_params) + else: + azure_client = client + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=azure_client.api_key, + additional_args={ + "headers": {"Authorization": f"Bearer {azure_client.api_key}"}, + "api_base": azure_client._base_url._uri_reference, + "acompletion": True, + "complete_input_dict": data, + }, + ) + response = await azure_client.chat.completions.create( + **data, timeout=timeout + ) + # return response + streamwrapper = CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider="azure", + logging_obj=logging_obj, + ) + return streamwrapper ## DO NOT make this into an async for ... loop, it will yield an async generator, which won't raise errors if the response fails + except Exception as e: + if hasattr(e, "status_code"): + raise AzureOpenAIError(status_code=e.status_code, message=str(e)) + else: + raise AzureOpenAIError(status_code=500, message=str(e)) + + async def aembedding( + self, + data: dict, + model_response: ModelResponse, + azure_client_params: dict, + api_key: str, + input: list, + client=None, + logging_obj=None, + timeout=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncAzureOpenAI(**azure_client_params) + else: + openai_aclient = client + response = await openai_aclient.embeddings.create(**data, timeout=timeout) + stringified_response = response.model_dump() + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=stringified_response, + ) + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + response_type="embedding", + ) + except Exception as e: + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=str(e), + ) + raise e + + def embedding( + self, + model: str, + input: list, + api_key: str, + api_base: str, + api_version: str, + timeout: float, + logging_obj=None, + model_response=None, + optional_params=None, + azure_ad_token: Optional[str] = None, + client=None, + aembedding=None, + ): + super().embedding() + exception_mapping_worked = False + if self._client_session is None: + self._client_session = self.create_client_session() + try: + data = {"model": model, "input": input, **optional_params} + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError( + status_code=422, message="max retries must be an int" + ) + + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "http_client": litellm.client_session, + "max_retries": max_retries, + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": {"api_key": api_key, "azure_ad_token": azure_ad_token}, + }, + ) + + if aembedding == True: + response = self.aembedding( + data=data, + input=input, + logging_obj=logging_obj, + api_key=api_key, + model_response=model_response, + azure_client_params=azure_client_params, + timeout=timeout, + ) + return response + if client is None: + azure_client = AzureOpenAI(**azure_client_params) # type: ignore + else: + azure_client = client + ## COMPLETION CALL + response = azure_client.embeddings.create(**data, timeout=timeout) # type: ignore + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data, "api_base": api_base}, + original_response=response, + ) + + return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="embedding") # type: ignore + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise AzureOpenAIError(status_code=e.status_code, message=str(e)) + else: + raise AzureOpenAIError(status_code=500, message=str(e)) + + async def aimage_generation( + self, + data: dict, + model_response: ModelResponse, + azure_client_params: dict, + api_key: str, + input: list, + client=None, + logging_obj=None, + timeout=None, + ): + response = None + try: + if client is None: + client_session = litellm.aclient_session or httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + ) + openai_aclient = AsyncAzureOpenAI( + http_client=client_session, **azure_client_params + ) + else: + openai_aclient = client + response = await openai_aclient.images.generate(**data, timeout=timeout) + stringified_response = response.model_dump() + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=stringified_response, + ) + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + response_type="image_generation", + ) + except Exception as e: + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=str(e), + ) + raise e + + def image_generation( + self, + prompt: str, + timeout: float, + model: Optional[str] = None, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + api_version: Optional[str] = None, + model_response: Optional[litellm.utils.ImageResponse] = None, + azure_ad_token: Optional[str] = None, + logging_obj=None, + optional_params=None, + client=None, + aimg_generation=None, + ): + exception_mapping_worked = False + try: + if model and len(model) > 0: + model = model + else: + model = None + data = {"model": model, "prompt": prompt, **optional_params} + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise AzureOpenAIError( + status_code=422, message="max retries must be an int" + ) + + # init AzureOpenAI Client + azure_client_params = { + "api_version": api_version, + "azure_endpoint": api_base, + "azure_deployment": model, + "max_retries": max_retries, + "timeout": timeout, + } + if api_key is not None: + azure_client_params["api_key"] = api_key + elif azure_ad_token is not None: + azure_client_params["azure_ad_token"] = azure_ad_token + + if aimg_generation == True: + response = self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout) # type: ignore + return response + + if client is None: + client_session = litellm.client_session or httpx.Client( + transport=CustomHTTPTransport(), + ) + azure_client = AzureOpenAI(http_client=client_session, **azure_client_params) # type: ignore + else: + azure_client = client + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=azure_client.api_key, + additional_args={ + "headers": {"Authorization": f"Bearer {azure_client.api_key}"}, + "api_base": azure_client._base_url._uri_reference, + "acompletion": False, + "complete_input_dict": data, + }, + ) + + ## COMPLETION CALL + response = azure_client.images.generate(**data, timeout=timeout) # type: ignore + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + # return response + return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="image_generation") # type: ignore + except AzureOpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise AzureOpenAIError(status_code=e.status_code, message=str(e)) + else: + raise AzureOpenAIError(status_code=500, message=str(e)) + + async def ahealth_check( + self, + model: Optional[str], + api_key: str, + api_base: str, + api_version: str, + timeout: float, + mode: str, + messages: Optional[list] = None, + input: Optional[list] = None, + prompt: Optional[str] = None, + ): + client_session = litellm.aclient_session or httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), # handle dall-e-2 calls + ) + if "gateway.ai.cloudflare.com" in api_base: + ## build base url - assume api base includes resource name + if not api_base.endswith("/"): + api_base += "/" + api_base += f"{model}" + client = AsyncAzureOpenAI( + base_url=api_base, + api_version=api_version, + api_key=api_key, + timeout=timeout, + http_client=client_session, + ) + model = None + # cloudflare ai gateway, needs model=None + else: + client = AsyncAzureOpenAI( + api_version=api_version, + azure_endpoint=api_base, + api_key=api_key, + timeout=timeout, + http_client=client_session, + ) + + # only run this check if it's not cloudflare ai gateway + if model is None and mode != "image_generation": + raise Exception("model is not set") + + completion = None + + if mode == "completion": + completion = await client.completions.with_raw_response.create( + model=model, # type: ignore + prompt=prompt, # type: ignore + ) + elif mode == "chat": + if messages is None: + raise Exception("messages is not set") + completion = await client.chat.completions.with_raw_response.create( + model=model, # type: ignore + messages=messages, # type: ignore + ) + elif mode == "embedding": + if input is None: + raise Exception("input is not set") + completion = await client.embeddings.with_raw_response.create( + model=model, # type: ignore + input=input, # type: ignore + ) + elif mode == "image_generation": + if prompt is None: + raise Exception("prompt is not set") + completion = await client.images.with_raw_response.generate( + model=model, # type: ignore + prompt=prompt, # type: ignore + ) + else: + raise Exception("mode not set") + response = {} + + if completion is None or not hasattr(completion, "headers"): + raise Exception("invalid completion response") + + if ( + completion.headers.get("x-ratelimit-remaining-requests", None) is not None + ): # not provided for dall-e requests + response["x-ratelimit-remaining-requests"] = completion.headers[ + "x-ratelimit-remaining-requests" + ] + + if completion.headers.get("x-ratelimit-remaining-tokens", None) is not None: + response["x-ratelimit-remaining-tokens"] = completion.headers[ + "x-ratelimit-remaining-tokens" + ] + return response diff --git a/litellm/llms/base.py b/litellm/llms/base.py new file mode 100644 index 0000000000000000000000000000000000000000..62b8069f06363bd325714744c1ddec2baf9ad1df --- /dev/null +++ b/litellm/llms/base.py @@ -0,0 +1,45 @@ +## This is a template base class to be used for adding new LLM providers via API calls +import litellm +import httpx +from typing import Optional + + +class BaseLLM: + _client_session: Optional[httpx.Client] = None + + def create_client_session(self): + if litellm.client_session: + _client_session = litellm.client_session + else: + _client_session = httpx.Client() + + return _client_session + + def create_aclient_session(self): + if litellm.aclient_session: + _aclient_session = litellm.aclient_session + else: + _aclient_session = httpx.AsyncClient() + + return _aclient_session + + def __exit__(self): + if hasattr(self, "_client_session"): + self._client_session.close() + + async def __aexit__(self, exc_type, exc_val, exc_tb): + if hasattr(self, "_aclient_session"): + await self._aclient_session.aclose() + + def validate_environment(self): # set up the environment required to run the model + pass + + def completion( + self, *args, **kwargs + ): # logic for parsing in - calling - parsing out model completion calls + pass + + def embedding( + self, *args, **kwargs + ): # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/baseten.py b/litellm/llms/baseten.py new file mode 100644 index 0000000000000000000000000000000000000000..b94491014592b9594809f7f259eb1466c5ad569a --- /dev/null +++ b/litellm/llms/baseten.py @@ -0,0 +1,164 @@ +import os +import json +from enum import Enum +import requests +import time +from typing import Callable +from litellm.utils import ModelResponse, Usage + + +class BasetenError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Api-Key {api_key}" + return headers + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + completion_url_fragment_1 = "https://app.baseten.co/models/" + completion_url_fragment_2 = "/predict" + model = model + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + data = { + "inputs": prompt, + "prompt": prompt, + "parameters": optional_params, + "stream": True + if "stream" in optional_params and optional_params["stream"] == True + else False, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url_fragment_1 + model + completion_url_fragment_2, + headers=headers, + data=json.dumps(data), + stream=True + if "stream" in optional_params and optional_params["stream"] == True + else False, + ) + if "text/event-stream" in response.headers["Content-Type"] or ( + "stream" in optional_params and optional_params["stream"] == True + ): + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise BasetenError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + if "model_output" in completion_response: + if ( + isinstance(completion_response["model_output"], dict) + and "data" in completion_response["model_output"] + and isinstance(completion_response["model_output"]["data"], list) + ): + model_response["choices"][0]["message"][ + "content" + ] = completion_response["model_output"]["data"][0] + elif isinstance(completion_response["model_output"], str): + model_response["choices"][0]["message"][ + "content" + ] = completion_response["model_output"] + elif "completion" in completion_response and isinstance( + completion_response["completion"], str + ): + model_response["choices"][0]["message"][ + "content" + ] = completion_response["completion"] + elif isinstance(completion_response, list) and len(completion_response) > 0: + if "generated_text" not in completion_response: + raise BasetenError( + message=f"Unable to parse response. Original response: {response.text}", + status_code=response.status_code, + ) + model_response["choices"][0]["message"][ + "content" + ] = completion_response[0]["generated_text"] + ## GETTING LOGPROBS + if ( + "details" in completion_response[0] + and "tokens" in completion_response[0]["details"] + ): + model_response.choices[0].finish_reason = completion_response[0][ + "details" + ]["finish_reason"] + sum_logprob = 0 + for token in completion_response[0]["details"]["tokens"]: + sum_logprob += token["logprob"] + model_response["choices"][0]["message"]._logprobs = sum_logprob + else: + raise BasetenError( + message=f"Unable to parse response. Original response: {response.text}", + status_code=response.status_code, + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"]["content"]) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py new file mode 100644 index 0000000000000000000000000000000000000000..85b820c9e832acefdabb678a13c86c04fee11a2d --- /dev/null +++ b/litellm/llms/bedrock.py @@ -0,0 +1,799 @@ +import json, copy, types +import os +from enum import Enum +import time +from typing import Callable, Optional, Any, Union +import litellm +from litellm.utils import ModelResponse, get_secret, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt +import httpx + + +class BedrockError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://us-west-2.console.aws.amazon.com/bedrock" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class AmazonTitanConfig: + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-text-express-v1 + + Supported Params for the Amazon Titan models: + + - `maxTokenCount` (integer) max tokens, + - `stopSequences` (string[]) list of stop sequence strings + - `temperature` (float) temperature for model, + - `topP` (int) top p for model + """ + + maxTokenCount: Optional[int] = None + stopSequences: Optional[list] = None + temperature: Optional[float] = None + topP: Optional[int] = None + + def __init__( + self, + maxTokenCount: Optional[int] = None, + stopSequences: Optional[list] = None, + temperature: Optional[float] = None, + topP: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +class AmazonAnthropicConfig: + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude + + Supported Params for the Amazon / Anthropic models: + + - `max_tokens_to_sample` (integer) max tokens, + - `temperature` (float) model temperature, + - `top_k` (integer) top k, + - `top_p` (integer) top p, + - `stop_sequences` (string[]) list of stop sequences - e.g. ["\\n\\nHuman:"], + - `anthropic_version` (string) version of anthropic for bedrock - e.g. "bedrock-2023-05-31" + """ + + max_tokens_to_sample: Optional[int] = litellm.max_tokens + stop_sequences: Optional[list] = None + temperature: Optional[float] = None + top_k: Optional[int] = None + top_p: Optional[int] = None + anthropic_version: Optional[str] = None + + def __init__( + self, + max_tokens_to_sample: Optional[int] = None, + stop_sequences: Optional[list] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[int] = None, + anthropic_version: Optional[str] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +class AmazonCohereConfig: + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=command + + Supported Params for the Amazon / Cohere models: + + - `max_tokens` (integer) max tokens, + - `temperature` (float) model temperature, + - `return_likelihood` (string) n/a + """ + + max_tokens: Optional[int] = None + temperature: Optional[float] = None + return_likelihood: Optional[str] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + temperature: Optional[float] = None, + return_likelihood: Optional[str] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +class AmazonAI21Config: + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra + + Supported Params for the Amazon / AI21 models: + + - `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`. + + - `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding. + + - `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass. + + - `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional. + + - `frequencyPenalty` (object): Placeholder for frequency penalty object. + + - `presencePenalty` (object): Placeholder for presence penalty object. + + - `countPenalty` (object): Placeholder for count penalty object. + """ + + maxTokens: Optional[int] = None + temperature: Optional[float] = None + topP: Optional[float] = None + stopSequences: Optional[list] = None + frequencePenalty: Optional[dict] = None + presencePenalty: Optional[dict] = None + countPenalty: Optional[dict] = None + + def __init__( + self, + maxTokens: Optional[int] = None, + temperature: Optional[float] = None, + topP: Optional[float] = None, + stopSequences: Optional[list] = None, + frequencePenalty: Optional[dict] = None, + presencePenalty: Optional[dict] = None, + countPenalty: Optional[dict] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + + +class AmazonLlamaConfig: + """ + Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=meta.llama2-13b-chat-v1 + + Supported Params for the Amazon / Meta Llama models: + + - `max_gen_len` (integer) max tokens, + - `temperature` (float) temperature for model, + - `top_p` (float) top p for model + """ + + max_gen_len: Optional[int] = None + temperature: Optional[float] = None + topP: Optional[float] = None + + def __init__( + self, + maxTokenCount: Optional[int] = None, + temperature: Optional[float] = None, + topP: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def init_bedrock_client( + region_name=None, + aws_access_key_id: Optional[str] = None, + aws_secret_access_key: Optional[str] = None, + aws_region_name: Optional[str] = None, + aws_bedrock_runtime_endpoint: Optional[str] = None, +): + # check for custom AWS_REGION_NAME and use it if not passed to init_bedrock_client + litellm_aws_region_name = get_secret("AWS_REGION_NAME", None) + standard_aws_region_name = get_secret("AWS_REGION", None) + + ## CHECK IS 'os.environ/' passed in + # Define the list of parameters to check + params_to_check = [ + aws_access_key_id, + aws_secret_access_key, + aws_region_name, + aws_bedrock_runtime_endpoint, + ] + + # Iterate over parameters and update if needed + for i, param in enumerate(params_to_check): + if param and param.startswith("os.environ/"): + params_to_check[i] = get_secret(param) + # Assign updated values back to parameters + ( + aws_access_key_id, + aws_secret_access_key, + aws_region_name, + aws_bedrock_runtime_endpoint, + ) = params_to_check + if region_name: + pass + elif aws_region_name: + region_name = aws_region_name + elif litellm_aws_region_name: + region_name = litellm_aws_region_name + elif standard_aws_region_name: + region_name = standard_aws_region_name + else: + raise BedrockError( + message="AWS region not set: set AWS_REGION_NAME or AWS_REGION env variable or in .env file", + status_code=401, + ) + + # check for custom AWS_BEDROCK_RUNTIME_ENDPOINT and use it if not passed to init_bedrock_client + env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT") + if aws_bedrock_runtime_endpoint: + endpoint_url = aws_bedrock_runtime_endpoint + elif env_aws_bedrock_runtime_endpoint: + endpoint_url = env_aws_bedrock_runtime_endpoint + else: + endpoint_url = f"https://bedrock-runtime.{region_name}.amazonaws.com" + + import boto3 + + if aws_access_key_id != None: + # uses auth params passed to completion + # aws_access_key_id is not None, assume user is trying to auth using litellm.completion + + client = boto3.client( + service_name="bedrock-runtime", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=region_name, + endpoint_url=endpoint_url, + ) + else: + # aws_access_key_id is None, assume user is trying to auth using env variables + # boto3 automatically reads env variables + + client = boto3.client( + service_name="bedrock-runtime", + region_name=region_name, + endpoint_url=endpoint_url, + ) + + return client + + +def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict): + # handle anthropic prompts using anthropic constants + if provider == "anthropic": + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages, + ) + else: + prompt = prompt_factory( + model=model, messages=messages, custom_llm_provider="anthropic" + ) + else: + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + return prompt + + +""" +BEDROCK AUTH Keys/Vars +os.environ['AWS_ACCESS_KEY_ID'] = "" +os.environ['AWS_SECRET_ACCESS_KEY'] = "" +""" + + +# set os.environ['AWS_REGION_NAME'] = + + +def completion( + model: str, + messages: list, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + exception_mapping_worked = False + try: + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + aws_bedrock_runtime_endpoint = optional_params.pop( + "aws_bedrock_runtime_endpoint", None + ) + + # use passed in BedrockRuntime.Client if provided, otherwise create a new one + client = optional_params.pop("aws_bedrock_client", None) + + # only init client, if user did not pass one + if client is None: + client = init_bedrock_client( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + ) + + model = model + modelId = ( + optional_params.pop("model_id", None) or model + ) # default to model if not passed + provider = model.split(".")[0] + prompt = convert_messages_to_prompt( + model, messages, provider, custom_prompt_dict + ) + inference_params = copy.deepcopy(optional_params) + stream = inference_params.pop("stream", False) + if provider == "anthropic": + ## LOAD CONFIG + config = litellm.AmazonAnthropicConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + data = json.dumps({"prompt": prompt, **inference_params}) + elif provider == "ai21": + ## LOAD CONFIG + config = litellm.AmazonAI21Config.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + data = json.dumps({"prompt": prompt, **inference_params}) + elif provider == "cohere": + ## LOAD CONFIG + config = litellm.AmazonCohereConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + if optional_params.get("stream", False) == True: + inference_params[ + "stream" + ] = True # cohere requires stream = True in inference params + data = json.dumps({"prompt": prompt, **inference_params}) + elif provider == "meta": + ## LOAD CONFIG + config = litellm.AmazonLlamaConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + data = json.dumps({"prompt": prompt, **inference_params}) + elif provider == "amazon": # amazon titan + ## LOAD CONFIG + config = litellm.AmazonTitanConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > amazon_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + data = json.dumps( + { + "inputText": prompt, + "textGenerationConfig": inference_params, + } + ) + else: + data = json.dumps({}) + + ## COMPLETION CALL + accept = "application/json" + contentType = "application/json" + if stream == True: + if provider == "ai21": + ## LOGGING + request_str = f""" + response = client.invoke_model( + body={data}, + modelId={modelId}, + accept=accept, + contentType=contentType + ) + """ + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={ + "complete_input_dict": data, + "request_str": request_str, + }, + ) + + response = client.invoke_model( + body=data, modelId=modelId, accept=accept, contentType=contentType + ) + + response = response.get("body").read() + return response + else: + ## LOGGING + request_str = f""" + response = client.invoke_model_with_response_stream( + body={data}, + modelId={modelId}, + accept=accept, + contentType=contentType + ) + """ + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={ + "complete_input_dict": data, + "request_str": request_str, + }, + ) + + response = client.invoke_model_with_response_stream( + body=data, modelId=modelId, accept=accept, contentType=contentType + ) + response = response.get("body") + return response + try: + ## LOGGING + request_str = f""" + response = client.invoke_model( + body={data}, + modelId={modelId}, + accept=accept, + contentType=contentType + ) + """ + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={ + "complete_input_dict": data, + "request_str": request_str, + }, + ) + response = client.invoke_model( + body=data, modelId=modelId, accept=accept, contentType=contentType + ) + except client.exceptions.ValidationException as e: + if "The provided model identifier is invalid" in str(e): + raise BedrockError(status_code=404, message=str(e)) + raise BedrockError(status_code=400, message=str(e)) + except Exception as e: + raise BedrockError(status_code=500, message=str(e)) + + response_body = json.loads(response.get("body").read()) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=json.dumps(response_body), + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + outputText = "default" + if provider == "ai21": + outputText = response_body.get("completions")[0].get("data").get("text") + elif provider == "anthropic": + outputText = response_body["completion"] + model_response["finish_reason"] = response_body["stop_reason"] + elif provider == "cohere": + outputText = response_body["generations"][0]["text"] + elif provider == "meta": + outputText = response_body["generation"] + else: # amazon titan + outputText = response_body.get("results")[0].get("outputText") + + response_metadata = response.get("ResponseMetadata", {}) + if response_metadata.get("HTTPStatusCode", 500) >= 400: + raise BedrockError( + message=outputText, + status_code=response_metadata.get("HTTPStatusCode", 500), + ) + else: + try: + if len(outputText) > 0: + model_response["choices"][0]["message"]["content"] = outputText + except: + raise BedrockError( + message=json.dumps(outputText), + status_code=response_metadata.get("HTTPStatusCode", 500), + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + except BedrockError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if exception_mapping_worked: + raise e + else: + import traceback + + raise BedrockError(status_code=500, message=traceback.format_exc()) + + +def _embedding_func_single( + model: str, + input: str, + client: Any, + optional_params=None, + encoding=None, + logging_obj=None, +): + # logic for parsing in - calling - parsing out model embedding calls + ## FORMAT EMBEDDING INPUT ## + provider = model.split(".")[0] + inference_params = copy.deepcopy(optional_params) + inference_params.pop( + "user", None + ) # make sure user is not passed in for bedrock call + modelId = ( + optional_params.pop("model_id", None) or model + ) # default to model if not passed + if provider == "amazon": + input = input.replace(os.linesep, " ") + data = {"inputText": input, **inference_params} + # data = json.dumps(data) + elif provider == "cohere": + inference_params["input_type"] = inference_params.get( + "input_type", "search_document" + ) # aws bedrock example default - https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/providers?model=cohere.embed-english-v3 + data = {"texts": [input], **inference_params} # type: ignore + body = json.dumps(data).encode("utf-8") + ## LOGGING + request_str = f""" + response = client.invoke_model( + body={body}, + modelId={modelId}, + accept="*/*", + contentType="application/json", + )""" # type: ignore + logging_obj.pre_call( + input=input, + api_key="", # boto3 is used for init. + additional_args={ + "complete_input_dict": {"model": modelId, "texts": input}, + "request_str": request_str, + }, + ) + try: + response = client.invoke_model( + body=body, + modelId=modelId, + accept="*/*", + contentType="application/json", + ) + response_body = json.loads(response.get("body").read()) + ## LOGGING + logging_obj.post_call( + input=input, + api_key="", + additional_args={"complete_input_dict": data}, + original_response=json.dumps(response_body), + ) + if provider == "cohere": + response = response_body.get("embeddings") + # flatten list + response = [item for sublist in response for item in sublist] + return response + elif provider == "amazon": + return response_body.get("embedding") + except Exception as e: + raise BedrockError( + message=f"Embedding Error with model {model}: {e}", status_code=500 + ) + + +def embedding( + model: str, + input: Union[list, str], + api_key: Optional[str] = None, + logging_obj=None, + model_response=None, + optional_params=None, + encoding=None, +): + ### BOTO3 INIT ### + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + aws_bedrock_runtime_endpoint = optional_params.pop( + "aws_bedrock_runtime_endpoint", None + ) + + # use passed in BedrockRuntime.Client if provided, otherwise create a new one + client = init_bedrock_client( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + ) + if type(input) == str: + embeddings = [ + _embedding_func_single( + model, + input, + optional_params=optional_params, + client=client, + logging_obj=logging_obj, + ) + ] + else: + ## Embedding Call + embeddings = [ + _embedding_func_single( + model, + i, + optional_params=optional_params, + client=client, + logging_obj=logging_obj, + ) + for i in input + ] # [TODO]: make these parallel calls + + ## Populate OpenAI compliant dictionary + embedding_response = [] + for idx, embedding in enumerate(embeddings): + embedding_response.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding, + } + ) + model_response["object"] = "list" + model_response["data"] = embedding_response + model_response["model"] = model + input_tokens = 0 + + input_str = "".join(input) + + input_tokens += len(encoding.encode(input_str)) + + usage = Usage( + prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens + 0 + ) + model_response.usage = usage + + return model_response diff --git a/litellm/llms/cloudflare.py b/litellm/llms/cloudflare.py new file mode 100644 index 0000000000000000000000000000000000000000..a9e60bb7e09c23c44ac5fe2048ec438b435cb152 --- /dev/null +++ b/litellm/llms/cloudflare.py @@ -0,0 +1,176 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +import httpx +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class CloudflareError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="https://api.cloudflare.com") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class CloudflareConfig: + max_tokens: Optional[int] = None + stream: Optional[bool] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + stream: Optional[bool] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing CloudflareError API Key - A call is being made to cloudflare but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "content-type": "application/json", + "Authorization": "Bearer " + api_key, + } + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.CloudflareConfig.get_config() + for k, v in config.items(): + if k not in optional_params: + optional_params[k] = v + + print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}") + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + + # cloudflare adds the model to the api base + api_base = api_base + model + + data = { + "messages": messages, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "headers": headers, + "api_base": api_base, + "complete_input_dict": data, + }, + ) + + ## COMPLETION CALL + if "stream" in optional_params and optional_params["stream"] == True: + response = requests.post( + api_base, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"], + ) + return response.iter_lines() + else: + response = requests.post(api_base, headers=headers, data=json.dumps(data)) + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + if response.status_code != 200: + raise CloudflareError( + status_code=response.status_code, message=response.text + ) + completion_response = response.json() + + model_response["choices"][0]["message"]["content"] = completion_response[ + "result" + ]["response"] + + ## CALCULATING USAGE + print_verbose( + f"CALCULATING CLOUDFLARE TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}" + ) + prompt_tokens = litellm.utils.get_token_count(messages=messages, model=model) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = "cloudflare/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/cohere.py b/litellm/llms/cohere.py new file mode 100644 index 0000000000000000000000000000000000000000..40b65439b215b9fac54f50575fd426b480437127 --- /dev/null +++ b/litellm/llms/cohere.py @@ -0,0 +1,293 @@ +import os, types +import json +from enum import Enum +import requests +import time, traceback +from typing import Callable, Optional +from litellm.utils import ModelResponse, Choices, Message, Usage +import litellm +import httpx + + +class CohereError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.cohere.ai/v1/generate" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class CohereConfig: + """ + Reference: https://docs.cohere.com/reference/generate + + The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters: + + - `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5. + + - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20. + + - `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END. + + - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75. + + - `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc. + + - `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text. + + - `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text. + + - `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0. + + - `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0. + + - `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens. + + - `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared. + + - `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE. + + - `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233} + """ + + num_generations: Optional[int] = None + max_tokens: Optional[int] = None + truncate: Optional[str] = None + temperature: Optional[int] = None + preset: Optional[str] = None + end_sequences: Optional[list] = None + stop_sequences: Optional[list] = None + k: Optional[int] = None + p: Optional[int] = None + frequency_penalty: Optional[int] = None + presence_penalty: Optional[int] = None + return_likelihoods: Optional[str] = None + logit_bias: Optional[dict] = None + + def __init__( + self, + num_generations: Optional[int] = None, + max_tokens: Optional[int] = None, + truncate: Optional[str] = None, + temperature: Optional[int] = None, + preset: Optional[str] = None, + end_sequences: Optional[list] = None, + stop_sequences: Optional[list] = None, + k: Optional[int] = None, + p: Optional[int] = None, + frequency_penalty: Optional[int] = None, + presence_penalty: Optional[int] = None, + return_likelihoods: Optional[str] = None, + logit_bias: Optional[dict] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + completion_url = api_base + model = model + prompt = " ".join(message["content"] for message in messages) + + ## Load Config + config = litellm.CohereConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": completion_url, + }, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"] if "stream" in optional_params else False, + ) + ## error handling for cohere calls + if response.status_code != 200: + raise CohereError(message=response.text, status_code=response.status_code) + + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise CohereError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + choices_list = [] + for idx, item in enumerate(completion_response["generations"]): + if len(item["text"]) > 0: + message_obj = Message(content=item["text"]) + else: + message_obj = Message(content=None) + choice_obj = Choices( + finish_reason=item["finish_reason"], + index=idx + 1, + message=message_obj, + ) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + raise CohereError( + message=response.text, status_code=response.status_code + ) + + ## CALCULATING USAGE + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding( + model: str, + input: list, + api_key: Optional[str] = None, + logging_obj=None, + model_response=None, + encoding=None, + optional_params=None, +): + headers = validate_environment(api_key) + embed_url = "https://api.cohere.ai/v1/embed" + model = model + data = {"model": model, "texts": input, **optional_params} + + if "3" in model and "input_type" not in data: + # cohere v3 embedding models require input_type, if no input_type is provided, default to "search_document" + data["input_type"] = "search_document" + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post(embed_url, headers=headers, data=json.dumps(data)) + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + """ + response + { + 'object': "list", + 'data': [ + + ] + 'model', + 'usage' + } + """ + if response.status_code != 200: + raise CohereError(message=response.text, status_code=response.status_code) + embeddings = response.json()["embeddings"] + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + model_response["object"] = "list" + model_response["data"] = output_data + model_response["model"] = model + input_tokens = 0 + for text in input: + input_tokens += len(encoding.encode(text)) + + model_response["usage"] = { + "prompt_tokens": input_tokens, + "total_tokens": input_tokens, + } + return model_response diff --git a/litellm/llms/custom_httpx/azure_dall_e_2.py b/litellm/llms/custom_httpx/azure_dall_e_2.py new file mode 100644 index 0000000000000000000000000000000000000000..a62e1d666ddf0d7ce0f17a80426850d30f0bc916 --- /dev/null +++ b/litellm/llms/custom_httpx/azure_dall_e_2.py @@ -0,0 +1,136 @@ +import time, json, httpx, asyncio + + +class AsyncCustomHTTPTransport(httpx.AsyncHTTPTransport): + """ + Async implementation of custom http transport + """ + + async def handle_async_request(self, request: httpx.Request) -> httpx.Response: + if "images/generations" in request.url.path and request.url.params[ + "api-version" + ] in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict + "2023-06-01-preview", + "2023-07-01-preview", + "2023-08-01-preview", + "2023-09-01-preview", + "2023-10-01-preview", + ]: + request.url = request.url.copy_with( + path="/openai/images/generations:submit" + ) + response = await super().handle_async_request(request) + operation_location_url = response.headers["operation-location"] + request.url = httpx.URL(operation_location_url) + request.method = "GET" + response = await super().handle_async_request(request) + await response.aread() + + timeout_secs: int = 120 + start_time = time.time() + while response.json()["status"] not in ["succeeded", "failed"]: + if time.time() - start_time > timeout_secs: + timeout = { + "error": { + "code": "Timeout", + "message": "Operation polling timed out.", + } + } + return httpx.Response( + status_code=400, + headers=response.headers, + content=json.dumps(timeout).encode("utf-8"), + request=request, + ) + + time.sleep(int(response.headers.get("retry-after")) or 10) + response = await super().handle_async_request(request) + await response.aread() + + if response.json()["status"] == "failed": + error_data = response.json() + return httpx.Response( + status_code=400, + headers=response.headers, + content=json.dumps(error_data).encode("utf-8"), + request=request, + ) + + result = response.json()["result"] + return httpx.Response( + status_code=200, + headers=response.headers, + content=json.dumps(result).encode("utf-8"), + request=request, + ) + return await super().handle_async_request(request) + + +class CustomHTTPTransport(httpx.HTTPTransport): + """ + This class was written as a workaround to support dall-e-2 on openai > v1.x + + Refer to this issue for more: https://github.com/openai/openai-python/issues/692 + """ + + def handle_request( + self, + request: httpx.Request, + ) -> httpx.Response: + if "images/generations" in request.url.path and request.url.params[ + "api-version" + ] in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict + "2023-06-01-preview", + "2023-07-01-preview", + "2023-08-01-preview", + "2023-09-01-preview", + "2023-10-01-preview", + ]: + request.url = request.url.copy_with( + path="/openai/images/generations:submit" + ) + response = super().handle_request(request) + operation_location_url = response.headers["operation-location"] + request.url = httpx.URL(operation_location_url) + request.method = "GET" + response = super().handle_request(request) + response.read() + + timeout_secs: int = 120 + start_time = time.time() + while response.json()["status"] not in ["succeeded", "failed"]: + if time.time() - start_time > timeout_secs: + timeout = { + "error": { + "code": "Timeout", + "message": "Operation polling timed out.", + } + } + return httpx.Response( + status_code=400, + headers=response.headers, + content=json.dumps(timeout).encode("utf-8"), + request=request, + ) + + time.sleep(int(response.headers.get("retry-after")) or 10) + response = super().handle_request(request) + response.read() + + if response.json()["status"] == "failed": + error_data = response.json() + return httpx.Response( + status_code=400, + headers=response.headers, + content=json.dumps(error_data).encode("utf-8"), + request=request, + ) + + result = response.json()["result"] + return httpx.Response( + status_code=200, + headers=response.headers, + content=json.dumps(result).encode("utf-8"), + request=request, + ) + return super().handle_request(request) diff --git a/litellm/llms/custom_httpx/bedrock_async.py b/litellm/llms/custom_httpx/bedrock_async.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/litellm/llms/gemini.py b/litellm/llms/gemini.py new file mode 100644 index 0000000000000000000000000000000000000000..863fb4baffa31dd6df2cdad477aff3d8636e3d6e --- /dev/null +++ b/litellm/llms/gemini.py @@ -0,0 +1,222 @@ +import os, types, traceback, copy +import json +from enum import Enum +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage +import litellm +import sys, httpx +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class GeminiError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", + url="https://developers.generativeai.google/api/python/google/generativeai/chat", + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class GeminiConfig: + """ + Reference: https://ai.google.dev/api/python/google/generativeai/GenerationConfig + + The class `GeminiConfig` provides configuration for the Gemini's API interface. Here are the parameters: + + - `candidate_count` (int): Number of generated responses to return. + + - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response. + + - `max_output_tokens` (int): The maximum number of tokens to include in a candidate. If unset, this will default to output_token_limit specified in the model's specification. + + - `temperature` (float): Controls the randomness of the output. Note: The default value varies by model, see the Model.temperature attribute of the Model returned the genai.get_model function. Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model. + + - `top_p` (float): Optional. The maximum cumulative probability of tokens to consider when sampling. + + - `top_k` (int): Optional. The maximum number of tokens to consider when sampling. + """ + + candidate_count: Optional[int] = None + stop_sequences: Optional[list] = None + max_output_tokens: Optional[int] = None + temperature: Optional[float] = None + top_p: Optional[float] = None + top_k: Optional[int] = None + + def __init__( + self, + candidate_count: Optional[int] = None, + stop_sequences: Optional[list] = None, + max_output_tokens: Optional[int] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + api_key, + encoding, + logging_obj, + custom_prompt_dict: dict, + acompletion: bool = False, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + try: + import google.generativeai as genai + except: + raise Exception( + "Importing google.generativeai failed, please run 'pip install -q google-generativeai" + ) + genai.configure(api_key=api_key) + + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages, + ) + else: + prompt = prompt_factory( + model=model, messages=messages, custom_llm_provider="gemini" + ) + + ## Load Config + inference_params = copy.deepcopy(optional_params) + inference_params.pop( + "stream", None + ) # palm does not support streaming, so we handle this by fake streaming in main.py + config = litellm.GeminiConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > gemini_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": {"inference_params": inference_params}}, + ) + ## COMPLETION CALL + try: + _model = genai.GenerativeModel(f"models/{model}") + response = _model.generate_content( + contents=prompt, + generation_config=genai.types.GenerationConfig(**inference_params), + ) + except Exception as e: + raise GeminiError( + message=str(e), + status_code=500, + ) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response, + additional_args={"complete_input_dict": {}}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + completion_response = response + try: + choices_list = [] + for idx, item in enumerate(completion_response.candidates): + if len(item.content.parts) > 0: + message_obj = Message(content=item.content.parts[0].text) + else: + message_obj = Message(content=None) + choice_obj = Choices(index=idx + 1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + traceback.print_exc() + raise GeminiError( + message=traceback.format_exc(), status_code=response.status_code + ) + + try: + completion_response = model_response["choices"][0]["message"].get("content") + if completion_response is None: + raise Exception + except: + original_response = f"response: {response}" + if hasattr(response, "candidates"): + original_response = f"response: {response.candidates}" + if "SAFETY" in original_response: + original_response += "\nThe candidate content was flagged for safety reasons." + elif "RECITATION" in original_response: + original_response += "\nThe candidate content was flagged for recitation reasons." + raise GeminiError( + status_code=400, + message=f"No response received. Original response - {original_response}", + ) + + ## CALCULATING USAGE + prompt_str = "" + for m in messages: + if isinstance(m["content"], str): + prompt_str += m["content"] + elif isinstance(m["content"], list): + for content in m["content"]: + if content["type"] == "text": + prompt_str += content["text"] + prompt_tokens = len(encoding.encode(prompt_str)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = "gemini/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/huggingface_llms_metadata/hf_conversational_models.txt b/litellm/llms/huggingface_llms_metadata/hf_conversational_models.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa978352b2337ab58b744287921c60ec8bde66cf --- /dev/null +++ b/litellm/llms/huggingface_llms_metadata/hf_conversational_models.txt @@ -0,0 +1,2523 @@ +0xDEADBEA7/DialoGPT-small-rick +1Basco/DialoGPT-small-jake +2early4coffee/DialoGPT-medium-deadpool +2early4coffee/DialoGPT-small-deadpool +2gud/DialogGPT-small-Koopsbot +ABBHISHEK/DialoGPT-small-harrypotter +AIDynamics/DialoGPT-medium-MentorDealerGuy +AJ/DialoGPT-small-ricksanchez +AJ/rick-discord-bot +AJ/rick-sanchez-bot +AJ-Dude/DialoGPT-small-harrypotter +AK270802/DialoGPT-small-harrypotter +ATGdev/DialoGPT-small-harrypotter +AVeryRealHuman/DialoGPT-small-TonyStark +AbhinavSaiTheGreat/DialoGPT-small-harrypotter +AccurateIsaiah/DialoGPT-small-jefftastic +AccurateIsaiah/DialoGPT-small-mozark +AccurateIsaiah/DialoGPT-small-mozarkv2 +AccurateIsaiah/DialoGPT-small-sinclair +AdharshJolly/HarryPotterBot-Model +AdrianGzz/DialoGPT-small-harrypotter +Aero/Tsubomi-Haruno +AetherIT/DialoGPT-small-Hal +AiPorter/DialoGPT-small-Back_to_the_future +Aibox/DialoGPT-small-rick +Akjder/DialoGPT-small-harrypotter +AllwynJ/HarryBoy +AnthonyNelson/DialoGPT-small-ricksanchez +Apisate/DialoGPT-small-jordan +ArJakusz/DialoGPT-small-stark +Aran/DialoGPT-medium-harrypotter +Aran/DialoGPT-small-harrypotter +Arcktosh/DialoGPT-small-rick +AriakimTaiyo/DialoGPT-cultured-Kumiko +AriakimTaiyo/DialoGPT-medium-Kumiko +AriakimTaiyo/DialoGPT-revised-Kumiko +AriakimTaiyo/DialoGPT-small-Kumiko +AriakimTaiyo/DialoGPT-small-Rikka +ArtemisZealot/DialoGTP-small-Qkarin +Aruden/DialoGPT-medium-harrypotterall +Aspect11/DialoGPT-Medium-LiSBot +Asuramaru/DialoGPT-small-rintohsaka +Atchuth/DialoGPT-small-MichaelBot +Augustvember/WOKKAWOKKA +Augustvember/WokkaBot3 +Augustvember/test +Augustvember/wokka2 +Augustvember/wokka4 +Augustvember/wokka5 +Augustvember/wokkabottest2 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+siddanshchawla/Llama-2-7b-chat-finetune_inference +aman-mehra/gpt2-medium-finetune-squad-ep-0.2-lr-2e-06-wd-0.0001-glb_sd-1-data_sd-7-fx_head +FPHam/Jackson_The_Formalizer_13b_GPTQ +TheBlake/Llama-2-7b +gwlms/t5-efficient-base-dewiki-v1-germeval14 +notaphoenix/argument-transfer-conservative_l0.2 +notaphoenix/argument-transfer-conservative_l0.5 +cmu-mlsp/guanaco-7B-HF-gpt3.5-atk2-first_last +cmu-mlsp/vicuna-7b-v1.5-gpt3.5-atk2-first_last +vjeronymo2/monot5-3b-msmarco-10k-half +ryanyard/llama-2-7b-miniguanaco +luozhuanggary/vicuna-7b-v1.5-sft-math-merged +Globaly/globaly-1-llama2-13b-OpenOrca-v0.2 +cmu-mlsp/guanaco-13B-HF-gpt3.5-first_last-global +research-dump/t5-base_hoax_timestamp_classifier_v1 +wtang06/mpt-125m-c4 +PocketDoc/Dans-MysteryModel-13b-exl2-6.0bpw +Asap7772/sft-review-model-20230927-215151 +Asap7772/sft-review-model-20230927-220132 +Asap7772/sft-review-model-20230927-220131 +Mintrz/Loobe-3 +badokorach/flan-t5-small-qa-9 +nick-1234/llama-2-7b-finetuned-for-news_comments_generation +lajosd/llama-2-7b-miniguanaco +aman-mehra/gpt2-medium-finetune-squad-ep-2.0-lr-3e-06-wd-0.0001-glb_sd-1-data_sd-500-fx_head diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py new file mode 100644 index 0000000000000000000000000000000000000000..eb8ce38b9791fa4e1fc1af9b6b9ec8e8c689f302 --- /dev/null +++ b/litellm/llms/huggingface_restapi.py @@ -0,0 +1,750 @@ +## Uses the huggingface text generation inference API +import os, copy, types +import json +from enum import Enum +import httpx, requests +from .base import BaseLLM +import time +import litellm +from typing import Callable, Dict, List, Any +from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, Usage +from typing import Optional +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class HuggingfaceError(Exception): + def __init__( + self, + status_code, + message, + request: Optional[httpx.Request] = None, + response: Optional[httpx.Response] = None, + ): + self.status_code = status_code + self.message = message + if request is not None: + self.request = request + else: + self.request = httpx.Request( + method="POST", url="https://api-inference.huggingface.co/models" + ) + if response is not None: + self.response = response + else: + self.response = httpx.Response( + status_code=status_code, request=self.request + ) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class HuggingfaceConfig: + """ + Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate + """ + + best_of: Optional[int] = None + decoder_input_details: Optional[bool] = None + details: Optional[bool] = True # enables returning logprobs + best of + max_new_tokens: Optional[int] = None + repetition_penalty: Optional[float] = None + return_full_text: Optional[ + bool + ] = False # by default don't return the input as part of the output + seed: Optional[int] = None + temperature: Optional[float] = None + top_k: Optional[int] = None + top_n_tokens: Optional[int] = None + top_p: Optional[int] = None + truncate: Optional[int] = None + typical_p: Optional[float] = None + watermark: Optional[bool] = None + + def __init__( + self, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + details: Optional[bool] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = None, + seed: Optional[int] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[int] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def output_parser(generated_text: str): + """ + Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens. + + Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763 + """ + chat_template_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "", ""] + for token in chat_template_tokens: + if generated_text.strip().startswith(token): + generated_text = generated_text.replace(token, "", 1) + if generated_text.endswith(token): + generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1] + return generated_text + + +tgi_models_cache = None +conv_models_cache = None + + +def read_tgi_conv_models(): + try: + global tgi_models_cache, conv_models_cache + # Check if the cache is already populated + # so we don't keep on reading txt file if there are 1k requests + if (tgi_models_cache is not None) and (conv_models_cache is not None): + return tgi_models_cache, conv_models_cache + # If not, read the file and populate the cache + tgi_models = set() + script_directory = os.path.dirname(os.path.abspath(__file__)) + # Construct the file path relative to the script's directory + file_path = os.path.join( + script_directory, + "huggingface_llms_metadata", + "hf_text_generation_models.txt", + ) + + with open(file_path, "r") as file: + for line in file: + tgi_models.add(line.strip()) + + # Cache the set for future use + tgi_models_cache = tgi_models + + # If not, read the file and populate the cache + file_path = os.path.join( + script_directory, + "huggingface_llms_metadata", + "hf_conversational_models.txt", + ) + conv_models = set() + with open(file_path, "r") as file: + for line in file: + conv_models.add(line.strip()) + # Cache the set for future use + conv_models_cache = conv_models + return tgi_models, conv_models + except: + return set(), set() + + +def get_hf_task_for_model(model): + # read text file, cast it to set + # read the file called "huggingface_llms_metadata/hf_text_generation_models.txt" + tgi_models, conversational_models = read_tgi_conv_models() + if model in tgi_models: + return "text-generation-inference" + elif model in conversational_models: + return "conversational" + elif "roneneldan/TinyStories" in model: + return None + else: + return "text-generation-inference" # default to tgi + + +class Huggingface(BaseLLM): + _client_session: Optional[httpx.Client] = None + _aclient_session: Optional[httpx.AsyncClient] = None + + def __init__(self) -> None: + super().__init__() + + def validate_environment(self, api_key, headers): + default_headers = { + "content-type": "application/json", + } + if api_key and headers is None: + default_headers[ + "Authorization" + ] = f"Bearer {api_key}" # Huggingface Inference Endpoint default is to accept bearer tokens + headers = default_headers + elif headers: + headers = headers + else: + headers = default_headers + return headers + + def convert_to_model_response_object( + self, + completion_response, + model_response, + task, + optional_params, + encoding, + input_text, + model, + ): + if task == "conversational": + if len(completion_response["generated_text"]) > 0: # type: ignore + model_response["choices"][0]["message"][ + "content" + ] = completion_response[ + "generated_text" + ] # type: ignore + elif task == "text-generation-inference": + if ( + not isinstance(completion_response, list) + or not isinstance(completion_response[0], dict) + or "generated_text" not in completion_response[0] + ): + raise HuggingfaceError( + status_code=422, + message=f"response is not in expected format - {completion_response}", + ) + + if len(completion_response[0]["generated_text"]) > 0: + model_response["choices"][0]["message"]["content"] = output_parser( + completion_response[0]["generated_text"] + ) + ## GETTING LOGPROBS + FINISH REASON + if ( + "details" in completion_response[0] + and "tokens" in completion_response[0]["details"] + ): + model_response.choices[0].finish_reason = completion_response[0][ + "details" + ]["finish_reason"] + sum_logprob = 0 + for token in completion_response[0]["details"]["tokens"]: + if token["logprob"] != None: + sum_logprob += token["logprob"] + model_response["choices"][0]["message"]._logprob = sum_logprob + if "best_of" in optional_params and optional_params["best_of"] > 1: + if ( + "details" in completion_response[0] + and "best_of_sequences" in completion_response[0]["details"] + ): + choices_list = [] + for idx, item in enumerate( + completion_response[0]["details"]["best_of_sequences"] + ): + sum_logprob = 0 + for token in item["tokens"]: + if token["logprob"] != None: + sum_logprob += token["logprob"] + if len(item["generated_text"]) > 0: + message_obj = Message( + content=output_parser(item["generated_text"]), + logprobs=sum_logprob, + ) + else: + message_obj = Message(content=None) + choice_obj = Choices( + finish_reason=item["finish_reason"], + index=idx + 1, + message=message_obj, + ) + choices_list.append(choice_obj) + model_response["choices"].extend(choices_list) + else: + if len(completion_response[0]["generated_text"]) > 0: + model_response["choices"][0]["message"]["content"] = output_parser( + completion_response[0]["generated_text"] + ) + ## CALCULATING USAGE + prompt_tokens = 0 + try: + prompt_tokens = len( + encoding.encode(input_text) + ) ##[TODO] use the llama2 tokenizer here + except: + # this should remain non blocking we should not block a response returning if calculating usage fails + pass + output_text = model_response["choices"][0]["message"].get("content", "") + if output_text is not None and len(output_text) > 0: + completion_tokens = 0 + try: + completion_tokens = len( + encoding.encode( + model_response["choices"][0]["message"].get("content", "") + ) + ) ##[TODO] use the llama2 tokenizer here + except: + # this should remain non blocking we should not block a response returning if calculating usage fails + pass + else: + completion_tokens = 0 + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + model_response._hidden_params["original_response"] = completion_response + return model_response + + def completion( + self, + model: str, + messages: list, + api_base: Optional[str], + headers: Optional[dict], + model_response: ModelResponse, + print_verbose: Callable, + timeout: float, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + acompletion: bool = False, + optional_params=None, + litellm_params=None, + logger_fn=None, + ): + super().completion() + exception_mapping_worked = False + try: + headers = self.validate_environment(api_key, headers) + task = get_hf_task_for_model(model) + print_verbose(f"{model}, {task}") + completion_url = "" + input_text = "" + if "https" in model: + completion_url = model + elif api_base: + completion_url = api_base + elif "HF_API_BASE" in os.environ: + completion_url = os.getenv("HF_API_BASE", "") + elif "HUGGINGFACE_API_BASE" in os.environ: + completion_url = os.getenv("HUGGINGFACE_API_BASE", "") + else: + completion_url = f"https://api-inference.huggingface.co/models/{model}" + + ## Load Config + config = litellm.HuggingfaceConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > huggingfaceConfig(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ### MAP INPUT PARAMS + if task == "conversational": + inference_params = copy.deepcopy(optional_params) + inference_params.pop("details") + inference_params.pop("return_full_text") + past_user_inputs = [] + generated_responses = [] + text = "" + for message in messages: + if message["role"] == "user": + if text != "": + past_user_inputs.append(text) + text = message["content"] + elif message["role"] == "assistant" or message["role"] == "system": + generated_responses.append(message["content"]) + data = { + "inputs": { + "text": text, + "past_user_inputs": past_user_inputs, + "generated_responses": generated_responses, + }, + "parameters": inference_params, + } + input_text = "".join(message["content"] for message in messages) + elif task == "text-generation-inference": + # always send "details" and "return_full_text" as params + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", None), + initial_prompt_value=model_prompt_details.get( + "initial_prompt_value", "" + ), + final_prompt_value=model_prompt_details.get( + "final_prompt_value", "" + ), + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + data = { + "inputs": prompt, + "parameters": optional_params, + "stream": True + if "stream" in optional_params and optional_params["stream"] == True + else False, + } + input_text = prompt + else: + # Non TGI and Conversational llms + # We need this branch, it removes 'details' and 'return_full_text' from params + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get( + "initial_prompt_value", "" + ), + final_prompt_value=model_prompt_details.get( + "final_prompt_value", "" + ), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + inference_params = copy.deepcopy(optional_params) + inference_params.pop("details") + inference_params.pop("return_full_text") + data = { + "inputs": prompt, + "parameters": inference_params, + "stream": True + if "stream" in optional_params and optional_params["stream"] == True + else False, + } + input_text = prompt + ## LOGGING + logging_obj.pre_call( + input=input_text, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "task": task, + "headers": headers, + "api_base": completion_url, + "acompletion": acompletion, + }, + ) + ## COMPLETION CALL + if acompletion is True: + ### ASYNC STREAMING + if optional_params.get("stream", False): + return self.async_streaming(logging_obj=logging_obj, api_base=completion_url, data=data, headers=headers, model_response=model_response, model=model, timeout=timeout) # type: ignore + else: + ### ASYNC COMPLETION + return self.acompletion(api_base=completion_url, data=data, headers=headers, model_response=model_response, task=task, encoding=encoding, input_text=input_text, model=model, optional_params=optional_params, timeout=timeout) # type: ignore + ### SYNC STREAMING + if "stream" in optional_params and optional_params["stream"] == True: + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"], + ) + return response.iter_lines() + ### SYNC COMPLETION + else: + response = requests.post( + completion_url, headers=headers, data=json.dumps(data) + ) + + ## Some servers might return streaming responses even though stream was not set to true. (e.g. Baseten) + is_streamed = False + if ( + response.__dict__["headers"].get("Content-Type", "") + == "text/event-stream" + ): + is_streamed = True + + # iterate over the complete streamed response, and return the final answer + if is_streamed: + streamed_response = CustomStreamWrapper( + completion_stream=response.iter_lines(), + model=model, + custom_llm_provider="huggingface", + logging_obj=logging_obj, + ) + content = "" + for chunk in streamed_response: + content += chunk["choices"][0]["delta"]["content"] + completion_response: List[Dict[str, Any]] = [ + {"generated_text": content} + ] + ## LOGGING + logging_obj.post_call( + input=input_text, + api_key=api_key, + original_response=completion_response, + additional_args={"complete_input_dict": data, "task": task}, + ) + else: + ## LOGGING + logging_obj.post_call( + input=input_text, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data, "task": task}, + ) + ## RESPONSE OBJECT + try: + completion_response = response.json() + if isinstance(completion_response, dict): + completion_response = [completion_response] + except: + import traceback + + raise HuggingfaceError( + message=f"Original Response received: {response.text}; Stacktrace: {traceback.format_exc()}", + status_code=response.status_code, + ) + print_verbose(f"response: {completion_response}") + if ( + isinstance(completion_response, dict) + and "error" in completion_response + ): + print_verbose(f"completion error: {completion_response['error']}") + print_verbose(f"response.status_code: {response.status_code}") + raise HuggingfaceError( + message=completion_response["error"], + status_code=response.status_code, + ) + return self.convert_to_model_response_object( + completion_response=completion_response, + model_response=model_response, + task=task, + optional_params=optional_params, + encoding=encoding, + input_text=input_text, + model=model, + ) + except HuggingfaceError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if exception_mapping_worked: + raise e + else: + import traceback + + raise HuggingfaceError(status_code=500, message=traceback.format_exc()) + + async def acompletion( + self, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + task: str, + encoding: Any, + input_text: str, + model: str, + optional_params: dict, + timeout: float + ): + response = None + try: + async with httpx.AsyncClient(timeout=timeout) as client: + response = await client.post( + url=api_base, json=data, headers=headers + ) + response_json = response.json() + if response.status_code != 200: + raise HuggingfaceError( + status_code=response.status_code, + message=response.text, + request=response.request, + response=response, + ) + + ## RESPONSE OBJECT + return self.convert_to_model_response_object( + completion_response=response_json, + model_response=model_response, + task=task, + encoding=encoding, + input_text=input_text, + model=model, + optional_params=optional_params, + ) + except Exception as e: + if isinstance(e, httpx.TimeoutException): + raise HuggingfaceError(status_code=500, message="Request Timeout Error") + elif response is not None and hasattr(response, "text"): + raise HuggingfaceError( + status_code=500, + message=f"{str(e)}\n\nOriginal Response: {response.text}", + ) + else: + raise HuggingfaceError(status_code=500, message=f"{str(e)}") + + async def async_streaming( + self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + model: str, + timeout: float + ): + async with httpx.AsyncClient(timeout=timeout) as client: + response = client.stream( + "POST", url=f"{api_base}", json=data, headers=headers + ) + async with response as r: + if r.status_code != 200: + raise HuggingfaceError( + status_code=r.status_code, + message="An error occurred while streaming", + ) + streamwrapper = CustomStreamWrapper( + completion_stream=r.aiter_lines(), + model=model, + custom_llm_provider="huggingface", + logging_obj=logging_obj, + ) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + + def embedding( + self, + model: str, + input: list, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + logging_obj=None, + model_response=None, + encoding=None, + ): + super().embedding() + headers = self.validate_environment(api_key, headers=None) + # print_verbose(f"{model}, {task}") + embed_url = "" + if "https" in model: + embed_url = model + elif api_base: + embed_url = api_base + elif "HF_API_BASE" in os.environ: + embed_url = os.getenv("HF_API_BASE", "") + elif "HUGGINGFACE_API_BASE" in os.environ: + embed_url = os.getenv("HUGGINGFACE_API_BASE", "") + else: + embed_url = f"https://api-inference.huggingface.co/models/{model}" + + if "sentence-transformers" in model: + if len(input) == 0: + raise HuggingfaceError( + status_code=400, + message="sentence transformers requires 2+ sentences", + ) + data = { + "inputs": { + "source_sentence": input[0], + "sentences": [ + "That is a happy dog", + "That is a very happy person", + "Today is a sunny day", + ], + } + } + else: + data = {"inputs": input} # type: ignore + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": embed_url, + }, + ) + ## COMPLETION CALL + response = requests.post(embed_url, headers=headers, data=json.dumps(data)) + + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + embeddings = response.json() + + if "error" in embeddings: + raise HuggingfaceError(status_code=500, message=embeddings["error"]) + + output_data = [] + if "similarities" in embeddings: + for idx, embedding in embeddings["similarities"]: + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding, # flatten list returned from hf + } + ) + else: + for idx, embedding in enumerate(embeddings): + if isinstance(embedding, float): + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding, # flatten list returned from hf + } + ) + elif isinstance(embedding, list) and isinstance(embedding[0], float): + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding, # flatten list returned from hf + } + ) + else: + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding[0][ + 0 + ], # flatten list returned from hf + } + ) + model_response["object"] = "list" + model_response["data"] = output_data + model_response["model"] = model + input_tokens = 0 + for text in input: + input_tokens += len(encoding.encode(text)) + + model_response["usage"] = { + "prompt_tokens": input_tokens, + "total_tokens": input_tokens, + } + return model_response diff --git a/litellm/llms/maritalk.py b/litellm/llms/maritalk.py new file mode 100644 index 0000000000000000000000000000000000000000..77267b13d1658b912bb7a3f79448dda28fed4a28 --- /dev/null +++ b/litellm/llms/maritalk.py @@ -0,0 +1,189 @@ +import os, types +import json +from enum import Enum +import requests +import time, traceback +from typing import Callable, Optional, List +from litellm.utils import ModelResponse, Choices, Message, Usage +import litellm + + +class MaritalkError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class MaritTalkConfig: + """ + The class `MaritTalkConfig` provides configuration for the MaritTalk's API interface. Here are the parameters: + + - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default is 1. + + - `model` (string): The model used for conversation. Default is 'maritalk'. + + - `do_sample` (boolean): If set to True, the API will generate a response using sampling. Default is True. + + - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.7. + + - `top_p` (number): Selection threshold for token inclusion based on cumulative probability. Default is 0.95. + + - `repetition_penalty` (number): Penalty for repetition in the generated conversation. Default is 1. + + - `stopping_tokens` (list of string): List of tokens where the conversation can be stopped/stopped. + """ + + max_tokens: Optional[int] = None + model: Optional[str] = None + do_sample: Optional[bool] = None + temperature: Optional[float] = None + top_p: Optional[float] = None + repetition_penalty: Optional[float] = None + stopping_tokens: Optional[List[str]] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + model: Optional[str] = None, + do_sample: Optional[bool] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + repetition_penalty: Optional[float] = None, + stopping_tokens: Optional[List[str]] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Key {api_key}" + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + completion_url = api_base + model = model + + ## Load Config + config = litellm.MaritTalkConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > maritalk_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "messages": messages, + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"] if "stream" in optional_params else False, + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + completion_response = response.json() + if "error" in completion_response: + raise MaritalkError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + if len(completion_response["answer"]) > 0: + model_response["choices"][0]["message"][ + "content" + ] = completion_response["answer"] + except Exception as e: + raise MaritalkError( + message=response.text, status_code=response.status_code + ) + + ## CALCULATING USAGE + prompt = "".join(m["content"] for m in messages) + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding( + model: str, + input: list, + api_key: Optional[str] = None, + logging_obj=None, + model_response=None, + encoding=None, +): + pass diff --git a/litellm/llms/nlp_cloud.py b/litellm/llms/nlp_cloud.py new file mode 100644 index 0000000000000000000000000000000000000000..f827975cefc0a6081ba5529e44cecd3ec7e8b4e9 --- /dev/null +++ b/litellm/llms/nlp_cloud.py @@ -0,0 +1,243 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, Usage + + +class NLPCloudError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class NLPCloudConfig: + """ + Reference: https://docs.nlpcloud.com/#generation + + - `max_length` (int): Optional. The maximum number of tokens that the generated text should contain. + + - `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text. + + - `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence. + + - `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result. + + - `remove_input` (boolean): Optional. Whether to remove the input text from the result. + + - `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated. + + - `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities. + + - `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. + + - `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering. + + - `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times. + + - `num_beams` (int): Optional. Number of beams for beam search. + + - `num_return_sequences` (int): Optional. The number of independently computed returned sequences. + """ + + max_length: Optional[int] = None + length_no_input: Optional[bool] = None + end_sequence: Optional[str] = None + remove_end_sequence: Optional[bool] = None + remove_input: Optional[bool] = None + bad_words: Optional[list] = None + temperature: Optional[float] = None + top_p: Optional[float] = None + top_k: Optional[int] = None + repetition_penalty: Optional[float] = None + num_beams: Optional[int] = None + num_return_sequences: Optional[int] = None + + def __init__( + self, + max_length: Optional[int] = None, + length_no_input: Optional[bool] = None, + end_sequence: Optional[str] = None, + remove_end_sequence: Optional[bool] = None, + remove_input: Optional[bool] = None, + bad_words: Optional[list] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + repetition_penalty: Optional[float] = None, + num_beams: Optional[int] = None, + num_return_sequences: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Token {api_key}" + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, + default_max_tokens_to_sample=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.NLPCloudConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + completion_url_fragment_1 = api_base + completion_url_fragment_2 = "/generation" + model = model + text = " ".join(message["content"] for message in messages) + + data = { + "text": text, + **optional_params, + } + + completion_url = completion_url_fragment_1 + model + completion_url_fragment_2 + + ## LOGGING + logging_obj.pre_call( + input=text, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": completion_url, + }, + ) + ## COMPLETION CALL + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"] if "stream" in optional_params else False, + ) + if "stream" in optional_params and optional_params["stream"] == True: + return clean_and_iterate_chunks(response) + else: + ## LOGGING + logging_obj.post_call( + input=text, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + try: + completion_response = response.json() + except: + raise NLPCloudError(message=response.text, status_code=response.status_code) + if "error" in completion_response: + raise NLPCloudError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + if len(completion_response["generated_text"]) > 0: + model_response["choices"][0]["message"][ + "content" + ] = completion_response["generated_text"] + except: + raise NLPCloudError( + message=json.dumps(completion_response), + status_code=response.status_code, + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = completion_response["nb_input_tokens"] + completion_tokens = completion_response["nb_generated_tokens"] + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +# def clean_and_iterate_chunks(response): +# def process_chunk(chunk): +# print(f"received chunk: {chunk}") +# cleaned_chunk = chunk.decode("utf-8") +# # Perform further processing based on your needs +# return cleaned_chunk + + +# for line in response.iter_lines(): +# if line: +# yield process_chunk(line) +def clean_and_iterate_chunks(response): + buffer = b"" + + for chunk in response.iter_content(chunk_size=1024): + if not chunk: + break + + buffer += chunk + while b"\x00" in buffer: + buffer = buffer.replace(b"\x00", b"") + yield buffer.decode("utf-8") + buffer = b"" + + # No more data expected, yield any remaining data in the buffer + if buffer: + yield buffer.decode("utf-8") + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py new file mode 100644 index 0000000000000000000000000000000000000000..0f99622b32e6723b9bbaf513d1dfa963e20222ef --- /dev/null +++ b/litellm/llms/ollama.py @@ -0,0 +1,400 @@ +import requests, types, time +import json, uuid +import traceback +from typing import Optional +import litellm +import httpx, aiohttp, asyncio +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class OllamaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="http://localhost:11434") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class OllamaConfig: + """ + Reference: https://github.com/jmorganca/ollama/blob/main/docs/api.md#parameters + + The class `OllamaConfig` provides the configuration for the Ollama's API interface. Below are the parameters: + + - `mirostat` (int): Enable Mirostat sampling for controlling perplexity. Default is 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. Example usage: mirostat 0 + + - `mirostat_eta` (float): Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. Default: 0.1. Example usage: mirostat_eta 0.1 + + - `mirostat_tau` (float): Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. Default: 5.0. Example usage: mirostat_tau 5.0 + + - `num_ctx` (int): Sets the size of the context window used to generate the next token. Default: 2048. Example usage: num_ctx 4096 + + - `num_gqa` (int): The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Example usage: num_gqa 1 + + - `num_gpu` (int): The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Example usage: num_gpu 0 + + - `num_thread` (int): Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Example usage: num_thread 8 + + - `repeat_last_n` (int): Sets how far back for the model to look back to prevent repetition. Default: 64, 0 = disabled, -1 = num_ctx. Example usage: repeat_last_n 64 + + - `repeat_penalty` (float): Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. Default: 1.1. Example usage: repeat_penalty 1.1 + + - `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7 + + - `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:" + + - `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1 + + - `num_predict` (int): Maximum number of tokens to predict when generating text. Default: 128, -1 = infinite generation, -2 = fill context. Example usage: num_predict 42 + + - `top_k` (int): Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. Default: 40. Example usage: top_k 40 + + - `top_p` (float): Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. Default: 0.9. Example usage: top_p 0.9 + + - `system` (string): system prompt for model (overrides what is defined in the Modelfile) + + - `template` (string): the full prompt or prompt template (overrides what is defined in the Modelfile) + """ + + mirostat: Optional[int] = None + mirostat_eta: Optional[float] = None + mirostat_tau: Optional[float] = None + num_ctx: Optional[int] = None + num_gqa: Optional[int] = None + num_thread: Optional[int] = None + repeat_last_n: Optional[int] = None + repeat_penalty: Optional[float] = None + temperature: Optional[float] = None + stop: Optional[ + list + ] = None # stop is a list based on this - https://github.com/jmorganca/ollama/pull/442 + tfs_z: Optional[float] = None + num_predict: Optional[int] = None + top_k: Optional[int] = None + top_p: Optional[float] = None + system: Optional[str] = None + template: Optional[str] = None + + def __init__( + self, + mirostat: Optional[int] = None, + mirostat_eta: Optional[float] = None, + mirostat_tau: Optional[float] = None, + num_ctx: Optional[int] = None, + num_gqa: Optional[int] = None, + num_thread: Optional[int] = None, + repeat_last_n: Optional[int] = None, + repeat_penalty: Optional[float] = None, + temperature: Optional[float] = None, + stop: Optional[list] = None, + tfs_z: Optional[float] = None, + num_predict: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + system: Optional[str] = None, + template: Optional[str] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +# ollama implementation +def get_ollama_response( + api_base="http://localhost:11434", + model="llama2", + prompt="Why is the sky blue?", + optional_params=None, + logging_obj=None, + acompletion: bool = False, + model_response=None, + encoding=None, +): + if api_base.endswith("/api/generate"): + url = api_base + else: + url = f"{api_base}/api/generate" + + ## Load Config + config = litellm.OllamaConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + optional_params["stream"] = optional_params.get("stream", False) + data = {"model": model, "prompt": prompt, **optional_params} + ## LOGGING + logging_obj.pre_call( + input=None, + api_key=None, + additional_args={ + "api_base": url, + "complete_input_dict": data, + "headers": {}, + "acompletion": acompletion, + }, + ) + if acompletion is True: + if optional_params.get("stream", False) == True: + response = ollama_async_streaming( + url=url, + data=data, + model_response=model_response, + encoding=encoding, + logging_obj=logging_obj, + ) + else: + response = ollama_acompletion( + url=url, + data=data, + model_response=model_response, + encoding=encoding, + logging_obj=logging_obj, + ) + return response + elif optional_params.get("stream", False) == True: + return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj) + + response = requests.post(url=f"{url}", json=data, timeout=litellm.request_timeout) + if response.status_code != 200: + raise OllamaError(status_code=response.status_code, message=response.text) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response.text, + additional_args={ + "headers": None, + "api_base": api_base, + }, + ) + + response_json = response.json() + + ## RESPONSE OBJECT + model_response["choices"][0]["finish_reason"] = "stop" + if optional_params.get("format", "") == "json": + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": {"arguments": response_json["response"], "name": ""}, + "type": "function", + } + ], + ) + model_response["choices"][0]["message"] = message + else: + model_response["choices"][0]["message"]["content"] = response_json["response"] + model_response["created"] = int(time.time()) + model_response["model"] = "ollama/" + model + prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt))) # type: ignore + completion_tokens = response_json["eval_count"] + model_response["usage"] = litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + return model_response + + +def ollama_completion_stream(url, data, logging_obj): + with httpx.stream( + url=url, json=data, method="POST", timeout=litellm.request_timeout + ) as response: + try: + if response.status_code != 200: + raise OllamaError( + status_code=response.status_code, message=response.text + ) + + streamwrapper = litellm.CustomStreamWrapper( + completion_stream=response.iter_lines(), + model=data["model"], + custom_llm_provider="ollama", + logging_obj=logging_obj, + ) + for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: + raise e + + +async def ollama_async_streaming(url, data, model_response, encoding, logging_obj): + try: + client = httpx.AsyncClient() + async with client.stream( + url=f"{url}", json=data, method="POST", timeout=litellm.request_timeout + ) as response: + if response.status_code != 200: + raise OllamaError( + status_code=response.status_code, message=response.text + ) + + streamwrapper = litellm.CustomStreamWrapper( + completion_stream=response.aiter_lines(), + model=data["model"], + custom_llm_provider="ollama", + logging_obj=logging_obj, + ) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: + traceback.print_exc() + + +async def ollama_acompletion(url, data, model_response, encoding, logging_obj): + data["stream"] = False + try: + timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes + async with aiohttp.ClientSession(timeout=timeout) as session: + resp = await session.post(url, json=data) + + if resp.status != 200: + text = await resp.text() + raise OllamaError(status_code=resp.status, message=text) + + ## LOGGING + logging_obj.post_call( + input=data["prompt"], + api_key="", + original_response=resp.text, + additional_args={ + "headers": None, + "api_base": url, + }, + ) + + response_json = await resp.json() + ## RESPONSE OBJECT + model_response["choices"][0]["finish_reason"] = "stop" + if data.get("format", "") == "json": + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": response_json["response"], + "name": "", + }, + "type": "function", + } + ], + ) + model_response["choices"][0]["message"] = message + else: + model_response["choices"][0]["message"]["content"] = response_json[ + "response" + ] + model_response["created"] = int(time.time()) + model_response["model"] = "ollama/" + data["model"] + prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"]))) # type: ignore + completion_tokens = response_json["eval_count"] + model_response["usage"] = litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + return model_response + except Exception as e: + traceback.print_exc() + raise e + + +async def ollama_aembeddings( + api_base="http://localhost:11434", + model="llama2", + prompt="Why is the sky blue?", + optional_params=None, + logging_obj=None, + model_response=None, + encoding=None, +): + if api_base.endswith("/api/embeddings"): + url = api_base + else: + url = f"{api_base}/api/embeddings" + + ## Load Config + config = litellm.OllamaConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + data = { + "model": model, + "prompt": prompt, + } + ## LOGGING + logging_obj.pre_call( + input=None, + api_key=None, + additional_args={"api_base": url, "complete_input_dict": data, "headers": {}}, + ) + timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes + async with aiohttp.ClientSession(timeout=timeout) as session: + response = await session.post(url, json=data) + + if response.status != 200: + text = await response.text() + raise OllamaError(status_code=response.status, message=text) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response.text, + additional_args={ + "headers": None, + "api_base": api_base, + }, + ) + + response_json = await response.json() + embeddings = response_json["embedding"] + ## RESPONSE OBJECT + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + model_response["object"] = "list" + model_response["data"] = output_data + model_response["model"] = model + + input_tokens = len(encoding.encode(prompt)) + + model_response["usage"] = { + "prompt_tokens": input_tokens, + "total_tokens": input_tokens, + } + return model_response diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..1ff93649f044fd51dcd5235eca820f1f5ee31cc0 --- /dev/null +++ b/litellm/llms/ollama_chat.py @@ -0,0 +1,333 @@ +import requests, types, time +import json, uuid +import traceback +from typing import Optional +import litellm +import httpx, aiohttp, asyncio +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class OllamaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="http://localhost:11434") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class OllamaConfig: + """ + Reference: https://github.com/jmorganca/ollama/blob/main/docs/api.md#parameters + + The class `OllamaConfig` provides the configuration for the Ollama's API interface. Below are the parameters: + + - `mirostat` (int): Enable Mirostat sampling for controlling perplexity. Default is 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. Example usage: mirostat 0 + + - `mirostat_eta` (float): Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. Default: 0.1. Example usage: mirostat_eta 0.1 + + - `mirostat_tau` (float): Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. Default: 5.0. Example usage: mirostat_tau 5.0 + + - `num_ctx` (int): Sets the size of the context window used to generate the next token. Default: 2048. Example usage: num_ctx 4096 + + - `num_gqa` (int): The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Example usage: num_gqa 1 + + - `num_gpu` (int): The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Example usage: num_gpu 0 + + - `num_thread` (int): Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Example usage: num_thread 8 + + - `repeat_last_n` (int): Sets how far back for the model to look back to prevent repetition. Default: 64, 0 = disabled, -1 = num_ctx. Example usage: repeat_last_n 64 + + - `repeat_penalty` (float): Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. Default: 1.1. Example usage: repeat_penalty 1.1 + + - `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7 + + - `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:" + + - `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1 + + - `num_predict` (int): Maximum number of tokens to predict when generating text. Default: 128, -1 = infinite generation, -2 = fill context. Example usage: num_predict 42 + + - `top_k` (int): Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. Default: 40. Example usage: top_k 40 + + - `top_p` (float): Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. Default: 0.9. Example usage: top_p 0.9 + + - `system` (string): system prompt for model (overrides what is defined in the Modelfile) + + - `template` (string): the full prompt or prompt template (overrides what is defined in the Modelfile) + """ + + mirostat: Optional[int] = None + mirostat_eta: Optional[float] = None + mirostat_tau: Optional[float] = None + num_ctx: Optional[int] = None + num_gqa: Optional[int] = None + num_thread: Optional[int] = None + repeat_last_n: Optional[int] = None + repeat_penalty: Optional[float] = None + temperature: Optional[float] = None + stop: Optional[ + list + ] = None # stop is a list based on this - https://github.com/jmorganca/ollama/pull/442 + tfs_z: Optional[float] = None + num_predict: Optional[int] = None + top_k: Optional[int] = None + top_p: Optional[float] = None + system: Optional[str] = None + template: Optional[str] = None + + def __init__( + self, + mirostat: Optional[int] = None, + mirostat_eta: Optional[float] = None, + mirostat_tau: Optional[float] = None, + num_ctx: Optional[int] = None, + num_gqa: Optional[int] = None, + num_thread: Optional[int] = None, + repeat_last_n: Optional[int] = None, + repeat_penalty: Optional[float] = None, + temperature: Optional[float] = None, + stop: Optional[list] = None, + tfs_z: Optional[float] = None, + num_predict: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + system: Optional[str] = None, + template: Optional[str] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +# ollama implementation +def get_ollama_response( + api_base="http://localhost:11434", + model="llama2", + messages=None, + optional_params=None, + logging_obj=None, + acompletion: bool = False, + model_response=None, + encoding=None, +): + if api_base.endswith("/api/chat"): + url = api_base + else: + url = f"{api_base}/api/chat" + + ## Load Config + config = litellm.OllamaConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + optional_params["stream"] = optional_params.get("stream", False) + data = {"model": model, "messages": messages, **optional_params} + ## LOGGING + logging_obj.pre_call( + input=None, + api_key=None, + additional_args={ + "api_base": url, + "complete_input_dict": data, + "headers": {}, + "acompletion": acompletion, + }, + ) + if acompletion is True: + if optional_params.get("stream", False) == True: + response = ollama_async_streaming( + url=url, + data=data, + model_response=model_response, + encoding=encoding, + logging_obj=logging_obj, + ) + else: + response = ollama_acompletion( + url=url, + data=data, + model_response=model_response, + encoding=encoding, + logging_obj=logging_obj, + ) + return response + elif optional_params.get("stream", False) == True: + return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj) + + response = requests.post( + url=f"{url}", + json=data, + ) + if response.status_code != 200: + raise OllamaError(status_code=response.status_code, message=response.text) + + ## LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response=response.text, + additional_args={ + "headers": None, + "api_base": api_base, + }, + ) + + response_json = response.json() + + ## RESPONSE OBJECT + model_response["choices"][0]["finish_reason"] = "stop" + if data.get("format", "") == "json": + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": response_json["message"]["content"], + "name": "", + }, + "type": "function", + } + ], + ) + model_response["choices"][0]["message"] = message + else: + model_response["choices"][0]["message"] = response_json["message"] + model_response["created"] = int(time.time()) + model_response["model"] = "ollama/" + model + prompt_tokens = response_json["prompt_eval_count"] # type: ignore + completion_tokens = response_json["eval_count"] + model_response["usage"] = litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + return model_response + + +def ollama_completion_stream(url, data, logging_obj): + with httpx.stream( + url=url, json=data, method="POST", timeout=litellm.request_timeout + ) as response: + try: + if response.status_code != 200: + raise OllamaError( + status_code=response.status_code, message=response.iter_lines() + ) + + streamwrapper = litellm.CustomStreamWrapper( + completion_stream=response.iter_lines(), + model=data["model"], + custom_llm_provider="ollama_chat", + logging_obj=logging_obj, + ) + for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: + raise e + + +async def ollama_async_streaming(url, data, model_response, encoding, logging_obj): + try: + client = httpx.AsyncClient() + async with client.stream( + url=f"{url}", json=data, method="POST", timeout=litellm.request_timeout + ) as response: + if response.status_code != 200: + raise OllamaError( + status_code=response.status_code, message=response.text + ) + + streamwrapper = litellm.CustomStreamWrapper( + completion_stream=response.aiter_lines(), + model=data["model"], + custom_llm_provider="ollama_chat", + logging_obj=logging_obj, + ) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: + traceback.print_exc() + + +async def ollama_acompletion(url, data, model_response, encoding, logging_obj): + data["stream"] = False + try: + timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes + async with aiohttp.ClientSession(timeout=timeout) as session: + resp = await session.post(url, json=data) + + if resp.status != 200: + text = await resp.text() + raise OllamaError(status_code=resp.status, message=text) + + response_json = await resp.json() + + ## LOGGING + logging_obj.post_call( + input=data, + api_key="", + original_response=response_json, + additional_args={ + "headers": None, + "api_base": url, + }, + ) + + ## RESPONSE OBJECT + model_response["choices"][0]["finish_reason"] = "stop" + if data.get("format", "") == "json": + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": response_json["message"]["content"], + "name": "", + }, + "type": "function", + } + ], + ) + model_response["choices"][0]["message"] = message + else: + model_response["choices"][0]["message"] = response_json["message"] + model_response["created"] = int(time.time()) + model_response["model"] = "ollama/" + data["model"] + prompt_tokens = response_json["prompt_eval_count"] # type: ignore + completion_tokens = response_json["eval_count"] + model_response["usage"] = litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + return model_response + except Exception as e: + traceback.print_exc() + raise e diff --git a/litellm/llms/oobabooga.py b/litellm/llms/oobabooga.py new file mode 100644 index 0000000000000000000000000000000000000000..2a6e9c9ac1bd83c693248e7abb794acbeba7a207 --- /dev/null +++ b/litellm/llms/oobabooga.py @@ -0,0 +1,183 @@ +import os +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class OobaboogaError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +def validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Token {api_key}" + return headers + + +def completion( + model: str, + messages: list, + api_base: Optional[str], + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, + default_max_tokens_to_sample=None, +): + headers = validate_environment(api_key) + if "https" in model: + completion_url = model + elif api_base: + completion_url = api_base + else: + raise OobaboogaError( + status_code=404, + message="API Base not set. Set one via completion(..,api_base='your-api-url')", + ) + model = model + + completion_url = completion_url + "/v1/chat/completions" + data = { + "messages": messages, + **optional_params, + } + ## LOGGING + + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={"complete_input_dict": data}, + ) + ## COMPLETION CALL + + response = requests.post( + completion_url, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream"] if "stream" in optional_params else False, + ) + if "stream" in optional_params and optional_params["stream"] == True: + return response.iter_lines() + else: + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + try: + completion_response = response.json() + except: + raise OobaboogaError( + message=response.text, status_code=response.status_code + ) + if "error" in completion_response: + raise OobaboogaError( + message=completion_response["error"], + status_code=response.status_code, + ) + else: + try: + model_response["choices"][0]["message"][ + "content" + ] = completion_response["choices"][0]["message"]["content"] + except: + raise OobaboogaError( + message=json.dumps(completion_response), + status_code=response.status_code, + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=completion_response["usage"]["prompt_tokens"], + completion_tokens=completion_response["usage"]["completion_tokens"], + total_tokens=completion_response["usage"]["total_tokens"], + ) + model_response.usage = usage + return model_response + + +def embedding( + model: str, + input: list, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + logging_obj=None, + model_response=None, + optional_params=None, + encoding=None, +): + # Create completion URL + if "https" in model: + embeddings_url = model + elif api_base: + embeddings_url = f"{api_base}/v1/embeddings" + else: + raise OobaboogaError( + status_code=404, + message="API Base not set. Set one via completion(..,api_base='your-api-url')", + ) + + # Prepare request data + data = {"input": input} + if optional_params: + data.update(optional_params) + + # Logging before API call + if logging_obj: + logging_obj.pre_call( + input=input, api_key=api_key, additional_args={"complete_input_dict": data} + ) + + # Send POST request + headers = validate_environment(api_key) + response = requests.post(embeddings_url, headers=headers, json=data) + if not response.ok: + raise OobaboogaError(message=response.text, status_code=response.status_code) + completion_response = response.json() + + # Check for errors in response + if "error" in completion_response: + raise OobaboogaError( + message=completion_response["error"], + status_code=completion_response.get("status_code", 500), + ) + + # Process response data + model_response["data"] = [ + { + "embedding": completion_response["data"][0]["embedding"], + "index": 0, + "object": "embedding", + } + ] + + num_tokens = len(completion_response["data"][0]["embedding"]) + # Adding metadata to response + model_response.usage = Usage(prompt_tokens=num_tokens, total_tokens=num_tokens) + model_response["object"] = "list" + model_response["model"] = model + + return model_response diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..9285bf6f55ce37f5a3b2c5c0b6778a03bedb57dd --- /dev/null +++ b/litellm/llms/openai.py @@ -0,0 +1,1047 @@ +from typing import Optional, Union, Any +import types, time, json +import httpx +from .base import BaseLLM +from litellm.utils import ( + ModelResponse, + Choices, + Message, + CustomStreamWrapper, + convert_to_model_response_object, + Usage, +) +from typing import Callable, Optional +import aiohttp, requests +import litellm +from .prompt_templates.factory import prompt_factory, custom_prompt +from openai import OpenAI, AsyncOpenAI + + +class OpenAIError(Exception): + def __init__( + self, + status_code, + message, + request: Optional[httpx.Request] = None, + response: Optional[httpx.Response] = None, + ): + self.status_code = status_code + self.message = message + if request: + self.request = request + else: + self.request = httpx.Request(method="POST", url="https://api.openai.com/v1") + if response: + self.response = response + else: + self.response = httpx.Response( + status_code=status_code, request=self.request + ) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class OpenAIConfig: + """ + Reference: https://platform.openai.com/docs/api-reference/chat/create + + The class `OpenAIConfig` provides configuration for the OpenAI's Chat API interface. Below are the parameters: + + - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition. + + - `function_call` (string or object): This optional parameter controls how the model calls functions. + + - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs. + + - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. + + - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. + + - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message. + + - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics. + + - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. + + - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. + + - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. + """ + + frequency_penalty: Optional[int] = None + function_call: Optional[Union[str, dict]] = None + functions: Optional[list] = None + logit_bias: Optional[dict] = None + max_tokens: Optional[int] = None + n: Optional[int] = None + presence_penalty: Optional[int] = None + stop: Optional[Union[str, list]] = None + temperature: Optional[int] = None + top_p: Optional[int] = None + + def __init__( + self, + frequency_penalty: Optional[int] = None, + function_call: Optional[Union[str, dict]] = None, + functions: Optional[list] = None, + logit_bias: Optional[dict] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[int] = None, + stop: Optional[Union[str, list]] = None, + temperature: Optional[int] = None, + top_p: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +class OpenAITextCompletionConfig: + """ + Reference: https://platform.openai.com/docs/api-reference/completions/create + + The class `OpenAITextCompletionConfig` provides configuration for the OpenAI's text completion API interface. Below are the parameters: + + - `best_of` (integer or null): This optional parameter generates server-side completions and returns the one with the highest log probability per token. + + - `echo` (boolean or null): This optional parameter will echo back the prompt in addition to the completion. + + - `frequency_penalty` (number or null): Defaults to 0. It is a numbers from -2.0 to 2.0, where positive values decrease the model's likelihood to repeat the same line. + + - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. + + - `logprobs` (integer or null): This optional parameter includes the log probabilities on the most likely tokens as well as the chosen tokens. + + - `max_tokens` (integer or null): This optional parameter sets the maximum number of tokens to generate in the completion. + + - `n` (integer or null): This optional parameter sets how many completions to generate for each prompt. + + - `presence_penalty` (number or null): Defaults to 0 and can be between -2.0 and 2.0. Positive values increase the model's likelihood to talk about new topics. + + - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. + + - `suffix` (string or null): Defines the suffix that comes after a completion of inserted text. + + - `temperature` (number or null): This optional parameter defines the sampling temperature to use. + + - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. + """ + + best_of: Optional[int] = None + echo: Optional[bool] = None + frequency_penalty: Optional[int] = None + logit_bias: Optional[dict] = None + logprobs: Optional[int] = None + max_tokens: Optional[int] = None + n: Optional[int] = None + presence_penalty: Optional[int] = None + stop: Optional[Union[str, list]] = None + suffix: Optional[str] = None + temperature: Optional[float] = None + top_p: Optional[float] = None + + def __init__( + self, + best_of: Optional[int] = None, + echo: Optional[bool] = None, + frequency_penalty: Optional[int] = None, + logit_bias: Optional[dict] = None, + logprobs: Optional[int] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[int] = None, + stop: Optional[Union[str, list]] = None, + suffix: Optional[str] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +class OpenAIChatCompletion(BaseLLM): + def __init__(self) -> None: + super().__init__() + + def completion( + self, + model_response: ModelResponse, + timeout: float, + model: Optional[str] = None, + messages: Optional[list] = None, + print_verbose: Optional[Callable] = None, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + acompletion: bool = False, + logging_obj=None, + optional_params=None, + litellm_params=None, + logger_fn=None, + headers: Optional[dict] = None, + custom_prompt_dict: dict = {}, + client=None, + ): + super().completion() + exception_mapping_worked = False + try: + if headers: + optional_params["extra_headers"] = headers + if model is None or messages is None: + raise OpenAIError(status_code=422, message=f"Missing model or messages") + + if not isinstance(timeout, float): + raise OpenAIError( + status_code=422, message=f"Timeout needs to be a float" + ) + + for _ in range( + 2 + ): # if call fails due to alternating messages, retry with reformatted message + data = {"model": model, "messages": messages, **optional_params} + + try: + max_retries = data.pop("max_retries", 2) + if acompletion is True: + if optional_params.get("stream", False): + return self.async_streaming( + logging_obj=logging_obj, + headers=headers, + data=data, + model=model, + api_base=api_base, + api_key=api_key, + timeout=timeout, + client=client, + max_retries=max_retries, + ) + else: + return self.acompletion( + data=data, + headers=headers, + logging_obj=logging_obj, + model_response=model_response, + api_base=api_base, + api_key=api_key, + timeout=timeout, + client=client, + max_retries=max_retries, + ) + elif optional_params.get("stream", False): + return self.streaming( + logging_obj=logging_obj, + headers=headers, + data=data, + model=model, + api_base=api_base, + api_key=api_key, + timeout=timeout, + client=client, + max_retries=max_retries, + ) + else: + if not isinstance(max_retries, int): + raise OpenAIError( + status_code=422, message="max retries must be an int" + ) + if client is None: + openai_client = OpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.client_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_client = client + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=openai_client.api_key, + additional_args={ + "headers": headers, + "api_base": openai_client._base_url._uri_reference, + "acompletion": acompletion, + "complete_input_dict": data, + }, + ) + + response = openai_client.chat.completions.create(**data, timeout=timeout) # type: ignore + stringified_response = response.model_dump() + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=stringified_response, + additional_args={"complete_input_dict": data}, + ) + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + ) + except Exception as e: + if "Conversation roles must alternate user/assistant" in str( + e + ) or "user and assistant roles should be alternating" in str(e): + # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility + new_messages = [] + for i in range(len(messages) - 1): + new_messages.append(messages[i]) + if messages[i]["role"] == messages[i + 1]["role"]: + if messages[i]["role"] == "user": + new_messages.append( + {"role": "assistant", "content": ""} + ) + else: + new_messages.append({"role": "user", "content": ""}) + new_messages.append(messages[-1]) + messages = new_messages + elif "Last message must have role `user`" in str(e): + new_messages = messages + new_messages.append({"role": "user", "content": ""}) + messages = new_messages + else: + raise e + except OpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise OpenAIError(status_code=e.status_code, message=str(e)) + else: + raise OpenAIError(status_code=500, message=str(e)) + + async def acompletion( + self, + data: dict, + model_response: ModelResponse, + timeout: float, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + client=None, + max_retries=None, + logging_obj=None, + headers=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.aclient_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_aclient = client + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=openai_aclient.api_key, + additional_args={ + "headers": {"Authorization": f"Bearer {openai_aclient.api_key}"}, + "api_base": openai_aclient._base_url._uri_reference, + "acompletion": True, + "complete_input_dict": data, + }, + ) + + response = await openai_aclient.chat.completions.create( + **data, timeout=timeout + ) + stringified_response = response.model_dump() + logging_obj.post_call( + input=data["messages"], + api_key=api_key, + original_response=stringified_response, + additional_args={"complete_input_dict": data}, + ) + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + ) + except Exception as e: + raise e + + def streaming( + self, + logging_obj, + timeout: float, + data: dict, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + client=None, + max_retries=None, + headers=None, + ): + if client is None: + openai_client = OpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.client_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_client = client + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=api_key, + additional_args={ + "headers": headers, + "api_base": api_base, + "acompletion": False, + "complete_input_dict": data, + }, + ) + response = openai_client.chat.completions.create(**data, timeout=timeout) + streamwrapper = CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider="openai", + logging_obj=logging_obj, + ) + return streamwrapper + + async def async_streaming( + self, + logging_obj, + timeout: float, + data: dict, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + client=None, + max_retries=None, + headers=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.aclient_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_aclient = client + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=api_key, + additional_args={ + "headers": headers, + "api_base": api_base, + "acompletion": True, + "complete_input_dict": data, + }, + ) + + response = await openai_aclient.chat.completions.create( + **data, timeout=timeout + ) + streamwrapper = CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider="openai", + logging_obj=logging_obj, + ) + return streamwrapper + except ( + Exception + ) as e: # need to exception handle here. async exceptions don't get caught in sync functions. + if response is not None and hasattr(response, "text"): + raise OpenAIError( + status_code=500, + message=f"{str(e)}\n\nOriginal Response: {response.text}", + ) + else: + if type(e).__name__ == "ReadTimeout": + raise OpenAIError(status_code=408, message=f"{type(e).__name__}") + elif hasattr(e, "status_code"): + raise OpenAIError(status_code=e.status_code, message=str(e)) + else: + raise OpenAIError(status_code=500, message=f"{str(e)}") + + async def aembedding( + self, + input: list, + data: dict, + model_response: ModelResponse, + timeout: float, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + client=None, + max_retries=None, + logging_obj=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.aclient_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_aclient = client + response = await openai_aclient.embeddings.create(**data, timeout=timeout) # type: ignore + stringified_response = response.model_dump() + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=stringified_response, + ) + return convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, response_type="embedding") # type: ignore + except Exception as e: + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + original_response=str(e), + ) + raise e + + def embedding( + self, + model: str, + input: list, + timeout: float, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + model_response: Optional[litellm.utils.EmbeddingResponse] = None, + logging_obj=None, + optional_params=None, + client=None, + aembedding=None, + ): + super().embedding() + exception_mapping_worked = False + try: + model = model + data = {"model": model, "input": input, **optional_params} + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise OpenAIError(status_code=422, message="max retries must be an int") + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data, "api_base": api_base}, + ) + + if aembedding == True: + response = self.aembedding(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore + return response + if client is None: + openai_client = OpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.client_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_client = client + + ## COMPLETION CALL + response = openai_client.embeddings.create(**data, timeout=timeout) # type: ignore + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="embedding") # type: ignore + except OpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise OpenAIError(status_code=e.status_code, message=str(e)) + else: + raise OpenAIError(status_code=500, message=str(e)) + + async def aimage_generation( + self, + prompt: str, + data: dict, + model_response: ModelResponse, + timeout: float, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + client=None, + max_retries=None, + logging_obj=None, + ): + response = None + try: + if client is None: + openai_aclient = AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.aclient_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_aclient = client + response = await openai_aclient.images.generate(**data, timeout=timeout) # type: ignore + stringified_response = response.model_dump() + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=stringified_response, + ) + return convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, response_type="image_generation") # type: ignore + except Exception as e: + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + original_response=str(e), + ) + raise e + + def image_generation( + self, + model: Optional[str], + prompt: str, + timeout: float, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + model_response: Optional[litellm.utils.ImageResponse] = None, + logging_obj=None, + optional_params=None, + client=None, + aimg_generation=None, + ): + exception_mapping_worked = False + try: + model = model + data = {"model": model, "prompt": prompt, **optional_params} + max_retries = data.pop("max_retries", 2) + if not isinstance(max_retries, int): + raise OpenAIError(status_code=422, message="max retries must be an int") + + if aimg_generation == True: + response = self.aimage_generation(data=data, prompt=prompt, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore + return response + + if client is None: + openai_client = OpenAI( + api_key=api_key, + base_url=api_base, + http_client=litellm.client_session, + timeout=timeout, + max_retries=max_retries, + ) + else: + openai_client = client + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=openai_client.api_key, + additional_args={ + "headers": {"Authorization": f"Bearer {openai_client.api_key}"}, + "api_base": openai_client._base_url._uri_reference, + "acompletion": True, + "complete_input_dict": data, + }, + ) + + ## COMPLETION CALL + response = openai_client.images.generate(**data, timeout=timeout) # type: ignore + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + # return response + return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="image_generation") # type: ignore + except OpenAIError as e: + exception_mapping_worked = True + raise e + except Exception as e: + if hasattr(e, "status_code"): + raise OpenAIError(status_code=e.status_code, message=str(e)) + else: + raise OpenAIError(status_code=500, message=str(e)) + + async def ahealth_check( + self, + model: Optional[str], + api_key: str, + timeout: float, + mode: str, + messages: Optional[list] = None, + input: Optional[list] = None, + prompt: Optional[str] = None, + ): + client = AsyncOpenAI(api_key=api_key, timeout=timeout) + if model is None and mode != "image_generation": + raise Exception("model is not set") + + completion = None + + if mode == "completion": + completion = await client.completions.with_raw_response.create( + model=model, # type: ignore + prompt=prompt, # type: ignore + ) + elif mode == "chat": + if messages is None: + raise Exception("messages is not set") + completion = await client.chat.completions.with_raw_response.create( + model=model, # type: ignore + messages=messages, # type: ignore + ) + elif mode == "embedding": + if input is None: + raise Exception("input is not set") + completion = await client.embeddings.with_raw_response.create( + model=model, # type: ignore + input=input, # type: ignore + ) + elif mode == "image_generation": + if prompt is None: + raise Exception("prompt is not set") + completion = await client.images.with_raw_response.generate( + model=model, # type: ignore + prompt=prompt, # type: ignore + ) + else: + raise Exception("mode not set") + response = {} + + if completion is None or not hasattr(completion, "headers"): + raise Exception("invalid completion response") + + if ( + completion.headers.get("x-ratelimit-remaining-requests", None) is not None + ): # not provided for dall-e requests + response["x-ratelimit-remaining-requests"] = completion.headers[ + "x-ratelimit-remaining-requests" + ] + + if completion.headers.get("x-ratelimit-remaining-tokens", None) is not None: + response["x-ratelimit-remaining-tokens"] = completion.headers[ + "x-ratelimit-remaining-tokens" + ] + return response + + +class OpenAITextCompletion(BaseLLM): + _client_session: httpx.Client + + def __init__(self) -> None: + super().__init__() + self._client_session = self.create_client_session() + + def validate_environment(self, api_key): + headers = { + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + + def convert_to_model_response_object( + self, + response_object: Optional[dict] = None, + model_response_object: Optional[ModelResponse] = None, + ): + try: + ## RESPONSE OBJECT + if response_object is None or model_response_object is None: + raise ValueError("Error in response object format") + choice_list = [] + for idx, choice in enumerate(response_object["choices"]): + message = Message(content=choice["text"], role="assistant") + choice = Choices( + finish_reason=choice["finish_reason"], index=idx, message=message + ) + choice_list.append(choice) + model_response_object.choices = choice_list + + if "usage" in response_object: + model_response_object.usage = response_object["usage"] + + if "id" in response_object: + model_response_object.id = response_object["id"] + + if "model" in response_object: + model_response_object.model = response_object["model"] + + model_response_object._hidden_params[ + "original_response" + ] = response_object # track original response, if users make a litellm.text_completion() request, we can return the original response + return model_response_object + except Exception as e: + raise e + + def completion( + self, + model_response: ModelResponse, + api_key: str, + model: str, + messages: list, + timeout: float, + print_verbose: Optional[Callable] = None, + api_base: Optional[str] = None, + logging_obj=None, + acompletion: bool = False, + optional_params=None, + litellm_params=None, + logger_fn=None, + headers: Optional[dict] = None, + ): + super().completion() + exception_mapping_worked = False + try: + if headers is None: + headers = self.validate_environment(api_key=api_key) + if model is None or messages is None: + raise OpenAIError(status_code=422, message=f"Missing model or messages") + + api_base = f"{api_base}/completions" + + if ( + len(messages) > 0 + and "content" in messages[0] + and type(messages[0]["content"]) == list + ): + prompt = messages[0]["content"] + else: + prompt = " ".join([message["content"] for message in messages]) # type: ignore + + # don't send max retries to the api, if set + optional_params.pop("max_retries", None) + + data = {"model": model, "prompt": prompt, **optional_params} + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "headers": headers, + "api_base": api_base, + "complete_input_dict": data, + }, + ) + if acompletion == True: + if optional_params.get("stream", False): + return self.async_streaming( + logging_obj=logging_obj, + api_base=api_base, + data=data, + headers=headers, + model_response=model_response, + model=model, + timeout=timeout, + ) + else: + return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, prompt=prompt, api_key=api_key, logging_obj=logging_obj, model=model, timeout=timeout) # type: ignore + elif optional_params.get("stream", False): + return self.streaming( + logging_obj=logging_obj, + api_base=api_base, + data=data, + headers=headers, + model_response=model_response, + model=model, + timeout=timeout, + ) + else: + response = httpx.post( + url=f"{api_base}", json=data, headers=headers, timeout=timeout + ) + if response.status_code != 200: + raise OpenAIError( + status_code=response.status_code, message=response.text + ) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_base": api_base, + }, + ) + + ## RESPONSE OBJECT + return self.convert_to_model_response_object( + response_object=response.json(), + model_response_object=model_response, + ) + except Exception as e: + raise e + + async def acompletion( + self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + prompt: str, + api_key: str, + model: str, + timeout: float, + ): + async with httpx.AsyncClient(timeout=timeout) as client: + try: + response = await client.post( + api_base, + json=data, + headers=headers, + timeout=litellm.request_timeout, + ) + response_json = response.json() + if response.status_code != 200: + raise OpenAIError( + status_code=response.status_code, message=response.text + ) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_base": api_base, + }, + ) + + ## RESPONSE OBJECT + return self.convert_to_model_response_object( + response_object=response_json, model_response_object=model_response + ) + except Exception as e: + raise e + + def streaming( + self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + model: str, + timeout: float, + ): + with httpx.stream( + url=f"{api_base}", + json=data, + headers=headers, + method="POST", + timeout=timeout, + ) as response: + if response.status_code != 200: + raise OpenAIError( + status_code=response.status_code, message=response.text + ) + + streamwrapper = CustomStreamWrapper( + completion_stream=response.iter_lines(), + model=model, + custom_llm_provider="text-completion-openai", + logging_obj=logging_obj, + ) + for transformed_chunk in streamwrapper: + yield transformed_chunk + + async def async_streaming( + self, + logging_obj, + api_base: str, + data: dict, + headers: dict, + model_response: ModelResponse, + model: str, + timeout: float, + ): + client = httpx.AsyncClient() + async with client.stream( + url=f"{api_base}", + json=data, + headers=headers, + method="POST", + timeout=timeout, + ) as response: + try: + if response.status_code != 200: + raise OpenAIError( + status_code=response.status_code, message=response.text + ) + + streamwrapper = CustomStreamWrapper( + completion_stream=response.aiter_lines(), + model=model, + custom_llm_provider="text-completion-openai", + logging_obj=logging_obj, + ) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: + raise e diff --git a/litellm/llms/openrouter.py b/litellm/llms/openrouter.py new file mode 100644 index 0000000000000000000000000000000000000000..b6ec4024fd26915b0ae29bb76f1fddfbc6c733ad --- /dev/null +++ b/litellm/llms/openrouter.py @@ -0,0 +1,41 @@ +from typing import List, Dict +import types + + +class OpenrouterConfig: + """ + Reference: https://openrouter.ai/docs#format + + """ + + # OpenRouter-only parameters + extra_body: Dict[str, List[str]] = {"transforms": []} # default transforms to [] + + def __init__( + self, + transforms: List[str] = [], + models: List[str] = [], + route: str = "", + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } diff --git a/litellm/llms/palm.py b/litellm/llms/palm.py new file mode 100644 index 0000000000000000000000000000000000000000..060e6dca1509676ba3f0be5185a93a7d4813410d --- /dev/null +++ b/litellm/llms/palm.py @@ -0,0 +1,200 @@ +import os, types, traceback, copy +import json +from enum import Enum +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage +import litellm +import sys, httpx + + +class PalmError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", + url="https://developers.generativeai.google/api/python/google/generativeai/chat", + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class PalmConfig: + """ + Reference: https://developers.generativeai.google/api/python/google/generativeai/chat + + The class `PalmConfig` provides configuration for the Palm's API interface. Here are the parameters: + + - `context` (string): Text that should be provided to the model first, to ground the response. This could be a prompt to guide the model's responses. + + - `examples` (list): Examples of what the model should generate. They are treated identically to conversation messages except that they take precedence over the history in messages if the total input size exceeds the model's input_token_limit. + + - `temperature` (float): Controls the randomness of the output. Must be positive. Higher values produce a more random and varied response. A temperature of zero will be deterministic. + + - `candidate_count` (int): Maximum number of generated response messages to return. This value must be between [1, 8], inclusive. Only unique candidates are returned. + + - `top_k` (int): The API uses combined nucleus and top-k sampling. `top_k` sets the maximum number of tokens to sample from on each step. + + - `top_p` (float): The API uses combined nucleus and top-k sampling. `top_p` configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. + + - `max_output_tokens` (int): Sets the maximum number of tokens to be returned in the output + """ + + context: Optional[str] = None + examples: Optional[list] = None + temperature: Optional[float] = None + candidate_count: Optional[int] = None + top_k: Optional[int] = None + top_p: Optional[float] = None + max_output_tokens: Optional[int] = None + + def __init__( + self, + context: Optional[str] = None, + examples: Optional[list] = None, + temperature: Optional[float] = None, + candidate_count: Optional[int] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + max_output_tokens: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + api_key, + encoding, + logging_obj, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + try: + import google.generativeai as palm + except: + raise Exception( + "Importing google.generativeai failed, please run 'pip install -q google-generativeai" + ) + palm.configure(api_key=api_key) + + model = model + + ## Load Config + inference_params = copy.deepcopy(optional_params) + inference_params.pop( + "stream", None + ) # palm does not support streaming, so we handle this by fake streaming in main.py + config = litellm.PalmConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > palm_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + prompt = "" + for message in messages: + if "role" in message: + if message["role"] == "user": + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + else: + prompt += f"{message['content']}" + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": {"inference_params": inference_params}}, + ) + ## COMPLETION CALL + try: + response = palm.generate_text(prompt=prompt, **inference_params) + except Exception as e: + raise PalmError( + message=str(e), + status_code=500, + ) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response, + additional_args={"complete_input_dict": {}}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + completion_response = response + try: + choices_list = [] + for idx, item in enumerate(completion_response.candidates): + if len(item["output"]) > 0: + message_obj = Message(content=item["output"]) + else: + message_obj = Message(content=None) + choice_obj = Choices(index=idx + 1, message=message_obj) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + except Exception as e: + traceback.print_exc() + raise PalmError( + message=traceback.format_exc(), status_code=response.status_code + ) + + try: + completion_response = model_response["choices"][0]["message"].get("content") + except: + raise PalmError( + status_code=400, + message=f"No response received. Original response - {response}", + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = "palm/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/petals.py b/litellm/llms/petals.py new file mode 100644 index 0000000000000000000000000000000000000000..bc30306a6d506e232255f54e44afed8481fff527 --- /dev/null +++ b/litellm/llms/petals.py @@ -0,0 +1,212 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class PetalsError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class PetalsConfig: + """ + Reference: https://github.com/petals-infra/chat.petals.dev#post-apiv1generate + The `PetalsConfig` class encapsulates the configuration for the Petals API. The properties of this class are described below: + + - `max_length` (integer): This represents the maximum length of the generated text (including the prefix) in tokens. + + - `max_new_tokens` (integer): This represents the maximum number of newly generated tokens (excluding the prefix). + + The generation parameters are compatible with `.generate()` from Hugging Face's Transformers library: + + - `do_sample` (boolean, optional): If set to 0 (default), the API runs greedy generation. If set to 1, the API performs sampling using the parameters below: + + - `temperature` (float, optional): This value sets the temperature for sampling. + + - `top_k` (integer, optional): This value sets the limit for top-k sampling. + + - `top_p` (float, optional): This value sets the limit for top-p (nucleus) sampling. + + - `repetition_penalty` (float, optional): This helps apply the repetition penalty during text generation, as discussed in this paper. + """ + + max_length: Optional[int] = None + max_new_tokens: Optional[ + int + ] = litellm.max_tokens # petals requires max tokens to be set + do_sample: Optional[bool] = None + temperature: Optional[float] = None + top_k: Optional[int] = None + top_p: Optional[float] = None + repetition_penalty: Optional[float] = None + + def __init__( + self, + max_length: Optional[int] = None, + max_new_tokens: Optional[ + int + ] = litellm.max_tokens, # petals requires max tokens to be set + do_sample: Optional[bool] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + repetition_penalty: Optional[float] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def completion( + model: str, + messages: list, + api_base: Optional[str], + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params=None, + stream=False, + litellm_params=None, + logger_fn=None, +): + ## Load Config + config = litellm.PetalsConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > petals_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + if model in litellm.custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = litellm.custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + if api_base: + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={ + "complete_input_dict": optional_params, + "api_base": api_base, + }, + ) + data = {"model": model, "inputs": prompt, **optional_params} + + ## COMPLETION CALL + response = requests.post(api_base, data=data) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response.text, + additional_args={"complete_input_dict": optional_params}, + ) + + ## RESPONSE OBJECT + try: + output_text = response.json()["outputs"] + except Exception as e: + PetalsError(status_code=response.status_code, message=str(e)) + + else: + try: + import torch + from transformers import AutoTokenizer + from petals import AutoDistributedModelForCausalLM # type: ignore + except: + raise Exception( + "Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals" + ) + + model = model + + tokenizer = AutoTokenizer.from_pretrained( + model, use_fast=False, add_bos_token=False + ) + model_obj = AutoDistributedModelForCausalLM.from_pretrained(model) + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": optional_params}, + ) + + ## COMPLETION CALL + inputs = tokenizer(prompt, return_tensors="pt")["input_ids"] + + # optional params: max_new_tokens=1,temperature=0.9, top_p=0.6 + outputs = model_obj.generate(inputs, **optional_params) + + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=outputs, + additional_args={"complete_input_dict": optional_params}, + ) + ## RESPONSE OBJECT + output_text = tokenizer.decode(outputs[0]) + + if len(output_text) > 0: + model_response["choices"][0]["message"]["content"] = output_text + + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..43fbe87240957dfe96dd84cea61796476104f5aa --- /dev/null +++ b/litellm/llms/prompt_templates/factory.py @@ -0,0 +1,643 @@ +from enum import Enum +import requests, traceback +import json +from jinja2 import Template, exceptions, Environment, meta +from typing import Optional, Any + + +def default_pt(messages): + return " ".join(message["content"] for message in messages) + + +# alpaca prompt template - for models like mythomax, etc. +def alpaca_pt(messages): + prompt = custom_prompt( + role_dict={ + "system": { + "pre_message": "### Instruction:\n", + "post_message": "\n\n", + }, + "user": { + "pre_message": "### Instruction:\n", + "post_message": "\n\n", + }, + "assistant": {"pre_message": "### Response:\n", "post_message": "\n\n"}, + }, + bos_token="", + eos_token="", + messages=messages, + ) + return prompt + + +# Llama2 prompt template +def llama_2_chat_pt(messages): + prompt = custom_prompt( + role_dict={ + "system": { + "pre_message": "[INST] <>\n", + "post_message": "\n<>\n [/INST]\n", + }, + "user": { # follow this format https://github.com/facebookresearch/llama/blob/77062717054710e352a99add63d160274ce670c6/llama/generation.py#L348 + "pre_message": "[INST] ", + "post_message": " [/INST]\n", + }, + "assistant": { + "post_message": "\n" # follows this - https://replicate.com/blog/how-to-prompt-llama + }, + }, + messages=messages, + bos_token="", + eos_token="", + ) + return prompt + + +def ollama_pt( + model, messages +): # https://github.com/jmorganca/ollama/blob/af4cf55884ac54b9e637cd71dadfe9b7a5685877/docs/modelfile.md#template + if "instruct" in model: + prompt = custom_prompt( + role_dict={ + "system": {"pre_message": "### System:\n", "post_message": "\n"}, + "user": { + "pre_message": "### User:\n", + "post_message": "\n", + }, + "assistant": { + "pre_message": "### Response:\n", + "post_message": "\n", + }, + }, + final_prompt_value="### Response:", + messages=messages, + ) + elif "llava" in model: + prompt = "" + images = [] + for message in messages: + if isinstance(message["content"], str): + prompt += message["content"] + elif isinstance(message["content"], list): + # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models + for element in message["content"]: + if isinstance(element, dict): + if element["type"] == "text": + prompt += element["text"] + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + images.append(image_url) + return {"prompt": prompt, "images": images} + else: + prompt = "".join( + m["content"] + if isinstance(m["content"], str) is str + else "".join(m["content"]) + for m in messages + ) + return prompt + + +def mistral_instruct_pt(messages): + prompt = custom_prompt( + initial_prompt_value="", + role_dict={ + "system": {"pre_message": "[INST]", "post_message": "[/INST]"}, + "user": {"pre_message": "[INST]", "post_message": "[/INST]"}, + "assistant": {"pre_message": "[INST]", "post_message": "[/INST]"}, + }, + final_prompt_value="", + messages=messages, + ) + return prompt + + +# Falcon prompt template - from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py#L110 +def falcon_instruct_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += message["content"] + else: + prompt += ( + message["role"] + + ":" + + message["content"].replace("\r\n", "\n").replace("\n\n", "\n") + ) + prompt += "\n\n" + + return prompt + + +def falcon_chat_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += "System: " + message["content"] + elif message["role"] == "assistant": + prompt += "Falcon: " + message["content"] + elif message["role"] == "user": + prompt += "User: " + message["content"] + + return prompt + + +# MPT prompt template - from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py#L110 +def mpt_chat_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += "<|im_start|>system" + message["content"] + "<|im_end|>" + "\n" + elif message["role"] == "assistant": + prompt += "<|im_start|>assistant" + message["content"] + "<|im_end|>" + "\n" + elif message["role"] == "user": + prompt += "<|im_start|>user" + message["content"] + "<|im_end|>" + "\n" + return prompt + + +# WizardCoder prompt template - https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0#prompt-format +def wizardcoder_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += message["content"] + "\n\n" + elif message["role"] == "user": # map to 'Instruction' + prompt += "### Instruction:\n" + message["content"] + "\n\n" + elif message["role"] == "assistant": # map to 'Response' + prompt += "### Response:\n" + message["content"] + "\n\n" + return prompt + + +# Phind-CodeLlama prompt template - https://huggingface.co/Phind/Phind-CodeLlama-34B-v2#how-to-prompt-the-model +def phind_codellama_pt(messages): + prompt = "" + for message in messages: + if message["role"] == "system": + prompt += "### System Prompt\n" + message["content"] + "\n\n" + elif message["role"] == "user": + prompt += "### User Message\n" + message["content"] + "\n\n" + elif message["role"] == "assistant": + prompt += "### Assistant\n" + message["content"] + "\n\n" + return prompt + + +def hf_chat_template(model: str, messages: list, chat_template: Optional[Any] = None): + ## get the tokenizer config from huggingface + bos_token = "" + eos_token = "" + if chat_template is None: + + def _get_tokenizer_config(hf_model_name): + url = ( + f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" + ) + # Make a GET request to fetch the JSON data + response = requests.get(url) + if response.status_code == 200: + # Parse the JSON data + tokenizer_config = json.loads(response.content) + return {"status": "success", "tokenizer": tokenizer_config} + else: + return {"status": "failure"} + + tokenizer_config = _get_tokenizer_config(model) + if ( + tokenizer_config["status"] == "failure" + or "chat_template" not in tokenizer_config["tokenizer"] + ): + raise Exception("No chat template found") + ## read the bos token, eos token and chat template from the json + tokenizer_config = tokenizer_config["tokenizer"] + bos_token = tokenizer_config["bos_token"] + eos_token = tokenizer_config["eos_token"] + chat_template = tokenizer_config["chat_template"] + + def raise_exception(message): + raise Exception(f"Error message - {message}") + + # Create a template object from the template text + env = Environment() + env.globals["raise_exception"] = raise_exception + try: + template = env.from_string(chat_template) + except Exception as e: + raise e + + def _is_system_in_template(): + try: + # Try rendering the template with a system message + response = template.render( + messages=[{"role": "system", "content": "test"}], + eos_token="", + bos_token="", + ) + return True + + # This will be raised if Jinja attempts to render the system message and it can't + except: + return False + + try: + # Render the template with the provided values + if _is_system_in_template(): + rendered_text = template.render( + bos_token=bos_token, eos_token=eos_token, messages=messages + ) + else: + # treat a system message as a user message, if system not in template + try: + reformatted_messages = [] + for message in messages: + if message["role"] == "system": + reformatted_messages.append( + {"role": "user", "content": message["content"]} + ) + else: + reformatted_messages.append(message) + rendered_text = template.render( + bos_token=bos_token, + eos_token=eos_token, + messages=reformatted_messages, + ) + except Exception as e: + if "Conversation roles must alternate user/assistant" in str(e): + # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility + new_messages = [] + for i in range(len(reformatted_messages) - 1): + new_messages.append(reformatted_messages[i]) + if ( + reformatted_messages[i]["role"] + == reformatted_messages[i + 1]["role"] + ): + if reformatted_messages[i]["role"] == "user": + new_messages.append( + {"role": "assistant", "content": ""} + ) + else: + new_messages.append({"role": "user", "content": ""}) + new_messages.append(reformatted_messages[-1]) + rendered_text = template.render( + bos_token=bos_token, eos_token=eos_token, messages=new_messages + ) + return rendered_text + except Exception as e: + raise Exception(f"Error rendering template - {str(e)}") + + +# Anthropic template +def claude_2_1_pt( + messages: list, +): # format - https://docs.anthropic.com/claude/docs/how-to-use-system-prompts + """ + Claude v2.1 allows system prompts (no Human: needed), but requires it be followed by Human: + - you can't just pass a system message + - you can't pass a system message and follow that with an assistant message + if system message is passed in, you can only do system, human, assistant or system, human + + if a system message is passed in and followed by an assistant message, insert a blank human message between them. + + Additionally, you can "put words in Claude's mouth" by ending with an assistant message. + See: https://docs.anthropic.com/claude/docs/put-words-in-claudes-mouth + """ + + class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + + prompt = "" + for idx, message in enumerate(messages): + if message["role"] == "user": + prompt += f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}" + elif message["role"] == "system": + prompt += f"{message['content']}" + elif message["role"] == "assistant": + if idx > 0 and messages[idx - 1]["role"] == "system": + prompt += f"{AnthropicConstants.HUMAN_PROMPT.value}" # Insert a blank human message + prompt += f"{AnthropicConstants.AI_PROMPT.value}{message['content']}" + if messages[-1]["role"] != "assistant": + prompt += f"{AnthropicConstants.AI_PROMPT.value}" # prompt must end with \"\n\nAssistant: " turn + return prompt + + +### TOGETHER AI + + +def get_model_info(token, model): + try: + headers = {"Authorization": f"Bearer {token}"} + response = requests.get("https://api.together.xyz/models/info", headers=headers) + if response.status_code == 200: + model_info = response.json() + for m in model_info: + if m["name"].lower().strip() == model.strip(): + return m["config"].get("prompt_format", None), m["config"].get( + "chat_template", None + ) + return None, None + else: + return None, None + except Exception as e: # safely fail a prompt template request + return None, None + + +def format_prompt_togetherai(messages, prompt_format, chat_template): + if prompt_format is None: + return default_pt(messages) + + human_prompt, assistant_prompt = prompt_format.split("{prompt}") + + if chat_template is not None: + prompt = hf_chat_template( + model=None, messages=messages, chat_template=chat_template + ) + elif prompt_format is not None: + prompt = custom_prompt( + role_dict={}, + messages=messages, + initial_prompt_value=human_prompt, + final_prompt_value=assistant_prompt, + ) + else: + prompt = default_pt(messages) + return prompt + + +### + + +def anthropic_pt( + messages: list, +): # format - https://docs.anthropic.com/claude/reference/complete_post + """ + You can "put words in Claude's mouth" by ending with an assistant message. + See: https://docs.anthropic.com/claude/docs/put-words-in-claudes-mouth + """ + class AnthropicConstants(Enum): + HUMAN_PROMPT = "\n\nHuman: " + AI_PROMPT = "\n\nAssistant: " + + prompt = "" + for idx, message in enumerate( + messages + ): # needs to start with `\n\nHuman: ` and end with `\n\nAssistant: ` + if message["role"] == "user": + prompt += f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}" + elif message["role"] == "system": + prompt += f"{AnthropicConstants.HUMAN_PROMPT.value}{message['content']}" + else: + prompt += f"{AnthropicConstants.AI_PROMPT.value}{message['content']}" + if ( + idx == 0 and message["role"] == "assistant" + ): # ensure the prompt always starts with `\n\nHuman: ` + prompt = f"{AnthropicConstants.HUMAN_PROMPT.value}" + prompt + if messages[-1]["role"] != "assistant": + prompt += f"{AnthropicConstants.AI_PROMPT.value}" + return prompt + + +def _load_image_from_url(image_url): + try: + from PIL import Image + except: + raise Exception("gemini image conversion failed please run `pip install Pillow`") + from io import BytesIO + try: + # Send a GET request to the image URL + response = requests.get(image_url) + response.raise_for_status() # Raise an exception for HTTP errors + + # Check the response's content type to ensure it is an image + content_type = response.headers.get('content-type') + if not content_type or 'image' not in content_type: + raise ValueError(f"URL does not point to a valid image (content-type: {content_type})") + + # Load the image from the response content + return Image.open(BytesIO(response.content)) + + except requests.RequestException as e: + print(f"Request failed: {e}") + except UnidentifiedImageError: + print("Cannot identify image file (it may not be a supported image format or might be corrupted).") + except ValueError as e: + print(e) + + +def _gemini_vision_convert_messages(messages: list): + """ + Converts given messages for GPT-4 Vision to Gemini format. + + Args: + messages (list): The messages to convert. Each message can be a dictionary with a "content" key. The content can be a string or a list of elements. If it is a string, it will be concatenated to the prompt. If it is a list, each element will be processed based on its type: + - If the element is a dictionary with a "type" key equal to "text", its "text" value will be concatenated to the prompt. + - If the element is a dictionary with a "type" key equal to "image_url", its "image_url" value will be added to the list of images. + + Returns: + tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images). + """ + try: + from PIL import Image + except: + raise Exception("gemini image conversion failed please run `pip install Pillow`") + + try: + + # given messages for gpt-4 vision, convert them for gemini + # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_python.ipynb + prompt = "" + images = [] + for message in messages: + if isinstance(message["content"], str): + prompt += message["content"] + elif isinstance(message["content"], list): + # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models + for element in message["content"]: + if isinstance(element, dict): + if element["type"] == "text": + prompt += element["text"] + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + images.append(image_url) + # processing images passed to gemini + processed_images = [] + for img in images: + if "https:/" in img: + # Case 1: Image from URL + image = _load_image_from_url(img) + processed_images.append(image) + else: + # Case 2: Image filepath (e.g. temp.jpeg) given + image = Image.open(img) + processed_images.append(image) + content = [prompt] + processed_images + return content + except Exception as e: + raise e + + +def gemini_text_image_pt(messages: list): + """ + { + "contents":[ + { + "parts":[ + {"text": "What is this picture?"}, + { + "inline_data": { + "mime_type":"image/jpeg", + "data": "'$(base64 -w0 image.jpg)'" + } + } + ] + } + ] + } + """ + try: + import google.generativeai as genai + except: + raise Exception( + "Importing google.generativeai failed, please run 'pip install -q google-generativeai" + ) + + prompt = "" + images = [] + for message in messages: + if isinstance(message["content"], str): + prompt += message["content"] + elif isinstance(message["content"], list): + # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models + for element in message["content"]: + if isinstance(element, dict): + if element["type"] == "text": + prompt += element["text"] + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + images.append(image_url) + + content = [prompt] + images + return content + + +# Function call template +def function_call_prompt(messages: list, functions: list): + function_prompt = ( + "Produce JSON OUTPUT ONLY! The following functions are available to you:" + ) + for function in functions: + function_prompt += f"""\n{function}\n""" + + function_added_to_prompt = False + for message in messages: + if "system" in message["role"]: + message["content"] += f"""{function_prompt}""" + function_added_to_prompt = True + + if function_added_to_prompt == False: + messages.append({"role": "system", "content": f"""{function_prompt}"""}) + + return messages + + +# Custom prompt template +def custom_prompt( + role_dict: dict, + messages: list, + initial_prompt_value: str = "", + final_prompt_value: str = "", + bos_token: str = "", + eos_token: str = "", +): + prompt = bos_token + initial_prompt_value + bos_open = True + ## a bos token is at the start of a system / human message + ## an eos token is at the end of the assistant response to the message + for message in messages: + role = message["role"] + + if role in ["system", "human"] and not bos_open: + prompt += bos_token + bos_open = True + + pre_message_str = ( + role_dict[role]["pre_message"] + if role in role_dict and "pre_message" in role_dict[role] + else "" + ) + post_message_str = ( + role_dict[role]["post_message"] + if role in role_dict and "post_message" in role_dict[role] + else "" + ) + prompt += pre_message_str + message["content"] + post_message_str + + if role == "assistant": + prompt += eos_token + bos_open = False + + prompt += final_prompt_value + return prompt + + +def prompt_factory( + model: str, + messages: list, + custom_llm_provider: Optional[str] = None, + api_key: Optional[str] = None, +): + original_model_name = model + model = model.lower() + if custom_llm_provider == "ollama": + return ollama_pt(model=model, messages=messages) + elif custom_llm_provider == "anthropic": + if any(_ in model for _ in ["claude-2.1","claude-v2:1"]): + return claude_2_1_pt(messages=messages) + else: + return anthropic_pt(messages=messages) + elif custom_llm_provider == "together_ai": + prompt_format, chat_template = get_model_info(token=api_key, model=model) + return format_prompt_togetherai( + messages=messages, prompt_format=prompt_format, chat_template=chat_template + ) + elif custom_llm_provider == "gemini": + if model == "gemini-pro-vision": + return _gemini_vision_convert_messages(messages=messages) + else: + return gemini_text_image_pt(messages=messages) + try: + if "meta-llama/llama-2" in model and "chat" in model: + return llama_2_chat_pt(messages=messages) + elif ( + "tiiuae/falcon" in model + ): # Note: for the instruct models, it's best to use a User: .., Assistant:.. approach in your prompt template. + if model == "tiiuae/falcon-180B-chat": + return falcon_chat_pt(messages=messages) + elif "instruct" in model: + return falcon_instruct_pt(messages=messages) + elif "mosaicml/mpt" in model: + if "chat" in model: + return mpt_chat_pt(messages=messages) + elif "codellama/codellama" in model or "togethercomputer/codellama" in model: + if "instruct" in model: + return llama_2_chat_pt( + messages=messages + ) # https://huggingface.co/blog/codellama#conversational-instructions + elif "wizardlm/wizardcoder" in model: + return wizardcoder_pt(messages=messages) + elif "phind/phind-codellama" in model: + return phind_codellama_pt(messages=messages) + elif "togethercomputer/llama-2" in model and ( + "instruct" in model or "chat" in model + ): + return llama_2_chat_pt(messages=messages) + elif model in [ + "gryphe/mythomax-l2-13b", + "gryphe/mythomix-l2-13b", + "gryphe/mythologic-l2-13b", + ]: + return alpaca_pt(messages=messages) + else: + return hf_chat_template(original_model_name, messages) + except Exception as e: + return default_pt( + messages=messages + ) # default that covers Bloom, T-5, any non-chat tuned model (e.g. base Llama2) diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..bc296bfd08ad08c73186017c770e61944fe6001f --- /dev/null +++ b/litellm/llms/replicate.py @@ -0,0 +1,359 @@ +import os, types +import json +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage +import litellm +import httpx +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class ReplicateError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.replicate.com/v1/deployments" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class ReplicateConfig: + """ + Reference: https://replicate.com/meta/llama-2-70b-chat/api + - `prompt` (string): The prompt to send to the model. + + - `system_prompt` (string): The system prompt to send to the model. This is prepended to the prompt and helps guide system behavior. Default value: `You are a helpful assistant`. + + - `max_new_tokens` (integer): Maximum number of tokens to generate. Typically, a word is made up of 2-3 tokens. Default value: `128`. + + - `min_new_tokens` (integer): Minimum number of tokens to generate. To disable, set to `-1`. A word is usually 2-3 tokens. Default value: `-1`. + + - `temperature` (number): Adjusts the randomness of outputs. Values greater than 1 increase randomness, 0 is deterministic, and 0.75 is a reasonable starting value. Default value: `0.75`. + + - `top_p` (number): During text decoding, it samples from the top `p` percentage of most likely tokens. Reduce this to ignore less probable tokens. Default value: `0.9`. + + - `top_k` (integer): During text decoding, samples from the top `k` most likely tokens. Reduce this to ignore less probable tokens. Default value: `50`. + + - `stop_sequences` (string): A comma-separated list of sequences to stop generation at. For example, inputting ',' will cease generation at the first occurrence of either 'end' or ''. + + - `seed` (integer): This is the seed for the random generator. Leave it blank to randomize the seed. + + - `debug` (boolean): If set to `True`, it provides debugging output in logs. + + Please note that Replicate's mapping of these parameters can be inconsistent across different models, indicating that not all of these parameters may be available for use with all models. + """ + + system_prompt: Optional[str] = None + max_new_tokens: Optional[int] = None + min_new_tokens: Optional[int] = None + temperature: Optional[int] = None + top_p: Optional[int] = None + top_k: Optional[int] = None + stop_sequences: Optional[str] = None + seed: Optional[int] = None + debug: Optional[bool] = None + + def __init__( + self, + system_prompt: Optional[str] = None, + max_new_tokens: Optional[int] = None, + min_new_tokens: Optional[int] = None, + temperature: Optional[int] = None, + top_p: Optional[int] = None, + top_k: Optional[int] = None, + stop_sequences: Optional[str] = None, + seed: Optional[int] = None, + debug: Optional[bool] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +# Function to start a prediction and get the prediction URL +def start_prediction( + version_id, input_data, api_token, api_base, logging_obj, print_verbose +): + base_url = api_base + if "deployments" in version_id: + print_verbose("\nLiteLLM: Request to custom replicate deployment") + version_id = version_id.replace("deployments/", "") + base_url = f"https://api.replicate.com/v1/deployments/{version_id}" + print_verbose(f"Deployment base URL: {base_url}\n") + + headers = { + "Authorization": f"Token {api_token}", + "Content-Type": "application/json", + } + + initial_prediction_data = { + "version": version_id, + "input": input_data, + } + + ## LOGGING + logging_obj.pre_call( + input=input_data["prompt"], + api_key="", + additional_args={ + "complete_input_dict": initial_prediction_data, + "headers": headers, + "api_base": base_url, + }, + ) + + response = requests.post( + f"{base_url}/predictions", json=initial_prediction_data, headers=headers + ) + if response.status_code == 201: + response_data = response.json() + return response_data.get("urls", {}).get("get") + else: + raise ReplicateError( + response.status_code, f"Failed to start prediction {response.text}" + ) + + +# Function to handle prediction response (non-streaming) +def handle_prediction_response(prediction_url, api_token, print_verbose): + output_string = "" + headers = { + "Authorization": f"Token {api_token}", + "Content-Type": "application/json", + } + + status = "" + logs = "" + while True and (status not in ["succeeded", "failed", "canceled"]): + print_verbose(f"replicate: polling endpoint: {prediction_url}") + time.sleep(0.5) + response = requests.get(prediction_url, headers=headers) + if response.status_code == 200: + response_data = response.json() + if "output" in response_data: + output_string = "".join(response_data["output"]) + print_verbose(f"Non-streamed output:{output_string}") + status = response_data.get("status", None) + logs = response_data.get("logs", "") + if status == "failed": + replicate_error = response_data.get("error", "") + raise ReplicateError( + status_code=400, + message=f"Error: {replicate_error}, \nReplicate logs:{logs}", + ) + else: + # this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed" + print_verbose("Replicate: Failed to fetch prediction status and output.") + return output_string, logs + + +# Function to handle prediction response (streaming) +def handle_prediction_response_streaming(prediction_url, api_token, print_verbose): + previous_output = "" + output_string = "" + + headers = { + "Authorization": f"Token {api_token}", + "Content-Type": "application/json", + } + status = "" + while True and (status not in ["succeeded", "failed", "canceled"]): + time.sleep(0.5) # prevent being rate limited by replicate + print_verbose(f"replicate: polling endpoint: {prediction_url}") + response = requests.get(prediction_url, headers=headers) + if response.status_code == 200: + response_data = response.json() + status = response_data["status"] + if "output" in response_data: + output_string = "".join(response_data["output"]) + new_output = output_string[len(previous_output) :] + print_verbose(f"New chunk: {new_output}") + yield {"output": new_output, "status": status} + previous_output = output_string + status = response_data["status"] + if status == "failed": + replicate_error = response_data.get("error", "") + raise ReplicateError( + status_code=400, message=f"Error: {replicate_error}" + ) + else: + # this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed" + print_verbose( + f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}" + ) + + +# Function to extract version ID from model string +def model_to_version_id(model): + if ":" in model: + split_model = model.split(":") + return split_model[1] + return model + + +# Main function for prediction completion +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + logging_obj, + api_key, + encoding, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + # Start a prediction and get the prediction URL + version_id = model_to_version_id(model) + ## Load Config + config = litellm.ReplicateConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > replicate_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + system_prompt = None + if optional_params is not None and "supports_system_prompt" in optional_params: + supports_sys_prompt = optional_params.pop("supports_system_prompt") + else: + supports_sys_prompt = False + + if supports_sys_prompt: + for i in range(len(messages)): + if messages[i]["role"] == "system": + first_sys_message = messages.pop(i) + system_prompt = first_sys_message["content"] + break + + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + # If system prompt is supported, and a system prompt is provided, use it + if system_prompt is not None: + input_data = { + "prompt": prompt, + "system_prompt": system_prompt, + **optional_params, + } + # Otherwise, use the prompt as is + else: + input_data = {"prompt": prompt, **optional_params} + + ## COMPLETION CALL + ## Replicate Compeltion calls have 2 steps + ## Step1: Start Prediction: gets a prediction url + ## Step2: Poll prediction url for response + ## Step2: is handled with and without streaming + model_response["created"] = int( + time.time() + ) # for pricing this must remain right before calling api + prediction_url = start_prediction( + version_id, + input_data, + api_key, + api_base, + logging_obj=logging_obj, + print_verbose=print_verbose, + ) + print_verbose(prediction_url) + + # Handle the prediction response (streaming or non-streaming) + if "stream" in optional_params and optional_params["stream"] == True: + print_verbose("streaming request") + return handle_prediction_response_streaming( + prediction_url, api_key, print_verbose + ) + else: + result, logs = handle_prediction_response( + prediction_url, api_key, print_verbose + ) + model_response[ + "ended" + ] = time.time() # for pricing this must remain right after calling api + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=result, + additional_args={ + "complete_input_dict": input_data, + "logs": logs, + "api_base": prediction_url, + }, + ) + + print_verbose(f"raw model_response: {result}") + + if len(result) == 0: # edge case, where result from replicate is empty + result = " " + + ## Building RESPONSE OBJECT + if len(result) > 1: + model_response["choices"][0]["message"]["content"] = result + + # Calculate usage + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + model_response["model"] = "replicate/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +# # Example usage: +# response = completion( +# api_key="", +# messages=[{"content": "good morning"}], +# model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", +# model_response=ModelResponse(), +# print_verbose=print, +# logging_obj=print, # stub logging_obj +# optional_params={"stream": False} +# ) + +# print(response) diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py new file mode 100644 index 0000000000000000000000000000000000000000..1d341e7e92ded12dfbdc7d25cbe628d46854071b --- /dev/null +++ b/litellm/llms/sagemaker.py @@ -0,0 +1,400 @@ +import os, types +from enum import Enum +import json +import requests +import time +from typing import Callable, Optional, Any +import litellm +from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage +import sys +from copy import deepcopy +import httpx +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class SagemakerError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://us-west-2.console.aws.amazon.com/sagemaker" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class SagemakerConfig: + """ + Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb + """ + + max_new_tokens: Optional[int] = None + top_p: Optional[float] = None + temperature: Optional[float] = None + return_full_text: Optional[bool] = None + + def __init__( + self, + max_new_tokens: Optional[int] = None, + top_p: Optional[float] = None, + temperature: Optional[float] = None, + return_full_text: Optional[bool] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +""" +SAGEMAKER AUTH Keys/Vars +os.environ['AWS_ACCESS_KEY_ID'] = "" +os.environ['AWS_SECRET_ACCESS_KEY'] = "" +""" + +# set os.environ['AWS_REGION_NAME'] = + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + custom_prompt_dict={}, + hf_model_name=None, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + import boto3 + + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + + if aws_access_key_id != None: + # uses auth params passed to completion + # aws_access_key_id is not None, assume user is trying to auth using litellm.completion + client = boto3.client( + service_name="sagemaker-runtime", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region_name, + ) + else: + # aws_access_key_id is None, assume user is trying to auth using env variables + # boto3 automaticaly reads env variables + + # we need to read region name from env + # I assume majority of users use .env for auth + region_name = ( + get_secret("AWS_REGION_NAME") + or "us-west-2" # default to us-west-2 if user not specified + ) + client = boto3.client( + service_name="sagemaker-runtime", + region_name=region_name, + ) + + # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker + inference_params = deepcopy(optional_params) + inference_params.pop("stream", None) + + ## Load Config + config = litellm.SagemakerConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + model = model + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", None), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + messages=messages, + ) + else: + if hf_model_name is None: + if "llama-2" in model.lower(): # llama-2 model + if "chat" in model.lower(): # apply llama2 chat template + hf_model_name = "meta-llama/Llama-2-7b-chat-hf" + else: # apply regular llama2 template + hf_model_name = "meta-llama/Llama-2-7b" + hf_model_name = ( + hf_model_name or model + ) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt) + prompt = prompt_factory(model=hf_model_name, messages=messages) + + data = json.dumps({"inputs": prompt, "parameters": inference_params}).encode( + "utf-8" + ) + + ## LOGGING + request_str = f""" + response = client.invoke_endpoint( + EndpointName={model}, + ContentType="application/json", + Body={data}, + CustomAttributes="accept_eula=true", + ) + """ # type: ignore + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={ + "complete_input_dict": data, + "request_str": request_str, + "hf_model_name": hf_model_name, + }, + ) + ## COMPLETION CALL + try: + response = client.invoke_endpoint( + EndpointName=model, + ContentType="application/json", + Body=data, + CustomAttributes="accept_eula=true", + ) + except Exception as e: + raise SagemakerError(status_code=500, message=f"{str(e)}") + + response = response["Body"].read().decode("utf8") + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=response, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response}") + ## RESPONSE OBJECT + completion_response = json.loads(response) + try: + completion_response_choices = completion_response[0] + completion_output = "" + if "generation" in completion_response_choices: + completion_output += completion_response_choices["generation"] + elif "generated_text" in completion_response_choices: + completion_output += completion_response_choices["generated_text"] + + # check if the prompt template is part of output, if so - filter it out + if completion_output.startswith(prompt) and "" in prompt: + completion_output = completion_output.replace(prompt, "", 1) + + model_response["choices"][0]["message"]["content"] = completion_output + except: + raise SagemakerError( + message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}", + status_code=500, + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +# async def acompletion( +# client: Any, +# model_response: ModelResponse, +# model: str, +# logging_obj: Any, +# data: dict, +# hf_model_name: str, +# ): +# """ +# Use boto3 create_invocation_async endpoint +# """ +# ## LOGGING +# request_str = f""" +# response = client.invoke_endpoint( +# EndpointName={model}, +# ContentType="application/json", +# Body={data}, +# CustomAttributes="accept_eula=true", +# ) +# """ # type: ignore +# logging_obj.pre_call( +# input=data["prompt"], +# api_key="", +# additional_args={ +# "complete_input_dict": data, +# "request_str": request_str, +# "hf_model_name": hf_model_name, +# }, +# ) +# ## COMPLETION CALL +# try: +# response = client.invoke_endpoint( +# EndpointName=model, +# ContentType="application/json", +# Body=data, +# CustomAttributes="accept_eula=true", +# ) +# except Exception as e: +# raise SagemakerError(status_code=500, message=f"{str(e)}") + + +def embedding( + model: str, + input: list, + model_response: EmbeddingResponse, + print_verbose: Callable, + encoding, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + """ + Supports Huggingface Jumpstart embeddings like GPT-6B + """ + ### BOTO3 INIT + import boto3 + + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) + + if aws_access_key_id != None: + # uses auth params passed to completion + # aws_access_key_id is not None, assume user is trying to auth using litellm.completion + client = boto3.client( + service_name="sagemaker-runtime", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region_name, + ) + else: + # aws_access_key_id is None, assume user is trying to auth using env variables + # boto3 automaticaly reads env variables + + # we need to read region name from env + # I assume majority of users use .env for auth + region_name = ( + get_secret("AWS_REGION_NAME") + or "us-west-2" # default to us-west-2 if user not specified + ) + client = boto3.client( + service_name="sagemaker-runtime", + region_name=region_name, + ) + + # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker + inference_params = deepcopy(optional_params) + inference_params.pop("stream", None) + + ## Load Config + config = litellm.SagemakerConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + #### HF EMBEDDING LOGIC + data = json.dumps({"text_inputs": input}).encode("utf-8") + + ## LOGGING + request_str = f""" + response = client.invoke_endpoint( + EndpointName={model}, + ContentType="application/json", + Body={data}, + CustomAttributes="accept_eula=true", + )""" # type: ignore + logging_obj.pre_call( + input=input, + api_key="", + additional_args={"complete_input_dict": data, "request_str": request_str}, + ) + ## EMBEDDING CALL + try: + response = client.invoke_endpoint( + EndpointName=model, + ContentType="application/json", + Body=data, + CustomAttributes="accept_eula=true", + ) + except Exception as e: + raise SagemakerError(status_code=500, message=f"{str(e)}") + + response = json.loads(response["Body"].read().decode("utf8")) + ## LOGGING + logging_obj.post_call( + input=input, + api_key="", + original_response=response, + additional_args={"complete_input_dict": data}, + ) + + print_verbose(f"raw model_response: {response}") + if "embedding" not in response: + raise SagemakerError(status_code=500, message="embedding not found in response") + embeddings = response["embedding"] + + if not isinstance(embeddings, list): + raise SagemakerError( + status_code=422, message=f"Response not in expected format - {embeddings}" + ) + + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + + model_response["object"] = "list" + model_response["data"] = output_data + model_response["model"] = model + + input_tokens = 0 + for text in input: + input_tokens += len(encoding.encode(text)) + + model_response["usage"] = Usage( + prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens + ) + + return model_response diff --git a/litellm/llms/together_ai.py b/litellm/llms/together_ai.py new file mode 100644 index 0000000000000000000000000000000000000000..d4b85e9ca31e7c4ae33010f7b1228c6659b49332 --- /dev/null +++ b/litellm/llms/together_ai.py @@ -0,0 +1,230 @@ +import os, types +import json +from enum import Enum +import requests +import time +from typing import Callable, Optional +import litellm +import httpx +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class TogetherAIError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://api.together.xyz/inference" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class TogetherAIConfig: + """ + Reference: https://docs.together.ai/reference/inference + + The class `TogetherAIConfig` provides configuration for the TogetherAI's API interface. Here are the parameters: + + - `max_tokens` (int32, required): The maximum number of tokens to generate. + + - `stop` (string, optional): A string sequence that will truncate (stop) the inference text output. For example, "\n\n" will stop generation as soon as the model generates two newlines. + + - `temperature` (float, optional): A decimal number that determines the degree of randomness in the response. A value of 1 will always yield the same output. A temperature less than 1 favors more correctness and is appropriate for question answering or summarization. A value greater than 1 introduces more randomness in the output. + + - `top_p` (float, optional): The `top_p` (nucleus) parameter is used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities. It specifies a probability threshold, below which all less likely tokens are filtered out. This technique helps to maintain diversity and generate more fluent and natural-sounding text. + + - `top_k` (int32, optional): The `top_k` parameter is used to limit the number of choices for the next predicted word or token. It specifies the maximum number of tokens to consider at each step, based on their probability of occurrence. This technique helps to speed up the generation process and can improve the quality of the generated text by focusing on the most likely options. + + - `repetition_penalty` (float, optional): A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition. + + - `logprobs` (int32, optional): This parameter is not described in the prompt. + """ + + max_tokens: Optional[int] = None + stop: Optional[str] = None + temperature: Optional[int] = None + top_p: Optional[float] = None + top_k: Optional[int] = None + repetition_penalty: Optional[float] = None + logprobs: Optional[int] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + stop: Optional[str] = None, + temperature: Optional[int] = None, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + repetition_penalty: Optional[float] = None, + logprobs: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def validate_environment(api_key): + if api_key is None: + raise ValueError( + "Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params" + ) + headers = { + "accept": "application/json", + "content-type": "application/json", + "Authorization": "Bearer " + api_key, + } + return headers + + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + + ## Load Config + config = litellm.TogetherAIConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}") + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", {}), + initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + bos_token=model_prompt_details.get("bos_token", ""), + eos_token=model_prompt_details.get("eos_token", ""), + messages=messages, + ) + else: + prompt = prompt_factory( + model=model, + messages=messages, + api_key=api_key, + custom_llm_provider="together_ai", + ) # api key required to query together ai model list + + data = { + "model": model, + "prompt": prompt, + "request_type": "language-model-inference", + **optional_params, + } + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": api_base, + }, + ) + ## COMPLETION CALL + if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True: + response = requests.post( + api_base, + headers=headers, + data=json.dumps(data), + stream=optional_params["stream_tokens"], + ) + return response.iter_lines() + else: + response = requests.post(api_base, headers=headers, data=json.dumps(data)) + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + print_verbose(f"raw model_response: {response.text}") + ## RESPONSE OBJECT + if response.status_code != 200: + raise TogetherAIError( + status_code=response.status_code, message=response.text + ) + completion_response = response.json() + + if "error" in completion_response: + raise TogetherAIError( + message=json.dumps(completion_response), + status_code=response.status_code, + ) + elif "error" in completion_response["output"]: + raise TogetherAIError( + message=json.dumps(completion_response["output"]), + status_code=response.status_code, + ) + + if len(completion_response["output"]["choices"][0]["text"]) >= 0: + model_response["choices"][0]["message"]["content"] = completion_response[ + "output" + ]["choices"][0]["text"] + + ## CALCULATING USAGE + print_verbose( + f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}" + ) + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + if "finish_reason" in completion_response["output"]["choices"][0]: + model_response.choices[0].finish_reason = completion_response["output"][ + "choices" + ][0]["finish_reason"] + model_response["created"] = int(time.time()) + model_response["model"] = "together_ai/" + model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/tokenizers/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 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plot","Ġpe u","Ġback s","Ġreg exp","Test All","Ġcontin uity","temp s","sl a","US A","land marks","number ed","Ġunt er","Ġgr up","88 9","ĠPar a","Ġ17 2","ograph er","Ġcr ashed","Ġface book","DR AM","Ġhalf way","Ġsepar ating","Rep ublic","ĠKey word",")+ '.","ĠAcc uracy","embed ded","RA IN","Ġcit ing","Ġjournal ism","Inst ances","ĠCommission er","WE VER","Pop ulation","?? ??","Ñħ од","Ġtruck s","Ġconsum ing","Ġ#@ -","Small IntegerField","ани е","Ġbreat he","bott leneck","4 16","I ts","S ending","h q","l ia","o zy","Ġs queeze","Ġm asters","Ġd ug","Ġh acer","Ġ\" ''","ri ad","op acity","Ġ2 80","ĠB in","ĠH ob","out side","ĠE S","Ġres istant","ĠO NE","]) **","ĠJ a","pre amble","ĠV or","ann ers","Ġcomm its","mb l","enc il","ier a","ĠRe gression","ĠUn comment","ĠEx ternal","Ġdat um","Ġchar ity","TT TT","Def ines","ĠMan uel","ĠCor inth","Ġfore head","Ġcard io","Cre ator","Ġir regular","=', ')","PAR AMETER","ĠBack ground","ðŁĶ µ","Depend ency","0025 905","iox ide","Ġdiscre p","ðŁĮ ķ","# 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SHOT","ĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀĀ 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'),","/ :","5 72","; ','","? --","? >>",") |\\",") }$.",": +","; %","> %(","C ant","C ORS","D al","E gypt","F uel","G ust","G ran","G ithub","H IDE","I W","I j","K in","L DP","M ir","N EL","O c","O nt","P LE","R ae","R oster","S ah","S lices","U zbek","W on","W IND","] }\"","a ffected","b im","b ary","h sm","j ian","j xb","j sgotangco","l tr","l asses","l unch","m A","p ch","v ias","w olf","y rs","{ $","} =(","× ĺ","è ¸","é ¹","í Ĥ","in ctions","in deed","Ġt ablature","on ite","re j","he b","st ale","it ates","Ġc code","Ġc pus","de k","de queue","de creased","Ġf ip","Ġp val","Ġs name","Ġs cept","Ġb anning","ed io","Ġm adera","Ġm ús","Ġre pre","Ġre collection","Ġn op","Ġto xin","Ġi q","mp g","ot ify","Ġe con","Ġe ph","ol ing","ol ocation","ad opt","Ġg az","pe ech","ĠS ays","ĠS inger","ri am","ĠA j","ĠA FP","ĠC Script","ĠC ritic","if config","Ġv ener","Ġcon ferred","__ ))))","Ġy m","ke V","Ġ2 100","ĠP OT","ĠM ith","ĠM am","ĠM itch","(' '),","ĠN ero","ht able","ath s","ĠB org","ĠD ag","Ġpro bl","Ġor anges","ĠH G","ĠW ORD","Ġat ra","oc occus","ĠG n","ĠG ir","ĠG oes","ĠE nder","ĠE MT","def ining","ial ias","ip ad","pro ber","pro chen","Ġel icit","ĠO dysseus","Ġk sdk","data center","Ġ3 42","Ġ3 76","Ġ3 56","Ġwe eping","par er","Ġcl ung","Ġout skirts","Ġpre train","pre ci","Ġx ls","Ġro bbed","Ġun checked","Ġun important","hen ko","Ġ$ ^","ge ometric","ĠV argas","min im","ĠIn fer","Ġte lev","Ġdis pose","Ġass ur","11 786","Ġmy stic","max col","Ġcomm iss","ven ues","ific antly","Ġcre f",",\" \\\\","15 15","16 01","django apps","AL PH","Ġback pack","... «","99 98","Ġdist ressed","é l","reg r","bl ade","bl adder","17 01","net scaler","List Node","no ch","ins pections","Ġam mon","other word","az aki","ĠÐ ¤","\". '","ait i","To Use","')) ))","CO ST","ui sed","е Ñĩ","Time shift","Ġest ud","Char set","ĠDe vi","call iope","Ġax arr","))) /","Ġgame Display","ĠSh o","Ġpat ented","ĠSe al","del s","emp ted","Ġ16 777215","Ġincre ments","Ġbr as","IM ES","pen et","ÑĢ ани","åı ¤","ped ro","ze j","dev ic","Ġlaw ful","Ġdate fmt","Ġsw irling","gy m","cer ning",".... .....","ĠComm iss","Ġenc uent","cell ent","Ġdest in","ĠRes ize","Ġ13 95","Ad ic","Ġhard y","Ġhard core","ĠNot ably","Ġgovern ors","Comp ressed","Ġdesign ate","den ied","':' ',","Ġlayer ed","Ġda jax","uk es","87 22","Ġnormal izer","equal ities","Reg gie","Att acks","comple ter","LI BS","Ġign ition","Sc opes","NO OP","Ġsil houette","ida api","ĠDE FIN","cert ification","Ġfac ade","ouch ers","clean MergeVectors","Ġterm os","Ġfunc name","Ġsecret aries","vey ard","åĩ ı","Default Value","Default Deleter","SET S","produ kt","pdf s","filters flipped","MT cut","CP T","ĠModel Checkpoint","ĠSE Q","Rel ations","ĠMax Pool","ĠPal m","Ġple asures","Sim Hits","Ġut an","PF HT","Ġheavy weight","Ġcos a","PAR SE","Ġlif ts","het amine","bel ieve","ãĤĴ åıĸå¾Ĺ","EA ST","hu ang","ĠBig Query","Seq No","Func iones","Directory Item","Parse Mode","Mar ie","Ġliqu ids","Ġinstrument ation","ĠAre as","virtual ization","uten berg","ĠLand ing","Ġbrand ing","Ġreprodu cible","ĠIll umina","scroll command","Ġ-------------------------------- --------------","004 33","ĠCamb odia","Ro asted","ĠCast illo","LINK FLAGS","Ġinvent ions","ĠRom illy","âĻ ª","Ġstroke Width","Ans w","Install ation","Ġhonor able","Period s","Ġmx net","ĠDummy Request","ighth aven","Ġ}} None: + locals_ = locals() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + +def _get_image_bytes_from_url(image_url: str) -> bytes: + try: + response = requests.get(image_url) + response.raise_for_status() # Raise an error for bad responses (4xx and 5xx) + image_bytes = response.content + return image_bytes + except requests.exceptions.RequestException as e: + # Handle any request exceptions (e.g., connection error, timeout) + return b"" # Return an empty bytes object or handle the error as needed + + +def _load_image_from_url(image_url: str): + """ + Loads an image from a URL. + + Args: + image_url (str): The URL of the image. + + Returns: + Image: The loaded image. + """ + from vertexai.preview.generative_models import ( + GenerativeModel, + Part, + GenerationConfig, + Image, + ) + + image_bytes = _get_image_bytes_from_url(image_url) + return Image.from_bytes(image_bytes) + + +def _gemini_vision_convert_messages(messages: list): + """ + Converts given messages for GPT-4 Vision to Gemini format. + + Args: + messages (list): The messages to convert. Each message can be a dictionary with a "content" key. The content can be a string or a list of elements. If it is a string, it will be concatenated to the prompt. If it is a list, each element will be processed based on its type: + - If the element is a dictionary with a "type" key equal to "text", its "text" value will be concatenated to the prompt. + - If the element is a dictionary with a "type" key equal to "image_url", its "image_url" value will be added to the list of images. + + Returns: + tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images). + + Raises: + VertexAIError: If the import of the 'vertexai' module fails, indicating that 'google-cloud-aiplatform' needs to be installed. + Exception: If any other exception occurs during the execution of the function. + + Note: + This function is based on the code from the 'gemini/getting-started/intro_gemini_python.ipynb' notebook in the 'generative-ai' repository on GitHub. + The supported MIME types for images include 'image/png' and 'image/jpeg'. + + Examples: + >>> messages = [ + ... {"content": "Hello, world!"}, + ... {"content": [{"type": "text", "text": "This is a text message."}, {"type": "image_url", "image_url": "example.com/image.png"}]}, + ... ] + >>> _gemini_vision_convert_messages(messages) + ('Hello, world!This is a text message.', [, ]) + """ + try: + import vertexai + except: + raise VertexAIError( + status_code=400, + message="vertexai import failed please run `pip install google-cloud-aiplatform`", + ) + try: + from vertexai.preview.language_models import ( + ChatModel, + CodeChatModel, + InputOutputTextPair, + ) + from vertexai.language_models import TextGenerationModel, CodeGenerationModel + from vertexai.preview.generative_models import ( + GenerativeModel, + Part, + GenerationConfig, + Image, + ) + + # given messages for gpt-4 vision, convert them for gemini + # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_python.ipynb + prompt = "" + images = [] + for message in messages: + if isinstance(message["content"], str): + prompt += message["content"] + elif isinstance(message["content"], list): + # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models + for element in message["content"]: + if isinstance(element, dict): + if element["type"] == "text": + prompt += element["text"] + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + images.append(image_url) + # processing images passed to gemini + processed_images = [] + for img in images: + if "gs://" in img: + # Case 1: Images with Cloud Storage URIs + # The supported MIME types for images include image/png and image/jpeg. + part_mime = "image/png" if "png" in img else "image/jpeg" + google_clooud_part = Part.from_uri(img, mime_type=part_mime) + processed_images.append(google_clooud_part) + elif "https:/" in img: + # Case 2: Images with direct links + image = _load_image_from_url(img) + processed_images.append(image) + elif ".mp4" in img and "gs://" in img: + # Case 3: Videos with Cloud Storage URIs + part_mime = "video/mp4" + google_clooud_part = Part.from_uri(img, mime_type=part_mime) + processed_images.append(google_clooud_part) + return prompt, processed_images + except Exception as e: + raise e + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + vertex_project=None, + vertex_location=None, + optional_params=None, + litellm_params=None, + logger_fn=None, + acompletion: bool = False, +): + try: + import vertexai + except: + raise VertexAIError( + status_code=400, + message="vertexai import failed please run `pip install google-cloud-aiplatform`", + ) + try: + from vertexai.preview.language_models import ( + ChatModel, + CodeChatModel, + InputOutputTextPair, + ) + from vertexai.language_models import TextGenerationModel, CodeGenerationModel + from vertexai.preview.generative_models import ( + GenerativeModel, + Part, + GenerationConfig, + ) + from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types + + vertexai.init(project=vertex_project, location=vertex_location) + + ## Load Config + config = litellm.VertexAIConfig.get_config() + for k, v in config.items(): + if k not in optional_params: + optional_params[k] = v + + ## Process safety settings into format expected by vertex AI + safety_settings = None + if "safety_settings" in optional_params: + safety_settings = optional_params.pop("safety_settings") + if not isinstance(safety_settings, list): + raise ValueError("safety_settings must be a list") + if len(safety_settings) > 0 and not isinstance(safety_settings[0], dict): + raise ValueError("safety_settings must be a list of dicts") + safety_settings = [ + gapic_content_types.SafetySetting(x) for x in safety_settings + ] + + # vertexai does not use an API key, it looks for credentials.json in the environment + + prompt = " ".join( + [ + message["content"] + for message in messages + if isinstance(message["content"], str) + ] + ) + + mode = "" + + request_str = "" + response_obj = None + if ( + model in litellm.vertex_language_models + or model in litellm.vertex_vision_models + ): + llm_model = GenerativeModel(model) + mode = "vision" + request_str += f"llm_model = GenerativeModel({model})\n" + elif model in litellm.vertex_chat_models: + llm_model = ChatModel.from_pretrained(model) + mode = "chat" + request_str += f"llm_model = ChatModel.from_pretrained({model})\n" + elif model in litellm.vertex_text_models: + llm_model = TextGenerationModel.from_pretrained(model) + mode = "text" + request_str += f"llm_model = TextGenerationModel.from_pretrained({model})\n" + elif model in litellm.vertex_code_text_models: + llm_model = CodeGenerationModel.from_pretrained(model) + mode = "text" + request_str += f"llm_model = CodeGenerationModel.from_pretrained({model})\n" + else: # vertex_code_llm_models + llm_model = CodeChatModel.from_pretrained(model) + mode = "chat" + request_str += f"llm_model = CodeChatModel.from_pretrained({model})\n" + + if acompletion == True: # [TODO] expand support to vertex ai chat + text models + if optional_params.get("stream", False) is True: + # async streaming + return async_streaming( + llm_model=llm_model, + mode=mode, + prompt=prompt, + logging_obj=logging_obj, + request_str=request_str, + model=model, + model_response=model_response, + messages=messages, + print_verbose=print_verbose, + **optional_params, + ) + return async_completion( + llm_model=llm_model, + mode=mode, + prompt=prompt, + logging_obj=logging_obj, + request_str=request_str, + model=model, + model_response=model_response, + encoding=encoding, + messages=messages, + print_verbose=print_verbose, + **optional_params, + ) + + if mode == "vision": + print_verbose("\nMaking VertexAI Gemini Pro Vision Call") + print_verbose(f"\nProcessing input messages = {messages}") + tools = optional_params.pop("tools", None) + prompt, images = _gemini_vision_convert_messages(messages=messages) + content = [prompt] + images + if "stream" in optional_params and optional_params["stream"] == True: + stream = optional_params.pop("stream") + request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), safety_settings={safety_settings}, stream={stream})\n" + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + + model_response = llm_model.generate_content( + contents=content, + generation_config=GenerationConfig(**optional_params), + safety_settings=safety_settings, + stream=True, + tools=tools, + ) + optional_params["stream"] = True + return model_response + + request_str += f"response = llm_model.generate_content({content})\n" + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + + ## LLM Call + response = llm_model.generate_content( + contents=content, + generation_config=GenerationConfig(**optional_params), + safety_settings=safety_settings, + tools=tools, + ) + + if tools is not None and hasattr( + response.candidates[0].content.parts[0], "function_call" + ): + function_call = response.candidates[0].content.parts[0].function_call + args_dict = {} + for k, v in function_call.args.items(): + args_dict[k] = v + args_str = json.dumps(args_dict) + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": args_str, + "name": function_call.name, + }, + "type": "function", + } + ], + ) + completion_response = message + else: + completion_response = response.text + response_obj = response._raw_response + optional_params["tools"] = tools + elif mode == "chat": + chat = llm_model.start_chat() + request_str += f"chat = llm_model.start_chat()\n" + + if "stream" in optional_params and optional_params["stream"] == True: + # NOTE: VertexAI does not accept stream=True as a param and raises an error, + # we handle this by removing 'stream' from optional params and sending the request + # after we get the response we add optional_params["stream"] = True, since main.py needs to know it's a streaming response to then transform it for the OpenAI format + optional_params.pop( + "stream", None + ) # vertex ai raises an error when passing stream in optional params + request_str += ( + f"chat.send_message_streaming({prompt}, **{optional_params})\n" + ) + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + model_response = chat.send_message_streaming(prompt, **optional_params) + optional_params["stream"] = True + return model_response + + request_str += f"chat.send_message({prompt}, **{optional_params}).text\n" + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + completion_response = chat.send_message(prompt, **optional_params).text + elif mode == "text": + if "stream" in optional_params and optional_params["stream"] == True: + optional_params.pop( + "stream", None + ) # See note above on handling streaming for vertex ai + request_str += ( + f"llm_model.predict_streaming({prompt}, **{optional_params})\n" + ) + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + model_response = llm_model.predict_streaming(prompt, **optional_params) + optional_params["stream"] = True + return model_response + + request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n" + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + completion_response = llm_model.predict(prompt, **optional_params).text + + ## LOGGING + logging_obj.post_call( + input=prompt, api_key=None, original_response=completion_response + ) + + ## RESPONSE OBJECT + if isinstance(completion_response, litellm.Message): + model_response["choices"][0]["message"] = completion_response + elif len(str(completion_response)) > 0: + model_response["choices"][0]["message"]["content"] = str( + completion_response + ) + model_response["choices"][0]["message"]["content"] = str(completion_response) + model_response["created"] = int(time.time()) + model_response["model"] = model + ## CALCULATING USAGE + if model in litellm.vertex_language_models and response_obj is not None: + model_response["choices"][0].finish_reason = response_obj.candidates[ + 0 + ].finish_reason.name + usage = Usage( + prompt_tokens=response_obj.usage_metadata.prompt_token_count, + completion_tokens=response_obj.usage_metadata.candidates_token_count, + total_tokens=response_obj.usage_metadata.total_token_count, + ) + else: + # init prompt tokens + # this block attempts to get usage from response_obj if it exists, if not it uses the litellm token counter + prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 + if response_obj is not None: + if hasattr(response_obj, "usage_metadata") and hasattr( + response_obj.usage_metadata, "prompt_token_count" + ): + prompt_tokens = response_obj.usage_metadata.prompt_token_count + completion_tokens = ( + response_obj.usage_metadata.candidates_token_count + ) + else: + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode( + model_response["choices"][0]["message"].get("content", "") + ) + ) + + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + except Exception as e: + raise VertexAIError(status_code=500, message=str(e)) + + +async def async_completion( + llm_model, + mode: str, + prompt: str, + model: str, + model_response: ModelResponse, + logging_obj=None, + request_str=None, + encoding=None, + messages=None, + print_verbose=None, + **optional_params, +): + """ + Add support for acompletion calls for gemini-pro + """ + try: + from vertexai.preview.generative_models import GenerationConfig + + if mode == "vision": + print_verbose("\nMaking VertexAI Gemini Pro Vision Call") + print_verbose(f"\nProcessing input messages = {messages}") + tools = optional_params.pop("tools", None) + + prompt, images = _gemini_vision_convert_messages(messages=messages) + content = [prompt] + images + + request_str += f"response = llm_model.generate_content({content})\n" + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + + ## LLM Call + response = await llm_model._generate_content_async( + contents=content, + generation_config=GenerationConfig(**optional_params), + tools=tools, + ) + + if tools is not None and hasattr( + response.candidates[0].content.parts[0], "function_call" + ): + function_call = response.candidates[0].content.parts[0].function_call + args_dict = {} + for k, v in function_call.args.items(): + args_dict[k] = v + args_str = json.dumps(args_dict) + message = litellm.Message( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": args_str, + "name": function_call.name, + }, + "type": "function", + } + ], + ) + completion_response = message + else: + completion_response = response.text + response_obj = response._raw_response + optional_params["tools"] = tools + elif mode == "chat": + # chat-bison etc. + chat = llm_model.start_chat() + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + response_obj = await chat.send_message_async(prompt, **optional_params) + completion_response = response_obj.text + elif mode == "text": + # gecko etc. + request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n" + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + response_obj = await llm_model.predict_async(prompt, **optional_params) + completion_response = response_obj.text + + ## LOGGING + logging_obj.post_call( + input=prompt, api_key=None, original_response=completion_response + ) + + ## RESPONSE OBJECT + if isinstance(completion_response, litellm.Message): + model_response["choices"][0]["message"] = completion_response + elif len(str(completion_response)) > 0: + model_response["choices"][0]["message"]["content"] = str( + completion_response + ) + model_response["choices"][0]["message"]["content"] = str(completion_response) + model_response["created"] = int(time.time()) + model_response["model"] = model + ## CALCULATING USAGE + if model in litellm.vertex_language_models and response_obj is not None: + model_response["choices"][0].finish_reason = response_obj.candidates[ + 0 + ].finish_reason.name + usage = Usage( + prompt_tokens=response_obj.usage_metadata.prompt_token_count, + completion_tokens=response_obj.usage_metadata.candidates_token_count, + total_tokens=response_obj.usage_metadata.total_token_count, + ) + else: + # init prompt tokens + # this block attempts to get usage from response_obj if it exists, if not it uses the litellm token counter + prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 + if response_obj is not None: + if hasattr(response_obj, "usage_metadata") and hasattr( + response_obj.usage_metadata, "prompt_token_count" + ): + prompt_tokens = response_obj.usage_metadata.prompt_token_count + completion_tokens = ( + response_obj.usage_metadata.candidates_token_count + ) + else: + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode( + model_response["choices"][0]["message"].get("content", "") + ) + ) + + # set usage + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + except Exception as e: + raise VertexAIError(status_code=500, message=str(e)) + + +async def async_streaming( + llm_model, + mode: str, + prompt: str, + model: str, + model_response: ModelResponse, + logging_obj=None, + request_str=None, + messages=None, + print_verbose=None, + **optional_params, +): + """ + Add support for async streaming calls for gemini-pro + """ + from vertexai.preview.generative_models import GenerationConfig + + if mode == "vision": + stream = optional_params.pop("stream") + tools = optional_params.pop("tools", None) + print_verbose("\nMaking VertexAI Gemini Pro Vision Call") + print_verbose(f"\nProcessing input messages = {messages}") + + prompt, images = _gemini_vision_convert_messages(messages=messages) + content = [prompt] + images + request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), stream={stream})\n" + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + + response = await llm_model._generate_content_streaming_async( + contents=content, + generation_config=GenerationConfig(**optional_params), + tools=tools, + ) + optional_params["stream"] = True + optional_params["tools"] = tools + elif mode == "chat": + chat = llm_model.start_chat() + optional_params.pop( + "stream", None + ) # vertex ai raises an error when passing stream in optional params + request_str += ( + f"chat.send_message_streaming_async({prompt}, **{optional_params})\n" + ) + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + response = chat.send_message_streaming_async(prompt, **optional_params) + optional_params["stream"] = True + elif mode == "text": + optional_params.pop( + "stream", None + ) # See note above on handling streaming for vertex ai + request_str += ( + f"llm_model.predict_streaming_async({prompt}, **{optional_params})\n" + ) + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=None, + additional_args={ + "complete_input_dict": optional_params, + "request_str": request_str, + }, + ) + response = llm_model.predict_streaming_async(prompt, **optional_params) + + streamwrapper = CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider="vertex_ai", + logging_obj=logging_obj, + ) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/llms/vllm.py b/litellm/llms/vllm.py new file mode 100644 index 0000000000000000000000000000000000000000..2b130765b3e4b9e47177c15454002a87b9b5c716 --- /dev/null +++ b/litellm/llms/vllm.py @@ -0,0 +1,196 @@ +import os +import json +from enum import Enum +import requests +import time, httpx +from typing import Callable, Any +from litellm.utils import ModelResponse, Usage +from .prompt_templates.factory import prompt_factory, custom_prompt + +llm = None + + +class VLLMError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url="http://0.0.0.0:8000") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +# check if vllm is installed +def validate_environment(model: str): + global llm + try: + from vllm import LLM, SamplingParams # type: ignore + + if llm is None: + llm = LLM(model=model) + return llm, SamplingParams + except Exception as e: + raise VLLMError(status_code=0, message=str(e)) + + +def completion( + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + global llm + try: + llm, SamplingParams = validate_environment(model=model) + except Exception as e: + raise VLLMError(status_code=0, message=str(e)) + sampling_params = SamplingParams(**optional_params) + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages, + ) + else: + prompt = prompt_factory(model=model, messages=messages) + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key="", + additional_args={"complete_input_dict": sampling_params}, + ) + + if llm: + outputs = llm.generate(prompt, sampling_params) + else: + raise VLLMError( + status_code=0, message="Need to pass in a model name to initialize vllm" + ) + + ## COMPLETION CALL + if "stream" in optional_params and optional_params["stream"] == True: + return iter(outputs) + else: + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=outputs, + additional_args={"complete_input_dict": sampling_params}, + ) + print_verbose(f"raw model_response: {outputs}") + ## RESPONSE OBJECT + model_response["choices"][0]["message"]["content"] = outputs[0].outputs[0].text + + ## CALCULATING USAGE + prompt_tokens = len(outputs[0].prompt_token_ids) + completion_tokens = len(outputs[0].outputs[0].token_ids) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + return model_response + + +def batch_completions( + model: str, messages: list, optional_params=None, custom_prompt_dict={} +): + """ + Example usage: + import litellm + import os + from litellm import batch_completion + + + responses = batch_completion( + model="vllm/facebook/opt-125m", + messages = [ + [ + { + "role": "user", + "content": "good morning? " + } + ], + [ + { + "role": "user", + "content": "what's the time? " + } + ] + ] + ) + """ + try: + llm, SamplingParams = validate_environment(model=model) + except Exception as e: + error_str = str(e) + if "data parallel group is already initialized" in error_str: + pass + else: + raise VLLMError(status_code=0, message=error_str) + sampling_params = SamplingParams(**optional_params) + prompts = [] + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + for message in messages: + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=message, + ) + prompts.append(prompt) + else: + for message in messages: + prompt = prompt_factory(model=model, messages=message) + prompts.append(prompt) + + if llm: + outputs = llm.generate(prompts, sampling_params) + else: + raise VLLMError( + status_code=0, message="Need to pass in a model name to initialize vllm" + ) + + final_outputs = [] + for output in outputs: + model_response = ModelResponse() + ## RESPONSE OBJECT + model_response["choices"][0]["message"]["content"] = output.outputs[0].text + + ## CALCULATING USAGE + prompt_tokens = len(output.prompt_token_ids) + completion_tokens = len(output.outputs[0].token_ids) + + model_response["created"] = int(time.time()) + model_response["model"] = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + model_response.usage = usage + final_outputs.append(model_response) + return final_outputs + + +def embedding(): + # logic for parsing in - calling - parsing out model embedding calls + pass diff --git a/litellm/main.py b/litellm/main.py new file mode 100644 index 0000000000000000000000000000000000000000..2d9b4dc32187fa24f9dc130a19ad9ebc6a9e3510 --- /dev/null +++ b/litellm/main.py @@ -0,0 +1,3444 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you ! We ❤️ you! - Krrish & Ishaan + +import os, openai, sys, json, inspect, uuid, datetime, threading +from typing import Any, Literal, Union +from functools import partial + +import dotenv, traceback, random, asyncio, time, contextvars +from copy import deepcopy +import httpx +import litellm + +from litellm import ( # type: ignore + client, + exception_type, + get_optional_params, + get_litellm_params, + Logging, +) +from litellm.utils import ( + get_secret, + CustomStreamWrapper, + read_config_args, + completion_with_fallbacks, + get_llm_provider, + get_api_key, + mock_completion_streaming_obj, + convert_to_model_response_object, + token_counter, + Usage, + get_optional_params_embeddings, + get_optional_params_image_gen, +) +from .llms import ( + anthropic, + together_ai, + ai21, + sagemaker, + bedrock, + huggingface_restapi, + replicate, + aleph_alpha, + nlp_cloud, + baseten, + vllm, + ollama, + ollama_chat, + cloudflare, + cohere, + petals, + oobabooga, + openrouter, + palm, + gemini, + vertex_ai, + maritalk, +) +from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion +from .llms.azure import AzureChatCompletion +from .llms.huggingface_restapi import Huggingface +from .llms.prompt_templates.factory import ( + prompt_factory, + custom_prompt, + function_call_prompt, +) +import tiktoken +from concurrent.futures import ThreadPoolExecutor +from typing import Callable, List, Optional, Dict, Union, Mapping +from .caching import enable_cache, disable_cache, update_cache + +encoding = tiktoken.get_encoding("cl100k_base") +from litellm.utils import ( + get_secret, + CustomStreamWrapper, + TextCompletionStreamWrapper, + ModelResponse, + TextCompletionResponse, + TextChoices, + EmbeddingResponse, + read_config_args, + Choices, + Message, +) + +####### ENVIRONMENT VARIABLES ################### +dotenv.load_dotenv() # Loading env variables using dotenv +openai_chat_completions = OpenAIChatCompletion() +openai_text_completions = OpenAITextCompletion() +azure_chat_completions = AzureChatCompletion() +huggingface = Huggingface() +####### COMPLETION ENDPOINTS ################ + + +class LiteLLM: + def __init__( + self, + *, + api_key=None, + organization: Optional[str] = None, + base_url: Optional[str] = None, + timeout: Optional[float] = 600, + max_retries: Optional[int] = litellm.num_retries, + default_headers: Optional[Mapping[str, str]] = None, + ): + self.params = locals() + self.chat = Chat(self.params) + + +class Chat: + def __init__(self, params): + self.params = params + self.completions = Completions(self.params) + + +class Completions: + def __init__(self, params): + self.params = params + + def create(self, messages, model=None, **kwargs): + for k, v in kwargs.items(): + self.params[k] = v + model = model or self.params.get("model") + response = completion(model=model, messages=messages, **self.params) + return response + + +@client +async def acompletion( + model: str, + # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create + messages: List = [], + functions: Optional[List] = None, + function_call: Optional[str] = None, + timeout: Optional[Union[float, int]] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[dict] = None, + user: Optional[str] = None, + # openai v1.0+ new params + response_format: Optional[dict] = None, + seed: Optional[int] = None, + tools: Optional[List] = None, + tool_choice: Optional[str] = None, + logprobs: Optional[bool] = None, + top_logprobs: Optional[int] = None, + deployment_id=None, + # set api_base, api_version, api_key + base_url: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. + # Optional liteLLM function params + **kwargs, +): + """ + Asynchronously executes a litellm.completion() call for any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly) + + Parameters: + model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/ + messages (List): A list of message objects representing the conversation context (default is an empty list). + + OPTIONAL PARAMS + functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list). + function_call (str, optional): The name of the function to call within the conversation (default is an empty string). + temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0). + top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0). + n (int, optional): The number of completions to generate (default is 1). + stream (bool, optional): If True, return a streaming response (default is False). + stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens. + max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity). + presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far. + frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far. + logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion. + user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse. + metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc. + api_base (str, optional): Base URL for the API (default is None). + api_version (str, optional): API version (default is None). + api_key (str, optional): API key (default is None). + model_list (list, optional): List of api base, version, keys + + LITELLM Specific Params + mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None). + force_timeout (int, optional): The maximum execution time in seconds for the completion request (default is 600). + custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock" + Returns: + ModelResponse: A response object containing the generated completion and associated metadata. + + Notes: + - This function is an asynchronous version of the `completion` function. + - The `completion` function is called using `run_in_executor` to execute synchronously in the event loop. + - If `stream` is True, the function returns an async generator that yields completion lines. + """ + loop = asyncio.get_event_loop() + custom_llm_provider = None + # Adjusted to use explicit arguments instead of *args and **kwargs + completion_kwargs = { + "model": model, + "messages": messages, + "functions": functions, + "function_call": function_call, + "timeout": timeout, + "temperature": temperature, + "top_p": top_p, + "n": n, + "stream": stream, + "stop": stop, + "max_tokens": max_tokens, + "presence_penalty": presence_penalty, + "frequency_penalty": frequency_penalty, + "logit_bias": logit_bias, + "user": user, + "response_format": response_format, + "seed": seed, + "tools": tools, + "tool_choice": tool_choice, + "logprobs": logprobs, + "top_logprobs": top_logprobs, + "deployment_id": deployment_id, + "base_url": base_url, + "api_version": api_version, + "api_key": api_key, + "model_list": model_list, + "acompletion": True, # assuming this is a required parameter + } + try: + # Use a partial function to pass your keyword arguments + func = partial(completion, **completion_kwargs, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider( + model=model, api_base=kwargs.get("api_base", None) + ) + + if ( + custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "mistral" + or custom_llm_provider == "openrouter" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "text-completion-openai" + or custom_llm_provider == "huggingface" + or custom_llm_provider == "ollama" + or custom_llm_provider == "ollama_chat" + or custom_llm_provider == "vertex_ai" + ): # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all. + init_response = await loop.run_in_executor(None, func_with_context) + if isinstance(init_response, dict) or isinstance( + init_response, ModelResponse + ): ## CACHING SCENARIO + response = init_response + elif asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response # type: ignore + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) # type: ignore + # if kwargs.get("stream", False): # return an async generator + # return _async_streaming( + # response=response, + # model=model, + # custom_llm_provider=custom_llm_provider, + # args=args, + # ) + # else: + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=completion_kwargs, + ) + + +async def _async_streaming(response, model, custom_llm_provider, args): + try: + print_verbose(f"received response in _async_streaming: {response}") + async for line in response: + print_verbose(f"line in async streaming: {line}") + yield line + except Exception as e: + raise e + + +def mock_completion( + model: str, + messages: List, + stream: Optional[bool] = False, + mock_response: str = "This is a mock request", + **kwargs, +): + """ + Generate a mock completion response for testing or debugging purposes. + + This is a helper function that simulates the response structure of the OpenAI completion API. + + Parameters: + model (str): The name of the language model for which the mock response is generated. + messages (List): A list of message objects representing the conversation context. + stream (bool, optional): If True, returns a mock streaming response (default is False). + mock_response (str, optional): The content of the mock response (default is "This is a mock request"). + **kwargs: Additional keyword arguments that can be used but are not required. + + Returns: + litellm.ModelResponse: A ModelResponse simulating a completion response with the specified model, messages, and mock response. + + Raises: + Exception: If an error occurs during the generation of the mock completion response. + + Note: + - This function is intended for testing or debugging purposes to generate mock completion responses. + - If 'stream' is True, it returns a response that mimics the behavior of a streaming completion. + """ + try: + model_response = ModelResponse(stream=stream) + if stream is True: + # don't try to access stream object, + response = mock_completion_streaming_obj( + model_response, mock_response=mock_response, model=model + ) + return response + + model_response["choices"][0]["message"]["content"] = mock_response + model_response["created"] = int(time.time()) + model_response["model"] = model + return model_response + + except: + traceback.print_exc() + raise Exception("Mock completion response failed") + + +@client +def completion( + model: str, + # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create + messages: List = [], + timeout: Optional[Union[float, int]] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[dict] = None, + user: Optional[str] = None, + # openai v1.0+ new params + response_format: Optional[dict] = None, + seed: Optional[int] = None, + tools: Optional[List] = None, + tool_choice: Optional[str] = None, + logprobs: Optional[bool] = None, + top_logprobs: Optional[int] = None, + deployment_id=None, + # soon to be deprecated params by OpenAI + functions: Optional[List] = None, + function_call: Optional[str] = None, + # set api_base, api_version, api_key + base_url: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. + # Optional liteLLM function params + **kwargs, +) -> Union[ModelResponse, CustomStreamWrapper]: + """ + Perform a completion() using any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly) + Parameters: + model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/ + messages (List): A list of message objects representing the conversation context (default is an empty list). + + OPTIONAL PARAMS + functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list). + function_call (str, optional): The name of the function to call within the conversation (default is an empty string). + temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0). + top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0). + n (int, optional): The number of completions to generate (default is 1). + stream (bool, optional): If True, return a streaming response (default is False). + stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens. + max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity). + presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far. + frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far. + logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion. + user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse. + logprobs (bool, optional): Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message + top_logprobs (int, optional): An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used. + metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc. + api_base (str, optional): Base URL for the API (default is None). + api_version (str, optional): API version (default is None). + api_key (str, optional): API key (default is None). + model_list (list, optional): List of api base, version, keys + + LITELLM Specific Params + mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None). + custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock" + max_retries (int, optional): The number of retries to attempt (default is 0). + Returns: + ModelResponse: A response object containing the generated completion and associated metadata. + + Note: + - This function is used to perform completions() using the specified language model. + - It supports various optional parameters for customizing the completion behavior. + - If 'mock_response' is provided, a mock completion response is returned for testing or debugging. + """ + ######### unpacking kwargs ##################### + args = locals() + api_base = kwargs.get("api_base", None) + mock_response = kwargs.get("mock_response", None) + force_timeout = kwargs.get("force_timeout", 600) ## deprecated + logger_fn = kwargs.get("logger_fn", None) + verbose = kwargs.get("verbose", False) + custom_llm_provider = kwargs.get("custom_llm_provider", None) + litellm_logging_obj = kwargs.get("litellm_logging_obj", None) + id = kwargs.get("id", None) + metadata = kwargs.get("metadata", None) + model_info = kwargs.get("model_info", None) + proxy_server_request = kwargs.get("proxy_server_request", None) + fallbacks = kwargs.get("fallbacks", None) + headers = kwargs.get("headers", None) + num_retries = kwargs.get("num_retries", None) ## deprecated + max_retries = kwargs.get("max_retries", None) + context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None) + ### CUSTOM MODEL COST ### + input_cost_per_token = kwargs.get("input_cost_per_token", None) + output_cost_per_token = kwargs.get("output_cost_per_token", None) + ### CUSTOM PROMPT TEMPLATE ### + initial_prompt_value = kwargs.get("initial_prompt_value", None) + roles = kwargs.get("roles", None) + final_prompt_value = kwargs.get("final_prompt_value", None) + bos_token = kwargs.get("bos_token", None) + eos_token = kwargs.get("eos_token", None) + preset_cache_key = kwargs.get("preset_cache_key", None) + hf_model_name = kwargs.get("hf_model_name", None) + ### ASYNC CALLS ### + acompletion = kwargs.get("acompletion", False) + client = kwargs.get("client", None) + ######## end of unpacking kwargs ########### + openai_params = [ + "functions", + "function_call", + "temperature", + "temperature", + "top_p", + "n", + "stream", + "stop", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "user", + "request_timeout", + "api_base", + "api_version", + "api_key", + "deployment_id", + "organization", + "base_url", + "default_headers", + "timeout", + "response_format", + "seed", + "tools", + "tool_choice", + "max_retries", + "logprobs", + "top_logprobs", + ] + litellm_params = [ + "metadata", + "acompletion", + "caching", + "mock_response", + "api_key", + "api_version", + "api_base", + "force_timeout", + "logger_fn", + "verbose", + "custom_llm_provider", + "litellm_logging_obj", + "litellm_call_id", + "use_client", + "id", + "fallbacks", + "azure", + "headers", + "model_list", + "num_retries", + "context_window_fallback_dict", + "roles", + "final_prompt_value", + "bos_token", + "eos_token", + "request_timeout", + "complete_response", + "self", + "client", + "rpm", + "tpm", + "input_cost_per_token", + "output_cost_per_token", + "hf_model_name", + "model_info", + "proxy_server_request", + "preset_cache_key", + "caching_groups", + "ttl", + "cache", + ] + default_params = openai_params + litellm_params + non_default_params = { + k: v for k, v in kwargs.items() if k not in default_params + } # model-specific params - pass them straight to the model/provider + if mock_response: + return mock_completion( + model, messages, stream=stream, mock_response=mock_response + ) + if timeout is None: + timeout = ( + kwargs.get("request_timeout", None) or 600 + ) # set timeout for 10 minutes by default + timeout = float(timeout) + try: + if base_url is not None: + api_base = base_url + if max_retries is not None: # openai allows openai.OpenAI(max_retries=3) + num_retries = max_retries + logging = litellm_logging_obj + fallbacks = fallbacks or litellm.model_fallbacks + if fallbacks is not None: + return completion_with_fallbacks(**args) + if model_list is not None: + deployments = [ + m["litellm_params"] for m in model_list if m["model_name"] == model + ] + return batch_completion_models(deployments=deployments, **args) + if litellm.model_alias_map and model in litellm.model_alias_map: + model = litellm.model_alias_map[ + model + ] # update the model to the actual value if an alias has been passed in + model_response = ModelResponse() + if ( + kwargs.get("azure", False) == True + ): # don't remove flag check, to remain backwards compatible for repos like Codium + custom_llm_provider = "azure" + if deployment_id != None: # azure llms + model = deployment_id + custom_llm_provider = "azure" + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( + model=model, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + ) + if model_response is not None and hasattr(model_response, "_hidden_params"): + model_response._hidden_params["custom_llm_provider"] = custom_llm_provider + ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ### + if input_cost_per_token is not None and output_cost_per_token is not None: + litellm.register_model( + { + model: { + "input_cost_per_token": input_cost_per_token, + "output_cost_per_token": output_cost_per_token, + "litellm_provider": custom_llm_provider, + } + } + ) + ### BUILD CUSTOM PROMPT TEMPLATE -- IF GIVEN ### + custom_prompt_dict = {} # type: ignore + if ( + initial_prompt_value + or roles + or final_prompt_value + or bos_token + or eos_token + ): + custom_prompt_dict = {model: {}} + if initial_prompt_value: + custom_prompt_dict[model]["initial_prompt_value"] = initial_prompt_value + if roles: + custom_prompt_dict[model]["roles"] = roles + if final_prompt_value: + custom_prompt_dict[model]["final_prompt_value"] = final_prompt_value + if bos_token: + custom_prompt_dict[model]["bos_token"] = bos_token + if eos_token: + custom_prompt_dict[model]["eos_token"] = eos_token + model_api_key = get_api_key( + llm_provider=custom_llm_provider, dynamic_api_key=api_key + ) # get the api key from the environment if required for the model + + if dynamic_api_key is not None: + api_key = dynamic_api_key + # check if user passed in any of the OpenAI optional params + optional_params = get_optional_params( + functions=functions, + function_call=function_call, + temperature=temperature, + top_p=top_p, + n=n, + stream=stream, + stop=stop, + max_tokens=max_tokens, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + logit_bias=logit_bias, + user=user, + # params to identify the model + model=model, + custom_llm_provider=custom_llm_provider, + response_format=response_format, + seed=seed, + tools=tools, + tool_choice=tool_choice, + max_retries=max_retries, + logprobs=logprobs, + top_logprobs=top_logprobs, + **non_default_params, + ) + + if litellm.add_function_to_prompt and optional_params.get( + "functions_unsupported_model", None + ): # if user opts to add it to prompt, when API doesn't support function calling + functions_unsupported_model = optional_params.pop( + "functions_unsupported_model" + ) + messages = function_call_prompt( + messages=messages, functions=functions_unsupported_model + ) + + # For logging - save the values of the litellm-specific params passed in + litellm_params = get_litellm_params( + acompletion=acompletion, + api_key=api_key, + force_timeout=force_timeout, + logger_fn=logger_fn, + verbose=verbose, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + litellm_call_id=kwargs.get("litellm_call_id", None), + model_alias_map=litellm.model_alias_map, + completion_call_id=id, + metadata=metadata, + model_info=model_info, + proxy_server_request=proxy_server_request, + preset_cache_key=preset_cache_key, + ) + logging.update_environment_variables( + model=model, + user=user, + optional_params=optional_params, + litellm_params=litellm_params, + ) + if custom_llm_provider == "azure": + # azure configs + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") + + api_version = ( + api_version or litellm.api_version or get_secret("AZURE_API_VERSION") + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) + + azure_ad_token = optional_params.pop("azure_ad_token", None) or get_secret( + "AZURE_AD_TOKEN" + ) + + headers = headers or litellm.headers + + ## LOAD CONFIG - if set + config = litellm.AzureOpenAIConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + response = azure_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + api_version=api_version, + api_type=api_type, + azure_ad_token=azure_ad_token, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + client=client, # pass AsyncAzureOpenAI, AzureOpenAI client + ) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) + elif ( + model in litellm.open_ai_chat_completion_models + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "mistral" + or custom_llm_provider == "openai" + or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo + ): # allow user to make an openai call with a custom base + # note: if a user sets a custom base - we should ensure this works + # allow for the setting of dynamic and stateful api-bases + api_base = ( + api_base # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api base from there + or litellm.api_base + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + openai.organization = ( + litellm.organization + or get_secret("OPENAI_ORGANIZATION") + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or get_secret("OPENAI_API_KEY") + ) + + headers = headers or litellm.headers + + ## LOAD CONFIG - if set + config = litellm.OpenAIConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + + ## COMPLETION CALL + try: + response = openai_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, + custom_prompt_dict=custom_prompt_dict, + client=client, # pass AsyncOpenAI, OpenAI client + ) + except Exception as e: + ## LOGGING - log the original exception returned + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args={"headers": headers}, + ) + raise e + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={"headers": headers}, + ) + elif ( + custom_llm_provider == "text-completion-openai" + or "ft:babbage-002" in model + or "ft:davinci-002" in model # support for finetuned completion models + ): + openai.api_type = "openai" + + api_base = ( + api_base + or litellm.api_base + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + + openai.api_version = None + # set API KEY + + api_key = ( + api_key + or litellm.api_key + or litellm.openai_key + or get_secret("OPENAI_API_KEY") + ) + + headers = headers or litellm.headers + + ## LOAD CONFIG - if set + config = litellm.OpenAITextCompletionConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in + optional_params[k] = v + if litellm.organization: + openai.organization = litellm.organization + + if ( + len(messages) > 0 + and "content" in messages[0] + and type(messages[0]["content"]) == list + ): + # text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content'] + # https://platform.openai.com/docs/api-reference/completions/create + prompt = messages[0]["content"] + else: + prompt = " ".join([message["content"] for message in messages]) # type: ignore + ## COMPLETION CALL + model_response = openai_text_completions.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, + ) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=model_response, + additional_args={"headers": headers}, + ) + response = model_response + elif ( + "replicate" in model + or custom_llm_provider == "replicate" + or model in litellm.replicate_models + ): + # Setting the relevant API KEY for replicate, replicate defaults to using os.environ.get("REPLICATE_API_TOKEN") + replicate_key = None + replicate_key = ( + api_key + or litellm.replicate_key + or litellm.api_key + or get_secret("REPLICATE_API_KEY") + or get_secret("REPLICATE_API_TOKEN") + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("REPLICATE_API_BASE") + or "https://api.replicate.com/v1" + ) + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + + model_response = replicate.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, # for calculating input/output tokens + api_key=replicate_key, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict, + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + model_response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate") # type: ignore + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=replicate_key, + original_response=model_response, + ) + + response = model_response + + elif custom_llm_provider == "anthropic": + api_key = ( + api_key + or litellm.anthropic_key + or litellm.api_key + or os.environ.get("ANTHROPIC_API_KEY") + ) + api_base = ( + api_base + or litellm.api_base + or get_secret("ANTHROPIC_API_BASE") + or "https://api.anthropic.com/v1/complete" + ) + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + response = anthropic.completion( + model=model, + messages=messages, + api_base=api_base, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, # for calculating input/output tokens + api_key=api_key, + logging_obj=logging, + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + response, + model, + custom_llm_provider="anthropic", + logging_obj=logging, + ) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + response = response + elif custom_llm_provider == "nlp_cloud": + nlp_cloud_key = ( + api_key + or litellm.nlp_cloud_key + or get_secret("NLP_CLOUD_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("NLP_CLOUD_API_BASE") + or "https://api.nlpcloud.io/v1/gpu/" + ) + + response = nlp_cloud.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=nlp_cloud_key, + logging_obj=logging, + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + response, + model, + custom_llm_provider="nlp_cloud", + logging_obj=logging, + ) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + + response = response + elif custom_llm_provider == "aleph_alpha": + aleph_alpha_key = ( + api_key + or litellm.aleph_alpha_key + or get_secret("ALEPH_ALPHA_API_KEY") + or get_secret("ALEPHALPHA_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("ALEPH_ALPHA_API_BASE") + or "https://api.aleph-alpha.com/complete" + ) + + model_response = aleph_alpha.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + default_max_tokens_to_sample=litellm.max_tokens, + api_key=aleph_alpha_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="aleph_alpha", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "cohere": + cohere_key = ( + api_key + or litellm.cohere_key + or get_secret("COHERE_API_KEY") + or get_secret("CO_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("COHERE_API_BASE") + or "https://api.cohere.ai/v1/generate" + ) + + model_response = cohere.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=cohere_key, + logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="cohere", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "maritalk": + maritalk_key = ( + api_key + or litellm.maritalk_key + or get_secret("MARITALK_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("MARITALK_API_BASE") + or "https://chat.maritaca.ai/api/chat/inference" + ) + + model_response = maritalk.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=maritalk_key, + logging_obj=logging, + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="maritalk", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "huggingface": + custom_llm_provider = "huggingface" + huggingface_key = ( + api_key + or litellm.huggingface_key + or os.environ.get("HF_TOKEN") + or os.environ.get("HUGGINGFACE_API_KEY") + or litellm.api_key + ) + hf_headers = headers or litellm.headers + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + model_response = huggingface.completion( + model=model, + messages=messages, + api_base=api_base, # type: ignore + headers=hf_headers, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=huggingface_key, + acompletion=acompletion, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + ) + if ( + "stream" in optional_params + and optional_params["stream"] == True + and acompletion is False + ): + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="huggingface", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "oobabooga": + custom_llm_provider = "oobabooga" + model_response = oobabooga.completion( + model=model, + messages=messages, + model_response=model_response, + api_base=api_base, # type: ignore + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=None, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="oobabooga", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "openrouter": + api_base = api_base or litellm.api_base or "https://openrouter.ai/api/v1" + + api_key = ( + api_key + or litellm.api_key + or litellm.openrouter_key + or get_secret("OPENROUTER_API_KEY") + or get_secret("OR_API_KEY") + ) + + openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" + + openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM" + + headers = ( + headers + or litellm.headers + or { + "HTTP-Referer": openrouter_site_url, + "X-Title": openrouter_app_name, + } + ) + + ## Load Config + config = openrouter.OpenrouterConfig.get_config() + for k, v in config.items(): + if k == "extra_body": + # we use openai 'extra_body' to pass openrouter specific params - transforms, route, models + if "extra_body" in optional_params: + optional_params[k].update(v) + else: + optional_params[k] = v + elif k not in optional_params: + optional_params[k] = v + + data = {"model": model, "messages": messages, **optional_params} + + ## COMPLETION CALL + response = openai_chat_completions.completion( + model=model, + messages=messages, + headers=headers, + api_key=api_key, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + logging_obj=logging, + acompletion=acompletion, + timeout=timeout, + ) + ## LOGGING + logging.post_call( + input=messages, api_key=openai.api_key, original_response=response + ) + elif ( + custom_llm_provider == "together_ai" + or ("togethercomputer" in model) + or (model in litellm.together_ai_models) + ): + custom_llm_provider = "together_ai" + together_ai_key = ( + api_key + or litellm.togetherai_api_key + or get_secret("TOGETHER_AI_TOKEN") + or get_secret("TOGETHERAI_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("TOGETHERAI_API_BASE") + or "https://api.together.xyz/inference" + ) + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + + model_response = together_ai.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=together_ai_key, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict, + ) + if ( + "stream_tokens" in optional_params + and optional_params["stream_tokens"] == True + ): + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="together_ai", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "palm": + palm_api_key = api_key or get_secret("PALM_API_KEY") or litellm.api_key + + # palm does not support streaming as yet :( + model_response = palm.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=palm_api_key, + logging_obj=logging, + ) + # fake palm streaming + if "stream" in optional_params and optional_params["stream"] == True: + # fake streaming for palm + resp_string = model_response["choices"][0]["message"]["content"] + response = CustomStreamWrapper( + resp_string, model, custom_llm_provider="palm", logging_obj=logging + ) + return response + response = model_response + elif custom_llm_provider == "gemini": + gemini_api_key = ( + api_key + or get_secret("GEMINI_API_KEY") + or get_secret("PALM_API_KEY") # older palm api key should also work + or litellm.api_key + ) + + # palm does not support streaming as yet :( + model_response = gemini.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=gemini_api_key, + logging_obj=logging, + acompletion=acompletion, + custom_prompt_dict=custom_prompt_dict, + ) + response = model_response + elif custom_llm_provider == "vertex_ai": + vertex_ai_project = litellm.vertex_project or get_secret("VERTEXAI_PROJECT") + vertex_ai_location = litellm.vertex_location or get_secret( + "VERTEXAI_LOCATION" + ) + + model_response = vertex_ai.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + logging_obj=logging, + acompletion=acompletion, + ) + + if ( + "stream" in optional_params + and optional_params["stream"] == True + and acompletion == False + ): + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="vertex_ai", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "ai21": + custom_llm_provider = "ai21" + ai21_key = ( + api_key + or litellm.ai21_key + or os.environ.get("AI21_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("AI21_API_BASE") + or "https://api.ai21.com/studio/v1/" + ) + + model_response = ai21.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=ai21_key, + logging_obj=logging, + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="ai21", + logging_obj=logging, + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "sagemaker": + # boto3 reads keys from .env + model_response = sagemaker.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + custom_prompt_dict=custom_prompt_dict, + hf_model_name=hf_model_name, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + ) + if ( + "stream" in optional_params and optional_params["stream"] == True + ): ## [BETA] + # sagemaker does not support streaming as of now so we're faking streaming: + # https://discuss.huggingface.co/t/streaming-output-text-when-deploying-on-sagemaker/39611 + # "SageMaker is currently not supporting streaming responses." + + # fake streaming for sagemaker + print_verbose(f"ENTERS SAGEMAKER CUSTOMSTREAMWRAPPER") + resp_string = model_response["choices"][0]["message"]["content"] + response = CustomStreamWrapper( + resp_string, + model, + custom_llm_provider="sagemaker", + logging_obj=logging, + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "bedrock": + # boto3 reads keys from .env + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + response = bedrock.completion( + model=model, + messages=messages, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + if "ai21" in model: + response = CustomStreamWrapper( + response, + model, + custom_llm_provider="bedrock", + logging_obj=logging, + ) + else: + response = CustomStreamWrapper( + iter(response), + model, + custom_llm_provider="bedrock", + logging_obj=logging, + ) + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=None, + original_response=response, + ) + + ## RESPONSE OBJECT + response = response + elif custom_llm_provider == "vllm": + model_response = vllm.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + ) + + if ( + "stream" in optional_params and optional_params["stream"] == True + ): ## [BETA] + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="vllm", + logging_obj=logging, + ) + return response + + ## RESPONSE OBJECT + response = model_response + elif custom_llm_provider == "ollama": + api_base = ( + litellm.api_base + or api_base + or get_secret("OLLAMA_API_BASE") + or "http://localhost:11434" + ) + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details["roles"], + initial_prompt_value=model_prompt_details["initial_prompt_value"], + final_prompt_value=model_prompt_details["final_prompt_value"], + messages=messages, + ) + else: + prompt = prompt_factory( + model=model, + messages=messages, + custom_llm_provider=custom_llm_provider, + ) + if isinstance(prompt, dict): + # for multimode models - ollama/llava prompt_factory returns a dict { + # "prompt": prompt, + # "images": images + # } + prompt, images = prompt["prompt"], prompt["images"] + optional_params["images"] = images + + ## LOGGING + generator = ollama.get_ollama_response( + api_base, + model, + prompt, + optional_params, + logging_obj=logging, + acompletion=acompletion, + model_response=model_response, + encoding=encoding, + ) + if acompletion is True or optional_params.get("stream", False) == True: + return generator + + response = generator + elif custom_llm_provider == "ollama_chat": + api_base = ( + litellm.api_base + or api_base + or get_secret("OLLAMA_API_BASE") + or "http://localhost:11434" + ) + + ## LOGGING + generator = ollama_chat.get_ollama_response( + api_base, + model, + messages, + optional_params, + logging_obj=logging, + acompletion=acompletion, + model_response=model_response, + encoding=encoding, + ) + if acompletion is True or optional_params.get("stream", False) == True: + return generator + + response = generator + elif custom_llm_provider == "cloudflare": + api_key = ( + api_key + or litellm.cloudflare_api_key + or litellm.api_key + or get_secret("CLOUDFLARE_API_KEY") + ) + account_id = get_secret("CLOUDFLARE_ACCOUNT_ID") + api_base = ( + api_base + or litellm.api_base + or get_secret("CLOUDFLARE_API_BASE") + or f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/" + ) + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + response = cloudflare.completion( + model=model, + messages=messages, + api_base=api_base, + custom_prompt_dict=litellm.custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, # for calculating input/output tokens + api_key=api_key, + logging_obj=logging, + ) + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + response = CustomStreamWrapper( + response, + model, + custom_llm_provider="cloudflare", + logging_obj=logging, + ) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + response = response + elif ( + custom_llm_provider == "baseten" + or litellm.api_base == "https://app.baseten.co" + ): + custom_llm_provider = "baseten" + baseten_key = ( + api_key + or litellm.baseten_key + or os.environ.get("BASETEN_API_KEY") + or litellm.api_key + ) + + model_response = baseten.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + api_key=baseten_key, + logging_obj=logging, + ) + if inspect.isgenerator(model_response) or ( + "stream" in optional_params and optional_params["stream"] == True + ): + # don't try to access stream object, + response = CustomStreamWrapper( + model_response, + model, + custom_llm_provider="baseten", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "petals" or model in litellm.petals_models: + api_base = api_base or litellm.api_base + + custom_llm_provider = "petals" + stream = optional_params.pop("stream", False) + model_response = petals.completion( + model=model, + messages=messages, + api_base=api_base, + model_response=model_response, + print_verbose=print_verbose, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + encoding=encoding, + logging_obj=logging, + ) + if stream == True: ## [BETA] + # Fake streaming for petals + resp_string = model_response["choices"][0]["message"]["content"] + response = CustomStreamWrapper( + resp_string, + model, + custom_llm_provider="petals", + logging_obj=logging, + ) + return response + response = model_response + elif custom_llm_provider == "custom": + import requests + + url = litellm.api_base or api_base or "" + if url == None or url == "": + raise ValueError( + "api_base not set. Set api_base or litellm.api_base for custom endpoints" + ) + + """ + assume input to custom LLM api bases follow this format: + resp = requests.post( + api_base, + json={ + 'model': 'meta-llama/Llama-2-13b-hf', # model name + 'params': { + 'prompt': ["The capital of France is P"], + 'max_tokens': 32, + 'temperature': 0.7, + 'top_p': 1.0, + 'top_k': 40, + } + } + ) + + """ + prompt = " ".join([message["content"] for message in messages]) # type: ignore + resp = requests.post( + url, + json={ + "model": model, + "params": { + "prompt": [prompt], + "max_tokens": max_tokens, + "temperature": temperature, + "top_p": top_p, + "top_k": kwargs.get("top_k", 40), + }, + }, + ) + response_json = resp.json() + """ + assume all responses from custom api_bases of this format: + { + 'data': [ + { + 'prompt': 'The capital of France is P', + 'output': ['The capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France'], + 'params': {'temperature': 0.7, 'top_k': 40, 'top_p': 1}}], + 'message': 'ok' + } + ] + } + """ + string_response = response_json["data"][0]["output"][0] + ## RESPONSE OBJECT + model_response["choices"][0]["message"]["content"] = string_response + model_response["created"] = int(time.time()) + model_response["model"] = model + response = model_response + else: + raise ValueError( + f"Unable to map your input to a model. Check your input - {args}" + ) + return response + except Exception as e: + ## Map to OpenAI Exception + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=args, + ) + + +def completion_with_retries(*args, **kwargs): + """ + Executes a litellm.completion() with 3 retries + """ + try: + import tenacity + except Exception as e: + raise Exception( + f"tenacity import failed please run `pip install tenacity`. Error{e}" + ) + + num_retries = kwargs.pop("num_retries", 3) + retry_strategy = kwargs.pop("retry_strategy", "constant_retry") + original_function = kwargs.pop("original_function", completion) + if retry_strategy == "constant_retry": + retryer = tenacity.Retrying( + stop=tenacity.stop_after_attempt(num_retries), reraise=True + ) + elif retry_strategy == "exponential_backoff_retry": + retryer = tenacity.Retrying( + wait=tenacity.wait_exponential(multiplier=1, max=10), + stop=tenacity.stop_after_attempt(num_retries), + reraise=True, + ) + return retryer(original_function, *args, **kwargs) + + +async def acompletion_with_retries(*args, **kwargs): + """ + Executes a litellm.completion() with 3 retries + """ + try: + import tenacity + except Exception as e: + raise Exception( + f"tenacity import failed please run `pip install tenacity`. Error{e}" + ) + + num_retries = kwargs.pop("num_retries", 3) + retry_strategy = kwargs.pop("retry_strategy", "constant_retry") + original_function = kwargs.pop("original_function", completion) + if retry_strategy == "constant_retry": + retryer = tenacity.Retrying( + stop=tenacity.stop_after_attempt(num_retries), reraise=True + ) + elif retry_strategy == "exponential_backoff_retry": + retryer = tenacity.Retrying( + wait=tenacity.wait_exponential(multiplier=1, max=10), + stop=tenacity.stop_after_attempt(num_retries), + reraise=True, + ) + return await retryer(original_function, *args, **kwargs) + + +def batch_completion( + model: str, + # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create + messages: List = [], + functions: Optional[List] = None, + function_call: Optional[str] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[dict] = None, + user: Optional[str] = None, + deployment_id=None, + request_timeout: Optional[int] = None, + timeout: Optional[int] = 600, + # Optional liteLLM function params + **kwargs, +): + """ + Batch litellm.completion function for a given model. + + Args: + model (str): The model to use for generating completions. + messages (List, optional): List of messages to use as input for generating completions. Defaults to []. + functions (List, optional): List of functions to use as input for generating completions. Defaults to []. + function_call (str, optional): The function call to use as input for generating completions. Defaults to "". + temperature (float, optional): The temperature parameter for generating completions. Defaults to None. + top_p (float, optional): The top-p parameter for generating completions. Defaults to None. + n (int, optional): The number of completions to generate. Defaults to None. + stream (bool, optional): Whether to stream completions or not. Defaults to None. + stop (optional): The stop parameter for generating completions. Defaults to None. + max_tokens (float, optional): The maximum number of tokens to generate. Defaults to None. + presence_penalty (float, optional): The presence penalty for generating completions. Defaults to None. + frequency_penalty (float, optional): The frequency penalty for generating completions. Defaults to None. + logit_bias (dict, optional): The logit bias for generating completions. Defaults to {}. + user (str, optional): The user string for generating completions. Defaults to "". + deployment_id (optional): The deployment ID for generating completions. Defaults to None. + request_timeout (int, optional): The request timeout for generating completions. Defaults to None. + + Returns: + list: A list of completion results. + """ + args = locals() + batch_messages = messages + completions = [] + model = model + custom_llm_provider = None + if model.split("/", 1)[0] in litellm.provider_list: + custom_llm_provider = model.split("/", 1)[0] + model = model.split("/", 1)[1] + if custom_llm_provider == "vllm": + optional_params = get_optional_params( + functions=functions, + function_call=function_call, + temperature=temperature, + top_p=top_p, + n=n, + stream=stream, + stop=stop, + max_tokens=max_tokens, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + logit_bias=logit_bias, + user=user, + # params to identify the model + model=model, + custom_llm_provider=custom_llm_provider, + ) + results = vllm.batch_completions( + model=model, + messages=batch_messages, + custom_prompt_dict=litellm.custom_prompt_dict, + optional_params=optional_params, + ) + # all non VLLM models for batch completion models + else: + + def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + for i in range(0, len(lst), n): + yield lst[i : i + n] + + with ThreadPoolExecutor(max_workers=100) as executor: + for sub_batch in chunks(batch_messages, 100): + for message_list in sub_batch: + kwargs_modified = args.copy() + kwargs_modified["messages"] = message_list + original_kwargs = {} + if "kwargs" in kwargs_modified: + original_kwargs = kwargs_modified.pop("kwargs") + future = executor.submit( + completion, **kwargs_modified, **original_kwargs + ) + completions.append(future) + + # Retrieve the results from the futures + results = [future.result() for future in completions] + return results + + +# send one request to multiple models +# return as soon as one of the llms responds +def batch_completion_models(*args, **kwargs): + """ + Send a request to multiple language models concurrently and return the response + as soon as one of the models responds. + + Args: + *args: Variable-length positional arguments passed to the completion function. + **kwargs: Additional keyword arguments: + - models (str or list of str): The language models to send requests to. + - Other keyword arguments to be passed to the completion function. + + Returns: + str or None: The response from one of the language models, or None if no response is received. + + Note: + This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models. + It sends requests concurrently and returns the response from the first model that responds. + """ + import concurrent + + if "model" in kwargs: + kwargs.pop("model") + if "models" in kwargs: + models = kwargs["models"] + kwargs.pop("models") + futures = {} + with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor: + for model in models: + futures[model] = executor.submit( + completion, *args, model=model, **kwargs + ) + + for model, future in sorted( + futures.items(), key=lambda x: models.index(x[0]) + ): + if future.result() is not None: + return future.result() + elif "deployments" in kwargs: + deployments = kwargs["deployments"] + kwargs.pop("deployments") + kwargs.pop("model_list") + nested_kwargs = kwargs.pop("kwargs", {}) + futures = {} + with concurrent.futures.ThreadPoolExecutor( + max_workers=len(deployments) + ) as executor: + for deployment in deployments: + for key in kwargs.keys(): + if ( + key not in deployment + ): # don't override deployment values e.g. model name, api base, etc. + deployment[key] = kwargs[key] + kwargs = {**deployment, **nested_kwargs} + futures[deployment["model"]] = executor.submit(completion, **kwargs) + + while futures: + # wait for the first returned future + print_verbose("\n\n waiting for next result\n\n") + done, _ = concurrent.futures.wait( + futures.values(), return_when=concurrent.futures.FIRST_COMPLETED + ) + print_verbose(f"done list\n{done}") + for future in done: + try: + result = future.result() + return result + except Exception as e: + # if model 1 fails, continue with response from model 2, model3 + print_verbose( + f"\n\ngot an exception, ignoring, removing from futures" + ) + print_verbose(futures) + new_futures = {} + for key, value in futures.items(): + if future == value: + print_verbose(f"removing key{key}") + continue + else: + new_futures[key] = value + futures = new_futures + print_verbose(f"new futures{futures}") + continue + + print_verbose("\n\ndone looping through futures\n\n") + print_verbose(futures) + + return None # If no response is received from any model + + +def batch_completion_models_all_responses(*args, **kwargs): + """ + Send a request to multiple language models concurrently and return a list of responses + from all models that respond. + + Args: + *args: Variable-length positional arguments passed to the completion function. + **kwargs: Additional keyword arguments: + - models (str or list of str): The language models to send requests to. + - Other keyword arguments to be passed to the completion function. + + Returns: + list: A list of responses from the language models that responded. + + Note: + This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models. + It sends requests concurrently and collects responses from all models that respond. + """ + import concurrent.futures + + # ANSI escape codes for colored output + GREEN = "\033[92m" + RED = "\033[91m" + RESET = "\033[0m" + + if "model" in kwargs: + kwargs.pop("model") + if "models" in kwargs: + models = kwargs["models"] + kwargs.pop("models") + + responses = [] + + with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor: + for idx, model in enumerate(models): + future = executor.submit(completion, *args, model=model, **kwargs) + if future.result() is not None: + responses.append(future.result()) + + return responses + + +### EMBEDDING ENDPOINTS #################### +@client +async def aembedding(*args, **kwargs): + """ + Asynchronously calls the `embedding` function with the given arguments and keyword arguments. + + Parameters: + - `args` (tuple): Positional arguments to be passed to the `embedding` function. + - `kwargs` (dict): Keyword arguments to be passed to the `embedding` function. + + Returns: + - `response` (Any): The response returned by the `embedding` function. + """ + loop = asyncio.get_event_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + ### PASS ARGS TO Embedding ### + kwargs["aembedding"] = True + custom_llm_provider = None + try: + # Use a partial function to pass your keyword arguments + func = partial(embedding, *args, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider( + model=model, api_base=kwargs.get("api_base", None) + ) + + if ( + custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider == "xinference" + or custom_llm_provider == "voyage" + or custom_llm_provider == "mistral" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "openrouter" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "ollama" + ): # currently implemented aiohttp calls for just azure and openai, soon all. + # Await normally + init_response = await loop.run_in_executor(None, func_with_context) + if isinstance(init_response, dict) or isinstance( + init_response, ModelResponse + ): ## CACHING SCENARIO + response = init_response + elif asyncio.iscoroutine(init_response): + response = await init_response + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) + if response is not None and hasattr(response, "_hidden_params"): + response._hidden_params["custom_llm_provider"] = custom_llm_provider + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=args, + ) + + +@client +def embedding( + model, + input=[], + # Optional params + timeout=600, # default to 10 minutes + # set api_base, api_version, api_key + api_base: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + api_type: Optional[str] = None, + caching: bool = False, + user: Optional[str] = None, + custom_llm_provider=None, + litellm_call_id=None, + litellm_logging_obj=None, + logger_fn=None, + **kwargs, +): + """ + Embedding function that calls an API to generate embeddings for the given input. + + Parameters: + - model: The embedding model to use. + - input: The input for which embeddings are to be generated. + - timeout: The timeout value for the API call, default 10 mins + - litellm_call_id: The call ID for litellm logging. + - litellm_logging_obj: The litellm logging object. + - logger_fn: The logger function. + - api_base: Optional. The base URL for the API. + - api_version: Optional. The version of the API. + - api_key: Optional. The API key to use. + - api_type: Optional. The type of the API. + - caching: A boolean indicating whether to enable caching. + - custom_llm_provider: The custom llm provider. + + Returns: + - response: The response received from the API call. + + Raises: + - exception_type: If an exception occurs during the API call. + """ + azure = kwargs.get("azure", None) + client = kwargs.pop("client", None) + rpm = kwargs.pop("rpm", None) + tpm = kwargs.pop("tpm", None) + model_info = kwargs.get("model_info", None) + metadata = kwargs.get("metadata", None) + encoding_format = kwargs.get("encoding_format", None) + proxy_server_request = kwargs.get("proxy_server_request", None) + aembedding = kwargs.get("aembedding", None) + openai_params = [ + "user", + "request_timeout", + "api_base", + "api_version", + "api_key", + "deployment_id", + "organization", + "base_url", + "default_headers", + "timeout", + "max_retries", + "encoding_format", + ] + litellm_params = [ + "metadata", + "aembedding", + "caching", + "mock_response", + "api_key", + "api_version", + "api_base", + "force_timeout", + "logger_fn", + "verbose", + "custom_llm_provider", + "litellm_logging_obj", + "litellm_call_id", + "use_client", + "id", + "fallbacks", + "azure", + "headers", + "model_list", + "num_retries", + "context_window_fallback_dict", + "roles", + "final_prompt_value", + "bos_token", + "eos_token", + "request_timeout", + "complete_response", + "self", + "client", + "rpm", + "tpm", + "input_cost_per_token", + "output_cost_per_token", + "hf_model_name", + "proxy_server_request", + "model_info", + "preset_cache_key", + "caching_groups", + "ttl", + "cache", + ] + default_params = openai_params + litellm_params + non_default_params = { + k: v for k, v in kwargs.items() if k not in default_params + } # model-specific params - pass them straight to the model/provider + + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( + model=model, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + ) + optional_params = get_optional_params_embeddings( + user=user, + encoding_format=encoding_format, + custom_llm_provider=custom_llm_provider, + **non_default_params, + ) + try: + response = None + logging = litellm_logging_obj + logging.update_environment_variables( + model=model, + user=user, + optional_params=optional_params, + litellm_params={ + "timeout": timeout, + "azure": azure, + "litellm_call_id": litellm_call_id, + "logger_fn": logger_fn, + "proxy_server_request": proxy_server_request, + "model_info": model_info, + "metadata": metadata, + "aembedding": aembedding, + "preset_cache_key": None, + "stream_response": {}, + }, + ) + if azure == True or custom_llm_provider == "azure": + # azure configs + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") + + api_version = ( + api_version or litellm.api_version or get_secret("AZURE_API_VERSION") + ) + + azure_ad_token = kwargs.pop("azure_ad_token", None) or get_secret( + "AZURE_AD_TOKEN" + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_API_KEY") + ) + ## EMBEDDING CALL + response = azure_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + api_version=api_version, + azure_ad_token=azure_ad_token, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif ( + model in litellm.open_ai_embedding_models or custom_llm_provider == "openai" + ): + api_base = ( + api_base + or litellm.api_base + or get_secret("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + openai.organization = ( + litellm.organization + or get_secret("OPENAI_ORGANIZATION") + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + api_key + or litellm.api_key + or litellm.openai_key + or get_secret("OPENAI_API_KEY") + ) + api_type = "openai" + api_version = None + + ## EMBEDDING CALL + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif model in litellm.cohere_embedding_models: + cohere_key = ( + api_key + or litellm.cohere_key + or get_secret("COHERE_API_KEY") + or get_secret("CO_API_KEY") + or litellm.api_key + ) + response = cohere.embedding( + model=model, + input=input, + optional_params=optional_params, + encoding=encoding, + api_key=cohere_key, + logging_obj=logging, + model_response=EmbeddingResponse(), + ) + elif custom_llm_provider == "huggingface": + api_key = ( + api_key + or litellm.huggingface_key + or get_secret("HUGGINGFACE_API_KEY") + or litellm.api_key + ) + response = huggingface.embedding( + model=model, + input=input, + encoding=encoding, + api_key=api_key, + api_base=api_base, + logging_obj=logging, + model_response=EmbeddingResponse(), + ) + elif custom_llm_provider == "bedrock": + response = bedrock.embedding( + model=model, + input=input, + encoding=encoding, + logging_obj=logging, + optional_params=optional_params, + model_response=EmbeddingResponse(), + ) + elif custom_llm_provider == "oobabooga": + response = oobabooga.embedding( + model=model, + input=input, + encoding=encoding, + api_base=api_base, + logging_obj=logging, + optional_params=optional_params, + model_response=EmbeddingResponse(), + ) + elif custom_llm_provider == "ollama": + if aembedding == True: + response = ollama.ollama_aembeddings( + model=model, + prompt=input, + encoding=encoding, + logging_obj=logging, + optional_params=optional_params, + model_response=EmbeddingResponse(), + ) + elif custom_llm_provider == "sagemaker": + response = sagemaker.embedding( + model=model, + input=input, + encoding=encoding, + logging_obj=logging, + optional_params=optional_params, + model_response=EmbeddingResponse(), + print_verbose=print_verbose, + ) + elif custom_llm_provider == "mistral": + api_key = api_key or litellm.api_key or get_secret("MISTRAL_API_KEY") + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif custom_llm_provider == "voyage": + api_key = api_key or litellm.api_key or get_secret("VOYAGE_API_KEY") + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + elif custom_llm_provider == "xinference": + api_key = ( + api_key + or litellm.api_key + or get_secret("XINFERENCE_API_KEY") + or "stub-xinference-key" + ) # xinference does not need an api key, pass a stub key if user did not set one + api_base = ( + api_base + or litellm.api_base + or get_secret("XINFERENCE_API_BASE") + or "http://127.0.0.1:9997/v1" + ) + response = openai_chat_completions.embedding( + model=model, + input=input, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + ) + else: + args = locals() + raise ValueError(f"No valid embedding model args passed in - {args}") + if response is not None and hasattr(response, "_hidden_params"): + response._hidden_params["custom_llm_provider"] = custom_llm_provider + return response + except Exception as e: + ## LOGGING + logging.post_call( + input=input, + api_key=api_key, + original_response=str(e), + ) + ## Map to OpenAI Exception + raise exception_type( + model=model, + original_exception=e, + custom_llm_provider="azure" if azure == True else None, + ) + + +###### Text Completion ################ +@client +async def atext_completion(*args, **kwargs): + """ + Implemented to handle async streaming for the text completion endpoint + """ + loop = asyncio.get_event_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + ### PASS ARGS TO COMPLETION ### + kwargs["acompletion"] = True + custom_llm_provider = None + try: + # Use a partial function to pass your keyword arguments + func = partial(text_completion, *args, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider( + model=model, api_base=kwargs.get("api_base", None) + ) + + if ( + custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "mistral" + or custom_llm_provider == "openrouter" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "text-completion-openai" + or custom_llm_provider == "huggingface" + or custom_llm_provider == "ollama" + or custom_llm_provider == "vertex_ai" + ): # currently implemented aiohttp calls for just azure and openai, soon all. + # Await normally + response = await loop.run_in_executor(None, func_with_context) + if asyncio.iscoroutine(response): + response = await response + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) + if kwargs.get("stream", False) == True: # return an async generator + return TextCompletionStreamWrapper( + completion_stream=_async_streaming( + response=response, + model=model, + custom_llm_provider=custom_llm_provider, + args=args, + ), + model=model, + ) + else: + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=args, + ) + + +@client +def text_completion( + prompt: Union[ + str, List[Union[str, List[Union[str, List[int]]]]] + ], # Required: The prompt(s) to generate completions for. + model: Optional[str] = None, # Optional: either `model` or `engine` can be set + best_of: Optional[ + int + ] = None, # Optional: Generates best_of completions server-side. + echo: Optional[ + bool + ] = None, # Optional: Echo back the prompt in addition to the completion. + frequency_penalty: Optional[ + float + ] = None, # Optional: Penalize new tokens based on their existing frequency. + logit_bias: Optional[ + Dict[int, int] + ] = None, # Optional: Modify the likelihood of specified tokens. + logprobs: Optional[ + int + ] = None, # Optional: Include the log probabilities on the most likely tokens. + max_tokens: Optional[ + int + ] = None, # Optional: The maximum number of tokens to generate in the completion. + n: Optional[ + int + ] = None, # Optional: How many completions to generate for each prompt. + presence_penalty: Optional[ + float + ] = None, # Optional: Penalize new tokens based on whether they appear in the text so far. + stop: Optional[ + Union[str, List[str]] + ] = None, # Optional: Sequences where the API will stop generating further tokens. + stream: Optional[bool] = None, # Optional: Whether to stream back partial progress. + suffix: Optional[ + str + ] = None, # Optional: The suffix that comes after a completion of inserted text. + temperature: Optional[float] = None, # Optional: Sampling temperature to use. + top_p: Optional[float] = None, # Optional: Nucleus sampling parameter. + user: Optional[ + str + ] = None, # Optional: A unique identifier representing your end-user. + # set api_base, api_version, api_key + api_base: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + model_list: Optional[list] = None, # pass in a list of api_base,keys, etc. + # Optional liteLLM function params + custom_llm_provider: Optional[str] = None, + *args, + **kwargs, +): + global print_verbose + import copy + + """ + Generate text completions using the OpenAI API. + + Args: + model (str): ID of the model to use. + prompt (Union[str, List[Union[str, List[Union[str, List[int]]]]]): The prompt(s) to generate completions for. + best_of (Optional[int], optional): Generates best_of completions server-side. Defaults to 1. + echo (Optional[bool], optional): Echo back the prompt in addition to the completion. Defaults to False. + frequency_penalty (Optional[float], optional): Penalize new tokens based on their existing frequency. Defaults to 0. + logit_bias (Optional[Dict[int, int]], optional): Modify the likelihood of specified tokens. Defaults to None. + logprobs (Optional[int], optional): Include the log probabilities on the most likely tokens. Defaults to None. + max_tokens (Optional[int], optional): The maximum number of tokens to generate in the completion. Defaults to 16. + n (Optional[int], optional): How many completions to generate for each prompt. Defaults to 1. + presence_penalty (Optional[float], optional): Penalize new tokens based on whether they appear in the text so far. Defaults to 0. + stop (Optional[Union[str, List[str]]], optional): Sequences where the API will stop generating further tokens. Defaults to None. + stream (Optional[bool], optional): Whether to stream back partial progress. Defaults to False. + suffix (Optional[str], optional): The suffix that comes after a completion of inserted text. Defaults to None. + temperature (Optional[float], optional): Sampling temperature to use. Defaults to 1. + top_p (Optional[float], optional): Nucleus sampling parameter. Defaults to 1. + user (Optional[str], optional): A unique identifier representing your end-user. + Returns: + TextCompletionResponse: A response object containing the generated completion and associated metadata. + + Example: + Your example of how to use this function goes here. + """ + if "engine" in kwargs: + if model == None: + # only use engine when model not passed + model = kwargs["engine"] + kwargs.pop("engine") + + text_completion_response = TextCompletionResponse() + + optional_params: Dict[str, Any] = {} + # default values for all optional params are none, litellm only passes them to the llm when they are set to non None values + if best_of is not None: + optional_params["best_of"] = best_of + if echo is not None: + optional_params["echo"] = echo + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if logprobs is not None: + optional_params["logprobs"] = logprobs + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if n is not None: + optional_params["n"] = n + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if stop is not None: + optional_params["stop"] = stop + if stream is not None: + optional_params["stream"] = stream + if suffix is not None: + optional_params["suffix"] = suffix + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if user is not None: + optional_params["user"] = user + if api_base is not None: + optional_params["api_base"] = api_base + if api_version is not None: + optional_params["api_version"] = api_version + if api_key is not None: + optional_params["api_key"] = api_key + if custom_llm_provider is not None: + optional_params["custom_llm_provider"] = custom_llm_provider + + # get custom_llm_provider + _, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore + + if custom_llm_provider == "huggingface": + # if echo == True, for TGI llms we need to set top_n_tokens to 3 + if echo == True: + # for tgi llms + if "top_n_tokens" not in kwargs: + kwargs["top_n_tokens"] = 3 + + # processing prompt - users can pass raw tokens to OpenAI Completion() + if type(prompt) == list: + import concurrent.futures + + tokenizer = tiktoken.encoding_for_model("text-davinci-003") + ## if it's a 2d list - each element in the list is a text_completion() request + if len(prompt) > 0 and type(prompt[0]) == list: + responses = [None for x in prompt] # init responses + + def process_prompt(i, individual_prompt): + decoded_prompt = tokenizer.decode(individual_prompt) + all_params = {**kwargs, **optional_params} + response = text_completion( + model=model, + prompt=decoded_prompt, + num_retries=3, # ensure this does not fail for the batch + *args, + **all_params, + ) + + text_completion_response["id"] = response.get("id", None) + text_completion_response["object"] = "text_completion" + text_completion_response["created"] = response.get("created", None) + text_completion_response["model"] = response.get("model", None) + return response["choices"][0] + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [ + executor.submit(process_prompt, i, individual_prompt) + for i, individual_prompt in enumerate(prompt) + ] + for i, future in enumerate( + concurrent.futures.as_completed(futures) + ): + responses[i] = future.result() + text_completion_response.choices = responses + + return text_completion_response + # else: + # check if non default values passed in for best_of, echo, logprobs, suffix + # these are the params supported by Completion() but not ChatCompletion + + # default case, non OpenAI requests go through here + messages = [{"role": "system", "content": prompt}] + kwargs.pop("prompt", None) + response = completion( + model=model, + messages=messages, + *args, + **kwargs, + **optional_params, + ) + if kwargs.get("acompletion", False) == True: + return response + if stream == True or kwargs.get("stream", False) == True: + response = TextCompletionStreamWrapper(completion_stream=response, model=model) + return response + transformed_logprobs = None + # only supported for TGI models + try: + raw_response = response._hidden_params.get("original_response", None) + transformed_logprobs = litellm.utils.transform_logprobs(raw_response) + except Exception as e: + print_verbose(f"LiteLLM non blocking exception: {e}") + text_completion_response["id"] = response.get("id", None) + text_completion_response["object"] = "text_completion" + text_completion_response["created"] = response.get("created", None) + text_completion_response["model"] = response.get("model", None) + text_choices = TextChoices() + text_choices["text"] = response["choices"][0]["message"]["content"] + text_choices["index"] = response["choices"][0]["index"] + text_choices["logprobs"] = transformed_logprobs + text_choices["finish_reason"] = response["choices"][0]["finish_reason"] + text_completion_response["choices"] = [text_choices] + text_completion_response["usage"] = response.get("usage", None) + return text_completion_response + + +##### Moderation ####################### +def moderation(input: str, api_key: Optional[str] = None): + # only supports open ai for now + api_key = ( + api_key or litellm.api_key or litellm.openai_key or get_secret("OPENAI_API_KEY") + ) + openai.api_key = api_key + openai.api_type = "open_ai" # type: ignore + openai.api_version = None + openai.base_url = "https://api.openai.com/v1/" + response = openai.moderations.create(input=input) + return response + + +##### Image Generation ####################### +@client +async def aimage_generation(*args, **kwargs): + """ + Asynchronously calls the `image_generation` function with the given arguments and keyword arguments. + + Parameters: + - `args` (tuple): Positional arguments to be passed to the `embedding` function. + - `kwargs` (dict): Keyword arguments to be passed to the `embedding` function. + + Returns: + - `response` (Any): The response returned by the `embedding` function. + """ + loop = asyncio.get_event_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + ### PASS ARGS TO Image Generation ### + kwargs["aimg_generation"] = True + custom_llm_provider = None + try: + # Use a partial function to pass your keyword arguments + func = partial(image_generation, *args, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider( + model=model, api_base=kwargs.get("api_base", None) + ) + + # Await normally + init_response = await loop.run_in_executor(None, func_with_context) + if isinstance(init_response, dict) or isinstance( + init_response, ModelResponse + ): ## CACHING SCENARIO + response = init_response + elif asyncio.iscoroutine(init_response): + response = await init_response + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=args, + ) + + +@client +def image_generation( + prompt: str, + model: Optional[str] = None, + n: Optional[int] = None, + quality: Optional[str] = None, + response_format: Optional[str] = None, + size: Optional[str] = None, + style: Optional[str] = None, + user: Optional[str] = None, + timeout=600, # default to 10 minutes + api_key: Optional[str] = None, + api_base: Optional[str] = None, + api_version: Optional[str] = None, + litellm_logging_obj=None, + custom_llm_provider=None, + **kwargs, +): + """ + Maps the https://api.openai.com/v1/images/generations endpoint. + + Currently supports just Azure + OpenAI. + """ + try: + aimg_generation = kwargs.get("aimg_generation", False) + litellm_call_id = kwargs.get("litellm_call_id", None) + logger_fn = kwargs.get("logger_fn", None) + proxy_server_request = kwargs.get("proxy_server_request", None) + model_info = kwargs.get("model_info", None) + metadata = kwargs.get("metadata", {}) + + model_response = litellm.utils.ImageResponse() + if model is not None or custom_llm_provider is not None: + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore + else: + model = "dall-e-2" + custom_llm_provider = "openai" # default to dall-e-2 on openai + openai_params = [ + "user", + "request_timeout", + "api_base", + "api_version", + "api_key", + "deployment_id", + "organization", + "base_url", + "default_headers", + "timeout", + "max_retries", + "n", + "quality", + "size", + "style", + ] + litellm_params = [ + "metadata", + "aimg_generation", + "caching", + "mock_response", + "api_key", + "api_version", + "api_base", + "force_timeout", + "logger_fn", + "verbose", + "custom_llm_provider", + "litellm_logging_obj", + "litellm_call_id", + "use_client", + "id", + "fallbacks", + "azure", + "headers", + "model_list", + "num_retries", + "context_window_fallback_dict", + "roles", + "final_prompt_value", + "bos_token", + "eos_token", + "request_timeout", + "complete_response", + "self", + "client", + "rpm", + "tpm", + "input_cost_per_token", + "output_cost_per_token", + "hf_model_name", + "proxy_server_request", + "model_info", + "preset_cache_key", + "caching_groups", + "ttl", + "cache", + ] + default_params = openai_params + litellm_params + non_default_params = { + k: v for k, v in kwargs.items() if k not in default_params + } # model-specific params - pass them straight to the model/provider + optional_params = get_optional_params_image_gen( + n=n, + quality=quality, + response_format=response_format, + size=size, + style=style, + user=user, + custom_llm_provider=custom_llm_provider, + **non_default_params, + ) + logging = litellm_logging_obj + logging.update_environment_variables( + model=model, + user=user, + optional_params=optional_params, + litellm_params={ + "timeout": timeout, + "azure": False, + "litellm_call_id": litellm_call_id, + "logger_fn": logger_fn, + "proxy_server_request": proxy_server_request, + "model_info": model_info, + "metadata": metadata, + "preset_cache_key": None, + "stream_response": {}, + }, + ) + + if custom_llm_provider == "azure": + # azure configs + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") + + api_version = ( + api_version or litellm.api_version or get_secret("AZURE_API_VERSION") + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) + + azure_ad_token = optional_params.pop("azure_ad_token", None) or get_secret( + "AZURE_AD_TOKEN" + ) + + model_response = azure_chat_completions.image_generation( + model=model, + prompt=prompt, + timeout=timeout, + api_key=api_key, + api_base=api_base, + logging_obj=litellm_logging_obj, + optional_params=optional_params, + model_response=model_response, + api_version=api_version, + aimg_generation=aimg_generation, + ) + elif custom_llm_provider == "openai": + model_response = openai_chat_completions.image_generation( + model=model, + prompt=prompt, + timeout=timeout, + api_key=api_key, + api_base=api_base, + logging_obj=litellm_logging_obj, + optional_params=optional_params, + model_response=model_response, + aimg_generation=aimg_generation, + ) + + return model_response + except Exception as e: + ## Map to OpenAI Exception + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=locals(), + ) + + +##### Health Endpoints ####################### + + +async def ahealth_check( + model_params: dict, + mode: Optional[ + Literal["completion", "embedding", "image_generation", "chat"] + ] = None, + prompt: Optional[str] = None, + input: Optional[List] = None, + default_timeout: float = 6000, +): + """ + Support health checks for different providers. Return remaining rate limit, etc. + + For azure/openai -> completion.with_raw_response + For rest -> litellm.acompletion() + """ + try: + model: Optional[str] = model_params.get("model", None) + + if model is None: + raise Exception("model not set") + + model, custom_llm_provider, _, _ = get_llm_provider(model=model) + mode = mode or "chat" # default to chat completion calls + + if custom_llm_provider == "azure": + api_key = ( + model_params.get("api_key") + or get_secret("AZURE_API_KEY") + or get_secret("AZURE_OPENAI_API_KEY") + ) + + api_base = ( + model_params.get("api_base") + or get_secret("AZURE_API_BASE") + or get_secret("AZURE_OPENAI_API_BASE") + ) + + api_version = ( + model_params.get("api_version") + or get_secret("AZURE_API_VERSION") + or get_secret("AZURE_OPENAI_API_VERSION") + ) + + timeout = ( + model_params.get("timeout") + or litellm.request_timeout + or default_timeout + ) + + response = await azure_chat_completions.ahealth_check( + model=model, + messages=model_params.get( + "messages", None + ), # Replace with your actual messages list + api_key=api_key, + api_base=api_base, + api_version=api_version, + timeout=timeout, + mode=mode, + prompt=prompt, + input=input, + ) + elif ( + custom_llm_provider == "openai" + or custom_llm_provider == "text-completion-openai" + ): + api_key = model_params.get("api_key") or get_secret("OPENAI_API_KEY") + + timeout = ( + model_params.get("timeout") + or litellm.request_timeout + or default_timeout + ) + + response = await openai_chat_completions.ahealth_check( + model=model, + messages=model_params.get( + "messages", None + ), # Replace with your actual messages list + api_key=api_key, + timeout=timeout, + mode=mode, + prompt=prompt, + input=input, + ) + else: + if mode == "embedding": + model_params.pop("messages", None) + model_params["input"] = input + await litellm.aembedding(**model_params) + response = {} + elif mode == "image_generation": + model_params.pop("messages", None) + model_params["prompt"] = prompt + await litellm.aimage_generation(**model_params) + response = {} + else: # default to completion calls + await acompletion(**model_params) + response = {} # args like remaining ratelimit etc. + return response + except Exception as e: + return {"error": str(e)} + + +####### HELPER FUNCTIONS ################ +## Set verbose to true -> ```litellm.set_verbose = True``` +def print_verbose(print_statement): + try: + if litellm.set_verbose: + print(print_statement) # noqa + except: + pass + + +def config_completion(**kwargs): + if litellm.config_path != None: + config_args = read_config_args(litellm.config_path) + # overwrite any args passed in with config args + return completion(**kwargs, **config_args) + else: + raise ValueError( + "No config path set, please set a config path using `litellm.config_path = 'path/to/config.json'`" + ) + + +def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List] = None): + id = chunks[0]["id"] + object = chunks[0]["object"] + created = chunks[0]["created"] + model = chunks[0]["model"] + system_fingerprint = chunks[0].get("system_fingerprint", None) + finish_reason = chunks[-1]["choices"][0]["finish_reason"] + logprobs = chunks[-1]["choices"][0]["logprobs"] + + response = { + "id": id, + "object": object, + "created": created, + "model": model, + "system_fingerprint": system_fingerprint, + "choices": [ + { + "text": None, + "index": 0, + "logprobs": logprobs, + "finish_reason": finish_reason, + } + ], + "usage": { + "prompt_tokens": None, + "completion_tokens": None, + "total_tokens": None, + }, + } + content_list = [] + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + if ( + choice is not None + and hasattr(choice, "text") + and choice.get("text") is not None + ): + _choice = choice.get("text") + content_list.append(_choice) + + # Combine the "content" strings into a single string || combine the 'function' strings into a single string + combined_content = "".join(content_list) + + # Update the "content" field within the response dictionary + response["choices"][0]["text"] = combined_content + + if len(combined_content) > 0: + completion_output = combined_content + else: + completion_output = "" + # # Update usage information if needed + try: + response["usage"]["prompt_tokens"] = token_counter( + model=model, messages=messages + ) + except: # don't allow this failing to block a complete streaming response from being returned + print_verbose(f"token_counter failed, assuming prompt tokens is 0") + response["usage"]["prompt_tokens"] = 0 + response["usage"]["completion_tokens"] = token_counter( + model=model, + text=combined_content, + count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages + ) + response["usage"]["total_tokens"] = ( + response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"] + ) + return response + + +def stream_chunk_builder(chunks: list, messages: Optional[list] = None): + model_response = litellm.ModelResponse() + # set hidden params from chunk to model_response + if model_response is not None and hasattr(model_response, "_hidden_params"): + model_response._hidden_params = chunks[0].get("_hidden_params", {}) + id = chunks[0]["id"] + object = chunks[0]["object"] + created = chunks[0]["created"] + model = chunks[0]["model"] + system_fingerprint = chunks[0].get("system_fingerprint", None) + + if isinstance( + chunks[0]["choices"][0], litellm.utils.TextChoices + ): # route to the text completion logic + return stream_chunk_builder_text_completion(chunks=chunks, messages=messages) + role = chunks[0]["choices"][0]["delta"]["role"] + finish_reason = chunks[-1]["choices"][0]["finish_reason"] + + # Initialize the response dictionary + response = { + "id": id, + "object": object, + "created": created, + "model": model, + "system_fingerprint": system_fingerprint, + "choices": [ + { + "index": 0, + "message": {"role": role, "content": ""}, + "finish_reason": finish_reason, + } + ], + "usage": { + "prompt_tokens": 0, # Modify as needed + "completion_tokens": 0, # Modify as needed + "total_tokens": 0, # Modify as needed + }, + } + + # Extract the "content" strings from the nested dictionaries within "choices" + content_list = [] + combined_content = "" + combined_arguments = "" + + if ( + "tool_calls" in chunks[0]["choices"][0]["delta"] + and chunks[0]["choices"][0]["delta"]["tool_calls"] is not None + ): + argument_list = [] + delta = chunks[0]["choices"][0]["delta"] + message = response["choices"][0]["message"] + message["tool_calls"] = [] + id = None + name = None + type = None + tool_calls_list = [] + prev_index = 0 + prev_id = None + curr_id = None + curr_index = 0 + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + tool_calls = delta.get("tool_calls", "") + # Check if a tool call is present + if tool_calls and tool_calls[0].function is not None: + if tool_calls[0].id: + id = tool_calls[0].id + curr_id = id + if prev_id is None: + prev_id = curr_id + if tool_calls[0].index: + curr_index = tool_calls[0].index + if tool_calls[0].function.arguments: + # Now, tool_calls is expected to be a dictionary + arguments = tool_calls[0].function.arguments + argument_list.append(arguments) + if tool_calls[0].function.name: + name = tool_calls[0].function.name + if tool_calls[0].type: + type = tool_calls[0].type + if curr_index != prev_index: # new tool call + combined_arguments = "".join(argument_list) + tool_calls_list.append( + { + "id": prev_id, + "index": prev_index, + "function": {"arguments": combined_arguments, "name": name}, + "type": type, + } + ) + argument_list = [] # reset + prev_index = curr_index + prev_id = curr_id + + combined_arguments = "".join(argument_list) + tool_calls_list.append( + { + "id": id, + "function": {"arguments": combined_arguments, "name": name}, + "type": type, + } + ) + response["choices"][0]["message"]["content"] = None + response["choices"][0]["message"]["tool_calls"] = tool_calls_list + elif ( + "function_call" in chunks[0]["choices"][0]["delta"] + and chunks[0]["choices"][0]["delta"]["function_call"] is not None + ): + argument_list = [] + delta = chunks[0]["choices"][0]["delta"] + function_call = delta.get("function_call", "") + function_call_name = function_call.name + + message = response["choices"][0]["message"] + message["function_call"] = {} + message["function_call"]["name"] = function_call_name + + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + function_call = delta.get("function_call", "") + + # Check if a function call is present + if function_call: + # Now, function_call is expected to be a dictionary + arguments = function_call.arguments + argument_list.append(arguments) + + combined_arguments = "".join(argument_list) + response["choices"][0]["message"]["content"] = None + response["choices"][0]["message"]["function_call"][ + "arguments" + ] = combined_arguments + else: + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + content = delta.get("content", "") + if content == None: + continue # openai v1.0.0 sets content = None for chunks + content_list.append(content) + + # Combine the "content" strings into a single string || combine the 'function' strings into a single string + combined_content = "".join(content_list) + + # Update the "content" field within the response dictionary + response["choices"][0]["message"]["content"] = combined_content + + if len(combined_content) > 0: + completion_output = combined_content + elif len(combined_arguments) > 0: + completion_output = combined_arguments + else: + completion_output = "" + # # Update usage information if needed + try: + response["usage"]["prompt_tokens"] = token_counter( + model=model, messages=messages + ) + except: # don't allow this failing to block a complete streaming response from being returned + print_verbose(f"token_counter failed, assuming prompt tokens is 0") + response["usage"]["prompt_tokens"] = 0 + response["usage"]["completion_tokens"] = token_counter( + model=model, + text=completion_output, + count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages + ) + response["usage"]["total_tokens"] = ( + response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"] + ) + return convert_to_model_response_object( + response_object=response, model_response_object=model_response + ) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json new file mode 100644 index 0000000000000000000000000000000000000000..454b2504ad765d8f547ea1e2586d071f57c72a5a --- /dev/null +++ b/litellm/model_prices_and_context_window_backup.json @@ -0,0 +1,948 @@ +{ + 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"max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/replit-code-v1.5-3b": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "anyscale/mistralai/Mistral-7B-Instruct-v0.1": { + "max_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000015, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/HuggingFaceH4/zephyr-7b-beta": { + "max_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000015, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/meta-llama/Llama-2-7b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000015, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/meta-llama/Llama-2-13b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.00000025, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/meta-llama/Llama-2-70b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/codellama/CodeLlama-34b-Instruct-hf": { + "max_tokens": 16384, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001, + "litellm_provider": "anyscale", + "mode": "chat" + } +} diff --git a/litellm/proxy/.gitignore b/litellm/proxy/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..caa4783d9074da6624aae24c2ed0fd95c7111449 --- /dev/null +++ b/litellm/proxy/.gitignore @@ -0,0 +1,2 @@ +.env +secrets.toml \ No newline at end of file diff --git a/litellm/proxy/README.md b/litellm/proxy/README.md new file mode 100644 index 0000000000000000000000000000000000000000..92df6026c2ddfe74c187d964e5d38d34163ff699 --- /dev/null +++ b/litellm/proxy/README.md @@ -0,0 +1,31 @@ +# litellm-proxy + +A local, fast, and lightweight **OpenAI-compatible server** to call 100+ LLM APIs. + +## usage + +```shell +$ pip install litellm +``` +```shell +$ litellm --model ollama/codellama + +#INFO: Ollama running on http://0.0.0.0:8000 +``` + +## replace openai base +```python +import openai # openai v1.0.0+ +client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) +``` + +[**See how to call Huggingface,Bedrock,TogetherAI,Anthropic, etc.**](https://docs.litellm.ai/docs/simple_proxy) diff --git a/litellm/proxy/__init__.py b/litellm/proxy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834 --- /dev/null +++ b/litellm/proxy/__init__.py @@ -0,0 +1 @@ +from . import * diff --git a/litellm/proxy/_experimental/post_call_rules.py b/litellm/proxy/_experimental/post_call_rules.py new file mode 100644 index 0000000000000000000000000000000000000000..d5cbe31f1be8ea53eb9dabf254edad26227caf9c --- /dev/null +++ b/litellm/proxy/_experimental/post_call_rules.py @@ -0,0 +1,4 @@ +def my_custom_rule(input): # receives the model response + # if len(input) < 5: # trigger fallback if the model response is too short + return False + return True diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py new file mode 100644 index 0000000000000000000000000000000000000000..77f9aabced04563ca060c2a13e253f9955ef2406 --- /dev/null +++ b/litellm/proxy/_types.py @@ -0,0 +1,314 @@ +from pydantic import BaseModel, Extra, Field, root_validator +import enum +from typing import Optional, List, Union, Dict, Literal +from datetime import datetime +import uuid, json + + +class LiteLLMBase(BaseModel): + """ + Implements default functions, all pydantic objects should have. + """ + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + def fields_set(self): + try: + return self.model_fields_set # noqa + except: + # if using pydantic v1 + return self.__fields_set__ + + +######### Request Class Definition ###### +class ProxyChatCompletionRequest(LiteLLMBase): + model: str + messages: List[Dict[str, str]] + temperature: Optional[float] = None + top_p: Optional[float] = None + n: Optional[int] = None + stream: Optional[bool] = None + stop: Optional[List[str]] = None + max_tokens: Optional[int] = None + presence_penalty: Optional[float] = None + frequency_penalty: Optional[float] = None + logit_bias: Optional[Dict[str, float]] = None + user: Optional[str] = None + response_format: Optional[Dict[str, str]] = None + seed: Optional[int] = None + tools: Optional[List[str]] = None + tool_choice: Optional[str] = None + functions: Optional[List[str]] = None # soon to be deprecated + function_call: Optional[str] = None # soon to be deprecated + + # Optional LiteLLM params + caching: Optional[bool] = None + api_base: Optional[str] = None + api_version: Optional[str] = None + api_key: Optional[str] = None + num_retries: Optional[int] = None + context_window_fallback_dict: Optional[Dict[str, str]] = None + fallbacks: Optional[List[str]] = None + metadata: Optional[Dict[str, str]] = {} + deployment_id: Optional[str] = None + request_timeout: Optional[int] = None + + class Config: + extra = "allow" # allow params not defined here, these fall in litellm.completion(**kwargs) + + +class ModelInfoDelete(LiteLLMBase): + id: Optional[str] + + +class ModelInfo(LiteLLMBase): + id: Optional[str] + mode: Optional[Literal["embedding", "chat", "completion"]] + input_cost_per_token: Optional[float] = 0.0 + output_cost_per_token: Optional[float] = 0.0 + max_tokens: Optional[int] = 2048 # assume 2048 if not set + + # for azure models we need users to specify the base model, one azure you can call deployments - azure/my-random-model + # we look up the base model in model_prices_and_context_window.json + base_model: Optional[ + Literal[ + "gpt-4-1106-preview", + "gpt-4-32k", + "gpt-4", + "gpt-3.5-turbo-16k", + "gpt-3.5-turbo", + "text-embedding-ada-002", + ] + ] + + class Config: + extra = Extra.allow # Allow extra fields + protected_namespaces = () + + @root_validator(pre=True) + def set_model_info(cls, values): + if values.get("id") is None: + values.update({"id": str(uuid.uuid4())}) + if values.get("mode") is None: + values.update({"mode": None}) + if values.get("input_cost_per_token") is None: + values.update({"input_cost_per_token": None}) + if values.get("output_cost_per_token") is None: + values.update({"output_cost_per_token": None}) + if values.get("max_tokens") is None: + values.update({"max_tokens": None}) + if values.get("base_model") is None: + values.update({"base_model": None}) + return values + + +class ModelParams(LiteLLMBase): + model_name: str + litellm_params: dict + model_info: ModelInfo + + class Config: + protected_namespaces = () + + @root_validator(pre=True) + def set_model_info(cls, values): + if values.get("model_info") is None: + values.update({"model_info": ModelInfo()}) + return values + + +class GenerateKeyRequest(LiteLLMBase): + duration: Optional[str] = "1h" + models: Optional[list] = [] + aliases: Optional[dict] = {} + config: Optional[dict] = {} + spend: Optional[float] = 0 + user_id: Optional[str] = None + max_parallel_requests: Optional[int] = None + metadata: Optional[dict] = {} + + +class UpdateKeyRequest(LiteLLMBase): + key: str + duration: Optional[str] = None + models: Optional[list] = None + aliases: Optional[dict] = None + config: Optional[dict] = None + spend: Optional[float] = None + user_id: Optional[str] = None + max_parallel_requests: Optional[int] = None + metadata: Optional[dict] = {} + + +class UserAPIKeyAuth(LiteLLMBase): # the expected response object for user api key auth + """ + Return the row in the db + """ + + api_key: Optional[str] = None + models: list = [] + aliases: dict = {} + config: dict = {} + spend: Optional[float] = 0 + user_id: Optional[str] = None + max_parallel_requests: Optional[int] = None + duration: str = "1h" + metadata: dict = {} + + +class GenerateKeyResponse(LiteLLMBase): + key: str + expires: Optional[datetime] + user_id: str + + +class _DeleteKeyObject(LiteLLMBase): + key: str + + +class DeleteKeyRequest(LiteLLMBase): + keys: List[_DeleteKeyObject] + + +class NewUserRequest(GenerateKeyRequest): + max_budget: Optional[float] = None + + +class NewUserResponse(GenerateKeyResponse): + max_budget: Optional[float] = None + + +class KeyManagementSystem(enum.Enum): + GOOGLE_KMS = "google_kms" + AZURE_KEY_VAULT = "azure_key_vault" + LOCAL = "local" + + +class DynamoDBArgs(LiteLLMBase): + billing_mode: Literal["PROVISIONED_THROUGHPUT", "PAY_PER_REQUEST"] + read_capacity_units: Optional[int] = None + write_capacity_units: Optional[int] = None + region_name: str + user_table_name: str = "LiteLLM_UserTable" + key_table_name: str = "LiteLLM_VerificationToken" + config_table_name: str = "LiteLLM_Config" + + +class ConfigGeneralSettings(LiteLLMBase): + """ + Documents all the fields supported by `general_settings` in config.yaml + """ + + completion_model: Optional[str] = Field( + None, description="proxy level default model for all chat completion calls" + ) + key_management_system: Optional[KeyManagementSystem] = Field( + None, description="key manager to load keys from / decrypt keys with" + ) + use_google_kms: Optional[bool] = Field( + None, description="decrypt keys with google kms" + ) + use_azure_key_vault: Optional[bool] = Field( + None, description="load keys from azure key vault" + ) + master_key: Optional[str] = Field( + None, description="require a key for all calls to proxy" + ) + database_url: Optional[str] = Field( + None, + description="connect to a postgres db - needed for generating temporary keys + tracking spend / key", + ) + database_type: Optional[Literal["dynamo_db"]] = Field( + None, description="to use dynamodb instead of postgres db" + ) + database_args: Optional[DynamoDBArgs] = Field( + None, + description="custom args for instantiating dynamodb client - e.g. billing provision", + ) + otel: Optional[bool] = Field( + None, + description="[BETA] OpenTelemetry support - this might change, use with caution.", + ) + custom_auth: Optional[str] = Field( + None, + description="override user_api_key_auth with your own auth script - https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth", + ) + max_parallel_requests: Optional[int] = Field( + None, description="maximum parallel requests for each api key" + ) + infer_model_from_keys: Optional[bool] = Field( + None, + description="for `/models` endpoint, infers available model based on environment keys (e.g. OPENAI_API_KEY)", + ) + background_health_checks: Optional[bool] = Field( + None, description="run health checks in background" + ) + health_check_interval: int = Field( + 300, description="background health check interval in seconds" + ) + alerting: Optional[List] = Field( + None, + description="List of alerting integrations. Today, just slack - `alerting: ['slack']`", + ) + alerting_threshold: Optional[int] = Field( + None, + description="sends alerts if requests hang for 5min+", + ) + + +class ConfigYAML(LiteLLMBase): + """ + Documents all the fields supported by the config.yaml + """ + + environment_variables: Optional[dict] = Field( + None, + description="Object to pass in additional environment variables via POST request", + ) + model_list: Optional[List[ModelParams]] = Field( + None, + description="List of supported models on the server, with model-specific configs", + ) + litellm_settings: Optional[dict] = Field( + None, + description="litellm Module settings. See __init__.py for all, example litellm.drop_params=True, litellm.set_verbose=True, litellm.api_base, litellm.cache", + ) + general_settings: Optional[ConfigGeneralSettings] = None + + class Config: + protected_namespaces = () + + +class LiteLLM_VerificationToken(LiteLLMBase): + token: str + spend: float = 0.0 + expires: Union[str, None] + models: List[str] + aliases: Dict[str, str] = {} + config: Dict[str, str] = {} + user_id: Union[str, None] + max_parallel_requests: Union[int, None] + metadata: Dict[str, str] = {} + + +class LiteLLM_Config(LiteLLMBase): + param_name: str + param_value: Dict + + +class LiteLLM_UserTable(LiteLLMBase): + user_id: str + max_budget: Optional[float] + spend: float = 0.0 + user_email: Optional[str] + + @root_validator(pre=True) + def set_model_info(cls, values): + if values.get("spend") is None: + values.update({"spend": 0.0}) + return values diff --git a/litellm/proxy/admin_ui.py b/litellm/proxy/admin_ui.py new file mode 100644 index 0000000000000000000000000000000000000000..d50d8be9087707b37f374179ec96ada33b235415 --- /dev/null +++ b/litellm/proxy/admin_ui.py @@ -0,0 +1,202 @@ +def add_new_model(): + import streamlit as st + import json, requests, uuid + + model_name = st.text_input( + "Model Name - user-facing model name", placeholder="gpt-3.5-turbo" + ) + st.subheader("LiteLLM Params") + litellm_model_name = st.text_input( + "Model", placeholder="azure/gpt-35-turbo-us-east" + ) + litellm_api_key = st.text_input("API Key") + litellm_api_base = st.text_input( + "API Base", + placeholder="https://my-endpoint.openai.azure.com", + ) + litellm_api_version = st.text_input("API Version", placeholder="2023-07-01-preview") + litellm_params = json.loads( + st.text_area( + "Additional Litellm Params (JSON dictionary). [See all possible inputs](https://github.com/BerriAI/litellm/blob/3f15d7230fe8e7492c95a752963e7fbdcaf7bf98/litellm/main.py#L293)", + value={}, + ) + ) + st.subheader("Model Info") + mode_options = ("completion", "embedding", "image generation") + mode_selected = st.selectbox("Mode", mode_options) + model_info = json.loads( + st.text_area( + "Additional Model Info (JSON dictionary)", + value={}, + ) + ) + + if st.button("Submit"): + try: + model_info = { + "model_name": model_name, + "litellm_params": { + "model": litellm_model_name, + "api_key": litellm_api_key, + "api_base": litellm_api_base, + "api_version": litellm_api_version, + }, + "model_info": { + "id": str(uuid.uuid4()), + "mode": mode_selected, + }, + } + # Make the POST request to the specified URL + complete_url = "" + if st.session_state["api_url"].endswith("/"): + complete_url = f"{st.session_state['api_url']}model/new" + else: + complete_url = f"{st.session_state['api_url']}/model/new" + + headers = {"Authorization": f"Bearer {st.session_state['proxy_key']}"} + response = requests.post(complete_url, json=model_info, headers=headers) + + if response.status_code == 200: + st.success("Model added successfully!") + else: + st.error(f"Failed to add model. Status code: {response.status_code}") + + st.success("Form submitted successfully!") + except Exception as e: + raise e + + +def list_models(): + import streamlit as st + import requests + + # Check if the necessary configuration is available + if ( + st.session_state.get("api_url", None) is not None + and st.session_state.get("proxy_key", None) is not None + ): + # Make the GET request + try: + complete_url = "" + if isinstance(st.session_state["api_url"], str) and st.session_state[ + "api_url" + ].endswith("/"): + complete_url = f"{st.session_state['api_url']}models" + else: + complete_url = f"{st.session_state['api_url']}/models" + response = requests.get( + complete_url, + headers={"Authorization": f"Bearer {st.session_state['proxy_key']}"}, + ) + # Check if the request was successful + if response.status_code == 200: + models = response.json() + st.write(models) # or st.json(models) to pretty print the JSON + else: + st.error(f"Failed to get models. Status code: {response.status_code}") + except Exception as e: + st.error(f"An error occurred while requesting models: {e}") + else: + st.warning( + "Please configure the Proxy Endpoint and Proxy Key on the Proxy Setup page." + ) + + +def create_key(): + import streamlit as st + import json, requests, uuid + + if ( + st.session_state.get("api_url", None) is not None + and st.session_state.get("proxy_key", None) is not None + ): + duration = st.text_input("Duration - Can be in (h,m,s)", placeholder="1h") + + models = st.text_input("Models it can access (separated by comma)", value="") + models = models.split(",") if models else [] + + additional_params = json.loads( + st.text_area( + "Additional Key Params (JSON dictionary). [See all possible inputs](https://litellm-api.up.railway.app/#/key%20management/generate_key_fn_key_generate_post)", + value={}, + ) + ) + + if st.button("Submit"): + try: + key_post_body = { + "duration": duration, + "models": models, + **additional_params, + } + # Make the POST request to the specified URL + complete_url = "" + if st.session_state["api_url"].endswith("/"): + complete_url = f"{st.session_state['api_url']}key/generate" + else: + complete_url = f"{st.session_state['api_url']}/key/generate" + + headers = {"Authorization": f"Bearer {st.session_state['proxy_key']}"} + response = requests.post( + complete_url, json=key_post_body, headers=headers + ) + + if response.status_code == 200: + st.success(f"Key added successfully! - {response.json()}") + else: + st.error(f"Failed to add Key. Status code: {response.status_code}") + + st.success("Form submitted successfully!") + except Exception as e: + raise e + else: + st.warning( + "Please configure the Proxy Endpoint and Proxy Key on the Proxy Setup page." + ) + + +def streamlit_ui(): + import streamlit as st + + st.header("Admin Configuration") + + # Add a navigation sidebar + st.sidebar.title("Navigation") + page = st.sidebar.radio( + "Go to", ("Proxy Setup", "Add Models", "List Models", "Create Key") + ) + + # Initialize session state variables if not already present + if "api_url" not in st.session_state: + st.session_state["api_url"] = None + if "proxy_key" not in st.session_state: + st.session_state["proxy_key"] = None + + # Display different pages based on navigation selection + if page == "Proxy Setup": + # Use text inputs with intermediary variables + input_api_url = st.text_input( + "Proxy Endpoint", + value=st.session_state.get("api_url", ""), + placeholder="http://0.0.0.0:8000", + ) + input_proxy_key = st.text_input( + "Proxy Key", + value=st.session_state.get("proxy_key", ""), + placeholder="sk-...", + ) + # When the "Save" button is clicked, update the session state + if st.button("Save"): + st.session_state["api_url"] = input_api_url + st.session_state["proxy_key"] = input_proxy_key + st.success("Configuration saved!") + elif page == "Add Models": + add_new_model() + elif page == "List Models": + list_models() + elif page == "Create Key": + create_key() + + +if __name__ == "__main__": + streamlit_ui() diff --git a/litellm/proxy/db/base_client.py b/litellm/proxy/db/base_client.py new file mode 100644 index 0000000000000000000000000000000000000000..07f0ecdc47d756d71945add6295405acbd6cab70 --- /dev/null +++ b/litellm/proxy/db/base_client.py @@ -0,0 +1,53 @@ +from typing import Any, Literal, List + + +class CustomDB: + """ + Implements a base class that we expect any custom db implementation (e.g. DynamoDB) to follow + """ + + def __init__(self) -> None: + pass + + def get_data(self, key: str, table_name: Literal["user", "key", "config"]): + """ + Check if key valid + """ + pass + + def insert_data(self, value: Any, table_name: Literal["user", "key", "config"]): + """ + For new key / user logic + """ + pass + + def update_data( + self, key: str, value: Any, table_name: Literal["user", "key", "config"] + ): + """ + For cost tracking logic + """ + pass + + def delete_data( + self, keys: List[str], table_name: Literal["user", "key", "config"] + ): + """ + For /key/delete endpoint s + """ + + def connect( + self, + ): + """ + For connecting to db and creating / updating any tables + """ + pass + + def disconnect( + self, + ): + """ + For closing connection on server shutdown + """ + pass diff --git a/litellm/proxy/db/dynamo_db.py b/litellm/proxy/db/dynamo_db.py new file mode 100644 index 0000000000000000000000000000000000000000..c79fcd1061a49fb090daceccae7154ddf334f3b2 --- /dev/null +++ b/litellm/proxy/db/dynamo_db.py @@ -0,0 +1,277 @@ +from litellm.proxy.db.base_client import CustomDB +from litellm.proxy._types import ( + DynamoDBArgs, + LiteLLM_VerificationToken, + LiteLLM_Config, + LiteLLM_UserTable, +) +from litellm import get_secret +from typing import Any, List, Literal, Optional, Union +import json +from datetime import datetime + + +class DynamoDBWrapper(CustomDB): + from aiodynamo.credentials import Credentials, StaticCredentials + + credentials: Credentials + + def __init__(self, database_arguments: DynamoDBArgs): + from aiodynamo.client import Client + from aiodynamo.credentials import Credentials, StaticCredentials + from aiodynamo.http.httpx import HTTPX + from aiodynamo.models import ( + Throughput, + KeySchema, + KeySpec, + KeyType, + PayPerRequest, + ) + from yarl import URL + from aiodynamo.expressions import UpdateExpression, F, Value + from aiodynamo.models import ReturnValues + from aiodynamo.http.aiohttp import AIOHTTP + from aiohttp import ClientSession + + self.throughput_type = None + if database_arguments.billing_mode == "PAY_PER_REQUEST": + self.throughput_type = PayPerRequest() + elif database_arguments.billing_mode == "PROVISIONED_THROUGHPUT": + if ( + database_arguments.read_capacity_units is not None + and isinstance(database_arguments.read_capacity_units, int) + and database_arguments.write_capacity_units is not None + and isinstance(database_arguments.write_capacity_units, int) + ): + self.throughput_type = Throughput(read=database_arguments.read_capacity_units, write=database_arguments.write_capacity_units) # type: ignore + else: + raise Exception( + f"Invalid args passed in. Need to set both read_capacity_units and write_capacity_units. Args passed in - {database_arguments}" + ) + self.database_arguments = database_arguments + self.region_name = database_arguments.region_name + + async def connect(self): + """ + Connect to DB, and creating / updating any tables + """ + from aiodynamo.client import Client + from aiodynamo.credentials import Credentials, StaticCredentials + from aiodynamo.http.httpx import HTTPX + from aiodynamo.models import ( + Throughput, + KeySchema, + KeySpec, + KeyType, + PayPerRequest, + ) + from yarl import URL + from aiodynamo.expressions import UpdateExpression, F, Value + from aiodynamo.models import ReturnValues + from aiodynamo.http.aiohttp import AIOHTTP + from aiohttp import ClientSession + + async with ClientSession() as session: + client = Client(AIOHTTP(session), Credentials.auto(), self.region_name) + ## User + try: + error_occurred = False + table = client.table(self.database_arguments.user_table_name) + if not await table.exists(): + await table.create( + self.throughput_type, + KeySchema(hash_key=KeySpec("user_id", KeyType.string)), + ) + except Exception as e: + error_occurred = True + if error_occurred == True: + raise Exception( + f"Failed to create table - {self.database_arguments.user_table_name}.\nPlease create a new table called {self.database_arguments.user_table_name}\nAND set `hash_key` as 'user_id'" + ) + ## Token + try: + error_occurred = False + table = client.table(self.database_arguments.key_table_name) + if not await table.exists(): + await table.create( + self.throughput_type, + KeySchema(hash_key=KeySpec("token", KeyType.string)), + ) + except Exception as e: + error_occurred = True + if error_occurred == True: + raise Exception( + f"Failed to create table - {self.database_arguments.key_table_name}.\nPlease create a new table called {self.database_arguments.key_table_name}\nAND set `hash_key` as 'token'" + ) + ## Config + try: + error_occurred = False + table = client.table(self.database_arguments.config_table_name) + if not await table.exists(): + await table.create( + self.throughput_type, + KeySchema(hash_key=KeySpec("param_name", KeyType.string)), + ) + except Exception as e: + error_occurred = True + if error_occurred == True: + raise Exception( + f"Failed to create table - {self.database_arguments.config_table_name}.\nPlease create a new table called {self.database_arguments.config_table_name}\nAND set `hash_key` as 'param_name'" + ) + + async def insert_data( + self, value: Any, table_name: Literal["user", "key", "config"] + ): + from aiodynamo.client import Client + from aiodynamo.credentials import Credentials, StaticCredentials + from aiodynamo.http.httpx import HTTPX + from aiodynamo.models import ( + Throughput, + KeySchema, + KeySpec, + KeyType, + PayPerRequest, + ) + from yarl import URL + from aiodynamo.expressions import UpdateExpression, F, Value + from aiodynamo.models import ReturnValues + from aiodynamo.http.aiohttp import AIOHTTP + from aiohttp import ClientSession + + async with ClientSession() as session: + client = Client(AIOHTTP(session), Credentials.auto(), self.region_name) + table = None + if table_name == "user": + table = client.table(self.database_arguments.user_table_name) + elif table_name == "key": + table = client.table(self.database_arguments.key_table_name) + elif table_name == "config": + table = client.table(self.database_arguments.config_table_name) + + for k, v in value.items(): + if isinstance(v, datetime): + value[k] = v.isoformat() + + await table.put_item(item=value) + + async def get_data(self, key: str, table_name: Literal["user", "key", "config"]): + from aiodynamo.client import Client + from aiodynamo.credentials import Credentials, StaticCredentials + from aiodynamo.http.httpx import HTTPX + from aiodynamo.models import ( + Throughput, + KeySchema, + KeySpec, + KeyType, + PayPerRequest, + ) + from yarl import URL + from aiodynamo.expressions import UpdateExpression, F, Value + from aiodynamo.models import ReturnValues + from aiodynamo.http.aiohttp import AIOHTTP + from aiohttp import ClientSession + + async with ClientSession() as session: + client = Client(AIOHTTP(session), Credentials.auto(), self.region_name) + table = None + key_name = None + if table_name == "user": + table = client.table(self.database_arguments.user_table_name) + key_name = "user_id" + elif table_name == "key": + table = client.table(self.database_arguments.key_table_name) + key_name = "token" + elif table_name == "config": + table = client.table(self.database_arguments.config_table_name) + key_name = "param_name" + + response = await table.get_item({key_name: key}) + + new_response: Any = None + if table_name == "user": + new_response = LiteLLM_UserTable(**response) + elif table_name == "key": + new_response = {} + for k, v in response.items(): # handle json string + if ( + (k == "aliases" or k == "config" or k == "metadata") + and v is not None + and isinstance(v, str) + ): + new_response[k] = json.loads(v) + else: + new_response[k] = v + new_response = LiteLLM_VerificationToken(**new_response) + elif table_name == "config": + new_response = LiteLLM_Config(**response) + return new_response + + async def update_data( + self, key: str, value: dict, table_name: Literal["user", "key", "config"] + ): + from aiodynamo.client import Client + from aiodynamo.credentials import Credentials, StaticCredentials + from aiodynamo.http.httpx import HTTPX + from aiodynamo.models import ( + Throughput, + KeySchema, + KeySpec, + KeyType, + PayPerRequest, + ) + from yarl import URL + from aiodynamo.expressions import UpdateExpression, F, Value + from aiodynamo.models import ReturnValues + from aiodynamo.http.aiohttp import AIOHTTP + from aiohttp import ClientSession + + async with ClientSession() as session: + client = Client(AIOHTTP(session), Credentials.auto(), self.region_name) + table = None + key_name = None + try: + if table_name == "user": + table = client.table(self.database_arguments.user_table_name) + key_name = "user_id" + + elif table_name == "key": + table = client.table(self.database_arguments.key_table_name) + key_name = "token" + + elif table_name == "config": + table = client.table(self.database_arguments.config_table_name) + key_name = "param_name" + else: + raise Exception( + f"Invalid table name. Needs to be one of - {self.database_arguments.user_table_name}, {self.database_arguments.key_table_name}, {self.database_arguments.config_table_name}" + ) + except Exception as e: + raise Exception(f"Error connecting to table - {str(e)}") + + # Initialize an empty UpdateExpression + + actions: List = [] + for k, v in value.items(): + # Convert datetime object to ISO8601 string + if isinstance(v, datetime): + v = v.isoformat() + + # Accumulate updates + actions.append((F(k), Value(value=v))) + + update_expression = UpdateExpression(set_updates=actions) + # Perform the update in DynamoDB + result = await table.update_item( + key={key_name: key}, + update_expression=update_expression, + return_values=ReturnValues.none, + ) + return result + + async def delete_data( + self, keys: List[str], table_name: Literal["user", "key", "config"] + ): + """ + Not Implemented yet. + """ + return super().delete_data(keys, table_name) diff --git a/litellm/proxy/example_config_yaml/_health_check_test_config.yaml b/litellm/proxy/example_config_yaml/_health_check_test_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..56d532b1d51a30cbd48893c63e33826f20c95c02 --- /dev/null +++ b/litellm/proxy/example_config_yaml/_health_check_test_config.yaml @@ -0,0 +1,17 @@ +model_list: + - model_name: text-embedding-ada-002 + litellm_params: + model: azure/azure-embedding-model + api_base: "os.environ/AZURE_API_BASE" + api_key: "os.environ/AZURE_API_KEY" + api_version: "2023-07-01-preview" + model_info: + mode: embedding + base_model: text-embedding-ada-002 + +litellm_settings: + set_verbose: True + +general_settings: + background_health_checks: True # enable background health checks + health_check_interval: 300 # frequency of background health checks \ No newline at end of file diff --git a/litellm/proxy/example_config_yaml/aliases_config.yaml b/litellm/proxy/example_config_yaml/aliases_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..266f6cf22eef39e2ef0783211ead16de1c5d4576 --- /dev/null +++ b/litellm/proxy/example_config_yaml/aliases_config.yaml @@ -0,0 +1,30 @@ +model_list: + - model_name: text-davinci-003 + litellm_params: + model: ollama/zephyr + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 + temperature: 0.1 + max_tokens: 20 + + +# request to gpt-4, response from ollama/llama2 +# curl --location 'http://0.0.0.0:8000/chat/completions' \ +# --header 'Content-Type: application/json' \ +# --data ' { +# "model": "gpt-4", +# "messages": [ +# { +# "role": "user", +# "content": "what llm are you" +# } +# ], +# } +# ' +# + +# {"id":"chatcmpl-27c85cf0-ab09-4bcf-8cb1-0ee950520743","choices":[{"finish_reason":"stop","index":0,"message":{"content":" Hello! I'm just an AI, I don't have personal experiences or emotions like humans do. However, I can help you with any questions or tasks you may have! Is there something specific you'd like to know or discuss?","role":"assistant","_logprobs":null}}],"created":1700094955.373751,"model":"ollama/llama2","object":"chat.completion","system_fingerprint":null,"usage":{"prompt_tokens":12,"completion_tokens":47,"total_tokens":59},"_response_ms":8028.017999999999}% \ No newline at end of file diff --git a/litellm/proxy/example_config_yaml/azure_config.yaml b/litellm/proxy/example_config_yaml/azure_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd9ff9ac993ef7db74f0334490b48a634bce33ab --- /dev/null +++ b/litellm/proxy/example_config_yaml/azure_config.yaml @@ -0,0 +1,21 @@ +model_list: + - model_name: gpt-4-team1 + litellm_params: + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + api_key: os.environ/AZURE_API_KEY + tpm: 20_000 + timeout: 5 # 1 second timeout + stream_timeout: 0.5 # 0.5 second timeout for streaming requests + max_retries: 4 + - model_name: gpt-4-team2 + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY + api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ + tpm: 100_000 + timeout: 5 # 1 second timeout + stream_timeout: 0.5 # 0.5 second timeout for streaming requests + max_retries: 4 + diff --git a/litellm/proxy/example_config_yaml/custom_auth.py b/litellm/proxy/example_config_yaml/custom_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..416b66682a9e2bb5459b52ffc1a2d2db921da55f --- /dev/null +++ b/litellm/proxy/example_config_yaml/custom_auth.py @@ -0,0 +1,16 @@ +from litellm.proxy._types import UserAPIKeyAuth +from fastapi import Request +from dotenv import load_dotenv +import os + +load_dotenv() + + +async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: + try: + modified_master_key = f"{os.getenv('PROXY_MASTER_KEY')}-1234" + if api_key == modified_master_key: + return UserAPIKeyAuth(api_key=api_key) + raise Exception + except: + raise Exception diff --git a/litellm/proxy/example_config_yaml/custom_callbacks.py b/litellm/proxy/example_config_yaml/custom_callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..c3e1344d2caef706cdaa6bc7f3cca4f20ee158fb --- /dev/null +++ b/litellm/proxy/example_config_yaml/custom_callbacks.py @@ -0,0 +1,71 @@ +import sys, os, traceback + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +from litellm.integrations.custom_logger import CustomLogger +import litellm +import inspect + + +# This file includes the custom callbacks for LiteLLM Proxy +# Once defined, these can be passed in proxy_config.yaml +def print_verbose(print_statement): + if litellm.set_verbose: + print(print_statement) # noqa + + +class MyCustomHandler(CustomLogger): + def __init__(self): + blue_color_code = "\033[94m" + reset_color_code = "\033[0m" + print_verbose(f"{blue_color_code}Initialized LiteLLM custom logger") + try: + print_verbose(f"Logger Initialized with following methods:") + methods = [ + method + for method in dir(self) + if inspect.ismethod(getattr(self, method)) + ] + + # Pretty print_verbose the methods + for method in methods: + print_verbose(f" - {method}") + print_verbose(f"{reset_color_code}") + except: + pass + + def log_pre_api_call(self, model, messages, kwargs): + print_verbose(f"Pre-API Call") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print_verbose(f"Post-API Call") + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print_verbose(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print_verbose("On Success!") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print_verbose(f"On Async Success!") + response_cost = litellm.completion_cost(completion_response=response_obj) + assert response_cost > 0.0 + return + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + print_verbose(f"On Async Failure !") + except Exception as e: + print_verbose(f"Exception: {e}") + + +proxy_handler_instance = MyCustomHandler() + + +# need to set litellm.callbacks = [customHandler] # on the proxy + +# litellm.success_callback = [async_on_succes_logger] diff --git a/litellm/proxy/example_config_yaml/langfuse_config.yaml b/litellm/proxy/example_config_yaml/langfuse_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2a77b5adc464ca063cb6a5bda3eab6965b400cf --- /dev/null +++ b/litellm/proxy/example_config_yaml/langfuse_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + +litellm_settings: + drop_params: True + success_callback: ["langfuse"] # https://docs.litellm.ai/docs/observability/langfuse_integration + diff --git a/litellm/proxy/example_config_yaml/load_balancer.yaml b/litellm/proxy/example_config_yaml/load_balancer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..502b90ff92af6f381fece2096a3b352f9f0ada91 --- /dev/null +++ b/litellm/proxy/example_config_yaml/load_balancer.yaml @@ -0,0 +1,28 @@ +litellm_settings: + drop_params: True + +# Model-specific settings +model_list: # use the same model_name for using the litellm router. LiteLLM will use the router between gpt-3.5-turbo + - model_name: gpt-3.5-turbo # litellm will + litellm_params: + model: gpt-3.5-turbo + api_key: sk-uj6F + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: sk-Imn + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: openrouter/gpt-3.5-turbo + - model_name: mistral-7b-instruct + litellm_params: + model: mistralai/mistral-7b-instruct + +environment_variables: + REDIS_HOST: localhost + REDIS_PASSWORD: + REDIS_PORT: \ No newline at end of file diff --git a/litellm/proxy/example_config_yaml/opentelemetry_config.yaml b/litellm/proxy/example_config_yaml/opentelemetry_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92d3454d7d5fdb4e2a896aa47d01a19182043ee7 --- /dev/null +++ b/litellm/proxy/example_config_yaml/opentelemetry_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +general_settings: + otel: True # OpenTelemetry Logger this logs OTEL data to your collector diff --git a/litellm/proxy/example_config_yaml/simple_config.yaml b/litellm/proxy/example_config_yaml/simple_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14b39a12518e83600c24ee86a0cd5405ae48ee3c --- /dev/null +++ b/litellm/proxy/example_config_yaml/simple_config.yaml @@ -0,0 +1,4 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo \ No newline at end of file diff --git a/litellm/proxy/health_check.py b/litellm/proxy/health_check.py new file mode 100644 index 0000000000000000000000000000000000000000..b05bd4b6ab5fbadad4db50766c9e900581920ee1 --- /dev/null +++ b/litellm/proxy/health_check.py @@ -0,0 +1,93 @@ +# This file runs a health check for the LLM, used on litellm/proxy + +import asyncio +import random +from typing import Optional + +import litellm +import logging +from litellm._logging import print_verbose + + +logger = logging.getLogger(__name__) + + +ILLEGAL_DISPLAY_PARAMS = ["messages", "api_key", "prompt", "input"] + + +def _get_random_llm_message(): + """ + Get a random message from the LLM. + """ + messages = ["Hey how's it going?", "What's 1 + 1?"] + + return [{"role": "user", "content": random.choice(messages)}] + + +def _clean_litellm_params(litellm_params: dict): + """ + Clean the litellm params for display to users. + """ + return {k: v for k, v in litellm_params.items() if k not in ILLEGAL_DISPLAY_PARAMS} + + +async def _perform_health_check(model_list: list): + """ + Perform a health check for each model in the list. + """ + tasks = [] + for model in model_list: + litellm_params = model["litellm_params"] + model_info = model.get("model_info", {}) + litellm_params["messages"] = _get_random_llm_message() + mode = model_info.get("mode", None) + tasks.append( + litellm.ahealth_check( + litellm_params, + mode=mode, + prompt="test from litellm", + input=["test from litellm"], + ) + ) + + results = await asyncio.gather(*tasks) + + healthy_endpoints = [] + unhealthy_endpoints = [] + + for is_healthy, model in zip(results, model_list): + cleaned_litellm_params = _clean_litellm_params(model["litellm_params"]) + + if isinstance(is_healthy, dict) and "error" not in is_healthy: + healthy_endpoints.append({**cleaned_litellm_params, **is_healthy}) + elif isinstance(is_healthy, dict): + unhealthy_endpoints.append({**cleaned_litellm_params, **is_healthy}) + else: + unhealthy_endpoints.append(cleaned_litellm_params) + + return healthy_endpoints, unhealthy_endpoints + + +async def perform_health_check( + model_list: list, model: Optional[str] = None, cli_model: Optional[str] = None +): + """ + Perform a health check on the system. + + Returns: + (bool): True if the health check passes, False otherwise. + """ + if not model_list: + if cli_model: + model_list = [ + {"model_name": cli_model, "litellm_params": {"model": cli_model}} + ] + else: + return [], [] + + if model is not None: + model_list = [x for x in model_list if x["litellm_params"]["model"] == model] + + healthy_endpoints, unhealthy_endpoints = await _perform_health_check(model_list) + + return healthy_endpoints, unhealthy_endpoints diff --git a/litellm/proxy/hooks/__init__.py b/litellm/proxy/hooks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e690fd59145ce8900fd9ab8d8a996ee7d33834 --- /dev/null +++ b/litellm/proxy/hooks/__init__.py @@ -0,0 +1 @@ +from . import * diff --git a/litellm/proxy/hooks/max_budget_limiter.py b/litellm/proxy/hooks/max_budget_limiter.py new file mode 100644 index 0000000000000000000000000000000000000000..fa24c9f0f76d5b0a9df0a34b8bd4d0ee83d1538a --- /dev/null +++ b/litellm/proxy/hooks/max_budget_limiter.py @@ -0,0 +1,47 @@ +from typing import Optional +import litellm +from litellm.caching import DualCache +from litellm.proxy._types import UserAPIKeyAuth +from litellm.integrations.custom_logger import CustomLogger +from fastapi import HTTPException +import json, traceback + + +class MaxBudgetLimiter(CustomLogger): + # Class variables or attributes + def __init__(self): + pass + + def print_verbose(self, print_statement): + if litellm.set_verbose is True: + print(print_statement) # noqa + + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: str, + ): + try: + self.print_verbose(f"Inside Max Budget Limiter Pre-Call Hook") + cache_key = f"{user_api_key_dict.user_id}_user_api_key_user_id" + user_row = cache.get_cache(cache_key) + if user_row is None: # value not yet cached + return + max_budget = user_row["max_budget"] + curr_spend = user_row["spend"] + + if max_budget is None: + return + + if curr_spend is None: + return + + # CHECK IF REQUEST ALLOWED + if curr_spend >= max_budget: + raise HTTPException(status_code=429, detail="Max budget limit reached.") + except HTTPException as e: + raise e + except Exception as e: + traceback.print_exc() diff --git a/litellm/proxy/hooks/parallel_request_limiter.py b/litellm/proxy/hooks/parallel_request_limiter.py new file mode 100644 index 0000000000000000000000000000000000000000..38247cbe07cdeced3dc1a7574a16b6621963de70 --- /dev/null +++ b/litellm/proxy/hooks/parallel_request_limiter.py @@ -0,0 +1,108 @@ +from typing import Optional +import litellm +from litellm.caching import DualCache +from litellm.proxy._types import UserAPIKeyAuth +from litellm.integrations.custom_logger import CustomLogger +from fastapi import HTTPException + + +class MaxParallelRequestsHandler(CustomLogger): + user_api_key_cache = None + + # Class variables or attributes + def __init__(self): + pass + + def print_verbose(self, print_statement): + if litellm.set_verbose is True: + print(print_statement) # noqa + + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: str, + ): + self.print_verbose(f"Inside Max Parallel Request Pre-Call Hook") + api_key = user_api_key_dict.api_key + max_parallel_requests = user_api_key_dict.max_parallel_requests + + if api_key is None: + return + + if max_parallel_requests is None: + return + + self.user_api_key_cache = cache # save the api key cache for updating the value + + # CHECK IF REQUEST ALLOWED + request_count_api_key = f"{api_key}_request_count" + current = cache.get_cache(key=request_count_api_key) + self.print_verbose(f"current: {current}") + if current is None: + cache.set_cache(request_count_api_key, 1) + elif int(current) < max_parallel_requests: + # Increase count for this token + cache.set_cache(request_count_api_key, int(current) + 1) + else: + raise HTTPException( + status_code=429, detail="Max parallel request limit reached." + ) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.print_verbose(f"INSIDE ASYNC SUCCESS LOGGING") + user_api_key = kwargs["litellm_params"]["metadata"]["user_api_key"] + if user_api_key is None: + return + + if self.user_api_key_cache is None: + return + + request_count_api_key = f"{user_api_key}_request_count" + # check if it has collected an entire stream response + self.print_verbose( + f"'complete_streaming_response' is in kwargs: {'complete_streaming_response' in kwargs}" + ) + if "complete_streaming_response" in kwargs or kwargs["stream"] != True: + # Decrease count for this token + current = ( + self.user_api_key_cache.get_cache(key=request_count_api_key) or 1 + ) + new_val = current - 1 + self.print_verbose(f"updated_value in success call: {new_val}") + self.user_api_key_cache.set_cache(request_count_api_key, new_val) + except Exception as e: + self.print_verbose(e) # noqa + + async def async_log_failure_call( + self, user_api_key_dict: UserAPIKeyAuth, original_exception: Exception + ): + try: + self.print_verbose(f"Inside Max Parallel Request Failure Hook") + api_key = user_api_key_dict.api_key + if api_key is None: + return + + if self.user_api_key_cache is None: + return + + ## decrement call count if call failed + if ( + hasattr(original_exception, "status_code") + and original_exception.status_code == 429 + and "Max parallel request limit reached" in str(original_exception) + ): + pass # ignore failed calls due to max limit being reached + else: + request_count_api_key = f"{api_key}_request_count" + # Decrease count for this token + current = ( + self.user_api_key_cache.get_cache(key=request_count_api_key) or 1 + ) + new_val = current - 1 + self.print_verbose(f"updated_value in failure call: {new_val}") + self.user_api_key_cache.set_cache(request_count_api_key, new_val) + except Exception as e: + self.print_verbose(f"An exception occurred - {str(e)}") # noqa diff --git a/litellm/proxy/lambda.py b/litellm/proxy/lambda.py new file mode 100644 index 0000000000000000000000000000000000000000..6b278c4118865c80661cfd31c6f01563f86d2cbb --- /dev/null +++ b/litellm/proxy/lambda.py @@ -0,0 +1,4 @@ +from mangum import Mangum +from litellm.proxy.proxy_server import app + +handler = Mangum(app, lifespan="on") diff --git a/litellm/proxy/openapi.json b/litellm/proxy/openapi.json new file mode 100644 index 0000000000000000000000000000000000000000..95517182667f6032704045cb8b353ca010434479 --- /dev/null +++ b/litellm/proxy/openapi.json @@ -0,0 +1,237 @@ +{ + "openapi": "3.0.0", + "info": { + "version": "1.0.0", + "title": "LiteLLM API", + "description": "API for LiteLLM" + }, + "paths": { + "/chat/completions": { + "post": { + "summary": "Create chat completion for 100+ LLM APIs", + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": { + "model": { + "type": "string", + "description": "ID of the model to use" + }, + "messages": { + "type": "array", + "items": { + "type": "object", + "properties": { + "role": { + "type": "string", + "description": "The role of the message's author" + }, + "content": { + "type": "string", + "description": "The contents of the message" + }, + "name": { + "type": "string", + "description": "The name of the author of the message" + }, + "function_call": { + "type": "object", + "description": "The name and arguments of a function that should be called" + } + } + } + }, + "functions": { + "type": "array", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "The name of the function to be called" + }, + "description": { + "type": "string", + "description": "A description explaining what the function does" + }, + "parameters": { + "type": "object", + "description": "The parameters that the function accepts" + }, + "function_call": { + "type": "string", + "description": "Controls how the model responds to function calls" + } + } + } + }, + "temperature": { + "type": "number", + "description": "The sampling temperature to be used" + }, + "top_p": { + "type": "number", + "description": "An alternative to sampling with temperature" + }, + "n": { + "type": "integer", + "description": "The number of chat completion choices to generate for each input message" + }, + "stream": { + "type": "boolean", + "description": "If set to true, it sends partial message deltas" + }, + "stop": { + "type": "array", + "items": { + "type": "string" + }, + "description": "Up to 4 sequences where the API will stop generating further tokens" + }, + "max_tokens": { + "type": "integer", + "description": "The maximum number of tokens to generate in the chat completion" + }, + "presence_penalty": { + "type": "number", + "description": "It is used to penalize new tokens based on their existence in the text so far" + }, + "frequency_penalty": { + "type": "number", + "description": "It is used to penalize new tokens based on their frequency in the text so far" + }, + "logit_bias": { + "type": "object", + "description": "Used to modify the probability of specific tokens appearing in the completion" + }, + "user": { + "type": "string", + "description": "A unique identifier representing your end-user" + } + } + } + } + } + }, + "responses": { + "200": { + "description": "Successful operation", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": { + "choices": { + "type": "array", + "items": { + "type": "object", + "properties": { + "finish_reason": { + "type": "string" + }, + "index": { + "type": "integer" + }, + "message": { + "type": "object", + "properties": { + "role": { + "type": "string" + }, + "content": { + "type": "string" + } + } + } + } + } + }, + "created": { + "type": "string" + }, + "model": { + "type": "string" + }, + "usage": { + "type": "object", + "properties": { + "prompt_tokens": { + "type": "integer" + }, + "completion_tokens": { + "type": "integer" + }, + "total_tokens": { + "type": "integer" + } + } + } + } + } + } + } + }, + "500": { + "description": "Server error" + } + } + } +}, + + "/completions": { + "post": { + "summary": "Create completion", + "responses": { + "200": { + "description": "Successful operation" + }, + "500": { + "description": "Server error" + } + } + } + }, + "/models": { + "get": { + "summary": "Get models", + "responses": { + "200": { + "description": "Successful operation" + } + } + } + }, + + "/ollama_logs": { + "get": { + "summary": "Retrieve server logs for ollama models", + "responses": { + "200": { + "description": "Successful operation", + "content": { + "application/octet-stream": { + "schema": { + "type": "string", + "format": "binary" + } + } + } + } + } + } + }, + "/": { + "get": { + "summary": "Home", + "responses": { + "200": { + "description": "Successful operation" + } + } + } + } + } +} diff --git a/litellm/proxy/otel_config.yaml b/litellm/proxy/otel_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5b75697b7bd1e83e8f6d566f4de150b54a822d77 --- /dev/null +++ b/litellm/proxy/otel_config.yaml @@ -0,0 +1,30 @@ +receivers: + otlp: + protocols: + grpc: + endpoint: 0.0.0.0:4317 + +processors: + batch: + timeout: 1s + send_batch_size: 1024 + +exporters: + logging: + loglevel: debug + otlphttp/elastic: + endpoint: "https://3f51c0abae7743e2800df44a8a5f0997.apm.us-central1.gcp.cloud.es.io:443" + headers: + Authorization: "Bearer " + +service: + pipelines: + metrics: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + traces: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + logs: + receivers: [otlp] + exporters: [logging,otlphttp/elastic] \ No newline at end of file diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py new file mode 100644 index 0000000000000000000000000000000000000000..7cec8695237b0a5ebbd2048f655844d53166de77 --- /dev/null +++ b/litellm/proxy/proxy_cli.py @@ -0,0 +1,489 @@ +import click +import subprocess, traceback, json +import os, sys +import random +from datetime import datetime +import importlib +from dotenv import load_dotenv + +sys.path.append(os.getcwd()) + +config_filename = "litellm.secrets" + +load_dotenv() +from importlib import resources +import shutil + +telemetry = None + + +def run_ollama_serve(): + try: + command = ["ollama", "serve"] + + with open(os.devnull, "w") as devnull: + process = subprocess.Popen(command, stdout=devnull, stderr=devnull) + except Exception as e: + print( + f""" + LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve` + """ + ) # noqa + + +def is_port_in_use(port): + import socket + + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(("localhost", port)) == 0 + + +@click.command() +@click.option("--host", default="0.0.0.0", help="Host for the server to listen on.") +@click.option("--port", default=8000, help="Port to bind the server to.") +@click.option("--num_workers", default=1, help="Number of gunicorn workers to spin up") +@click.option("--api_base", default=None, help="API base URL.") +@click.option( + "--api_version", + default="2023-07-01-preview", + help="For azure - pass in the api version.", +) +@click.option( + "--model", "-m", default=None, help="The model name to pass to litellm expects" +) +@click.option( + "--alias", + default=None, + help='The alias for the model - use this to give a litellm model name (e.g. "huggingface/codellama/CodeLlama-7b-Instruct-hf") a more user-friendly name ("codellama")', +) +@click.option( + "--add_key", default=None, help="The model name to pass to litellm expects" +) +@click.option("--headers", default=None, help="headers for the API call") +@click.option("--save", is_flag=True, type=bool, help="Save the model-specific config") +@click.option( + "--debug", default=False, is_flag=True, type=bool, help="To debug the input" +) +@click.option( + "--detailed_debug", + default=False, + is_flag=True, + type=bool, + help="To view detailed debug logs", +) +@click.option( + "--use_queue", + default=False, + is_flag=True, + type=bool, + help="To use celery workers for async endpoints", +) +@click.option( + "--temperature", default=None, type=float, help="Set temperature for the model" +) +@click.option( + "--max_tokens", default=None, type=int, help="Set max tokens for the model" +) +@click.option( + "--request_timeout", + default=600, + type=int, + help="Set timeout in seconds for completion calls", +) +@click.option("--drop_params", is_flag=True, help="Drop any unmapped params") +@click.option( + "--add_function_to_prompt", + is_flag=True, + help="If function passed but unsupported, pass it as prompt", +) +@click.option( + "--config", + "-c", + default=None, + help="Path to the proxy configuration file (e.g. config.yaml). Usage `litellm --config config.yaml`", +) +@click.option( + "--max_budget", + default=None, + type=float, + help="Set max budget for API calls - works for hosted models like OpenAI, TogetherAI, Anthropic, etc.`", +) +@click.option( + "--telemetry", + default=True, + type=bool, + help="Helps us know if people are using this feature. Turn this off by doing `--telemetry False`", +) +@click.option( + "--version", + "-v", + default=False, + is_flag=True, + type=bool, + help="Print LiteLLM version", +) +@click.option( + "--health", + flag_value=True, + help="Make a chat/completions request to all llms in config.yaml", +) +@click.option( + "--test", + flag_value=True, + help="proxy chat completions url to make a test request to", +) +@click.option( + "--test_async", + default=False, + is_flag=True, + help="Calls async endpoints /queue/requests and /queue/response", +) +@click.option( + "--num_requests", + default=10, + type=int, + help="Number of requests to hit async endpoint with", +) +@click.option("--local", is_flag=True, default=False, help="for local debugging") +def run_server( + host, + port, + api_base, + api_version, + model, + alias, + add_key, + headers, + save, + debug, + detailed_debug, + temperature, + max_tokens, + request_timeout, + drop_params, + add_function_to_prompt, + config, + max_budget, + telemetry, + test, + local, + num_workers, + test_async, + num_requests, + use_queue, + health, + version, +): + global feature_telemetry + args = locals() + if local: + from proxy_server import app, save_worker_config, usage_telemetry + else: + try: + from .proxy_server import app, save_worker_config, usage_telemetry + except ImportError as e: + if "litellm[proxy]" in str(e): + # user is missing a proxy dependency, ask them to pip install litellm[proxy] + raise e + else: + # this is just a local/relative import error, user git cloned litellm + from proxy_server import app, save_worker_config, usage_telemetry + feature_telemetry = usage_telemetry + if version == True: + pkg_version = importlib.metadata.version("litellm") + click.echo(f"\nLiteLLM: Current Version = {pkg_version}\n") + return + if model and "ollama" in model and api_base is None: + run_ollama_serve() + if test_async is True: + import requests, concurrent, time + + api_base = f"http://{host}:{port}" + + def _make_openai_completion(): + data = { + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "Write a short poem about the moon"} + ], + } + + response = requests.post("http://0.0.0.0:8000/queue/request", json=data) + + response = response.json() + + while True: + try: + url = response["url"] + polling_url = f"{api_base}{url}" + polling_response = requests.get(polling_url) + polling_response = polling_response.json() + print("\n RESPONSE FROM POLLING JOB", polling_response) + status = polling_response["status"] + if status == "finished": + llm_response = polling_response["result"] + break + print( + f"POLLING JOB{polling_url}\nSTATUS: {status}, \n Response {polling_response}" + ) # noqa + time.sleep(0.5) + except Exception as e: + print("got exception in polling", e) + break + + # Number of concurrent calls (you can adjust this) + concurrent_calls = num_requests + + # List to store the futures of concurrent calls + futures = [] + start_time = time.time() + # Make concurrent calls + with concurrent.futures.ThreadPoolExecutor( + max_workers=concurrent_calls + ) as executor: + for _ in range(concurrent_calls): + futures.append(executor.submit(_make_openai_completion)) + + # Wait for all futures to complete + concurrent.futures.wait(futures) + + # Summarize the results + successful_calls = 0 + failed_calls = 0 + + for future in futures: + if future.done(): + if future.result() is not None: + successful_calls += 1 + else: + failed_calls += 1 + end_time = time.time() + print(f"Elapsed Time: {end_time-start_time}") + print(f"Load test Summary:") + print(f"Total Requests: {concurrent_calls}") + print(f"Successful Calls: {successful_calls}") + print(f"Failed Calls: {failed_calls}") + return + if health != False: + import requests + + print("\nLiteLLM: Health Testing models in config") + response = requests.get(url=f"http://{host}:{port}/health") + print(json.dumps(response.json(), indent=4)) + return + if test != False: + request_model = model or "gpt-3.5-turbo" + click.echo( + f"\nLiteLLM: Making a test ChatCompletions request to your proxy. Model={request_model}" + ) + import openai + + if test == True: # flag value set + api_base = f"http://{host}:{port}" + else: + api_base = test + client = openai.OpenAI(api_key="My API Key", base_url=api_base) + + response = client.chat.completions.create( + model=request_model, + messages=[ + { + "role": "user", + "content": "this is a test request, write a short poem", + } + ], + max_tokens=256, + ) + click.echo(f"\nLiteLLM: response from proxy {response}") + + print( + f"\n LiteLLM: Making a test ChatCompletions + streaming request to proxy. Model={request_model}" + ) + + response = client.chat.completions.create( + model=request_model, + messages=[ + { + "role": "user", + "content": "this is a test request, write a short poem", + } + ], + stream=True, + ) + for chunk in response: + click.echo(f"LiteLLM: streaming response from proxy {chunk}") + print("\n making completion request to proxy") + response = client.completions.create( + model=request_model, prompt="this is a test request, write a short poem" + ) + print(response) + + return + else: + if headers: + headers = json.loads(headers) + save_worker_config( + model=model, + alias=alias, + api_base=api_base, + api_version=api_version, + debug=debug, + detailed_debug=detailed_debug, + temperature=temperature, + max_tokens=max_tokens, + request_timeout=request_timeout, + max_budget=max_budget, + telemetry=telemetry, + drop_params=drop_params, + add_function_to_prompt=add_function_to_prompt, + headers=headers, + save=save, + config=config, + use_queue=use_queue, + ) + try: + import uvicorn + + if os.name == "nt": + pass + else: + import gunicorn.app.base + except: + raise ImportError( + "Uvicorn, gunicorn needs to be imported. Run - `pip 'litellm[proxy]'`" + ) + + if config is not None: + """ + Allow user to pass in db url via config + + read from there and save it to os.env['DATABASE_URL'] + """ + try: + import yaml + except: + raise ImportError( + "yaml needs to be imported. Run - `pip install 'litellm[proxy]'`" + ) + + if os.path.exists(config): + with open(config, "r") as config_file: + config = yaml.safe_load(config_file) + general_settings = config.get("general_settings", {}) + database_url = general_settings.get("database_url", None) + if database_url and database_url.startswith("os.environ/"): + original_dir = os.getcwd() + # set the working directory to where this script is + sys.path.insert( + 0, os.path.abspath("../..") + ) # Adds the parent directory to the system path - for litellm local dev + import litellm + + database_url = litellm.get_secret(database_url) + os.chdir(original_dir) + if database_url is not None and isinstance(database_url, str): + os.environ["DATABASE_URL"] = database_url + + if os.getenv("DATABASE_URL", None) is not None: + try: + subprocess.run(["prisma"], capture_output=True) + is_prisma_runnable = True + except FileNotFoundError: + is_prisma_runnable = False + + if is_prisma_runnable: + # run prisma db push, before starting server + # Save the current working directory + original_dir = os.getcwd() + # set the working directory to where this script is + abspath = os.path.abspath(__file__) + dname = os.path.dirname(abspath) + os.chdir(dname) + try: + subprocess.run( + ["prisma", "db", "push", "--accept-data-loss"] + ) # this looks like a weird edge case when prisma just wont start on render. we need to have the --accept-data-loss + finally: + os.chdir(original_dir) + else: + print( + f"Unable to connect to DB. DATABASE_URL found in environment, but prisma package not found." + ) + if port == 8000 and is_port_in_use(port): + port = random.randint(1024, 49152) + from litellm.proxy.proxy_server import app + + if os.name == "nt": + uvicorn.run(app, host=host, port=port) # run uvicorn + else: + import gunicorn.app.base + + # Gunicorn Application Class + class StandaloneApplication(gunicorn.app.base.BaseApplication): + def __init__(self, app, options=None): + self.options = options or {} # gunicorn options + self.application = app # FastAPI app + super().__init__() + + _endpoint_str = ( + f"curl --location 'http://0.0.0.0:{port}/chat/completions' \\" + ) + curl_command = ( + _endpoint_str + + """ + --header 'Content-Type: application/json' \\ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + }' + \n + """ + ) + print() # noqa + print( # noqa + f'\033[1;34mLiteLLM: Test your local proxy with: "litellm --test" This runs an openai.ChatCompletion request to your proxy [In a new terminal tab]\033[0m\n' + ) + print( # noqa + f"\033[1;34mLiteLLM: Curl Command Test for your local proxy\n {curl_command} \033[0m\n" + ) + print( + "\033[1;34mDocs: https://docs.litellm.ai/docs/simple_proxy\033[0m\n" + ) # noqa + print( # noqa + f"\033[1;34mSee all Router/Swagger docs on http://0.0.0.0:{port} \033[0m\n" + ) # noqa + + def load_config(self): + # note: This Loads the gunicorn config - has nothing to do with LiteLLM Proxy config + config = { + key: value + for key, value in self.options.items() + if key in self.cfg.settings and value is not None + } + for key, value in config.items(): + self.cfg.set(key.lower(), value) + + def load(self): + # gunicorn app function + return self.application + + gunicorn_options = { + "bind": f"{host}:{port}", + "workers": num_workers, # default is 1 + "worker_class": "uvicorn.workers.UvicornWorker", + "preload": True, # Add the preload flag, + "accesslog": "-", # Log to stdout + "access_log_format": '%(h)s %(l)s %(u)s %(t)s "%(r)s" %(s)s %(b)s', + } + StandaloneApplication( + app=app, options=gunicorn_options + ).run() # Run gunicorn + + +if __name__ == "__main__": + run_server() diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..81374c8ee248f4e4a0fa8e7f7e685ea1de2540bf --- /dev/null +++ b/litellm/proxy/proxy_config.yaml @@ -0,0 +1,54 @@ +model_list: + - model_name: azure-gpt-3.5 + litellm_params: + model: azure/chatgpt-v-2 + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + model_info: + mode: chat + input_cost_per_token: 0.0.00006 + output_cost_per_token: 0.00003 + max_tokens: 4096 + base_model: gpt-3.5-turbo + - model_name: BEDROCK_GROUP + litellm_params: + model: bedrock/cohere.command-text-v14 + - model_name: openai-gpt-3.5 + litellm_params: + model: gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + model_info: + mode: chat + - model_name: azure-cloudflare + litellm_params: + model: azure/chatgpt-v-2 + api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1 + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + - model_name: azure-embedding-model + litellm_params: + model: azure/azure-embedding-model + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + model_info: + mode: embedding + base_model: text-embedding-ada-002 + - model_name: text-embedding-ada-002 + litellm_params: + model: text-embedding-ada-002 + api_key: os.environ/OPENAI_API_KEY + model_info: + mode: embedding +litellm_settings: + fallbacks: [{"openai-gpt-3.5": ["azure-gpt-3.5"]}] + # cache: True + # setting callback class + # callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance] + +# general_settings: + +environment_variables: + # otel: True # OpenTelemetry Logger + # master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py new file mode 100644 index 0000000000000000000000000000000000000000..565abf799fa49ebab54b1059214f437a41ffa21d --- /dev/null +++ b/litellm/proxy/proxy_server.py @@ -0,0 +1,2640 @@ +import sys, os, platform, time, copy, re, asyncio, inspect +import threading, ast +import shutil, random, traceback, requests +from datetime import datetime, timedelta, timezone +from typing import Optional, List +import secrets, subprocess +import hashlib, uuid +import warnings +import importlib + +messages: list = [] +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path - for litellm local dev + +try: + import fastapi + import backoff + import yaml + import orjson + import logging +except ImportError as e: + raise ImportError(f"Missing dependency {e}. Run `pip install 'litellm[proxy]'`") + +import random + +list_of_messages = [ + "'The thing I wish you improved is...'", + "'A feature I really want is...'", + "'The worst thing about this product is...'", + "'This product would be better if...'", + "'I don't like how this works...'", + "'It would help me if you could add...'", + "'This feature doesn't meet my needs because...'", + "'I get frustrated when the product...'", +] + + +def generate_feedback_box(): + box_width = 60 + + # Select a random message + message = random.choice(list_of_messages) + + print() # noqa + print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m") # noqa + print("\033[1;37m" + "#" + " " * box_width + "#\033[0m") # noqa + print("\033[1;37m" + "# {:^59} #\033[0m".format(message)) # noqa + print( # noqa + "\033[1;37m" + + "# {:^59} #\033[0m".format("https://github.com/BerriAI/litellm/issues/new") + ) # noqa + print("\033[1;37m" + "#" + " " * box_width + "#\033[0m") # noqa + print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m") # noqa + print() # noqa + print(" Thank you for using LiteLLM! - Krrish & Ishaan") # noqa + print() # noqa + print() # noqa + print() # noqa + print( # noqa + "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" + ) # noqa + print() # noqa + print() # noqa + + +import litellm +from litellm.proxy.utils import ( + PrismaClient, + DBClient, + get_instance_fn, + ProxyLogging, + _cache_user_row, + send_email, +) +from litellm.proxy.secret_managers.google_kms import load_google_kms +import pydantic +from litellm.proxy._types import * +from litellm.caching import DualCache +from litellm.proxy.health_check import perform_health_check +from litellm._logging import verbose_router_logger, verbose_proxy_logger + +litellm.suppress_debug_info = True +from fastapi import ( + FastAPI, + Request, + HTTPException, + status, + Depends, + BackgroundTasks, + Header, + Response, +) +from fastapi.routing import APIRouter +from fastapi.security import OAuth2PasswordBearer +from fastapi.encoders import jsonable_encoder +from fastapi.responses import StreamingResponse, FileResponse, ORJSONResponse +from fastapi.middleware.cors import CORSMiddleware +from fastapi.security.api_key import APIKeyHeader +import json +import logging +from typing import Union + +app = FastAPI( + docs_url="/", + title="LiteLLM API", + description="Proxy Server to call 100+ LLMs in the OpenAI format\n\nAdmin Panel on [https://dashboard.litellm.ai/admin](https://dashboard.litellm.ai/admin)", +) +router = APIRouter() +origins = ["*"] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +from typing import Dict + +api_key_header = APIKeyHeader(name="Authorization", auto_error=False) +user_api_base = None +user_model = None +user_debug = False +user_max_tokens = None +user_request_timeout = None +user_temperature = None +user_telemetry = True +user_config = None +user_headers = None +user_config_file_path = f"config_{int(time.time())}.yaml" +local_logging = True # writes logs to a local api_log.json file for debugging +experimental = False +#### GLOBAL VARIABLES #### +llm_router: Optional[litellm.Router] = None +llm_model_list: Optional[list] = None +general_settings: dict = {} +log_file = "api_log.json" +worker_config = None +master_key = None +otel_logging = False +prisma_client: Optional[PrismaClient] = None +custom_db_client: Optional[DBClient] = None +user_api_key_cache = DualCache() +user_custom_auth = None +use_background_health_checks = None +use_queue = False +health_check_interval = None +health_check_results = {} +queue: List = [] +### INITIALIZE GLOBAL LOGGING OBJECT ### +proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache) +### REDIS QUEUE ### +async_result = None +celery_app_conn = None +celery_fn = None # Redis Queue for handling requests +### logger ### + + +def usage_telemetry( + feature: str, +): # helps us know if people are using this feature. Set `litellm --telemetry False` to your cli call to turn this off + if user_telemetry: + data = {"feature": feature} # "local_proxy_server" + threading.Thread( + target=litellm.utils.litellm_telemetry, args=(data,), daemon=True + ).start() + + +def _get_bearer_token(api_key: str): + assert api_key.startswith("Bearer ") # ensure Bearer token passed in + api_key = api_key.replace("Bearer ", "") # extract the token + return api_key + + +def _get_pydantic_json_dict(pydantic_obj: BaseModel) -> dict: + try: + return pydantic_obj.model_dump() # type: ignore + except: + # if using pydantic v1 + return pydantic_obj.dict() + + +async def user_api_key_auth( + request: Request, api_key: str = fastapi.Security(api_key_header) +) -> UserAPIKeyAuth: + global master_key, prisma_client, llm_model_list, user_custom_auth, custom_db_client + try: + if isinstance(api_key, str): + api_key = _get_bearer_token(api_key=api_key) + ### USER-DEFINED AUTH FUNCTION ### + if user_custom_auth is not None: + response = await user_custom_auth(request=request, api_key=api_key) + return UserAPIKeyAuth.model_validate(response) + ### LITELLM-DEFINED AUTH FUNCTION ### + if master_key is None: + if isinstance(api_key, str): + return UserAPIKeyAuth(api_key=api_key) + else: + return UserAPIKeyAuth() + + route: str = request.url.path + if route == "/user/auth": + if general_settings.get("allow_user_auth", False) == True: + return UserAPIKeyAuth() + else: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="'allow_user_auth' not set or set to False", + ) + + if api_key is None: # only require api key if master key is set + raise Exception(f"No api key passed in.") + + # note: never string compare api keys, this is vulenerable to a time attack. Use secrets.compare_digest instead + is_master_key_valid = secrets.compare_digest(api_key, master_key) + if is_master_key_valid: + return UserAPIKeyAuth(api_key=master_key) + + if route.startswith("/config/") and not is_master_key_valid: + raise Exception(f"Only admin can modify config") + + if ( + (route.startswith("/key/") or route.startswith("/user/")) + or route.startswith("/model/") + and not is_master_key_valid + and general_settings.get("allow_user_auth", False) != True + ): + raise Exception( + f"If master key is set, only master key can be used to generate, delete, update or get info for new keys/users" + ) + + if ( + prisma_client is None and custom_db_client is None + ): # if both master key + user key submitted, and user key != master key, and no db connected, raise an error + raise Exception("No connected db.") + + ## check for cache hit (In-Memory Cache) + valid_token = user_api_key_cache.get_cache(key=api_key) + verbose_proxy_logger.debug(f"valid_token from cache: {valid_token}") + if valid_token is None: + ## check db + verbose_proxy_logger.debug(f"api key: {api_key}") + if prisma_client is not None: + valid_token = await prisma_client.get_data( + token=api_key, + ) + + expires = datetime.utcnow().replace(tzinfo=timezone.utc) + elif custom_db_client is not None: + valid_token = await custom_db_client.get_data( + key=api_key, table_name="key" + ) + # Token exists, now check expiration. + if valid_token.expires is not None: + expiry_time = datetime.fromisoformat(valid_token.expires) + if expiry_time >= datetime.utcnow(): + # Token exists and is not expired. + return response + else: + # Token exists but is expired. + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="expired user key", + ) + verbose_proxy_logger.debug(f"valid token from prisma: {valid_token}") + user_api_key_cache.set_cache(key=api_key, value=valid_token, ttl=60) + elif valid_token is not None: + verbose_proxy_logger.debug(f"API Key Cache Hit!") + if valid_token: + litellm.model_alias_map = valid_token.aliases + config = valid_token.config + if config != {}: + model_list = config.get("model_list", []) + llm_model_list = model_list + verbose_proxy_logger.debug( + f"\n new llm router model list {llm_model_list}" + ) + if ( + len(valid_token.models) == 0 + ): # assume an empty model list means all models are allowed to be called + pass + else: + try: + data = await request.json() + except json.JSONDecodeError: + data = {} # Provide a default value, such as an empty dictionary + model = data.get("model", None) + if model in litellm.model_alias_map: + model = litellm.model_alias_map[model] + if model and model not in valid_token.models: + raise Exception(f"Token not allowed to access model") + api_key = valid_token.token + valid_token_dict = _get_pydantic_json_dict(valid_token) + valid_token_dict.pop("token", None) + """ + asyncio create task to update the user api key cache with the user db table as well + + This makes the user row data accessible to pre-api call hooks. + """ + if prisma_client is not None: + asyncio.create_task( + _cache_user_row( + user_id=valid_token.user_id, + cache=user_api_key_cache, + db=prisma_client, + ) + ) + elif custom_db_client is not None: + asyncio.create_task( + _cache_user_row( + user_id=valid_token.user_id, + cache=user_api_key_cache, + db=custom_db_client, + ) + ) + return UserAPIKeyAuth(api_key=api_key, **valid_token_dict) + else: + raise Exception(f"Invalid token") + except Exception as e: + # verbose_proxy_logger.debug(f"An exception occurred - {traceback.format_exc()}") + traceback.print_exc() + if isinstance(e, HTTPException): + raise e + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="invalid user key", + ) + + +def prisma_setup(database_url: Optional[str]): + global prisma_client, proxy_logging_obj, user_api_key_cache + + if database_url is not None: + try: + prisma_client = PrismaClient( + database_url=database_url, proxy_logging_obj=proxy_logging_obj + ) + except Exception as e: + raise e + + +def load_from_azure_key_vault(use_azure_key_vault: bool = False): + if use_azure_key_vault is False: + return + + try: + from azure.keyvault.secrets import SecretClient + from azure.identity import ClientSecretCredential + + # Set your Azure Key Vault URI + KVUri = os.getenv("AZURE_KEY_VAULT_URI", None) + + # Set your Azure AD application/client ID, client secret, and tenant ID + client_id = os.getenv("AZURE_CLIENT_ID", None) + client_secret = os.getenv("AZURE_CLIENT_SECRET", None) + tenant_id = os.getenv("AZURE_TENANT_ID", None) + + if ( + KVUri is not None + and client_id is not None + and client_secret is not None + and tenant_id is not None + ): + # Initialize the ClientSecretCredential + credential = ClientSecretCredential( + client_id=client_id, client_secret=client_secret, tenant_id=tenant_id + ) + + # Create the SecretClient using the credential + client = SecretClient(vault_url=KVUri, credential=credential) + + litellm.secret_manager_client = client + litellm._key_management_system = KeyManagementSystem.AZURE_KEY_VAULT + else: + raise Exception( + f"Missing KVUri or client_id or client_secret or tenant_id from environment" + ) + except Exception as e: + verbose_proxy_logger.debug( + "Error when loading keys from Azure Key Vault. Ensure you run `pip install azure-identity azure-keyvault-secrets`" + ) + + +def cost_tracking(): + global prisma_client, custom_db_client + if prisma_client is not None or custom_db_client is not None: + if isinstance(litellm.success_callback, list): + verbose_proxy_logger.debug("setting litellm success callback to track cost") + if (track_cost_callback) not in litellm.success_callback: # type: ignore + litellm.success_callback.append(track_cost_callback) # type: ignore + + +async def track_cost_callback( + kwargs, # kwargs to completion + completion_response: litellm.ModelResponse, # response from completion + start_time=None, + end_time=None, # start/end time for completion +): + global prisma_client, custom_db_client + try: + # check if it has collected an entire stream response + verbose_proxy_logger.debug( + f"kwargs stream: {kwargs.get('stream', None)} + complete streaming response: {kwargs.get('complete_streaming_response', None)}" + ) + if "complete_streaming_response" in kwargs: + # for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost + completion_response = kwargs["complete_streaming_response"] + response_cost = litellm.completion_cost( + completion_response=completion_response + ) + verbose_proxy_logger.debug(f"streaming response_cost {response_cost}") + user_api_key = kwargs["litellm_params"]["metadata"].get( + "user_api_key", None + ) + user_id = kwargs["litellm_params"]["metadata"].get( + "user_api_key_user_id", None + ) + if user_api_key and ( + prisma_client is not None or custom_db_client is not None + ): + await update_database(token=user_api_key, response_cost=response_cost) + elif kwargs["stream"] == False: # for non streaming responses + response_cost = litellm.completion_cost( + completion_response=completion_response + ) + user_api_key = kwargs["litellm_params"]["metadata"].get( + "user_api_key", None + ) + user_id = kwargs["litellm_params"]["metadata"].get( + "user_api_key_user_id", None + ) + if user_api_key and ( + prisma_client is not None or custom_db_client is not None + ): + await update_database( + token=user_api_key, response_cost=response_cost, user_id=user_id + ) + except Exception as e: + verbose_proxy_logger.debug(f"error in tracking cost callback - {str(e)}") + + +async def update_database(token, response_cost, user_id=None): + try: + verbose_proxy_logger.debug( + f"Enters prisma db call, token: {token}; user_id: {user_id}" + ) + + ### UPDATE USER SPEND ### + async def _update_user_db(): + if user_id is None: + return + if prisma_client is not None: + existing_spend_obj = await prisma_client.get_data(user_id=user_id) + elif custom_db_client is not None: + existing_spend_obj = await custom_db_client.get_data( + key=user_id, table_name="user" + ) + if existing_spend_obj is None: + existing_spend = 0 + else: + existing_spend = existing_spend_obj.spend + + # Calculate the new cost by adding the existing cost and response_cost + new_spend = existing_spend + response_cost + + verbose_proxy_logger.debug(f"new cost: {new_spend}") + # Update the cost column for the given user id + if prisma_client is not None: + await prisma_client.update_data( + user_id=user_id, data={"spend": new_spend} + ) + elif custom_db_client is not None: + await custom_db_client.update_data( + key=user_id, value={"spend": new_spend}, table_name="user" + ) + + ### UPDATE KEY SPEND ### + async def _update_key_db(): + if prisma_client is not None: + # Fetch the existing cost for the given token + existing_spend_obj = await prisma_client.get_data(token=token) + verbose_proxy_logger.debug(f"existing spend: {existing_spend_obj}") + if existing_spend_obj is None: + existing_spend = 0 + else: + existing_spend = existing_spend_obj.spend + # Calculate the new cost by adding the existing cost and response_cost + new_spend = existing_spend + response_cost + + verbose_proxy_logger.debug(f"new cost: {new_spend}") + # Update the cost column for the given token + await prisma_client.update_data(token=token, data={"spend": new_spend}) + elif custom_db_client is not None: + # Fetch the existing cost for the given token + existing_spend_obj = await custom_db_client.get_data( + key=token, table_name="key" + ) + verbose_proxy_logger.debug(f"existing spend: {existing_spend_obj}") + if existing_spend_obj is None: + existing_spend = 0 + else: + existing_spend = existing_spend_obj.spend + # Calculate the new cost by adding the existing cost and response_cost + new_spend = existing_spend + response_cost + + verbose_proxy_logger.debug(f"new cost: {new_spend}") + # Update the cost column for the given token + await custom_db_client.update_data( + key=token, value={"spend": new_spend}, table_name="key" + ) + + tasks = [] + tasks.append(_update_user_db()) + tasks.append(_update_key_db()) + await asyncio.gather(*tasks) + except Exception as e: + verbose_proxy_logger.debug( + f"Error updating Prisma database: {traceback.format_exc()}" + ) + pass + + +def run_ollama_serve(): + try: + command = ["ollama", "serve"] + + with open(os.devnull, "w") as devnull: + process = subprocess.Popen(command, stdout=devnull, stderr=devnull) + except Exception as e: + verbose_proxy_logger.debug( + f""" + LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve` + """ + ) + + +async def _run_background_health_check(): + """ + Periodically run health checks in the background on the endpoints. + + Update health_check_results, based on this. + """ + global health_check_results, llm_model_list, health_check_interval + while True: + healthy_endpoints, unhealthy_endpoints = await perform_health_check( + model_list=llm_model_list + ) + + # Update the global variable with the health check results + health_check_results["healthy_endpoints"] = healthy_endpoints + health_check_results["unhealthy_endpoints"] = unhealthy_endpoints + health_check_results["healthy_count"] = len(healthy_endpoints) + health_check_results["unhealthy_count"] = len(unhealthy_endpoints) + + await asyncio.sleep(health_check_interval) + + +class ProxyConfig: + """ + Abstraction class on top of config loading/updating logic. Gives us one place to control all config updating logic. + """ + + def __init__(self) -> None: + pass + + def is_yaml(self, config_file_path: str) -> bool: + if not os.path.isfile(config_file_path): + return False + + _, file_extension = os.path.splitext(config_file_path) + return file_extension.lower() == ".yaml" or file_extension.lower() == ".yml" + + async def get_config(self, config_file_path: Optional[str] = None) -> dict: + global prisma_client, user_config_file_path + + file_path = config_file_path or user_config_file_path + if config_file_path is not None: + user_config_file_path = config_file_path + # Load existing config + ## Yaml + if os.path.exists(f"{file_path}"): + with open(f"{file_path}", "r") as config_file: + config = yaml.safe_load(config_file) + else: + config = { + "model_list": [], + "general_settings": {}, + "router_settings": {}, + "litellm_settings": {}, + } + + ## DB + if ( + prisma_client is not None + and litellm.get_secret("SAVE_CONFIG_TO_DB", False) == True + ): + prisma_setup(database_url=None) # in case it's not been connected yet + _tasks = [] + keys = [ + "model_list", + "general_settings", + "router_settings", + "litellm_settings", + ] + for k in keys: + response = prisma_client.get_generic_data( + key="param_name", value=k, table_name="config" + ) + _tasks.append(response) + + responses = await asyncio.gather(*_tasks) + + return config + + async def save_config(self, new_config: dict): + global prisma_client, llm_router, user_config_file_path, llm_model_list, general_settings + # Load existing config + backup_config = await self.get_config() + + # Save the updated config + ## YAML + with open(f"{user_config_file_path}", "w") as config_file: + yaml.dump(new_config, config_file, default_flow_style=False) + + # update Router - verifies if this is a valid config + try: + ( + llm_router, + llm_model_list, + general_settings, + ) = await proxy_config.load_config( + router=llm_router, config_file_path=user_config_file_path + ) + except Exception as e: + traceback.print_exc() + # Revert to old config instead + with open(f"{user_config_file_path}", "w") as config_file: + yaml.dump(backup_config, config_file, default_flow_style=False) + raise HTTPException(status_code=400, detail="Invalid config passed in") + + ## DB - writes valid config to db + """ + - Do not write restricted params like 'api_key' to the database + - if api_key is passed, save that to the local environment or connected secret manage (maybe expose `litellm.save_secret()`) + """ + if ( + prisma_client is not None + and litellm.get_secret("SAVE_CONFIG_TO_DB", default_value=False) == True + ): + ### KEY REMOVAL ### + models = new_config.get("model_list", []) + for m in models: + if m.get("litellm_params", {}).get("api_key", None) is not None: + # pop the key + api_key = m["litellm_params"].pop("api_key") + # store in local env + key_name = f"LITELLM_MODEL_KEY_{uuid.uuid4()}" + os.environ[key_name] = api_key + # save the key name (not the value) + m["litellm_params"]["api_key"] = f"os.environ/{key_name}" + await prisma_client.insert_data(data=new_config, table_name="config") + + async def load_config( + self, router: Optional[litellm.Router], config_file_path: str + ): + """ + Load config values into proxy global state + """ + global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, use_background_health_checks, health_check_interval, use_queue, custom_db_client + + # Load existing config + config = await self.get_config(config_file_path=config_file_path) + ## PRINT YAML FOR CONFIRMING IT WORKS + printed_yaml = copy.deepcopy(config) + printed_yaml.pop("environment_variables", None) + + verbose_proxy_logger.debug( + f"Loaded config YAML (api_key and environment_variables are not shown):\n{json.dumps(printed_yaml, indent=2)}" + ) + + ## ENVIRONMENT VARIABLES + environment_variables = config.get("environment_variables", None) + if environment_variables: + for key, value in environment_variables.items(): + os.environ[key] = value + + ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..) + litellm_settings = config.get("litellm_settings", None) + if litellm_settings is None: + litellm_settings = {} + if litellm_settings: + # ANSI escape code for blue text + blue_color_code = "\033[94m" + reset_color_code = "\033[0m" + for key, value in litellm_settings.items(): + if key == "cache": + print(f"{blue_color_code}\nSetting Cache on Proxy") # noqa + from litellm.caching import Cache + + cache_params = {} + if "cache_params" in litellm_settings: + cache_params_in_config = litellm_settings["cache_params"] + # overwrie cache_params with cache_params_in_config + cache_params.update(cache_params_in_config) + + cache_type = cache_params.get("type", "redis") + + verbose_proxy_logger.debug(f"passed cache type={cache_type}") + + if cache_type == "redis": + cache_host = litellm.get_secret("REDIS_HOST", None) + cache_port = litellm.get_secret("REDIS_PORT", None) + cache_password = litellm.get_secret("REDIS_PASSWORD", None) + + cache_params.update( + { + "type": cache_type, + "host": cache_host, + "port": cache_port, + "password": cache_password, + } + ) + # Assuming cache_type, cache_host, cache_port, and cache_password are strings + print( # noqa + f"{blue_color_code}Cache Type:{reset_color_code} {cache_type}" + ) # noqa + print( # noqa + f"{blue_color_code}Cache Host:{reset_color_code} {cache_host}" + ) # noqa + print( # noqa + f"{blue_color_code}Cache Port:{reset_color_code} {cache_port}" + ) # noqa + print( # noqa + f"{blue_color_code}Cache Password:{reset_color_code} {cache_password}" + ) + print() # noqa + + # users can pass os.environ/ variables on the proxy - we should read them from the env + for key, value in cache_params.items(): + if type(value) is str and value.startswith("os.environ/"): + cache_params[key] = litellm.get_secret(value) + + ## to pass a complete url, or set ssl=True, etc. just set it as `os.environ[REDIS_URL] = `, _redis.py checks for REDIS specific environment variables + litellm.cache = Cache(**cache_params) + print( # noqa + f"{blue_color_code}Set Cache on LiteLLM Proxy: {vars(litellm.cache.cache)}{reset_color_code}" + ) + elif key == "callbacks": + litellm.callbacks = [ + get_instance_fn(value=value, config_file_path=config_file_path) + ] + verbose_proxy_logger.debug( + f"{blue_color_code} Initialized Callbacks - {litellm.callbacks} {reset_color_code}" + ) + elif key == "post_call_rules": + litellm.post_call_rules = [ + get_instance_fn(value=value, config_file_path=config_file_path) + ] + verbose_proxy_logger.debug( + f"litellm.post_call_rules: {litellm.post_call_rules}" + ) + elif key == "success_callback": + litellm.success_callback = [] + + # intialize success callbacks + for callback in value: + # user passed custom_callbacks.async_on_succes_logger. They need us to import a function + if "." in callback: + litellm.success_callback.append( + get_instance_fn(value=callback) + ) + # these are litellm callbacks - "langfuse", "sentry", "wandb" + else: + litellm.success_callback.append(callback) + verbose_proxy_logger.debug( + f"{blue_color_code} Initialized Success Callbacks - {litellm.success_callback} {reset_color_code}" + ) + elif key == "failure_callback": + litellm.failure_callback = [] + + # intialize success callbacks + for callback in value: + # user passed custom_callbacks.async_on_succes_logger. They need us to import a function + if "." in callback: + litellm.failure_callback.append( + get_instance_fn(value=callback) + ) + # these are litellm callbacks - "langfuse", "sentry", "wandb" + else: + litellm.failure_callback.append(callback) + verbose_proxy_logger.debug( + f"{blue_color_code} Initialized Success Callbacks - {litellm.failure_callback} {reset_color_code}" + ) + elif key == "cache_params": + # this is set in the cache branch + # see usage here: https://docs.litellm.ai/docs/proxy/caching + pass + else: + setattr(litellm, key, value) + + ## GENERAL SERVER SETTINGS (e.g. master key,..) # do this after initializing litellm, to ensure sentry logging works for proxylogging + general_settings = config.get("general_settings", {}) + if general_settings is None: + general_settings = {} + if general_settings: + ### LOAD SECRET MANAGER ### + key_management_system = general_settings.get("key_management_system", None) + if key_management_system is not None: + if key_management_system == KeyManagementSystem.AZURE_KEY_VAULT.value: + ### LOAD FROM AZURE KEY VAULT ### + load_from_azure_key_vault(use_azure_key_vault=True) + elif key_management_system == KeyManagementSystem.GOOGLE_KMS.value: + ### LOAD FROM GOOGLE KMS ### + load_google_kms(use_google_kms=True) + else: + raise ValueError("Invalid Key Management System selected") + ### [DEPRECATED] LOAD FROM GOOGLE KMS ### old way of loading from google kms + use_google_kms = general_settings.get("use_google_kms", False) + load_google_kms(use_google_kms=use_google_kms) + ### [DEPRECATED] LOAD FROM AZURE KEY VAULT ### old way of loading from azure secret manager + use_azure_key_vault = general_settings.get("use_azure_key_vault", False) + load_from_azure_key_vault(use_azure_key_vault=use_azure_key_vault) + ### ALERTING ### + proxy_logging_obj.update_values( + alerting=general_settings.get("alerting", None), + alerting_threshold=general_settings.get("alerting_threshold", 600), + ) + ### CONNECT TO DATABASE ### + database_url = general_settings.get("database_url", None) + if database_url and database_url.startswith("os.environ/"): + verbose_proxy_logger.debug(f"GOING INTO LITELLM.GET_SECRET!") + database_url = litellm.get_secret(database_url) + verbose_proxy_logger.debug(f"RETRIEVED DB URL: {database_url}") + ### MASTER KEY ### + master_key = general_settings.get( + "master_key", litellm.get_secret("LITELLM_MASTER_KEY", None) + ) + if master_key and master_key.startswith("os.environ/"): + master_key = litellm.get_secret(master_key) + ### CUSTOM API KEY AUTH ### + ## pass filepath + custom_auth = general_settings.get("custom_auth", None) + if custom_auth is not None: + user_custom_auth = get_instance_fn( + value=custom_auth, config_file_path=config_file_path + ) + ## dynamodb + database_type = general_settings.get("database_type", None) + if database_type is not None and ( + database_type == "dynamo_db" or database_type == "dynamodb" + ): + database_args = general_settings.get("database_args", None) + custom_db_client = DBClient( + custom_db_args=database_args, custom_db_type=database_type + ) + ## COST TRACKING ## + cost_tracking() + ### BACKGROUND HEALTH CHECKS ### + # Enable background health checks + use_background_health_checks = general_settings.get( + "background_health_checks", False + ) + health_check_interval = general_settings.get("health_check_interval", 300) + + router_params: dict = { + "num_retries": 3, + "cache_responses": litellm.cache + != None, # cache if user passed in cache values + } + ## MODEL LIST + model_list = config.get("model_list", None) + if model_list: + router_params["model_list"] = model_list + print( # noqa + f"\033[32mLiteLLM: Proxy initialized with Config, Set models:\033[0m" + ) # noqa + for model in model_list: + ### LOAD FROM os.environ/ ### + for k, v in model["litellm_params"].items(): + if isinstance(v, str) and v.startswith("os.environ/"): + model["litellm_params"][k] = litellm.get_secret(v) + print(f"\033[32m {model.get('model_name', '')}\033[0m") # noqa + litellm_model_name = model["litellm_params"]["model"] + litellm_model_api_base = model["litellm_params"].get("api_base", None) + if "ollama" in litellm_model_name and litellm_model_api_base is None: + run_ollama_serve() + + ## ROUTER SETTINGS (e.g. routing_strategy, ...) + router_settings = config.get("router_settings", None) + if router_settings and isinstance(router_settings, dict): + arg_spec = inspect.getfullargspec(litellm.Router) + # model list already set + exclude_args = { + "self", + "model_list", + } + + available_args = [x for x in arg_spec.args if x not in exclude_args] + + for k, v in router_settings.items(): + if k in available_args: + router_params[k] = v + + router = litellm.Router(**router_params) # type:ignore + return router, model_list, general_settings + + +proxy_config = ProxyConfig() + + +async def generate_key_helper_fn( + duration: Optional[str], + models: list, + aliases: dict, + config: dict, + spend: float, + max_budget: Optional[float] = None, + token: Optional[str] = None, + user_id: Optional[str] = None, + user_email: Optional[str] = None, + max_parallel_requests: Optional[int] = None, + metadata: Optional[dict] = {}, +): + global prisma_client, custom_db_client + + if prisma_client is None and custom_db_client is None: + raise Exception( + f"Connect Proxy to database to generate keys - https://docs.litellm.ai/docs/proxy/virtual_keys " + ) + + if token is None: + token = f"sk-{secrets.token_urlsafe(16)}" + + def _duration_in_seconds(duration: str): + match = re.match(r"(\d+)([smhd]?)", duration) + if not match: + raise ValueError("Invalid duration format") + + value, unit = match.groups() + value = int(value) + + if unit == "s": + return value + elif unit == "m": + return value * 60 + elif unit == "h": + return value * 3600 + elif unit == "d": + return value * 86400 + else: + raise ValueError("Unsupported duration unit") + + if duration is None: # allow tokens that never expire + expires = None + else: + duration_s = _duration_in_seconds(duration=duration) + expires = datetime.utcnow() + timedelta(seconds=duration_s) + + aliases_json = json.dumps(aliases) + config_json = json.dumps(config) + metadata_json = json.dumps(metadata) + user_id = user_id or str(uuid.uuid4()) + try: + # Create a new verification token (you may want to enhance this logic based on your needs) + user_data = { + "max_budget": max_budget, + "user_email": user_email, + "user_id": user_id, + "spend": spend, + } + key_data = { + "token": token, + "expires": expires, + "models": models, + "aliases": aliases_json, + "config": config_json, + "spend": spend, + "user_id": user_id, + "max_parallel_requests": max_parallel_requests, + "metadata": metadata_json, + } + if prisma_client is not None: + verification_token_data = dict(key_data) + verification_token_data.update(user_data) + verbose_proxy_logger.debug("PrismaClient: Before Insert Data") + await prisma_client.insert_data(data=verification_token_data) + elif custom_db_client is not None: + ## CREATE USER (If necessary) + await custom_db_client.insert_data(value=user_data, table_name="user") + ## CREATE KEY + await custom_db_client.insert_data(value=key_data, table_name="key") + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR) + return { + "token": token, + "expires": expires, + "user_id": user_id, + "max_budget": max_budget, + } + + +async def delete_verification_token(tokens: List): + global prisma_client + try: + if prisma_client: + # Assuming 'db' is your Prisma Client instance + deleted_tokens = await prisma_client.delete_data(tokens=tokens) + else: + raise Exception + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR) + return deleted_tokens + + +def save_worker_config(**data): + import json + + os.environ["WORKER_CONFIG"] = json.dumps(data) + + +async def initialize( + model=None, + alias=None, + api_base=None, + api_version=None, + debug=False, + detailed_debug=False, + temperature=None, + max_tokens=None, + request_timeout=600, + max_budget=None, + telemetry=False, + drop_params=True, + add_function_to_prompt=True, + headers=None, + save=False, + use_queue=False, + config=None, +): + global user_model, user_api_base, user_debug, user_detailed_debug, user_user_max_tokens, user_request_timeout, user_temperature, user_telemetry, user_headers, experimental, llm_model_list, llm_router, general_settings, master_key, user_custom_auth, prisma_client + generate_feedback_box() + user_model = model + user_debug = debug + if debug == True: # this needs to be first, so users can see Router init debugg + from litellm._logging import verbose_router_logger, verbose_proxy_logger + import logging + + # this must ALWAYS remain logging.INFO, DO NOT MODIFY THIS + + verbose_router_logger.setLevel(level=logging.INFO) # set router logs to info + verbose_proxy_logger.setLevel(level=logging.INFO) # set proxy logs to info + if detailed_debug == True: + from litellm._logging import verbose_router_logger, verbose_proxy_logger + import logging + + verbose_router_logger.setLevel(level=logging.DEBUG) # set router logs to info + verbose_proxy_logger.setLevel(level=logging.DEBUG) # set proxy logs to debug + litellm.set_verbose = True + elif debug == False and detailed_debug == False: + # users can control proxy debugging using env variable = 'LITELLM_LOG' + litellm_log_setting = os.environ.get("LITELLM_LOG", "") + if litellm_log_setting != None: + if litellm_log_setting.upper() == "INFO": + from litellm._logging import verbose_router_logger, verbose_proxy_logger + import logging + + # this must ALWAYS remain logging.INFO, DO NOT MODIFY THIS + + verbose_router_logger.setLevel( + level=logging.INFO + ) # set router logs to info + verbose_proxy_logger.setLevel( + level=logging.INFO + ) # set proxy logs to info + elif litellm_log_setting.upper() == "DEBUG": + from litellm._logging import verbose_router_logger, verbose_proxy_logger + import logging + + verbose_router_logger.setLevel( + level=logging.DEBUG + ) # set router logs to info + verbose_proxy_logger.setLevel( + level=logging.DEBUG + ) # set proxy logs to debug + litellm.set_verbose = True + + dynamic_config = {"general": {}, user_model: {}} + if config: + ( + llm_router, + llm_model_list, + general_settings, + ) = await proxy_config.load_config(router=llm_router, config_file_path=config) + if headers: # model-specific param + user_headers = headers + dynamic_config[user_model]["headers"] = headers + if api_base: # model-specific param + user_api_base = api_base + dynamic_config[user_model]["api_base"] = api_base + if api_version: + os.environ[ + "AZURE_API_VERSION" + ] = api_version # set this for azure - litellm can read this from the env + if max_tokens: # model-specific param + user_max_tokens = max_tokens + dynamic_config[user_model]["max_tokens"] = max_tokens + if temperature: # model-specific param + user_temperature = temperature + dynamic_config[user_model]["temperature"] = temperature + if request_timeout: + user_request_timeout = request_timeout + dynamic_config[user_model]["request_timeout"] = request_timeout + if alias: # model-specific param + dynamic_config[user_model]["alias"] = alias + if drop_params == True: # litellm-specific param + litellm.drop_params = True + dynamic_config["general"]["drop_params"] = True + if add_function_to_prompt == True: # litellm-specific param + litellm.add_function_to_prompt = True + dynamic_config["general"]["add_function_to_prompt"] = True + if max_budget: # litellm-specific param + litellm.max_budget = max_budget + dynamic_config["general"]["max_budget"] = max_budget + if experimental: + pass + user_telemetry = telemetry + usage_telemetry(feature="local_proxy_server") + + +# for streaming +def data_generator(response): + verbose_proxy_logger.debug("inside generator") + for chunk in response: + verbose_proxy_logger.debug(f"returned chunk: {chunk}") + try: + yield f"data: {json.dumps(chunk.dict())}\n\n" + except: + yield f"data: {json.dumps(chunk)}\n\n" + + +async def async_data_generator(response, user_api_key_dict): + verbose_proxy_logger.debug("inside generator") + try: + start_time = time.time() + async for chunk in response: + verbose_proxy_logger.debug(f"returned chunk: {chunk}") + try: + yield f"data: {json.dumps(chunk.dict())}\n\n" + except Exception as e: + yield f"data: {str(e)}\n\n" + + ### ALERTING ### + end_time = time.time() + asyncio.create_task( + proxy_logging_obj.response_taking_too_long( + start_time=start_time, end_time=end_time, type="slow_response" + ) + ) + + # Streaming is done, yield the [DONE] chunk + done_message = "[DONE]" + yield f"data: {done_message}\n\n" + except Exception as e: + yield f"data: {str(e)}\n\n" + + +def get_litellm_model_info(model: dict = {}): + model_info = model.get("model_info", {}) + model_to_lookup = model.get("litellm_params", {}).get("model", None) + try: + if "azure" in model_to_lookup: + model_to_lookup = model_info.get("base_model", None) + litellm_model_info = litellm.get_model_info(model_to_lookup) + return litellm_model_info + except: + # this should not block returning on /model/info + # if litellm does not have info on the model it should return {} + return {} + + +def parse_cache_control(cache_control): + cache_dict = {} + directives = cache_control.split(", ") + + for directive in directives: + if "=" in directive: + key, value = directive.split("=") + cache_dict[key] = value + else: + cache_dict[directive] = True + + return cache_dict + + +@router.on_event("startup") +async def startup_event(): + global prisma_client, master_key, use_background_health_checks, llm_router, llm_model_list, general_settings + import json + + ### LOAD MASTER KEY ### + # check if master key set in environment - load from there + master_key = litellm.get_secret("LITELLM_MASTER_KEY", None) + # check if DATABASE_URL in environment - load from there + if prisma_client is None: + prisma_setup(database_url=os.getenv("DATABASE_URL")) + + ### LOAD CONFIG ### + worker_config = litellm.get_secret("WORKER_CONFIG") + verbose_proxy_logger.debug(f"worker_config: {worker_config}") + # check if it's a valid file path + if os.path.isfile(worker_config): + if proxy_config.is_yaml(config_file_path=worker_config): + ( + llm_router, + llm_model_list, + general_settings, + ) = await proxy_config.load_config( + router=llm_router, config_file_path=worker_config + ) + else: + await initialize(**worker_config) + else: + # if not, assume it's a json string + worker_config = json.loads(os.getenv("WORKER_CONFIG")) + await initialize(**worker_config) + proxy_logging_obj._init_litellm_callbacks() # INITIALIZE LITELLM CALLBACKS ON SERVER STARTUP <- do this to catch any logging errors on startup, not when calls are being made + + if use_background_health_checks: + asyncio.create_task( + _run_background_health_check() + ) # start the background health check coroutine. + + verbose_proxy_logger.debug(f"prisma client - {prisma_client}") + if prisma_client is not None: + await prisma_client.connect() + + if custom_db_client is not None: + await custom_db_client.connect() + + if prisma_client is not None and master_key is not None: + # add master key to db + await generate_key_helper_fn( + duration=None, models=[], aliases={}, config={}, spend=0, token=master_key + ) + + if custom_db_client is not None and master_key is not None: + # add master key to db + await generate_key_helper_fn( + duration=None, models=[], aliases={}, config={}, spend=0, token=master_key + ) + + +#### API ENDPOINTS #### +@router.get( + "/v1/models", dependencies=[Depends(user_api_key_auth)], tags=["model management"] +) +@router.get( + "/models", dependencies=[Depends(user_api_key_auth)], tags=["model management"] +) # if project requires model list +def model_list(): + global llm_model_list, general_settings + all_models = [] + if general_settings.get("infer_model_from_keys", False): + all_models = litellm.utils.get_valid_models() + if llm_model_list: + all_models = list(set(all_models + [m["model_name"] for m in llm_model_list])) + if user_model is not None: + all_models += [user_model] + verbose_proxy_logger.debug(f"all_models: {all_models}") + ### CHECK OLLAMA MODELS ### + try: + response = requests.get("http://0.0.0.0:11434/api/tags") + models = response.json()["models"] + ollama_models = ["ollama/" + m["name"].replace(":latest", "") for m in models] + all_models.extend(ollama_models) + except Exception as e: + pass + return dict( + data=[ + { + "id": model, + "object": "model", + "created": 1677610602, + "owned_by": "openai", + } + for model in all_models + ], + object="list", + ) + + +@router.post( + "/v1/completions", dependencies=[Depends(user_api_key_auth)], tags=["completions"] +) +@router.post( + "/completions", dependencies=[Depends(user_api_key_auth)], tags=["completions"] +) +@router.post( + "/engines/{model:path}/completions", + dependencies=[Depends(user_api_key_auth)], + tags=["completions"], +) +async def completion( + request: Request, + fastapi_response: Response, + model: Optional[str] = None, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + background_tasks: BackgroundTasks = BackgroundTasks(), +): + global user_temperature, user_request_timeout, user_max_tokens, user_api_base + try: + body = await request.body() + body_str = body.decode() + try: + data = ast.literal_eval(body_str) + except: + data = json.loads(body_str) + + data["user"] = data.get("user", user_api_key_dict.user_id) + data["model"] = ( + general_settings.get("completion_model", None) # server default + or user_model # model name passed via cli args + or model # for azure deployments + or data["model"] # default passed in http request + ) + if user_model: + data["model"] = user_model + if "metadata" in data: + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + data["metadata"]["headers"] = dict(request.headers) + else: + data["metadata"] = { + "user_api_key": user_api_key_dict.api_key, + "user_api_key_user_id": user_api_key_dict.user_id, + } + data["metadata"]["headers"] = dict(request.headers) + # override with user settings, these are params passed via cli + if user_temperature: + data["temperature"] = user_temperature + if user_request_timeout: + data["request_timeout"] = user_request_timeout + if user_max_tokens: + data["max_tokens"] = user_max_tokens + if user_api_base: + data["api_base"] = user_api_base + + ### CALL HOOKS ### - modify incoming data before calling the model + data = await proxy_logging_obj.pre_call_hook( + user_api_key_dict=user_api_key_dict, data=data, call_type="completion" + ) + + start_time = time.time() + + ### ROUTE THE REQUESTs ### + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + # skip router if user passed their key + if "api_key" in data: + response = await litellm.atext_completion(**data) + elif ( + llm_router is not None and data["model"] in router_model_names + ): # model in router model list + response = await llm_router.atext_completion(**data) + elif ( + llm_router is not None + and llm_router.model_group_alias is not None + and data["model"] in llm_router.model_group_alias + ): # model set in model_group_alias + response = await llm_router.atext_completion(**data) + elif ( + llm_router is not None and data["model"] in llm_router.deployment_names + ): # model in router deployments, calling a specific deployment on the router + response = await llm_router.atext_completion( + **data, specific_deployment=True + ) + else: # router is not set + response = await litellm.atext_completion(**data) + + if hasattr(response, "_hidden_params"): + model_id = response._hidden_params.get("model_id", None) or "" + else: + model_id = "" + + verbose_proxy_logger.debug(f"final response: {response}") + if ( + "stream" in data and data["stream"] == True + ): # use generate_responses to stream responses + custom_headers = {"x-litellm-model-id": model_id} + return StreamingResponse( + async_data_generator( + user_api_key_dict=user_api_key_dict, + response=response, + ), + media_type="text/event-stream", + headers=custom_headers, + ) + + ### ALERTING ### + end_time = time.time() + asyncio.create_task( + proxy_logging_obj.response_taking_too_long( + start_time=start_time, end_time=end_time, type="slow_response" + ) + ) + + fastapi_response.headers["x-litellm-model-id"] = model_id + return response + except Exception as e: + verbose_proxy_logger.debug(f"EXCEPTION RAISED IN PROXY MAIN.PY") + verbose_proxy_logger.debug( + f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`" + ) + traceback.print_exc() + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException(status_code=status, detail=error_msg) + + +@router.post( + "/v1/chat/completions", + dependencies=[Depends(user_api_key_auth)], + tags=["chat/completions"], +) +@router.post( + "/chat/completions", + dependencies=[Depends(user_api_key_auth)], + tags=["chat/completions"], +) +@router.post( + "/openai/deployments/{model:path}/chat/completions", + dependencies=[Depends(user_api_key_auth)], + tags=["chat/completions"], +) # azure compatible endpoint +async def chat_completion( + request: Request, + fastapi_response: Response, + model: Optional[str] = None, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + background_tasks: BackgroundTasks = BackgroundTasks(), +): + global general_settings, user_debug, proxy_logging_obj, llm_model_list + try: + data = {} + body = await request.body() + body_str = body.decode() + try: + data = ast.literal_eval(body_str) + except: + data = json.loads(body_str) + + # Include original request and headers in the data + data["proxy_server_request"] = { + "url": str(request.url), + "method": request.method, + "headers": dict(request.headers), + "body": copy.copy(data), # use copy instead of deepcopy + } + + ## Cache Controls + headers = request.headers + verbose_proxy_logger.debug(f"Request Headers: {headers}") + cache_control_header = headers.get("Cache-Control", None) + if cache_control_header: + cache_dict = parse_cache_control(cache_control_header) + data["ttl"] = cache_dict.get("s-maxage") + + verbose_proxy_logger.debug(f"receiving data: {data}") + data["model"] = ( + general_settings.get("completion_model", None) # server default + or user_model # model name passed via cli args + or model # for azure deployments + or data["model"] # default passed in http request + ) + + # users can pass in 'user' param to /chat/completions. Don't override it + if data.get("user", None) is None and user_api_key_dict.user_id is not None: + # if users are using user_api_key_auth, set `user` in `data` + data["user"] = user_api_key_dict.user_id + + if "metadata" in data: + verbose_proxy_logger.debug(f'received metadata: {data["metadata"]}') + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + data["metadata"]["headers"] = dict(request.headers) + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + + global user_temperature, user_request_timeout, user_max_tokens, user_api_base + # override with user settings, these are params passed via cli + if user_temperature: + data["temperature"] = user_temperature + if user_request_timeout: + data["request_timeout"] = user_request_timeout + if user_max_tokens: + data["max_tokens"] = user_max_tokens + if user_api_base: + data["api_base"] = user_api_base + + ### CALL HOOKS ### - modify incoming data before calling the model + data = await proxy_logging_obj.pre_call_hook( + user_api_key_dict=user_api_key_dict, data=data, call_type="completion" + ) + + start_time = time.time() + + ### ROUTE THE REQUEST ### + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + # skip router if user passed their key + if "api_key" in data: + response = await litellm.acompletion(**data) + elif "user_config" in data: + # initialize a new router instance. make request using this Router + router_config = data.pop("user_config") + user_router = litellm.Router(**router_config) + response = await user_router.acompletion(**data) + elif ( + llm_router is not None and data["model"] in router_model_names + ): # model in router model list + response = await llm_router.acompletion(**data) + elif ( + llm_router is not None + and llm_router.model_group_alias is not None + and data["model"] in llm_router.model_group_alias + ): # model set in model_group_alias + response = await llm_router.acompletion(**data) + elif ( + llm_router is not None and data["model"] in llm_router.deployment_names + ): # model in router deployments, calling a specific deployment on the router + response = await llm_router.acompletion(**data, specific_deployment=True) + else: # router is not set + response = await litellm.acompletion(**data) + + if hasattr(response, "_hidden_params"): + model_id = response._hidden_params.get("model_id", None) or "" + else: + model_id = "" + + if ( + "stream" in data and data["stream"] == True + ): # use generate_responses to stream responses + custom_headers = {"x-litellm-model-id": model_id} + return StreamingResponse( + async_data_generator( + user_api_key_dict=user_api_key_dict, + response=response, + ), + media_type="text/event-stream", + headers=custom_headers, + ) + + ### ALERTING ### + end_time = time.time() + asyncio.create_task( + proxy_logging_obj.response_taking_too_long( + start_time=start_time, end_time=end_time, type="slow_response" + ) + ) + + fastapi_response.headers["x-litellm-model-id"] = model_id + return response + except Exception as e: + traceback.print_exc() + await proxy_logging_obj.post_call_failure_hook( + user_api_key_dict=user_api_key_dict, original_exception=e + ) + verbose_proxy_logger.debug( + f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`" + ) + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + if llm_router is not None and data.get("model", "") in router_model_names: + verbose_proxy_logger.debug("Results from router") + verbose_proxy_logger.debug("\nRouter stats") + verbose_proxy_logger.debug("\nTotal Calls made") + for key, value in llm_router.total_calls.items(): + verbose_proxy_logger.debug(f"{key}: {value}") + verbose_proxy_logger.debug("\nSuccess Calls made") + for key, value in llm_router.success_calls.items(): + verbose_proxy_logger.debug(f"{key}: {value}") + verbose_proxy_logger.debug("\nFail Calls made") + for key, value in llm_router.fail_calls.items(): + verbose_proxy_logger.debug(f"{key}: {value}") + if user_debug: + traceback.print_exc() + + if isinstance(e, HTTPException): + raise e + else: + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException(status_code=status, detail=error_msg) + + +@router.post( + "/v1/embeddings", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, + tags=["embeddings"], +) +@router.post( + "/embeddings", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, + tags=["embeddings"], +) +async def embeddings( + request: Request, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + background_tasks: BackgroundTasks = BackgroundTasks(), +): + global proxy_logging_obj + try: + # Use orjson to parse JSON data, orjson speeds up requests significantly + body = await request.body() + data = orjson.loads(body) + + # Include original request and headers in the data + data["proxy_server_request"] = { + "url": str(request.url), + "method": request.method, + "headers": dict(request.headers), + "body": copy.copy(data), # use copy instead of deepcopy + } + + if data.get("user", None) is None and user_api_key_dict.user_id is not None: + data["user"] = user_api_key_dict.user_id + + data["model"] = ( + general_settings.get("embedding_model", None) # server default + or user_model # model name passed via cli args + or data["model"] # default passed in http request + ) + if user_model: + data["model"] = user_model + if "metadata" in data: + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + if ( + "input" in data + and isinstance(data["input"], list) + and isinstance(data["input"][0], list) + and isinstance(data["input"][0][0], int) + ): # check if array of tokens passed in + # check if non-openai/azure model called - e.g. for langchain integration + if llm_model_list is not None and data["model"] in router_model_names: + for m in llm_model_list: + if m["model_name"] == data["model"] and ( + m["litellm_params"]["model"] in litellm.open_ai_embedding_models + or m["litellm_params"]["model"].startswith("azure/") + ): + pass + else: + # non-openai/azure embedding model called with token input + input_list = [] + for i in data["input"]: + input_list.append( + litellm.decode(model="gpt-3.5-turbo", tokens=i) + ) + data["input"] = input_list + break + + ### CALL HOOKS ### - modify incoming data / reject request before calling the model + data = await proxy_logging_obj.pre_call_hook( + user_api_key_dict=user_api_key_dict, data=data, call_type="embeddings" + ) + + start_time = time.time() + + ## ROUTE TO CORRECT ENDPOINT ## + # skip router if user passed their key + if "api_key" in data: + response = await litellm.aembedding(**data) + elif "user_config" in data: + # initialize a new router instance. make request using this Router + router_config = data.pop("user_config") + user_router = litellm.Router(**router_config) + response = await user_router.aembedding(**data) + elif ( + llm_router is not None and data["model"] in router_model_names + ): # model in router model list + response = await llm_router.aembedding(**data) + elif ( + llm_router is not None + and llm_router.model_group_alias is not None + and data["model"] in llm_router.model_group_alias + ): # model set in model_group_alias + response = await llm_router.aembedding( + **data + ) # ensure this goes the llm_router, router will do the correct alias mapping + elif ( + llm_router is not None and data["model"] in llm_router.deployment_names + ): # model in router deployments, calling a specific deployment on the router + response = await llm_router.aembedding(**data, specific_deployment=True) + else: + response = await litellm.aembedding(**data) + + ### ALERTING ### + end_time = time.time() + asyncio.create_task( + proxy_logging_obj.response_taking_too_long( + start_time=start_time, end_time=end_time, type="slow_response" + ) + ) + + return response + except Exception as e: + await proxy_logging_obj.post_call_failure_hook( + user_api_key_dict=user_api_key_dict, original_exception=e + ) + traceback.print_exc() + if isinstance(e, HTTPException): + raise e + else: + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException(status_code=status, detail=error_msg) + + +@router.post( + "/v1/images/generations", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, + tags=["image generation"], +) +@router.post( + "/images/generations", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, + tags=["image generation"], +) +async def image_generation( + request: Request, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + background_tasks: BackgroundTasks = BackgroundTasks(), +): + global proxy_logging_obj + try: + # Use orjson to parse JSON data, orjson speeds up requests significantly + body = await request.body() + data = orjson.loads(body) + + # Include original request and headers in the data + data["proxy_server_request"] = { + "url": str(request.url), + "method": request.method, + "headers": dict(request.headers), + "body": copy.copy(data), # use copy instead of deepcopy + } + + if data.get("user", None) is None and user_api_key_dict.user_id is not None: + data["user"] = user_api_key_dict.user_id + + data["model"] = ( + general_settings.get("image_generation_model", None) # server default + or user_model # model name passed via cli args + or data["model"] # default passed in http request + ) + if user_model: + data["model"] = user_model + if "metadata" in data: + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + + ### CALL HOOKS ### - modify incoming data / reject request before calling the model + data = await proxy_logging_obj.pre_call_hook( + user_api_key_dict=user_api_key_dict, data=data, call_type="embeddings" + ) + + start_time = time.time() + + ## ROUTE TO CORRECT ENDPOINT ## + # skip router if user passed their key + if "api_key" in data: + response = await litellm.aimage_generation(**data) + elif ( + llm_router is not None and data["model"] in router_model_names + ): # model in router model list + response = await llm_router.aimage_generation(**data) + elif ( + llm_router is not None and data["model"] in llm_router.deployment_names + ): # model in router deployments, calling a specific deployment on the router + response = await llm_router.aimage_generation( + **data, specific_deployment=True + ) + elif ( + llm_router is not None + and llm_router.model_group_alias is not None + and data["model"] in llm_router.model_group_alias + ): # model set in model_group_alias + response = await llm_router.aimage_generation( + **data + ) # ensure this goes the llm_router, router will do the correct alias mapping + else: + response = await litellm.aimage_generation(**data) + + ### ALERTING ### + end_time = time.time() + asyncio.create_task( + proxy_logging_obj.response_taking_too_long( + start_time=start_time, end_time=end_time, type="slow_response" + ) + ) + + return response + except Exception as e: + await proxy_logging_obj.post_call_failure_hook( + user_api_key_dict=user_api_key_dict, original_exception=e + ) + traceback.print_exc() + if isinstance(e, HTTPException): + raise e + else: + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException(status_code=status, detail=error_msg) + + +#### KEY MANAGEMENT #### + + +@router.post( + "/key/generate", + tags=["key management"], + dependencies=[Depends(user_api_key_auth)], + response_model=GenerateKeyResponse, +) +async def generate_key_fn( + request: Request, + data: GenerateKeyRequest, + Authorization: Optional[str] = Header(None), +): + """ + Generate an API key based on the provided data. + + Docs: https://docs.litellm.ai/docs/proxy/virtual_keys + + Parameters: + - duration: Optional[str] - Specify the length of time the token is valid for. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). **(Default is set to 1 hour.)** + - models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models) + - aliases: Optional[dict] - Any alias mappings, on top of anything in the config.yaml model list. - https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---upgradedowngrade-models + - config: Optional[dict] - any key-specific configs, overrides config in config.yaml + - spend: Optional[int] - Amount spent by key. Default is 0. Will be updated by proxy whenever key is used. https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---tracking-spend + - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x. + - metadata: Optional[dict] - Metadata for key, store information for key. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" } + + Returns: + - key: (str) The generated api key + - expires: (datetime) Datetime object for when key expires. + - user_id: (str) Unique user id - used for tracking spend across multiple keys for same user id. + """ + verbose_proxy_logger.debug("entered /key/generate") + data_json = data.json() # type: ignore + response = await generate_key_helper_fn(**data_json) + return GenerateKeyResponse( + key=response["token"], expires=response["expires"], user_id=response["user_id"] + ) + + +@router.post( + "/key/update", tags=["key management"], dependencies=[Depends(user_api_key_auth)] +) +async def update_key_fn(request: Request, data: UpdateKeyRequest): + """ + Update an existing key + """ + global prisma_client + try: + data_json: dict = data.json() + key = data_json.pop("key") + # get the row from db + if prisma_client is None: + raise Exception("Not connected to DB!") + + non_default_values = {k: v for k, v in data_json.items() if v is not None} + response = await prisma_client.update_data( + token=key, data={**non_default_values, "token": key} + ) + return {"key": key, **non_default_values} + # update based on remaining passed in values + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + + +@router.post( + "/key/delete", tags=["key management"], dependencies=[Depends(user_api_key_auth)] +) +async def delete_key_fn(request: Request, data: DeleteKeyRequest): + try: + keys = data.keys + + deleted_keys = await delete_verification_token(tokens=keys) + assert len(keys) == deleted_keys + return {"deleted_keys": keys} + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + + +@router.get( + "/key/info", tags=["key management"], dependencies=[Depends(user_api_key_auth)] +) +async def info_key_fn( + key: str = fastapi.Query(..., description="Key in the request parameters") +): + global prisma_client + try: + if prisma_client is None: + raise Exception( + f"Database not connected. Connect a database to your proxy - https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys" + ) + key_info = await prisma_client.get_data(token=key) + return {"key": key, "info": key_info} + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + + +#### USER MANAGEMENT #### +@router.post( + "/user/new", + tags=["user management"], + dependencies=[Depends(user_api_key_auth)], + response_model=NewUserResponse, +) +async def new_user(data: NewUserRequest): + """ + Use this to create a new user with a budget. + + Returns user id, budget + new key. + + Parameters: + - user_id: Optional[str] - Specify a user id. If not set, a unique id will be generated. + - max_budget: Optional[float] - Specify max budget for a given user. + - duration: Optional[str] - Specify the length of time the token is valid for. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). **(Default is set to 1 hour.)** + - models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models) + - aliases: Optional[dict] - Any alias mappings, on top of anything in the config.yaml model list. - https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---upgradedowngrade-models + - config: Optional[dict] - any key-specific configs, overrides config in config.yaml + - spend: Optional[int] - Amount spent by key. Default is 0. Will be updated by proxy whenever key is used. https://docs.litellm.ai/docs/proxy/virtual_keys#managing-auth---tracking-spend + - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x. + - metadata: Optional[dict] - Metadata for key, store information for key. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" } + + Returns: + - key: (str) The generated api key + - expires: (datetime) Datetime object for when key expires. + - user_id: (str) Unique user id - used for tracking spend across multiple keys for same user id. + - max_budget: (float|None) Max budget for given user. + """ + data_json = data.json() # type: ignore + response = await generate_key_helper_fn(**data_json) + return NewUserResponse( + key=response["token"], + expires=response["expires"], + user_id=response["user_id"], + max_budget=response["max_budget"], + ) + + +@router.post( + "/user/auth", tags=["user management"], dependencies=[Depends(user_api_key_auth)] +) +async def user_auth(request: Request): + """ + Allows UI ("https://dashboard.litellm.ai/", or self-hosted - os.getenv("LITELLM_HOSTED_UI")) to request a magic link to be sent to user email, for auth to proxy. + + Only allows emails from accepted email subdomains. + + Rate limit: 1 request every 60s. + + Only works, if you enable 'allow_user_auth' in general settings: + e.g.: + ```yaml + general_settings: + allow_user_auth: true + ``` + + Requirements: + SMTP server details saved in .env: + - os.environ["SMTP_HOST"] + - os.environ["SMTP_PORT"] + - os.environ["SMTP_USERNAME"] + - os.environ["SMTP_PASSWORD"] + - os.environ["SMTP_SENDER_EMAIL"] + """ + global prisma_client + + data = await request.json() # type: ignore + user_email = data["user_email"] + page_params = data["page"] + if user_email is None: + raise HTTPException(status_code=400, detail="User email is none") + + if prisma_client is None: # if no db connected, raise an error + raise Exception("No connected db.") + + ### Check if user email in user table + response = await prisma_client.get_generic_data( + key="user_email", value=user_email, table_name="users" + ) + ### if so - generate a 24 hr key with that user id + if response is not None: + user_id = response.user_id + response = await generate_key_helper_fn( + **{"duration": "24hr", "models": [], "aliases": {}, "config": {}, "spend": 0, "user_id": user_id} # type: ignore + ) + else: ### else - create new user + response = await generate_key_helper_fn( + **{"duration": "24hr", "models": [], "aliases": {}, "config": {}, "spend": 0, "user_email": user_email} # type: ignore + ) + + base_url = os.getenv("LITELLM_HOSTED_UI", "https://dashboard.litellm.ai/") + + params = { + "sender_name": "LiteLLM Proxy", + "sender_email": os.getenv("SMTP_SENDER_EMAIL"), + "receiver_email": user_email, + "subject": "Your Magic Link", + "html": f" Follow this link, to login:\n\n{base_url}user/?token={response['token']}&user_id={response['user_id']}&page={page_params}", + } + + await send_email(**params) + return "Email sent!" + + +@router.get( + "/user/info", tags=["user management"], dependencies=[Depends(user_api_key_auth)] +) +async def user_info( + user_id: str = fastapi.Query(..., description="User ID in the request parameters") +): + """ + Use this to get user information. (user row + all user key info) + """ + global prisma_client + try: + if prisma_client is None: + raise Exception( + f"Database not connected. Connect a database to your proxy - https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys" + ) + ## GET USER ROW ## + user_info = await prisma_client.get_data(user_id=user_id) + ## GET ALL KEYS ## + keys = await prisma_client.get_data( + user_id=user_id, table_name="key", query_type="find_all" + ) + return {"user_id": user_id, "user_info": user_info, "keys": keys} + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + + +@router.post( + "/user/update", tags=["user management"], dependencies=[Depends(user_api_key_auth)] +) +async def user_update(request: Request): + """ + [TODO]: Use this to update user budget + """ + pass + + +#### MODEL MANAGEMENT #### + + +#### [BETA] - This is a beta endpoint, format might change based on user feedback. - https://github.com/BerriAI/litellm/issues/964 +@router.post( + "/model/new", + description="Allows adding new models to the model list in the config.yaml", + tags=["model management"], + dependencies=[Depends(user_api_key_auth)], +) +async def add_new_model(model_params: ModelParams): + global llm_router, llm_model_list, general_settings, user_config_file_path, proxy_config + try: + # Load existing config + config = await proxy_config.get_config() + + verbose_proxy_logger.debug(f"User config path: {user_config_file_path}") + + verbose_proxy_logger.debug(f"Loaded config: {config}") + # Add the new model to the config + model_info = model_params.model_info.json() + model_info = {k: v for k, v in model_info.items() if v is not None} + config["model_list"].append( + { + "model_name": model_params.model_name, + "litellm_params": model_params.litellm_params, + "model_info": model_info, + } + ) + + verbose_proxy_logger.debug(f"updated model list: {config['model_list']}") + + # Save new config + await proxy_config.save_config(new_config=config) + return {"message": "Model added successfully"} + + except Exception as e: + traceback.print_exc() + if isinstance(e, HTTPException): + raise e + else: + raise HTTPException( + status_code=500, detail=f"Internal Server Error: {str(e)}" + ) + + +#### [BETA] - This is a beta endpoint, format might change based on user feedback https://github.com/BerriAI/litellm/issues/933. If you need a stable endpoint use /model/info +@router.get( + "/model/info", + description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", + tags=["model management"], + dependencies=[Depends(user_api_key_auth)], +) +@router.get( + "/v1/model/info", + description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", + tags=["model management"], + dependencies=[Depends(user_api_key_auth)], +) +async def model_info_v1(request: Request): + global llm_model_list, general_settings, user_config_file_path, proxy_config + + # Load existing config + config = await proxy_config.get_config() + + all_models = config["model_list"] + for model in all_models: + # provided model_info in config.yaml + model_info = model.get("model_info", {}) + + # read litellm model_prices_and_context_window.json to get the following: + # input_cost_per_token, output_cost_per_token, max_tokens + litellm_model_info = get_litellm_model_info(model=model) + for k, v in litellm_model_info.items(): + if k not in model_info: + model_info[k] = v + model["model_info"] = model_info + # don't return the api key + model["litellm_params"].pop("api_key", None) + + verbose_proxy_logger.debug(f"all_models: {all_models}") + return {"data": all_models} + + +#### [BETA] - This is a beta endpoint, format might change based on user feedback. - https://github.com/BerriAI/litellm/issues/964 +@router.post( + "/model/delete", + description="Allows deleting models in the model list in the config.yaml", + tags=["model management"], + dependencies=[Depends(user_api_key_auth)], +) +async def delete_model(model_info: ModelInfoDelete): + global llm_router, llm_model_list, general_settings, user_config_file_path, proxy_config + try: + if not os.path.exists(user_config_file_path): + raise HTTPException(status_code=404, detail="Config file does not exist.") + + # Load existing config + config = await proxy_config.get_config() + + # If model_list is not in the config, nothing can be deleted + if len(config.get("model_list", [])) == 0: + raise HTTPException( + status_code=400, detail="No model list available in the config." + ) + + # Check if the model with the specified model_id exists + model_to_delete = None + for model in config["model_list"]: + if model.get("model_info", {}).get("id", None) == model_info.id: + model_to_delete = model + break + + # If the model was not found, return an error + if model_to_delete is None: + raise HTTPException( + status_code=400, detail="Model with given model_id not found." + ) + + # Remove model from the list and save the updated config + config["model_list"].remove(model_to_delete) + + # Save updated config + config = await proxy_config.save_config(new_config=config) + return {"message": "Model deleted successfully"} + + except HTTPException as e: + # Re-raise the HTTP exceptions to be handled by FastAPI + raise + except Exception as e: + raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}") + + +#### EXPERIMENTAL QUEUING #### +async def _litellm_chat_completions_worker(data, user_api_key_dict): + """ + worker to make litellm completions calls + """ + while True: + try: + ### CALL HOOKS ### - modify incoming data before calling the model + data = await proxy_logging_obj.pre_call_hook( + user_api_key_dict=user_api_key_dict, data=data, call_type="completion" + ) + + verbose_proxy_logger.debug(f"_litellm_chat_completions_worker started") + ### ROUTE THE REQUEST ### + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + if ( + llm_router is not None and data["model"] in router_model_names + ): # model in router model list + response = await llm_router.acompletion(**data) + elif ( + llm_router is not None and data["model"] in llm_router.deployment_names + ): # model in router deployments, calling a specific deployment on the router + response = await llm_router.acompletion( + **data, specific_deployment=True + ) + elif ( + llm_router is not None + and llm_router.model_group_alias is not None + and data["model"] in llm_router.model_group_alias + ): # model set in model_group_alias + response = await llm_router.acompletion(**data) + else: # router is not set + response = await litellm.acompletion(**data) + + verbose_proxy_logger.debug(f"final response: {response}") + return response + except HTTPException as e: + verbose_proxy_logger.debug( + f"EXCEPTION RAISED IN _litellm_chat_completions_worker - {e.status_code}; {e.detail}" + ) + if ( + e.status_code == 429 + and "Max parallel request limit reached" in e.detail + ): + verbose_proxy_logger.debug(f"Max parallel request limit reached!") + timeout = litellm._calculate_retry_after( + remaining_retries=3, max_retries=3, min_timeout=1 + ) + await asyncio.sleep(timeout) + else: + raise e + + +@router.post( + "/queue/chat/completions", + tags=["experimental"], + dependencies=[Depends(user_api_key_auth)], +) +async def async_queue_request( + request: Request, + model: Optional[str] = None, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + background_tasks: BackgroundTasks = BackgroundTasks(), +): + global general_settings, user_debug, proxy_logging_obj + """ + v2 attempt at a background worker to handle queuing. + + Just supports /chat/completion calls currently. + + Now using a FastAPI background task + /chat/completions compatible endpoint + """ + try: + data = {} + data = await request.json() # type: ignore + + # Include original request and headers in the data + data["proxy_server_request"] = { + "url": str(request.url), + "method": request.method, + "headers": dict(request.headers), + "body": copy.copy(data), # use copy instead of deepcopy + } + + verbose_proxy_logger.debug(f"receiving data: {data}") + data["model"] = ( + general_settings.get("completion_model", None) # server default + or user_model # model name passed via cli args + or model # for azure deployments + or data["model"] # default passed in http request + ) + + # users can pass in 'user' param to /chat/completions. Don't override it + if data.get("user", None) is None and user_api_key_dict.user_id is not None: + # if users are using user_api_key_auth, set `user` in `data` + data["user"] = user_api_key_dict.user_id + + if "metadata" in data: + verbose_proxy_logger.debug(f'received metadata: {data["metadata"]}') + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = dict(request.headers) + data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id + + global user_temperature, user_request_timeout, user_max_tokens, user_api_base + # override with user settings, these are params passed via cli + if user_temperature: + data["temperature"] = user_temperature + if user_request_timeout: + data["request_timeout"] = user_request_timeout + if user_max_tokens: + data["max_tokens"] = user_max_tokens + if user_api_base: + data["api_base"] = user_api_base + + response = await asyncio.wait_for( + _litellm_chat_completions_worker( + data=data, user_api_key_dict=user_api_key_dict + ), + timeout=litellm.request_timeout, + ) + + if ( + "stream" in data and data["stream"] == True + ): # use generate_responses to stream responses + return StreamingResponse( + async_data_generator( + user_api_key_dict=user_api_key_dict, response=response + ), + media_type="text/event-stream", + ) + + return response + except Exception as e: + await proxy_logging_obj.post_call_failure_hook( + user_api_key_dict=user_api_key_dict, original_exception=e + ) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + + +@router.get( + "/ollama_logs", dependencies=[Depends(user_api_key_auth)], tags=["experimental"] +) +async def retrieve_server_log(request: Request): + filepath = os.path.expanduser("~/.ollama/logs/server.log") + return FileResponse(filepath) + + +#### BASIC ENDPOINTS #### +@router.post( + "/config/update", + tags=["config.yaml"], + dependencies=[Depends(user_api_key_auth)], +) +async def update_config(config_info: ConfigYAML): + """ + For Admin UI - allows admin to update config via UI + + Currently supports modifying General Settings + LiteLLM settings + """ + global llm_router, llm_model_list, general_settings, proxy_config, proxy_logging_obj + try: + # Load existing config + config = await proxy_config.get_config() + + backup_config = copy.deepcopy(config) + verbose_proxy_logger.debug(f"Loaded config: {config}") + + # update the general settings + if config_info.general_settings is not None: + config.setdefault("general_settings", {}) + updated_general_settings = config_info.general_settings.dict( + exclude_none=True + ) + config["general_settings"] = { + **updated_general_settings, + **config["general_settings"], + } + + if config_info.environment_variables is not None: + config.setdefault("environment_variables", {}) + updated_environment_variables = config_info.environment_variables + config["environment_variables"] = { + **updated_environment_variables, + **config["environment_variables"], + } + + # update the litellm settings + if config_info.litellm_settings is not None: + config.setdefault("litellm_settings", {}) + updated_litellm_settings = config_info.litellm_settings + config["litellm_settings"] = { + **updated_litellm_settings, + **config["litellm_settings"], + } + + # Save the updated config + await proxy_config.save_config(new_config=config) + + # Test new connections + ## Slack + if "slack" in config.get("general_settings", {}).get("alerting", []): + await proxy_logging_obj.alerting_handler( + message="This is a test", level="Low" + ) + return {"message": "Config updated successfully"} + except HTTPException as e: + raise e + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"An error occurred - {str(e)}") + + +@router.get("/config/yaml", tags=["config.yaml"]) +async def config_yaml_endpoint(config_info: ConfigYAML): + """ + This is a mock endpoint, to show what you can set in config.yaml details in the Swagger UI. + + Parameters: + + The config.yaml object has the following attributes: + - **model_list**: *Optional[List[ModelParams]]* - A list of supported models on the server, along with model-specific configurations. ModelParams includes "model_name" (name of the model), "litellm_params" (litellm-specific parameters for the model), and "model_info" (additional info about the model such as id, mode, cost per token, etc). + + - **litellm_settings**: *Optional[dict]*: Settings for the litellm module. You can specify multiple properties like "drop_params", "set_verbose", "api_base", "cache". + + - **general_settings**: *Optional[ConfigGeneralSettings]*: General settings for the server like "completion_model" (default model for chat completion calls), "use_azure_key_vault" (option to load keys from azure key vault), "master_key" (key required for all calls to proxy), and others. + + Please, refer to each class's description for a better understanding of the specific attributes within them. + + Note: This is a mock endpoint primarily meant for demonstration purposes, and does not actually provide or change any configurations. + """ + return {"hello": "world"} + + +@router.get("/test", tags=["health"]) +async def test_endpoint(request: Request): + """ + A test endpoint that pings the proxy server to check if it's healthy. + + Parameters: + request (Request): The incoming request. + + Returns: + dict: A dictionary containing the route of the request URL. + """ + # ping the proxy server to check if its healthy + return {"route": request.url.path} + + +@router.get("/health", tags=["health"], dependencies=[Depends(user_api_key_auth)]) +async def health_endpoint( + request: Request, + model: Optional[str] = fastapi.Query( + None, description="Specify the model name (optional)" + ), +): + """ + Check the health of all the endpoints in config.yaml + + To run health checks in the background, add this to config.yaml: + ``` + general_settings: + # ... other settings + background_health_checks: True + ``` + else, the health checks will be run on models when /health is called. + """ + global health_check_results, use_background_health_checks, user_model + + if llm_model_list is None: + # if no router set, check if user set a model using litellm --model ollama/llama2 + if user_model is not None: + healthy_endpoints, unhealthy_endpoints = await perform_health_check( + model_list=[], cli_model=user_model + ) + return { + "healthy_endpoints": healthy_endpoints, + "unhealthy_endpoints": unhealthy_endpoints, + "healthy_count": len(healthy_endpoints), + "unhealthy_count": len(unhealthy_endpoints), + } + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail={"error": "Model list not initialized"}, + ) + + if use_background_health_checks: + return health_check_results + else: + healthy_endpoints, unhealthy_endpoints = await perform_health_check( + llm_model_list, model + ) + + return { + "healthy_endpoints": healthy_endpoints, + "unhealthy_endpoints": unhealthy_endpoints, + "healthy_count": len(healthy_endpoints), + "unhealthy_count": len(unhealthy_endpoints), + } + + +@router.get("/health/readiness", tags=["health"]) +async def health_readiness(): + """ + Unprotected endpoint for checking if worker can receive requests + """ + global prisma_client + if prisma_client is not None: # if db passed in, check if it's connected + if prisma_client.db.is_connected() == True: + return {"status": "healthy", "db": "connected"} + else: + return {"status": "healthy", "db": "Not connected"} + raise HTTPException(status_code=503, detail="Service Unhealthy") + + +@router.get("/health/liveliness", tags=["health"]) +async def health_liveliness(): + """ + Unprotected endpoint for checking if worker is alive + """ + return "I'm alive!" + + +@router.get("/") +async def home(request: Request): + return "LiteLLM: RUNNING" + + +@router.get("/routes") +async def get_routes(): + """ + Get a list of available routes in the FastAPI application. + """ + routes = [] + for route in app.routes: + route_info = { + "path": route.path, + "methods": route.methods, + "name": route.name, + "endpoint": route.endpoint.__name__ if route.endpoint else None, + } + routes.append(route_info) + + return {"routes": routes} + + +@router.on_event("shutdown") +async def shutdown_event(): + global prisma_client, master_key, user_custom_auth + if prisma_client: + verbose_proxy_logger.debug("Disconnecting from Prisma") + await prisma_client.disconnect() + + ## RESET CUSTOM VARIABLES ## + cleanup_router_config_variables() + + +def cleanup_router_config_variables(): + global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, use_background_health_checks, health_check_interval + + # Set all variables to None + master_key = None + user_config_file_path = None + otel_logging = None + user_custom_auth = None + user_custom_auth_path = None + use_background_health_checks = None + health_check_interval = None + + +app.include_router(router) diff --git a/litellm/proxy/queue/celery_app.py b/litellm/proxy/queue/celery_app.py new file mode 100644 index 0000000000000000000000000000000000000000..9f59b6edf54829efd68f8b046e4353112f94e5a5 --- /dev/null +++ b/litellm/proxy/queue/celery_app.py @@ -0,0 +1,77 @@ +# from dotenv import load_dotenv + +# load_dotenv() +# import json, subprocess +# import psutil # Import the psutil library +# import atexit + +# try: +# ### OPTIONAL DEPENDENCIES ### - pip install redis and celery only when a user opts into using the async endpoints which require both +# from celery import Celery +# import redis +# except: +# import sys + +# subprocess.check_call([sys.executable, "-m", "pip", "install", "redis", "celery"]) + +# import time +# import sys, os + +# sys.path.insert( +# 0, os.path.abspath("../../..") +# ) # Adds the parent directory to the system path - for litellm local dev +# import litellm + +# # Redis connection setup +# pool = redis.ConnectionPool( +# host=os.getenv("REDIS_HOST"), +# port=os.getenv("REDIS_PORT"), +# password=os.getenv("REDIS_PASSWORD"), +# db=0, +# max_connections=5, +# ) +# redis_client = redis.Redis(connection_pool=pool) + +# # Celery setup +# celery_app = Celery( +# "tasks", +# broker=f"redis://default:{os.getenv('REDIS_PASSWORD')}@{os.getenv('REDIS_HOST')}:{os.getenv('REDIS_PORT')}", +# backend=f"redis://default:{os.getenv('REDIS_PASSWORD')}@{os.getenv('REDIS_HOST')}:{os.getenv('REDIS_PORT')}", +# ) +# celery_app.conf.update( +# broker_pool_limit=None, +# broker_transport_options={"connection_pool": pool}, +# result_backend_transport_options={"connection_pool": pool}, +# ) + + +# # Celery task +# @celery_app.task(name="process_job", max_retries=3) +# def process_job(*args, **kwargs): +# try: +# llm_router: litellm.Router = litellm.Router(model_list=kwargs.pop("llm_model_list")) # type: ignore +# response = llm_router.completion(*args, **kwargs) # type: ignore +# if isinstance(response, litellm.ModelResponse): +# response = response.model_dump_json() +# return json.loads(response) +# return str(response) +# except Exception as e: +# raise e + + +# # Ensure Celery workers are terminated when the script exits +# def cleanup(): +# try: +# # Get a list of all running processes +# for process in psutil.process_iter(attrs=["pid", "name"]): +# # Check if the process is a Celery worker process +# if process.info["name"] == "celery": +# print(f"Terminating Celery worker with PID {process.info['pid']}") +# # Terminate the Celery worker process +# psutil.Process(process.info["pid"]).terminate() +# except Exception as e: +# print(f"Error during cleanup: {e}") + + +# # Register the cleanup function to run when the script exits +# atexit.register(cleanup) diff --git a/litellm/proxy/queue/celery_worker.py b/litellm/proxy/queue/celery_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..41b0af5153211f610d70aaed50ef51016a1d922d --- /dev/null +++ b/litellm/proxy/queue/celery_worker.py @@ -0,0 +1,15 @@ +import os +from multiprocessing import Process + + +def run_worker(cwd): + os.chdir(cwd) + os.system( + "celery -A celery_app.celery_app worker --concurrency=120 --loglevel=info" + ) + + +def start_worker(cwd): + cwd += "/queue" + worker_process = Process(target=run_worker, args=(cwd,)) + worker_process.start() diff --git a/litellm/proxy/queue/rq_worker.py b/litellm/proxy/queue/rq_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..7f9b34aed5cc888fa489f1f87c18899e6bae3bea --- /dev/null +++ b/litellm/proxy/queue/rq_worker.py @@ -0,0 +1,34 @@ +# import sys, os +# from dotenv import load_dotenv + +# load_dotenv() +# # Add the path to the local folder to sys.path +# sys.path.insert( +# 0, os.path.abspath("../../..") +# ) # Adds the parent directory to the system path - for litellm local dev + + +# def start_rq_worker(): +# from rq import Worker, Queue, Connection +# from redis import Redis + +# # Set up RQ connection +# redis_conn = Redis( +# host=os.getenv("REDIS_HOST"), +# port=os.getenv("REDIS_PORT"), +# password=os.getenv("REDIS_PASSWORD"), +# ) +# print(redis_conn.ping()) # Should print True if connected successfully +# # Create a worker and add the queue +# try: +# queue = Queue(connection=redis_conn) +# worker = Worker([queue], connection=redis_conn) +# except Exception as e: +# print(f"Error setting up worker: {e}") +# exit() + +# with Connection(redis_conn): +# worker.work() + + +# start_rq_worker() diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma new file mode 100644 index 0000000000000000000000000000000000000000..d12cac8f20f3fded026de5868c7a57c9cc325b8f --- /dev/null +++ b/litellm/proxy/schema.prisma @@ -0,0 +1,33 @@ +datasource client { + provider = "postgresql" + url = env("DATABASE_URL") +} + +generator client { + provider = "prisma-client-py" +} + +model LiteLLM_UserTable { + user_id String @unique + max_budget Float? + spend Float @default(0.0) + user_email String? +} + +// required for token gen +model LiteLLM_VerificationToken { + token String @unique + spend Float @default(0.0) + expires DateTime? + models String[] + aliases Json @default("{}") + config Json @default("{}") + user_id String? + max_parallel_requests Int? + metadata Json @default("{}") +} + +model LiteLLM_Config { + param_name String @id + param_value Json? +} \ No newline at end of file diff --git a/litellm/proxy/secret_managers/google_kms.py b/litellm/proxy/secret_managers/google_kms.py new file mode 100644 index 0000000000000000000000000000000000000000..5e83d54c7f450ad5ea900e7c3a0797e4c2014bd3 --- /dev/null +++ b/litellm/proxy/secret_managers/google_kms.py @@ -0,0 +1,40 @@ +""" +This is a file for the Google KMS integration + +Relevant issue: https://github.com/BerriAI/litellm/issues/1235 + +Requires: +* `os.environ["GOOGLE_APPLICATION_CREDENTIALS"], os.environ["GOOGLE_KMS_RESOURCE_NAME"]` +* `pip install google-cloud-kms` +""" +import litellm, os +from typing import Optional +from litellm.proxy._types import KeyManagementSystem + + +def validate_environment(): + if "GOOGLE_APPLICATION_CREDENTIALS" not in os.environ: + raise ValueError( + "Missing required environment variable - GOOGLE_APPLICATION_CREDENTIALS" + ) + if "GOOGLE_KMS_RESOURCE_NAME" not in os.environ: + raise ValueError( + "Missing required environment variable - GOOGLE_KMS_RESOURCE_NAME" + ) + + +def load_google_kms(use_google_kms: Optional[bool]): + if use_google_kms is None or use_google_kms == False: + return + try: + from google.cloud import kms_v1 # type: ignore + + validate_environment() + + # Create the KMS client + client = kms_v1.KeyManagementServiceClient() + litellm.secret_manager_client = client + litellm._key_management_system = KeyManagementSystem.GOOGLE_KMS + litellm._google_kms_resource_name = os.getenv("GOOGLE_KMS_RESOURCE_NAME") + except Exception as e: + raise e diff --git a/litellm/proxy/start.sh b/litellm/proxy/start.sh new file mode 100644 index 0000000000000000000000000000000000000000..44df50aaabfbf3cf0fa38e50d1b2ecc34a5ddecc --- /dev/null +++ b/litellm/proxy/start.sh @@ -0,0 +1,2 @@ +#!/bin/bash +python3 proxy_cli.py \ No newline at end of file diff --git a/litellm/proxy/tests/bursty_load_test_completion.py b/litellm/proxy/tests/bursty_load_test_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..529f2ce9f6940a5553dc934c652dbc348cc402d3 --- /dev/null +++ b/litellm/proxy/tests/bursty_load_test_completion.py @@ -0,0 +1,50 @@ +import time, asyncio +from openai import AsyncOpenAI +import uuid +import traceback + + +litellm_client = AsyncOpenAI(api_key="test", base_url="http://0.0.0.0:8000") + + +async def litellm_completion(): + # Your existing code for litellm_completion goes here + try: + response = await litellm_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": f"This is a test: {uuid.uuid4()}" * 180} + ], # this is about 4k tokens per request + ) + return response + + except Exception as e: + # If there's an exception, log the error message + with open("error_log.txt", "a") as error_log: + error_log.write(f"Error during completion: {str(e)}\n") + pass + + +async def main(): + start = time.time() + n = 60 # Send 60 concurrent requests, each with 4k tokens = 240k Tokens + tasks = [litellm_completion() for _ in range(n)] + + chat_completions = await asyncio.gather(*tasks) + + successful_completions = [c for c in chat_completions if c is not None] + + # Write errors to error_log.txt + with open("error_log.txt", "a") as error_log: + for completion in chat_completions: + if isinstance(completion, str): + error_log.write(completion + "\n") + + print(n, time.time() - start, len(successful_completions)) + + +if __name__ == "__main__": + # Blank out contents of error_log.txt + open("error_log.txt", "w").close() + + asyncio.run(main()) diff --git a/litellm/proxy/tests/error_log.txt b/litellm/proxy/tests/error_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/litellm/proxy/tests/load_test_completion.py b/litellm/proxy/tests/load_test_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..f2f480c88915b550c366ae1a349d8a4ac4f29e0a --- /dev/null +++ b/litellm/proxy/tests/load_test_completion.py @@ -0,0 +1,53 @@ +import time, asyncio +from openai import AsyncOpenAI +import uuid +import traceback + + +litellm_client = AsyncOpenAI(base_url="http://0.0.0.0:8000", api_key="any") + + +async def litellm_completion(): + # Your existing code for litellm_completion goes here + try: + response = await litellm_client.chat.completions.create( + model="Azure OpenAI GPT-4 Canada-East (External)", + stream=True, + messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}], + ) + async for chunk in response: + print(chunk) + return response + + except Exception as e: + # If there's an exception, log the error message + with open("error_log.txt", "a") as error_log: + error_log.write(f"Error during completion: {str(e)}\n") + pass + + +async def main(): + for i in range(1000000): + start = time.time() + n = 1000 # Number of concurrent tasks + tasks = [litellm_completion() for _ in range(n)] + + chat_completions = await asyncio.gather(*tasks) + + successful_completions = [c for c in chat_completions if c is not None] + + # Write errors to error_log.txt + with open("error_log.txt", "a") as error_log: + for completion in chat_completions: + if isinstance(completion, str): + error_log.write(completion + "\n") + + print(n, time.time() - start, len(successful_completions)) + time.sleep(10) + + +if __name__ == "__main__": + # Blank out contents of error_log.txt + open("error_log.txt", "w").close() + + asyncio.run(main()) diff --git a/litellm/proxy/tests/load_test_embedding.py b/litellm/proxy/tests/load_test_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..6771af188fcaada685db833d7e34f376850306ea --- /dev/null +++ b/litellm/proxy/tests/load_test_embedding.py @@ -0,0 +1,105 @@ +# test time it takes to make 100 concurrent embedding requests to OpenaI + +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest + + +import litellm + +litellm.set_verbose = False + + +question = "embed this very long text" * 100 + + +# make X concurrent calls to litellm.completion(model=gpt-35-turbo, messages=[]), pick a random question in questions array. +# Allow me to tune X concurrent calls.. Log question, output/exception, response time somewhere +# show me a summary of requests made, success full calls, failed calls. For failed calls show me the exceptions + +import concurrent.futures +import random +import time + + +# Function to make concurrent calls to OpenAI API +def make_openai_completion(question): + try: + start_time = time.time() + import openai + + client = openai.OpenAI( + api_key=os.environ["OPENAI_API_KEY"] + ) # base_url="http://0.0.0.0:8000", + response = client.embeddings.create( + model="text-embedding-ada-002", + input=[question], + ) + print(response) + end_time = time.time() + + # Log the request details + # with open("request_log.txt", "a") as log_file: + # log_file.write( + # f"Question: {question[:100]}\nResponse ID:{response.id} Content:{response.choices[0].message.content[:10]}\nTime: {end_time - start_time:.2f} seconds\n\n" + # ) + + return response + except Exception as e: + # Log exceptions for failed calls + # with open("error_log.txt", "a") as error_log_file: + # error_log_file.write( + # f"\nException: {str(e)}\n\n" + # ) + return None + + +start_time = time.time() +# Number of concurrent calls (you can adjust this) +concurrent_calls = 500 + +# List to store the futures of concurrent calls +futures = [] + +# Make concurrent calls +with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_calls) as executor: + for _ in range(concurrent_calls): + futures.append(executor.submit(make_openai_completion, question)) + +# Wait for all futures to complete +concurrent.futures.wait(futures) + +# Summarize the results +successful_calls = 0 +failed_calls = 0 + +for future in futures: + if future.result() is not None: + successful_calls += 1 + else: + failed_calls += 1 + +end_time = time.time() +# Calculate the duration +duration = end_time - start_time + +print(f"Load test Summary:") +print(f"Total Requests: {concurrent_calls}") +print(f"Successful Calls: {successful_calls}") +print(f"Failed Calls: {failed_calls}") +print(f"Total Time: {duration:.2f} seconds") + +# Display content of the logs +with open("request_log.txt", "r") as log_file: + print("\nRequest Log:\n", log_file.read()) + +with open("error_log.txt", "r") as error_log_file: + print("\nError Log:\n", error_log_file.read()) diff --git a/litellm/proxy/tests/load_test_embedding_100.py b/litellm/proxy/tests/load_test_embedding_100.py new file mode 100644 index 0000000000000000000000000000000000000000..38ae8990aaa91e581a5ded939a1de34fff93fb1a --- /dev/null +++ b/litellm/proxy/tests/load_test_embedding_100.py @@ -0,0 +1,54 @@ +import time, asyncio +from openai import AsyncOpenAI +import uuid +import traceback + + +litellm_client = AsyncOpenAI(api_key="test", base_url="http://0.0.0.0:8000") + + +async def litellm_completion(): + # Your existing code for litellm_completion goes here + try: + print("starting embedding calls") + response = await litellm_client.embeddings.create( + model="text-embedding-ada-002", + input=[ + "hello who are you" * 2000, + "hello who are you tomorrow 1234" * 1000, + "hello who are you tomorrow 1234" * 1000, + ], + ) + print(response) + return response + + except Exception as e: + # If there's an exception, log the error message + with open("error_log.txt", "a") as error_log: + error_log.write(f"Error during completion: {str(e)}\n") + pass + + +async def main(): + start = time.time() + n = 100 # Number of concurrent tasks + tasks = [litellm_completion() for _ in range(n)] + + chat_completions = await asyncio.gather(*tasks) + + successful_completions = [c for c in chat_completions if c is not None] + + # Write errors to error_log.txt + with open("error_log.txt", "a") as error_log: + for completion in chat_completions: + if isinstance(completion, str): + error_log.write(completion + "\n") + + print(n, time.time() - start, len(successful_completions)) + + +if __name__ == "__main__": + # Blank out contents of error_log.txt + open("error_log.txt", "w").close() + + asyncio.run(main()) diff --git a/litellm/proxy/tests/load_test_embedding_proxy.py b/litellm/proxy/tests/load_test_embedding_proxy.py new file mode 100644 index 0000000000000000000000000000000000000000..32c90cb8e32b49399d81e7328aaaf9dd921480c5 --- /dev/null +++ b/litellm/proxy/tests/load_test_embedding_proxy.py @@ -0,0 +1,105 @@ +# test time it takes to make 100 concurrent embedding requests to OpenaI + +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest + + +import litellm + +litellm.set_verbose = False + + +question = "embed this very long text" * 100 + + +# make X concurrent calls to litellm.completion(model=gpt-35-turbo, messages=[]), pick a random question in questions array. +# Allow me to tune X concurrent calls.. Log question, output/exception, response time somewhere +# show me a summary of requests made, success full calls, failed calls. For failed calls show me the exceptions + +import concurrent.futures +import random +import time + + +# Function to make concurrent calls to OpenAI API +def make_openai_completion(question): + try: + start_time = time.time() + import openai + + client = openai.OpenAI( + api_key=os.environ["OPENAI_API_KEY"], base_url="http://0.0.0.0:8000" + ) # base_url="http://0.0.0.0:8000", + response = client.embeddings.create( + model="text-embedding-ada-002", + input=[question], + ) + print(response) + end_time = time.time() + + # Log the request details + # with open("request_log.txt", "a") as log_file: + # log_file.write( + # f"Question: {question[:100]}\nResponse ID:{response.id} Content:{response.choices[0].message.content[:10]}\nTime: {end_time - start_time:.2f} seconds\n\n" + # ) + + return response + except Exception as e: + # Log exceptions for failed calls + # with open("error_log.txt", "a") as error_log_file: + # error_log_file.write( + # f"\nException: {str(e)}\n\n" + # ) + return None + + +start_time = time.time() +# Number of concurrent calls (you can adjust this) +concurrent_calls = 500 + +# List to store the futures of concurrent calls +futures = [] + +# Make concurrent calls +with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_calls) as executor: + for _ in range(concurrent_calls): + futures.append(executor.submit(make_openai_completion, question)) + +# Wait for all futures to complete +concurrent.futures.wait(futures) + +# Summarize the results +successful_calls = 0 +failed_calls = 0 + +for future in futures: + if future.result() is not None: + successful_calls += 1 + else: + failed_calls += 1 +end_time = time.time() +# Calculate the duration +duration = end_time - start_time + + +print(f"Load test Summary:") +print(f"Total Requests: {concurrent_calls}") +print(f"Successful Calls: {successful_calls}") +print(f"Failed Calls: {failed_calls}") +print(f"Total Time: {duration:.2f} seconds") + +# # Display content of the logs +# with open("request_log.txt", "r") as log_file: +# print("\nRequest Log:\n", log_file.read()) + +# with open("error_log.txt", "r") as error_log_file: +# print("\nError Log:\n", error_log_file.read()) diff --git a/litellm/proxy/tests/load_test_q.py b/litellm/proxy/tests/load_test_q.py new file mode 100644 index 0000000000000000000000000000000000000000..657c273017ebe3bce8d681d3b7b5af5948731492 --- /dev/null +++ b/litellm/proxy/tests/load_test_q.py @@ -0,0 +1,120 @@ +import requests +import time +import os +from dotenv import load_dotenv + +load_dotenv() + + +# Set the base URL as needed +base_url = "https://api.litellm.ai" +# # Uncomment the line below if you want to switch to the local server +# base_url = "http://0.0.0.0:8000" + +# Step 1 Add a config to the proxy, generate a temp key +config = { + "model_list": [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": os.environ["OPENAI_API_KEY"], + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.environ["AZURE_API_KEY"], + "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", + "api_version": "2023-07-01-preview", + }, + }, + ] +} +print("STARTING LOAD TEST Q") +print(os.environ["AZURE_API_KEY"]) + +response = requests.post( + url=f"{base_url}/key/generate", + json={ + "config": config, + "duration": "30d", # default to 30d, set it to 30m if you want a temp key + }, + headers={"Authorization": "Bearer sk-hosted-litellm"}, +) + +print("\nresponse from generating key", response.text) +print("\n json response from gen key", response.json()) + +generated_key = response.json()["key"] +print("\ngenerated key for proxy", generated_key) + + +# Step 2: Queue 50 requests to the proxy, using your generated_key + +import concurrent.futures + + +def create_job_and_poll(request_num): + print(f"Creating a job on the proxy for request {request_num}") + job_response = requests.post( + url=f"{base_url}/queue/request", + json={ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "system", "content": "write a short poem"}, + ], + }, + headers={"Authorization": f"Bearer {generated_key}"}, + ) + print(job_response.status_code) + print(job_response.text) + print("\nResponse from creating job", job_response.text) + job_response = job_response.json() + job_id = job_response["id"] + polling_url = job_response["url"] + polling_url = f"{base_url}{polling_url}" + print(f"\nCreated Job {request_num}, Polling Url {polling_url}") + + # Poll each request + while True: + try: + print(f"\nPolling URL for request {request_num}", polling_url) + polling_response = requests.get( + url=polling_url, headers={"Authorization": f"Bearer {generated_key}"} + ) + print( + f"\nResponse from polling url for request {request_num}", + polling_response.text, + ) + polling_response = polling_response.json() + status = polling_response.get("status", None) + if status == "finished": + llm_response = polling_response["result"] + print(f"LLM Response for request {request_num}") + print(llm_response) + # Write the llm_response to load_test_log.txt + try: + with open("load_test_log.txt", "a") as response_file: + response_file.write( + f"Response for request: {request_num}\n{llm_response}\n\n" + ) + except Exception as e: + print("GOT EXCEPTION", e) + break + time.sleep(0.5) + except Exception as e: + print("got exception when polling", e) + + +# Number of requests +num_requests = 100 + +# Use ThreadPoolExecutor for parallel execution +with concurrent.futures.ThreadPoolExecutor(max_workers=num_requests) as executor: + # Create and poll each request in parallel + futures = [executor.submit(create_job_and_poll, i) for i in range(num_requests)] + + # Wait for all futures to complete + concurrent.futures.wait(futures) diff --git a/litellm/proxy/tests/request_log.txt b/litellm/proxy/tests/request_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/litellm/proxy/tests/test_async.py b/litellm/proxy/tests/test_async.py new file mode 100644 index 0000000000000000000000000000000000000000..65d289853ba6fa0a2fd795a6b16a8af33575eb27 --- /dev/null +++ b/litellm/proxy/tests/test_async.py @@ -0,0 +1,28 @@ +# # This tests the litelm proxy +# # it makes async Completion requests with streaming +# import openai + +# openai.base_url = "http://0.0.0.0:8000" +# openai.api_key = "temp-key" +# print(openai.base_url) + +# async def test_async_completion(): +# response = await ( +# model="gpt-3.5-turbo", +# prompt='this is a test request, write a short poem', +# ) +# print(response) + +# print("test_streaming") +# response = await openai.chat.completions.create( +# model="gpt-3.5-turbo", +# prompt='this is a test request, write a short poem', +# stream=True +# ) +# print(response) +# async for chunk in response: +# print(chunk) + + +# import asyncio +# asyncio.run(test_async_completion()) diff --git a/litellm/proxy/tests/test_langchain_request.py b/litellm/proxy/tests/test_langchain_request.py new file mode 100644 index 0000000000000000000000000000000000000000..e94a077cc8eee524023894f4d99ead39acbaddc6 --- /dev/null +++ b/litellm/proxy/tests/test_langchain_request.py @@ -0,0 +1,44 @@ +# # LOCAL TEST +# from langchain.chat_models import ChatOpenAI +# from langchain.prompts.chat import ( +# ChatPromptTemplate, +# HumanMessagePromptTemplate, +# SystemMessagePromptTemplate, +# ) +# from langchain.schema import HumanMessage, SystemMessage + +# chat = ChatOpenAI( +# openai_api_base="http://0.0.0.0:8000", +# model = "azure/chatgpt-v-2", +# temperature=0.1, +# extra_body={ +# "metadata": { +# "generation_name": "ishaan-generation-langchain-client", +# "generation_id": "langchain-client-gen-id22", +# "trace_id": "langchain-client-trace-id22", +# "trace_user_id": "langchain-client-user-id2" +# } +# } +# ) + +# messages = [ +# SystemMessage( +# content="You are a helpful assistant that im using to make a test request to." +# ), +# HumanMessage( +# content="test from litellm. tell me why it's amazing in 1 sentence" +# ), +# ] +# response = chat(messages) + +# print(response) + +# # claude_chat = ChatOpenAI( +# # openai_api_base="http://0.0.0.0:8000", +# # model = "claude-v1", +# # temperature=0.1 +# # ) + +# # response = claude_chat(messages) + +# # print(response) diff --git a/litellm/proxy/tests/test_openai_request.py b/litellm/proxy/tests/test_openai_request.py new file mode 100644 index 0000000000000000000000000000000000000000..16755db0b8d2a34958723150c36cb74d983def78 --- /dev/null +++ b/litellm/proxy/tests/test_openai_request.py @@ -0,0 +1,26 @@ +# import openai +# client = openai.OpenAI( +# api_key="anything", +# base_url="http://0.0.0.0:8000" +# ) + +# # request sent to model set on litellm proxy, `litellm --model` +# response = client.chat.completions.create( +# model="azure/chatgpt-v-2", +# messages = [ +# { +# "role": "user", +# "content": "this is a test request, write a short poem" +# } +# ], +# extra_body={ +# "metadata": { +# "generation_name": "ishaan-generation-openai-client", +# "generation_id": "openai-client-gen-id22", +# "trace_id": "openai-client-trace-id22", +# "trace_user_id": "openai-client-user-id2" +# } +# } +# ) + +# print(response) diff --git a/litellm/proxy/tests/test_q.py b/litellm/proxy/tests/test_q.py new file mode 100644 index 0000000000000000000000000000000000000000..5878f21ada7991210ded1a108577dbb4858ae9fb --- /dev/null +++ b/litellm/proxy/tests/test_q.py @@ -0,0 +1,84 @@ +import requests +import time +import os +from dotenv import load_dotenv + +load_dotenv() + + +# Set the base URL as needed +base_url = "https://api.litellm.ai" +# Uncomment the line below if you want to switch to the local server +# base_url = "http://0.0.0.0:8000" + +# Step 1 Add a config to the proxy, generate a temp key +config = { + "model_list": [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": os.environ["OPENAI_API_KEY"], + }, + } + ] +} + +response = requests.post( + url=f"{base_url}/key/generate", + json={ + "config": config, + "duration": "30d", # default to 30d, set it to 30m if you want a temp key + }, + headers={"Authorization": "Bearer sk-hosted-litellm"}, +) + +print("\nresponse from generating key", response.text) +print("\n json response from gen key", response.json()) + +generated_key = response.json()["key"] +print("\ngenerated key for proxy", generated_key) + +# Step 2: Queue a request to the proxy, using your generated_key +print("Creating a job on the proxy") +job_response = requests.post( + url=f"{base_url}/queue/request", + json={ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "system", + "content": f"You are a helpful assistant. What is your name", + }, + ], + }, + headers={"Authorization": f"Bearer {generated_key}"}, +) +print(job_response.status_code) +print(job_response.text) +print("\nResponse from creating job", job_response.text) +job_response = job_response.json() +job_id = job_response["id"] # type: ignore +polling_url = job_response["url"] # type: ignore +polling_url = f"{base_url}{polling_url}" +print("\nCreated Job, Polling Url", polling_url) + +# Step 3: Poll the request +while True: + try: + print("\nPolling URL", polling_url) + polling_response = requests.get( + url=polling_url, headers={"Authorization": f"Bearer {generated_key}"} + ) + print("\nResponse from polling url", polling_response.text) + polling_response = polling_response.json() + status = polling_response.get("status", None) # type: ignore + if status == "finished": + llm_response = polling_response["result"] # type: ignore + print("LLM Response") + print(llm_response) + break + time.sleep(0.5) + except Exception as e: + print("got exception in polling", e) + break diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c55453423bfaa5dfee9d27d4049d476f6f2781c0 --- /dev/null +++ b/litellm/proxy/utils.py @@ -0,0 +1,750 @@ +from typing import Optional, List, Any, Literal, Union +import os, subprocess, hashlib, importlib, asyncio, copy, json, aiohttp, httpx +import litellm, backoff +from litellm.proxy._types import UserAPIKeyAuth, DynamoDBArgs +from litellm.caching import DualCache +from litellm.proxy.hooks.parallel_request_limiter import MaxParallelRequestsHandler +from litellm.proxy.hooks.max_budget_limiter import MaxBudgetLimiter +from litellm.integrations.custom_logger import CustomLogger +from litellm.proxy.db.base_client import CustomDB +from fastapi import HTTPException, status +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart + + +def print_verbose(print_statement): + if litellm.set_verbose: + print(f"LiteLLM Proxy: {print_statement}") # noqa + + +### LOGGING ### +class ProxyLogging: + """ + Logging/Custom Handlers for proxy. + + Implemented mainly to: + - log successful/failed db read/writes + - support the max parallel request integration + """ + + def __init__(self, user_api_key_cache: DualCache): + ## INITIALIZE LITELLM CALLBACKS ## + self.call_details: dict = {} + self.call_details["user_api_key_cache"] = user_api_key_cache + self.max_parallel_request_limiter = MaxParallelRequestsHandler() + self.max_budget_limiter = MaxBudgetLimiter() + self.alerting: Optional[List] = None + self.alerting_threshold: float = 300 # default to 5 min. threshold + pass + + def update_values( + self, alerting: Optional[List], alerting_threshold: Optional[float] + ): + self.alerting = alerting + if alerting_threshold is not None: + self.alerting_threshold = alerting_threshold + + def _init_litellm_callbacks(self): + print_verbose(f"INITIALIZING LITELLM CALLBACKS!") + litellm.callbacks.append(self.max_parallel_request_limiter) + litellm.callbacks.append(self.max_budget_limiter) + for callback in litellm.callbacks: + if callback not in litellm.input_callback: + litellm.input_callback.append(callback) + if callback not in litellm.success_callback: + litellm.success_callback.append(callback) + if callback not in litellm.failure_callback: + litellm.failure_callback.append(callback) + if callback not in litellm._async_success_callback: + litellm._async_success_callback.append(callback) + if callback not in litellm._async_failure_callback: + litellm._async_failure_callback.append(callback) + + if ( + len(litellm.input_callback) > 0 + or len(litellm.success_callback) > 0 + or len(litellm.failure_callback) > 0 + ): + callback_list = list( + set( + litellm.input_callback + + litellm.success_callback + + litellm.failure_callback + ) + ) + litellm.utils.set_callbacks(callback_list=callback_list) + + async def pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + data: dict, + call_type: Literal["completion", "embeddings"], + ): + """ + Allows users to modify/reject the incoming request to the proxy, without having to deal with parsing Request body. + + Covers: + 1. /chat/completions + 2. /embeddings + 3. /image/generation + """ + ### ALERTING ### + asyncio.create_task(self.response_taking_too_long()) + + try: + for callback in litellm.callbacks: + if isinstance(callback, CustomLogger) and "async_pre_call_hook" in vars( + callback.__class__ + ): + response = await callback.async_pre_call_hook( + user_api_key_dict=user_api_key_dict, + cache=self.call_details["user_api_key_cache"], + data=data, + call_type=call_type, + ) + if response is not None: + data = response + + print_verbose(f"final data being sent to {call_type} call: {data}") + return data + except Exception as e: + raise e + + async def success_handler( + self, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + call_type: Literal["completion", "embeddings"], + start_time, + end_time, + ): + """ + Log successful API calls / db read/writes + """ + + pass + + async def response_taking_too_long( + self, + start_time: Optional[float] = None, + end_time: Optional[float] = None, + type: Literal["hanging_request", "slow_response"] = "hanging_request", + ): + if type == "hanging_request": + # Simulate a long-running operation that could take more than 5 minutes + await asyncio.sleep( + self.alerting_threshold + ) # Set it to 5 minutes - i'd imagine this might be different for streaming, non-streaming, non-completion (embedding + img) requests + + await self.alerting_handler( + message=f"Requests are hanging - {self.alerting_threshold}s+ request time", + level="Medium", + ) + + elif ( + type == "slow_response" and start_time is not None and end_time is not None + ): + if end_time - start_time > self.alerting_threshold: + await self.alerting_handler( + message=f"Responses are slow - {round(end_time-start_time,2)}s response time", + level="Low", + ) + + async def alerting_handler( + self, message: str, level: Literal["Low", "Medium", "High"] + ): + """ + Alerting based on thresholds: - https://github.com/BerriAI/litellm/issues/1298 + + - Responses taking too long + - Requests are hanging + - Calls are failing + - DB Read/Writes are failing + + Parameters: + level: str - Low|Medium|High - if calls might fail (Medium) or are failing (High); Currently, no alerts would be 'Low'. + message: str - what is the alert about + """ + formatted_message = f"Level: {level}\n\nMessage: {message}" + if self.alerting is None: + return + + for client in self.alerting: + if client == "slack": + slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL", None) + if slack_webhook_url is None: + raise Exception("Missing SLACK_WEBHOOK_URL from environment") + payload = {"text": formatted_message} + headers = {"Content-type": "application/json"} + async with aiohttp.ClientSession() as session: + async with session.post( + slack_webhook_url, json=payload, headers=headers + ) as response: + if response.status == 200: + pass + elif client == "sentry": + if litellm.utils.sentry_sdk_instance is not None: + litellm.utils.sentry_sdk_instance.capture_message(formatted_message) + else: + raise Exception("Missing SENTRY_DSN from environment") + + async def failure_handler(self, original_exception): + """ + Log failed db read/writes + + Currently only logs exceptions to sentry + """ + ### ALERTING ### + if isinstance(original_exception, HTTPException): + error_message = original_exception.detail + else: + error_message = str(original_exception) + asyncio.create_task( + self.alerting_handler( + message=f"DB read/write call failed: {error_message}", + level="High", + ) + ) + + if litellm.utils.capture_exception: + litellm.utils.capture_exception(error=original_exception) + + async def post_call_failure_hook( + self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth + ): + """ + Allows users to raise custom exceptions/log when a call fails, without having to deal with parsing Request body. + + Covers: + 1. /chat/completions + 2. /embeddings + 3. /image/generation + """ + + ### ALERTING ### + asyncio.create_task( + self.alerting_handler( + message=f"LLM API call failed: {str(original_exception)}", level="High" + ) + ) + + for callback in litellm.callbacks: + try: + if isinstance(callback, CustomLogger): + await callback.async_post_call_failure_hook( + user_api_key_dict=user_api_key_dict, + original_exception=original_exception, + ) + except Exception as e: + raise e + return + + +### DB CONNECTOR ### +# Define the retry decorator with backoff strategy +# Function to be called whenever a retry is about to happen +def on_backoff(details): + # The 'tries' key in the details dictionary contains the number of completed tries + print_verbose(f"Backing off... this was attempt #{details['tries']}") + + +class PrismaClient: + def __init__(self, database_url: str, proxy_logging_obj: ProxyLogging): + print_verbose( + "LiteLLM: DATABASE_URL Set in config, trying to 'pip install prisma'" + ) + ## init logging object + self.proxy_logging_obj = proxy_logging_obj + try: + from prisma import Prisma # type: ignore + except Exception as e: + os.environ["DATABASE_URL"] = database_url + # Save the current working directory + original_dir = os.getcwd() + # set the working directory to where this script is + abspath = os.path.abspath(__file__) + dname = os.path.dirname(abspath) + os.chdir(dname) + + try: + subprocess.run(["prisma", "generate"]) + subprocess.run( + ["prisma", "db", "push", "--accept-data-loss"] + ) # this looks like a weird edge case when prisma just wont start on render. we need to have the --accept-data-loss + except: + raise Exception( + f"Unable to run prisma commands. Run `pip install prisma`" + ) + finally: + os.chdir(original_dir) + # Now you can import the Prisma Client + from prisma import Prisma # type: ignore + + self.db = Prisma( + http={ + "limits": httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ) + } + ) # Client to connect to Prisma db + + def hash_token(self, token: str): + # Hash the string using SHA-256 + hashed_token = hashlib.sha256(token.encode()).hexdigest() + + return hashed_token + + def jsonify_object(self, data: dict) -> dict: + db_data = copy.deepcopy(data) + + for k, v in db_data.items(): + if isinstance(v, dict): + db_data[k] = json.dumps(v) + return db_data + + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def get_generic_data( + self, + key: str, + value: Any, + table_name: Literal["users", "keys", "config"], + ): + """ + Generic implementation of get data + """ + try: + if table_name == "users": + response = await self.db.litellm_usertable.find_first( + where={key: value} # type: ignore + ) + elif table_name == "keys": + response = await self.db.litellm_verificationtoken.find_first( # type: ignore + where={key: value} # type: ignore + ) + elif table_name == "config": + response = await self.db.litellm_config.find_first( # type: ignore + where={key: value} # type: ignore + ) + return response + except Exception as e: + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + raise e + + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def get_data( + self, + token: Optional[str] = None, + user_id: Optional[str] = None, + table_name: Optional[Literal["user", "key", "config"]] = None, + query_type: Literal["find_unique", "find_all"] = "find_unique", + ): + try: + print_verbose("PrismaClient: get_data") + + response: Any = None + if token is not None or (table_name is not None and table_name == "key"): + # check if plain text or hash + if token is not None: + hashed_token = token + if token.startswith("sk-"): + hashed_token = self.hash_token(token=token) + print_verbose("PrismaClient: find_unique") + if query_type == "find_unique": + response = await self.db.litellm_verificationtoken.find_unique( + where={"token": hashed_token} + ) + elif query_type == "find_all" and user_id is not None: + response = await self.db.litellm_verificationtoken.find_many( + where={"user_id": user_id} + ) + print_verbose(f"PrismaClient: response={response}") + if response is not None: + return response + else: + # Token does not exist. + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="invalid user key", + ) + elif user_id is not None: + response = await self.db.litellm_usertable.find_unique( # type: ignore + where={ + "user_id": user_id, + } + ) + return response + except Exception as e: + print_verbose(f"LiteLLM Prisma Client Exception: {e}") + import traceback + + traceback.print_exc() + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + raise e + + # Define a retrying strategy with exponential backoff + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def insert_data( + self, data: dict, table_name: Literal["user+key", "config"] = "user+key" + ): + """ + Add a key to the database. If it already exists, do nothing. + """ + try: + if table_name == "user+key": + token = data["token"] + hashed_token = self.hash_token(token=token) + db_data = self.jsonify_object(data=data) + db_data["token"] = hashed_token + max_budget = db_data.pop("max_budget", None) + user_email = db_data.pop("user_email", None) + print_verbose( + "PrismaClient: Before upsert into litellm_verificationtoken" + ) + new_verification_token = await self.db.litellm_verificationtoken.upsert( # type: ignore + where={ + "token": hashed_token, + }, + data={ + "create": {**db_data}, # type: ignore + "update": {}, # don't do anything if it already exists + }, + ) + + new_user_row = await self.db.litellm_usertable.upsert( + where={"user_id": data["user_id"]}, + data={ + "create": { + "user_id": data["user_id"], + "max_budget": max_budget, + "user_email": user_email, + }, + "update": {}, # don't do anything if it already exists + }, + ) + return new_verification_token + elif table_name == "config": + """ + For each param, + get the existing table values + + Add the new values + + Update DB + """ + tasks = [] + for k, v in data.items(): + updated_data = v + updated_data = json.dumps(updated_data) + updated_table_row = self.db.litellm_config.upsert( + where={"param_name": k}, + data={ + "create": {"param_name": k, "param_value": updated_data}, + "update": {"param_value": updated_data}, + }, + ) + + tasks.append(updated_table_row) + + await asyncio.gather(*tasks) + except Exception as e: + print_verbose(f"LiteLLM Prisma Client Exception: {e}") + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + raise e + + # Define a retrying strategy with exponential backoff + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def update_data( + self, + token: Optional[str] = None, + data: dict = {}, + user_id: Optional[str] = None, + ): + """ + Update existing data + """ + try: + db_data = self.jsonify_object(data=data) + if token is not None: + print_verbose(f"token: {token}") + # check if plain text or hash + if token.startswith("sk-"): + token = self.hash_token(token=token) + db_data["token"] = token + response = await self.db.litellm_verificationtoken.update( + where={"token": token}, # type: ignore + data={**db_data}, # type: ignore + ) + print_verbose("\033[91m" + f"DB write succeeded {response}" + "\033[0m") + return {"token": token, "data": db_data} + elif user_id is not None: + """ + If data['spend'] + data['user'], update the user table with spend info as well + """ + update_user_row = await self.db.litellm_usertable.update( + where={"user_id": user_id}, # type: ignore + data={**db_data}, # type: ignore + ) + return {"user_id": user_id, "data": db_data} + except Exception as e: + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + print_verbose("\033[91m" + f"DB write failed: {e}" + "\033[0m") + raise e + + # Define a retrying strategy with exponential backoff + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def delete_data(self, tokens: List): + """ + Allow user to delete a key(s) + """ + try: + hashed_tokens = [self.hash_token(token=token) for token in tokens] + await self.db.litellm_verificationtoken.delete_many( + where={"token": {"in": hashed_tokens}} + ) + return {"deleted_keys": tokens} + except Exception as e: + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + raise e + + # Define a retrying strategy with exponential backoff + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def connect(self): + try: + if self.db.is_connected() == False: + await self.db.connect() + except Exception as e: + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + raise e + + # Define a retrying strategy with exponential backoff + @backoff.on_exception( + backoff.expo, + Exception, # base exception to catch for the backoff + max_tries=3, # maximum number of retries + max_time=10, # maximum total time to retry for + on_backoff=on_backoff, # specifying the function to call on backoff + ) + async def disconnect(self): + try: + await self.db.disconnect() + except Exception as e: + asyncio.create_task( + self.proxy_logging_obj.failure_handler(original_exception=e) + ) + raise e + + +class DBClient: + """ + Routes requests for CustomAuth + + [TODO] route b/w customauth and prisma + """ + + def __init__( + self, custom_db_type: Literal["dynamo_db"], custom_db_args: dict + ) -> None: + if custom_db_type == "dynamo_db": + from litellm.proxy.db.dynamo_db import DynamoDBWrapper + + self.db = DynamoDBWrapper(database_arguments=DynamoDBArgs(**custom_db_args)) + + async def get_data(self, key: str, table_name: Literal["user", "key", "config"]): + """ + Check if key valid + """ + return await self.db.get_data(key=key, table_name=table_name) + + async def insert_data( + self, value: Any, table_name: Literal["user", "key", "config"] + ): + """ + For new key / user logic + """ + return await self.db.insert_data(value=value, table_name=table_name) + + async def update_data( + self, key: str, value: Any, table_name: Literal["user", "key", "config"] + ): + """ + For cost tracking logic + + key - hash_key value \n + value - dict with updated values + """ + return await self.db.update_data(key=key, value=value, table_name=table_name) + + async def delete_data( + self, keys: List[str], table_name: Literal["user", "key", "config"] + ): + """ + For /key/delete endpoints + """ + return await self.db.delete_data(keys=keys, table_name=table_name) + + async def connect(self): + """ + For connecting to db and creating / updating any tables + """ + return await self.db.connect() + + async def disconnect(self): + """ + For closing connection on server shutdown + """ + return await self.db.disconnect() + + +### CUSTOM FILE ### +def get_instance_fn(value: str, config_file_path: Optional[str] = None) -> Any: + try: + print_verbose(f"value: {value}") + # Split the path by dots to separate module from instance + parts = value.split(".") + + # The module path is all but the last part, and the instance_name is the last part + module_name = ".".join(parts[:-1]) + instance_name = parts[-1] + + # If config_file_path is provided, use it to determine the module spec and load the module + if config_file_path is not None: + directory = os.path.dirname(config_file_path) + module_file_path = os.path.join(directory, *module_name.split(".")) + module_file_path += ".py" + + spec = importlib.util.spec_from_file_location(module_name, module_file_path) + if spec is None: + raise ImportError( + f"Could not find a module specification for {module_file_path}" + ) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) # type: ignore + else: + # Dynamically import the module + module = importlib.import_module(module_name) + + # Get the instance from the module + instance = getattr(module, instance_name) + + return instance + except ImportError as e: + # Re-raise the exception with a user-friendly message + raise ImportError(f"Could not import {instance_name} from {module_name}") from e + except Exception as e: + raise e + + +### HELPER FUNCTIONS ### +async def _cache_user_row( + user_id: str, cache: DualCache, db: Union[PrismaClient, DBClient] +): + """ + Check if a user_id exists in cache, + if not retrieve it. + """ + print_verbose(f"Prisma: _cache_user_row, user_id: {user_id}") + cache_key = f"{user_id}_user_api_key_user_id" + response = cache.get_cache(key=cache_key) + if response is None: # Cache miss + if isinstance(db, PrismaClient): + user_row = await db.get_data(user_id=user_id) + elif isinstance(db, DBClient): + user_row = await db.get_data(key=user_id, table_name="user") + if user_row is not None: + print_verbose(f"User Row: {user_row}, type = {type(user_row)}") + if hasattr(user_row, "model_dump_json") and callable( + getattr(user_row, "model_dump_json") + ): + cache_value = user_row.model_dump_json() + cache.set_cache( + key=cache_key, value=cache_value, ttl=600 + ) # store for 10 minutes + return + + +async def send_email(sender_name, sender_email, receiver_email, subject, html): + """ + smtp_host, + smtp_port, + smtp_username, + smtp_password, + sender_name, + sender_email, + """ + ## SERVER SETUP ## + smtp_host = os.getenv("SMTP_HOST") + smtp_port = os.getenv("SMTP_PORT", 587) # default to port 587 + smtp_username = os.getenv("SMTP_USERNAME") + smtp_password = os.getenv("SMTP_PASSWORD") + ## EMAIL SETUP ## + email_message = MIMEMultipart() + email_message["From"] = f"{sender_name} <{sender_email}>" + email_message["To"] = receiver_email + email_message["Subject"] = subject + + # Attach the body to the email + email_message.attach(MIMEText(html, "html")) + + try: + print_verbose(f"SMTP Connection Init") + # Establish a secure connection with the SMTP server + with smtplib.SMTP(smtp_host, smtp_port) as server: + server.starttls() + + # Login to your email account + server.login(smtp_username, smtp_password) + + # Send the email + server.send_message(email_message) + + except Exception as e: + print_verbose("An error occurred while sending the email:", str(e)) diff --git a/litellm/requirements.txt b/litellm/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc833dd4befe4d013346e1b2418e3fe91406a7a8 --- /dev/null +++ b/litellm/requirements.txt @@ -0,0 +1 @@ +../requirements.txt \ No newline at end of file diff --git a/litellm/router.py b/litellm/router.py new file mode 100644 index 0000000000000000000000000000000000000000..d5b42343cc7878c778bf9f255e2c757c64b73d1c --- /dev/null +++ b/litellm/router.py @@ -0,0 +1,1864 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you ! We ❤️ you! - Krrish & Ishaan + +import copy, httpx +from datetime import datetime +from typing import Dict, List, Optional, Union, Literal, Any +import random, threading, time, traceback, uuid +import litellm, openai +from litellm.caching import RedisCache, InMemoryCache, DualCache + +import logging, asyncio +import inspect, concurrent +from openai import AsyncOpenAI +from collections import defaultdict +from litellm.router_strategy.least_busy import LeastBusyLoggingHandler +from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler +from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler +from litellm.llms.custom_httpx.azure_dall_e_2 import ( + CustomHTTPTransport, + AsyncCustomHTTPTransport, +) +from litellm.utils import ModelResponse, CustomStreamWrapper +import copy +from litellm._logging import verbose_router_logger +import logging + + +class Router: + """ + Example usage: + ```python + from litellm import Router + model_list = [ + { + "model_name": "azure-gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/", + "api_key": , + "api_version": , + "api_base": + }, + }, + { + "model_name": "azure-gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/", + "api_key": , + "api_version": , + "api_base": + }, + }, + { + "model_name": "openai-gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": , + }, + ] + + router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}]) + ``` + """ + + model_names: List = [] + cache_responses: Optional[bool] = False + default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour + num_retries: int = 0 + tenacity = None + leastbusy_logger: Optional[LeastBusyLoggingHandler] = None + lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None + + def __init__( + self, + model_list: Optional[list] = None, + ## CACHING ## + redis_url: Optional[str] = None, + redis_host: Optional[str] = None, + redis_port: Optional[int] = None, + redis_password: Optional[str] = None, + cache_responses: Optional[bool] = False, + cache_kwargs: dict = {}, # additional kwargs to pass to RedisCache (see caching.py) + caching_groups: Optional[ + List[tuple] + ] = None, # if you want to cache across model groups + client_ttl: int = 3600, # ttl for cached clients - will re-initialize after this time in seconds + ## RELIABILITY ## + num_retries: int = 0, + timeout: Optional[float] = None, + default_litellm_params={}, # default params for Router.chat.completion.create + set_verbose: bool = False, + fallbacks: List = [], + allowed_fails: Optional[int] = None, + context_window_fallbacks: List = [], + model_group_alias: Optional[dict] = {}, + retry_after: int = 0, # min time to wait before retrying a failed request + routing_strategy: Literal[ + "simple-shuffle", + "least-busy", + "usage-based-routing", + "latency-based-routing", + ] = "simple-shuffle", + routing_strategy_args: dict = {}, # just for latency-based routing + ) -> None: + self.set_verbose = set_verbose + self.deployment_names: List = ( + [] + ) # names of models under litellm_params. ex. azure/chatgpt-v-2 + self.deployment_latency_map = {} + ### CACHING ### + cache_type: Literal["local", "redis"] = "local" # default to an in-memory cache + redis_cache = None + cache_config = {} + self.client_ttl = client_ttl + if redis_url is not None or ( + redis_host is not None + and redis_port is not None + and redis_password is not None + ): + cache_type = "redis" + + if redis_url is not None: + cache_config["url"] = redis_url + + if redis_host is not None: + cache_config["host"] = redis_host + + if redis_port is not None: + cache_config["port"] = str(redis_port) # type: ignore + + if redis_password is not None: + cache_config["password"] = redis_password + + # Add additional key-value pairs from cache_kwargs + cache_config.update(cache_kwargs) + redis_cache = RedisCache(**cache_config) + + if cache_responses: + if litellm.cache is None: + # the cache can be initialized on the proxy server. We should not overwrite it + litellm.cache = litellm.Cache(type=cache_type, **cache_config) # type: ignore + self.cache_responses = cache_responses + self.cache = DualCache( + redis_cache=redis_cache, in_memory_cache=InMemoryCache() + ) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc. + + if model_list: + model_list = copy.deepcopy(model_list) + self.set_model_list(model_list) + self.healthy_deployments: List = self.model_list + for m in model_list: + self.deployment_latency_map[m["litellm_params"]["model"]] = 0 + + self.allowed_fails = allowed_fails or litellm.allowed_fails + self.failed_calls = ( + InMemoryCache() + ) # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown + self.num_retries = num_retries or litellm.num_retries or 0 + self.timeout = timeout or litellm.request_timeout + self.retry_after = retry_after + self.routing_strategy = routing_strategy + self.fallbacks = fallbacks or litellm.fallbacks + self.context_window_fallbacks = ( + context_window_fallbacks or litellm.context_window_fallbacks + ) + self.model_exception_map: dict = ( + {} + ) # dict to store model: list exceptions. self.exceptions = {"gpt-3.5": ["API KEY Error", "Rate Limit Error", "good morning error"]} + self.total_calls: defaultdict = defaultdict( + int + ) # dict to store total calls made to each model + self.fail_calls: defaultdict = defaultdict( + int + ) # dict to store fail_calls made to each model + self.success_calls: defaultdict = defaultdict( + int + ) # dict to store success_calls made to each model + self.previous_models: List = ( + [] + ) # list to store failed calls (passed in as metadata to next call) + self.model_group_alias: dict = ( + model_group_alias or {} + ) # dict to store aliases for router, ex. {"gpt-4": "gpt-3.5-turbo"}, all requests with gpt-4 -> get routed to gpt-3.5-turbo group + + # make Router.chat.completions.create compatible for openai.chat.completions.create + self.chat = litellm.Chat(params=default_litellm_params) + + # default litellm args + self.default_litellm_params = default_litellm_params + self.default_litellm_params.setdefault("timeout", timeout) + self.default_litellm_params.setdefault("max_retries", 0) + self.default_litellm_params.setdefault("metadata", {}).update( + {"caching_groups": caching_groups} + ) + + ### ROUTING SETUP ### + if routing_strategy == "least-busy": + self.leastbusy_logger = LeastBusyLoggingHandler( + router_cache=self.cache, model_list=self.model_list + ) + ## add callback + if isinstance(litellm.input_callback, list): + litellm.input_callback.append(self.leastbusy_logger) # type: ignore + else: + litellm.input_callback = [self.leastbusy_logger] # type: ignore + if isinstance(litellm.callbacks, list): + litellm.callbacks.append(self.leastbusy_logger) # type: ignore + elif routing_strategy == "usage-based-routing": + self.lowesttpm_logger = LowestTPMLoggingHandler( + router_cache=self.cache, model_list=self.model_list + ) + if isinstance(litellm.callbacks, list): + litellm.callbacks.append(self.lowesttpm_logger) # type: ignore + elif routing_strategy == "latency-based-routing": + self.lowestlatency_logger = LowestLatencyLoggingHandler( + router_cache=self.cache, + model_list=self.model_list, + routing_args=routing_strategy_args, + ) + if isinstance(litellm.callbacks, list): + litellm.callbacks.append(self.lowestlatency_logger) # type: ignore + ## COOLDOWNS ## + if isinstance(litellm.failure_callback, list): + litellm.failure_callback.append(self.deployment_callback_on_failure) + else: + litellm.failure_callback = [self.deployment_callback_on_failure] + verbose_router_logger.debug( + f"Intialized router with Routing strategy: {self.routing_strategy}\n" + ) + + ### COMPLETION, EMBEDDING, IMG GENERATION FUNCTIONS + + def completion( + self, model: str, messages: List[Dict[str, str]], **kwargs + ) -> Union[ModelResponse, CustomStreamWrapper]: + """ + Example usage: + response = router.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}] + """ + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_function"] = self._completion + timeout = kwargs.get("request_timeout", self.timeout) + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: + # Submit the function to the executor with a timeout + future = executor.submit(self.function_with_fallbacks, **kwargs) + response = future.result(timeout=timeout) # type: ignore + + return response + except Exception as e: + raise e + + def _completion(self, model: str, messages: List[Dict[str, str]], **kwargs): + try: + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment( + model=model, + messages=messages, + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("metadata", {}).update( + {"deployment": deployment["litellm_params"]["model"]} + ) + data = deployment["litellm_params"].copy() + kwargs["model_info"] = deployment.get("model_info", {}) + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + potential_model_client = self._get_client( + deployment=deployment, kwargs=kwargs + ) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + + return litellm.completion( + **{ + **data, + "messages": messages, + "caching": self.cache_responses, + "client": model_client, + **kwargs, + } + ) + except Exception as e: + raise e + + async def acompletion(self, model: str, messages: List[Dict[str, str]], **kwargs): + try: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_function"] = self._acompletion + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + + response = await self.async_function_with_fallbacks(**kwargs) + + return response + except Exception as e: + raise e + + async def _acompletion(self, model: str, messages: List[Dict[str, str]], **kwargs): + model_name = None + try: + verbose_router_logger.debug( + f"Inside _acompletion()- model: {model}; kwargs: {kwargs}" + ) + deployment = self.get_available_deployment( + model=model, + messages=messages, + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("metadata", {}).update( + {"deployment": deployment["litellm_params"]["model"]} + ) + kwargs["model_info"] = deployment.get("model_info", {}) + data = deployment["litellm_params"].copy() + model_name = data["model"] + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + potential_model_client = self._get_client( + deployment=deployment, kwargs=kwargs, client_type="async" + ) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + self.total_calls[model_name] += 1 + response = await litellm.acompletion( + **{ + **data, + "messages": messages, + "caching": self.cache_responses, + "client": model_client, + "timeout": self.timeout, + **kwargs, + } + ) + self.success_calls[model_name] += 1 + verbose_router_logger.info( + f"litellm.acompletion(model={model_name})\033[32m 200 OK\033[0m" + ) + return response + except Exception as e: + verbose_router_logger.info( + f"litellm.acompletion(model={model_name})\033[31m Exception {str(e)}\033[0m" + ) + if model_name is not None: + self.fail_calls[model_name] += 1 + raise e + + def image_generation(self, prompt: str, model: str, **kwargs): + try: + kwargs["model"] = model + kwargs["prompt"] = prompt + kwargs["original_function"] = self._image_generation + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + response = self.function_with_fallbacks(**kwargs) + + return response + except Exception as e: + raise e + + def _image_generation(self, prompt: str, model: str, **kwargs): + try: + verbose_router_logger.debug( + f"Inside _image_generation()- model: {model}; kwargs: {kwargs}" + ) + deployment = self.get_available_deployment( + model=model, + messages=[{"role": "user", "content": "prompt"}], + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("metadata", {}).update( + {"deployment": deployment["litellm_params"]["model"]} + ) + kwargs["model_info"] = deployment.get("model_info", {}) + data = deployment["litellm_params"].copy() + model_name = data["model"] + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + potential_model_client = self._get_client( + deployment=deployment, kwargs=kwargs, client_type="async" + ) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + + self.total_calls[model_name] += 1 + response = litellm.image_generation( + **{ + **data, + "prompt": prompt, + "caching": self.cache_responses, + "client": model_client, + **kwargs, + } + ) + self.success_calls[model_name] += 1 + verbose_router_logger.info( + f"litellm.image_generation(model={model_name})\033[32m 200 OK\033[0m" + ) + return response + except Exception as e: + verbose_router_logger.info( + f"litellm.image_generation(model={model_name})\033[31m Exception {str(e)}\033[0m" + ) + if model_name is not None: + self.fail_calls[model_name] += 1 + raise e + + async def aimage_generation(self, prompt: str, model: str, **kwargs): + try: + kwargs["model"] = model + kwargs["prompt"] = prompt + kwargs["original_function"] = self._aimage_generation + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + response = await self.async_function_with_fallbacks(**kwargs) + + return response + except Exception as e: + raise e + + async def _aimage_generation(self, prompt: str, model: str, **kwargs): + try: + verbose_router_logger.debug( + f"Inside _image_generation()- model: {model}; kwargs: {kwargs}" + ) + deployment = self.get_available_deployment( + model=model, + messages=[{"role": "user", "content": "prompt"}], + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("metadata", {}).update( + {"deployment": deployment["litellm_params"]["model"]} + ) + kwargs["model_info"] = deployment.get("model_info", {}) + data = deployment["litellm_params"].copy() + model_name = data["model"] + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + potential_model_client = self._get_client( + deployment=deployment, kwargs=kwargs, client_type="async" + ) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + + self.total_calls[model_name] += 1 + response = await litellm.aimage_generation( + **{ + **data, + "prompt": prompt, + "caching": self.cache_responses, + "client": model_client, + **kwargs, + } + ) + self.success_calls[model_name] += 1 + verbose_router_logger.info( + f"litellm.aimage_generation(model={model_name})\033[32m 200 OK\033[0m" + ) + return response + except Exception as e: + verbose_router_logger.info( + f"litellm.aimage_generation(model={model_name})\033[31m Exception {str(e)}\033[0m" + ) + if model_name is not None: + self.fail_calls[model_name] += 1 + raise e + + def text_completion( + self, + model: str, + prompt: str, + is_retry: Optional[bool] = False, + is_fallback: Optional[bool] = False, + is_async: Optional[bool] = False, + **kwargs, + ): + try: + kwargs["model"] = model + kwargs["prompt"] = prompt + kwargs["original_function"] = self._acompletion + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + + messages = [{"role": "user", "content": prompt}] + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment( + model=model, + messages=messages, + specific_deployment=kwargs.pop("specific_deployment", None), + ) + + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + # call via litellm.completion() + return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore + except Exception as e: + if self.num_retries > 0: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_function"] = self.completion + return self.function_with_retries(**kwargs) + else: + raise e + + async def atext_completion( + self, + model: str, + prompt: str, + is_retry: Optional[bool] = False, + is_fallback: Optional[bool] = False, + is_async: Optional[bool] = False, + **kwargs, + ): + try: + kwargs["model"] = model + kwargs["prompt"] = prompt + kwargs["original_function"] = self._atext_completion + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + response = await self.async_function_with_fallbacks(**kwargs) + + return response + except Exception as e: + raise e + + async def _atext_completion(self, model: str, prompt: str, **kwargs): + try: + verbose_router_logger.debug( + f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}" + ) + deployment = self.get_available_deployment( + model=model, + messages=[{"role": "user", "content": prompt}], + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("metadata", {}).update( + {"deployment": deployment["litellm_params"]["model"]} + ) + kwargs["model_info"] = deployment.get("model_info", {}) + data = deployment["litellm_params"].copy() + model_name = data["model"] + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + potential_model_client = self._get_client( + deployment=deployment, kwargs=kwargs, client_type="async" + ) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + self.total_calls[model_name] += 1 + response = await litellm.atext_completion( + **{ + **data, + "prompt": prompt, + "caching": self.cache_responses, + "client": model_client, + "timeout": self.timeout, + **kwargs, + } + ) + self.success_calls[model_name] += 1 + verbose_router_logger.info( + f"litellm.atext_completion(model={model_name})\033[32m 200 OK\033[0m" + ) + return response + except Exception as e: + verbose_router_logger.info( + f"litellm.atext_completion(model={model_name})\033[31m Exception {str(e)}\033[0m" + ) + if model_name is not None: + self.fail_calls[model_name] += 1 + raise e + + def embedding( + self, + model: str, + input: Union[str, List], + is_async: Optional[bool] = False, + **kwargs, + ) -> Union[List[float], None]: + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment( + model=model, + input=input, + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("model_info", {}) + kwargs.setdefault("metadata", {}).update( + {"model_group": model, "deployment": deployment["litellm_params"]["model"]} + ) # [TODO]: move to using async_function_with_fallbacks + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + potential_model_client = self._get_client(deployment=deployment, kwargs=kwargs) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + return litellm.embedding( + **{ + **data, + "input": input, + "caching": self.cache_responses, + "client": model_client, + **kwargs, + } + ) + + async def aembedding( + self, + model: str, + input: Union[str, List], + is_async: Optional[bool] = True, + **kwargs, + ) -> Union[List[float], None]: + try: + kwargs["model"] = model + kwargs["input"] = input + kwargs["original_function"] = self._aembedding + kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) + timeout = kwargs.get("request_timeout", self.timeout) + kwargs.setdefault("metadata", {}).update({"model_group": model}) + response = await self.async_function_with_fallbacks(**kwargs) + return response + except Exception as e: + raise e + + async def _aembedding(self, input: Union[str, List], model: str, **kwargs): + try: + verbose_router_logger.debug( + f"Inside _aembedding()- model: {model}; kwargs: {kwargs}" + ) + deployment = self.get_available_deployment( + model=model, + input=input, + specific_deployment=kwargs.pop("specific_deployment", None), + ) + kwargs.setdefault("metadata", {}).update( + {"deployment": deployment["litellm_params"]["model"]} + ) + kwargs["model_info"] = deployment.get("model_info", {}) + data = deployment["litellm_params"].copy() + model_name = data["model"] + for k, v in self.default_litellm_params.items(): + if ( + k not in kwargs + ): # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + potential_model_client = self._get_client( + deployment=deployment, kwargs=kwargs, client_type="async" + ) + # check if provided keys == client keys # + dynamic_api_key = kwargs.get("api_key", None) + if ( + dynamic_api_key is not None + and potential_model_client is not None + and dynamic_api_key != potential_model_client.api_key + ): + model_client = None + else: + model_client = potential_model_client + + self.total_calls[model_name] += 1 + response = await litellm.aembedding( + **{ + **data, + "input": input, + "caching": self.cache_responses, + "client": model_client, + **kwargs, + } + ) + self.success_calls[model_name] += 1 + verbose_router_logger.info( + f"litellm.aembedding(model={model_name})\033[32m 200 OK\033[0m" + ) + return response + except Exception as e: + verbose_router_logger.info( + f"litellm.aembedding(model={model_name})\033[31m Exception {str(e)}\033[0m" + ) + if model_name is not None: + self.fail_calls[model_name] += 1 + raise e + + async def async_function_with_fallbacks(self, *args, **kwargs): + """ + Try calling the function_with_retries + If it fails after num_retries, fall back to another model group + """ + model_group = kwargs.get("model") + fallbacks = kwargs.get("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.get( + "context_window_fallbacks", self.context_window_fallbacks + ) + try: + response = await self.async_function_with_retries(*args, **kwargs) + verbose_router_logger.debug(f"Async Response: {response}") + return response + except Exception as e: + verbose_router_logger.debug(f"Traceback{traceback.format_exc()}") + original_exception = e + fallback_model_group = None + try: + if ( + hasattr(e, "status_code") and e.status_code == 400 + ): # don't retry a malformed request + raise e + verbose_router_logger.debug(f"Trying to fallback b/w models") + if ( + isinstance(e, litellm.ContextWindowExceededError) + and context_window_fallbacks is not None + ): + fallback_model_group = None + for ( + item + ) in context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + + if fallback_model_group is None: + raise original_exception + + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + kwargs["model"] = mg + response = await self.async_function_with_retries( + *args, **kwargs + ) + return response + except Exception as e: + pass + elif fallbacks is not None: + verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}") + for item in fallbacks: + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + if fallback_model_group is None: + verbose_router_logger.info( + f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}" + ) + raise original_exception + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + ## LOGGING + kwargs = self.log_retry(kwargs=kwargs, e=original_exception) + verbose_router_logger.info( + f"Falling back to model_group = {mg}" + ) + kwargs["model"] = mg + kwargs["metadata"]["model_group"] = mg + response = await self.async_function_with_retries( + *args, **kwargs + ) + return response + except Exception as e: + raise e + except Exception as e: + verbose_router_logger.debug(f"An exception occurred - {str(e)}") + traceback.print_exc() + raise original_exception + + async def async_function_with_retries(self, *args, **kwargs): + verbose_router_logger.debug( + f"Inside async function with retries: args - {args}; kwargs - {kwargs}" + ) + original_function = kwargs.pop("original_function") + fallbacks = kwargs.pop("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.pop( + "context_window_fallbacks", self.context_window_fallbacks + ) + verbose_router_logger.debug( + f"async function w/ retries: original_function - {original_function}" + ) + num_retries = kwargs.pop("num_retries") + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = await original_function(*args, **kwargs) + return response + except Exception as e: + original_exception = e + ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR w/ fallbacks available / Bad Request Error + if ( + isinstance(original_exception, litellm.ContextWindowExceededError) + and context_window_fallbacks is None + ) or ( + isinstance(original_exception, openai.RateLimitError) + and fallbacks is not None + ): + raise original_exception + ### RETRY + #### check if it should retry + back-off if required + if "No models available" in str(e): + timeout = litellm._calculate_retry_after( + remaining_retries=num_retries, + max_retries=num_retries, + min_timeout=self.retry_after, + ) + await asyncio.sleep(timeout) + elif hasattr(original_exception, "status_code") and litellm._should_retry( + status_code=original_exception.status_code + ): + if hasattr(original_exception, "response") and hasattr( + original_exception.response, "headers" + ): + timeout = litellm._calculate_retry_after( + remaining_retries=num_retries, + max_retries=num_retries, + response_headers=original_exception.response.headers, + min_timeout=self.retry_after, + ) + else: + timeout = litellm._calculate_retry_after( + remaining_retries=num_retries, + max_retries=num_retries, + min_timeout=self.retry_after, + ) + await asyncio.sleep(timeout) + else: + raise original_exception + + ## LOGGING + if num_retries > 0: + kwargs = self.log_retry(kwargs=kwargs, e=original_exception) + + for current_attempt in range(num_retries): + verbose_router_logger.debug( + f"retrying request. Current attempt - {current_attempt}; num retries: {num_retries}" + ) + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = await original_function(*args, **kwargs) + if inspect.iscoroutinefunction( + response + ): # async errors are often returned as coroutines + response = await response + return response + + except Exception as e: + ## LOGGING + kwargs = self.log_retry(kwargs=kwargs, e=e) + remaining_retries = num_retries - current_attempt + if "No models available" in str(e): + timeout = litellm._calculate_retry_after( + remaining_retries=remaining_retries, + max_retries=num_retries, + min_timeout=self.retry_after, + ) + await asyncio.sleep(timeout) + elif ( + hasattr(e, "status_code") + and hasattr(e, "response") + and litellm._should_retry(status_code=e.status_code) + ): + if hasattr(e.response, "headers"): + timeout = litellm._calculate_retry_after( + remaining_retries=remaining_retries, + max_retries=num_retries, + response_headers=e.response.headers, + min_timeout=self.retry_after, + ) + else: + timeout = litellm._calculate_retry_after( + remaining_retries=remaining_retries, + max_retries=num_retries, + min_timeout=self.retry_after, + ) + await asyncio.sleep(timeout) + else: + raise e + raise original_exception + + def function_with_fallbacks(self, *args, **kwargs): + """ + Try calling the function_with_retries + If it fails after num_retries, fall back to another model group + """ + model_group = kwargs.get("model") + fallbacks = kwargs.get("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.get( + "context_window_fallbacks", self.context_window_fallbacks + ) + try: + response = self.function_with_retries(*args, **kwargs) + return response + except Exception as e: + original_exception = e + verbose_router_logger.debug(f"An exception occurs {original_exception}") + try: + verbose_router_logger.debug( + f"Trying to fallback b/w models. Initial model group: {model_group}" + ) + if ( + isinstance(e, litellm.ContextWindowExceededError) + and context_window_fallbacks is not None + ): + fallback_model_group = None + + for ( + item + ) in context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + + if fallback_model_group is None: + raise original_exception + + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + ## LOGGING + kwargs = self.log_retry(kwargs=kwargs, e=original_exception) + kwargs["model"] = mg + response = self.function_with_fallbacks(*args, **kwargs) + return response + except Exception as e: + pass + elif fallbacks is not None: + verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}") + fallback_model_group = None + for item in fallbacks: + if list(item.keys())[0] == model_group: + fallback_model_group = item[model_group] + break + + if fallback_model_group is None: + raise original_exception + + for mg in fallback_model_group: + """ + Iterate through the model groups and try calling that deployment + """ + try: + ## LOGGING + kwargs = self.log_retry(kwargs=kwargs, e=original_exception) + kwargs["model"] = mg + response = self.function_with_fallbacks(*args, **kwargs) + return response + except Exception as e: + raise e + except Exception as e: + raise e + raise original_exception + + def function_with_retries(self, *args, **kwargs): + """ + Try calling the model 3 times. Shuffle between available deployments. + """ + verbose_router_logger.debug( + f"Inside function with retries: args - {args}; kwargs - {kwargs}" + ) + original_function = kwargs.pop("original_function") + num_retries = kwargs.pop("num_retries") + fallbacks = kwargs.pop("fallbacks", self.fallbacks) + context_window_fallbacks = kwargs.pop( + "context_window_fallbacks", self.context_window_fallbacks + ) + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = original_function(*args, **kwargs) + return response + except Exception as e: + original_exception = e + verbose_router_logger.debug( + f"num retries in function with retries: {num_retries}" + ) + ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR + if ( + isinstance(original_exception, litellm.ContextWindowExceededError) + and context_window_fallbacks is None + ) or ( + isinstance(original_exception, openai.RateLimitError) + and fallbacks is not None + ): + raise original_exception + ## LOGGING + if num_retries > 0: + kwargs = self.log_retry(kwargs=kwargs, e=original_exception) + ### RETRY + for current_attempt in range(num_retries): + verbose_router_logger.debug( + f"retrying request. Current attempt - {current_attempt}; retries left: {num_retries}" + ) + try: + # if the function call is successful, no exception will be raised and we'll break out of the loop + response = original_function(*args, **kwargs) + return response + + except Exception as e: + ## LOGGING + kwargs = self.log_retry(kwargs=kwargs, e=e) + remaining_retries = num_retries - current_attempt + if "No models available" in str(e): + timeout = litellm._calculate_retry_after( + remaining_retries=remaining_retries, + max_retries=num_retries, + min_timeout=self.retry_after, + ) + time.sleep(timeout) + elif ( + hasattr(e, "status_code") + and hasattr(e, "response") + and litellm._should_retry(status_code=e.status_code) + ): + if hasattr(e.response, "headers"): + timeout = litellm._calculate_retry_after( + remaining_retries=remaining_retries, + max_retries=num_retries, + response_headers=e.response.headers, + min_timeout=self.retry_after, + ) + else: + timeout = litellm._calculate_retry_after( + remaining_retries=remaining_retries, + max_retries=num_retries, + min_timeout=self.retry_after, + ) + time.sleep(timeout) + else: + raise e + raise original_exception + + ### HELPER FUNCTIONS + + def deployment_callback_on_failure( + self, + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, + end_time, # start/end time + ): + try: + exception = kwargs.get("exception", None) + exception_type = type(exception) + exception_status = getattr(exception, "status_code", "") + exception_cause = getattr(exception, "__cause__", "") + exception_message = getattr(exception, "message", "") + exception_str = ( + str(exception_type) + + "Status: " + + str(exception_status) + + "Message: " + + str(exception_cause) + + str(exception_message) + + "Full exception" + + str(exception) + ) + model_name = kwargs.get("model", None) # i.e. gpt35turbo + custom_llm_provider = kwargs.get("litellm_params", {}).get( + "custom_llm_provider", None + ) # i.e. azure + metadata = kwargs.get("litellm_params", {}).get("metadata", None) + _model_info = kwargs.get("litellm_params", {}).get("model_info", {}) + if isinstance(_model_info, dict): + deployment_id = _model_info.get("id", None) + self._set_cooldown_deployments( + deployment_id + ) # setting deployment_id in cooldown deployments + if metadata: + deployment = metadata.get("deployment", None) + deployment_exceptions = self.model_exception_map.get(deployment, []) + deployment_exceptions.append(exception_str) + self.model_exception_map[deployment] = deployment_exceptions + verbose_router_logger.debug("\nEXCEPTION FOR DEPLOYMENTS\n") + verbose_router_logger.debug(self.model_exception_map) + for model in self.model_exception_map: + verbose_router_logger.debug( + f"Model {model} had {len(self.model_exception_map[model])} exception" + ) + if custom_llm_provider: + model_name = f"{custom_llm_provider}/{model_name}" + + except Exception as e: + raise e + + def log_retry(self, kwargs: dict, e: Exception) -> dict: + """ + When a retry or fallback happens, log the details of the just failed model call - similar to Sentry breadcrumbing + """ + try: + # Log failed model as the previous model + previous_model = { + "exception_type": type(e).__name__, + "exception_string": str(e), + } + for ( + k, + v, + ) in ( + kwargs.items() + ): # log everything in kwargs except the old previous_models value - prevent nesting + if k != "metadata": + previous_model[k] = v + elif k == "metadata" and isinstance(v, dict): + previous_model["metadata"] = {} # type: ignore + for metadata_k, metadata_v in kwargs["metadata"].items(): + if metadata_k != "previous_models": + previous_model[k][metadata_k] = metadata_v # type: ignore + self.previous_models.append(previous_model) + kwargs["metadata"]["previous_models"] = self.previous_models + return kwargs + except Exception as e: + raise e + + def _set_cooldown_deployments(self, deployment: Optional[str] = None): + """ + Add a model to the list of models being cooled down for that minute, if it exceeds the allowed fails / minute + """ + if deployment is None: + return + + current_minute = datetime.now().strftime("%H-%M") + # get current fails for deployment + # update the number of failed calls + # if it's > allowed fails + # cooldown deployment + current_fails = self.failed_calls.get_cache(key=deployment) or 0 + updated_fails = current_fails + 1 + verbose_router_logger.debug( + f"Attempting to add {deployment} to cooldown list. updated_fails: {updated_fails}; self.allowed_fails: {self.allowed_fails}" + ) + if updated_fails > self.allowed_fails: + # get the current cooldown list for that minute + cooldown_key = f"{current_minute}:cooldown_models" # group cooldown models by minute to reduce number of redis calls + cached_value = self.cache.get_cache(key=cooldown_key) + + verbose_router_logger.debug(f"adding {deployment} to cooldown models") + # update value + try: + if deployment in cached_value: + pass + else: + cached_value = cached_value + [deployment] + # save updated value + self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=1) + except: + cached_value = [deployment] + # save updated value + self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=1) + else: + self.failed_calls.set_cache(key=deployment, value=updated_fails, ttl=1) + + def _get_cooldown_deployments(self): + """ + Get the list of models being cooled down for this minute + """ + current_minute = datetime.now().strftime("%H-%M") + # get the current cooldown list for that minute + cooldown_key = f"{current_minute}:cooldown_models" + + # ---------------------- + # Return cooldown models + # ---------------------- + cooldown_models = self.cache.get_cache(key=cooldown_key) or [] + + verbose_router_logger.debug(f"retrieve cooldown models: {cooldown_models}") + return cooldown_models + + def set_client(self, model: dict): + """ + Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278 + """ + client_ttl = self.client_ttl + litellm_params = model.get("litellm_params", {}) + model_name = litellm_params.get("model") + model_id = model["model_info"]["id"] + #### for OpenAI / Azure we need to initalize the Client for High Traffic ######## + custom_llm_provider = litellm_params.get("custom_llm_provider") + custom_llm_provider = custom_llm_provider or model_name.split("/", 1)[0] or "" + default_api_base = None + default_api_key = None + if custom_llm_provider in litellm.openai_compatible_providers: + _, custom_llm_provider, api_key, api_base = litellm.get_llm_provider( + model=model_name + ) + default_api_base = api_base + default_api_key = api_key + if ( + model_name in litellm.open_ai_chat_completion_models + or custom_llm_provider in litellm.openai_compatible_providers + or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "openai" + or "ft:gpt-3.5-turbo" in model_name + or model_name in litellm.open_ai_embedding_models + ): + # glorified / complicated reading of configs + # user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env + # we do this here because we init clients for Azure, OpenAI and we need to set the right key + api_key = litellm_params.get("api_key") or default_api_key + if api_key and api_key.startswith("os.environ/"): + api_key_env_name = api_key.replace("os.environ/", "") + api_key = litellm.get_secret(api_key_env_name) + litellm_params["api_key"] = api_key + + api_base = litellm_params.get("api_base") + base_url = litellm_params.get("base_url") + api_base = ( + api_base or base_url or default_api_base + ) # allow users to pass in `api_base` or `base_url` for azure + if api_base and api_base.startswith("os.environ/"): + api_base_env_name = api_base.replace("os.environ/", "") + api_base = litellm.get_secret(api_base_env_name) + litellm_params["api_base"] = api_base + + api_version = litellm_params.get("api_version") + if api_version and api_version.startswith("os.environ/"): + api_version_env_name = api_version.replace("os.environ/", "") + api_version = litellm.get_secret(api_version_env_name) + litellm_params["api_version"] = api_version + + timeout = litellm_params.pop("timeout", None) + if isinstance(timeout, str) and timeout.startswith("os.environ/"): + timeout_env_name = timeout.replace("os.environ/", "") + timeout = litellm.get_secret(timeout_env_name) + litellm_params["timeout"] = timeout + + stream_timeout = litellm_params.pop( + "stream_timeout", timeout + ) # if no stream_timeout is set, default to timeout + if isinstance(stream_timeout, str) and stream_timeout.startswith( + "os.environ/" + ): + stream_timeout_env_name = stream_timeout.replace("os.environ/", "") + stream_timeout = litellm.get_secret(stream_timeout_env_name) + litellm_params["stream_timeout"] = stream_timeout + + max_retries = litellm_params.pop("max_retries", 2) + if isinstance(max_retries, str) and max_retries.startswith("os.environ/"): + max_retries_env_name = max_retries.replace("os.environ/", "") + max_retries = litellm.get_secret(max_retries_env_name) + litellm_params["max_retries"] = max_retries + + if "azure" in model_name: + if api_base is None: + raise ValueError( + f"api_base is required for Azure OpenAI. Set it on your config. Model - {model}" + ) + if api_version is None: + api_version = "2023-07-01-preview" + if "gateway.ai.cloudflare.com" in api_base: + if not api_base.endswith("/"): + api_base += "/" + azure_model = model_name.replace("azure/", "") + api_base += f"{azure_model}" + cache_key = f"{model_id}_async_client" + _client = openai.AsyncAzureOpenAI( + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + http_client=httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + cache_key = f"{model_id}_client" + _client = openai.AzureOpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + http_client=httpx.Client( + transport=CustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + # streaming clients can have diff timeouts + cache_key = f"{model_id}_stream_async_client" + _client = openai.AsyncAzureOpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries, + http_client=httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + cache_key = f"{model_id}_stream_client" + _client = openai.AzureOpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries, + http_client=httpx.Client( + transport=CustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + else: + verbose_router_logger.debug( + f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{api_key}" + ) + + cache_key = f"{model_id}_async_client" + _client = openai.AsyncAzureOpenAI( # type: ignore + api_key=api_key, + azure_endpoint=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + http_client=httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + cache_key = f"{model_id}_client" + _client = openai.AzureOpenAI( # type: ignore + api_key=api_key, + azure_endpoint=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + http_client=httpx.Client( + transport=CustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + # streaming clients should have diff timeouts + cache_key = f"{model_id}_stream_async_client" + _client = openai.AsyncAzureOpenAI( # type: ignore + api_key=api_key, + azure_endpoint=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries, + http_client=httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + cache_key = f"{model_id}_stream_client" + _client = openai.AzureOpenAI( # type: ignore + api_key=api_key, + azure_endpoint=api_base, + api_version=api_version, + timeout=stream_timeout, + max_retries=max_retries, + http_client=httpx.Client( + transport=CustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + else: + verbose_router_logger.debug( + f"Initializing OpenAI Client for {model_name}, Api Base:{str(api_base)}, Api Key:{api_key}" + ) + cache_key = f"{model_id}_async_client" + _client = openai.AsyncOpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + timeout=timeout, + max_retries=max_retries, + http_client=httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + cache_key = f"{model_id}_client" + _client = openai.OpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + timeout=timeout, + max_retries=max_retries, + http_client=httpx.Client( + transport=CustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + # streaming clients should have diff timeouts + cache_key = f"{model_id}_stream_async_client" + _client = openai.AsyncOpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + timeout=stream_timeout, + max_retries=max_retries, + http_client=httpx.AsyncClient( + transport=AsyncCustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + # streaming clients should have diff timeouts + cache_key = f"{model_id}_stream_client" + _client = openai.OpenAI( # type: ignore + api_key=api_key, + base_url=api_base, + timeout=stream_timeout, + max_retries=max_retries, + http_client=httpx.Client( + transport=CustomHTTPTransport(), + limits=httpx.Limits( + max_connections=1000, max_keepalive_connections=100 + ), + ), # type: ignore + ) + self.cache.set_cache( + key=cache_key, + value=_client, + ttl=client_ttl, + local_only=True, + ) # cache for 1 hr + + def set_model_list(self, model_list: list): + self.model_list = copy.deepcopy(model_list) + # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works + import os + + for model in self.model_list: + #### MODEL ID INIT ######## + model_info = model.get("model_info", {}) + model_info["id"] = model_info.get("id", str(uuid.uuid4())) + model["model_info"] = model_info + #### DEPLOYMENT NAMES INIT ######## + self.deployment_names.append(model["litellm_params"]["model"]) + ############ Users can either pass tpm/rpm as a litellm_param or a router param ########### + # for get_available_deployment, we use the litellm_param["rpm"] + # in this snippet we also set rpm to be a litellm_param + if ( + model["litellm_params"].get("rpm") is None + and model.get("rpm") is not None + ): + model["litellm_params"]["rpm"] = model.get("rpm") + if ( + model["litellm_params"].get("tpm") is None + and model.get("tpm") is not None + ): + model["litellm_params"]["tpm"] = model.get("tpm") + + #### VALIDATE MODEL ######## + # check if model provider in supported providers + ( + _model, + custom_llm_provider, + dynamic_api_key, + api_base, + ) = litellm.get_llm_provider( + model=model["litellm_params"]["model"], + custom_llm_provider=model["litellm_params"].get( + "custom_llm_provider", None + ), + ) + + if custom_llm_provider not in litellm.provider_list: + raise Exception(f"Unsupported provider - {custom_llm_provider}") + + self.set_client(model=model) + + verbose_router_logger.debug(f"\nInitialized Model List {self.model_list}") + self.model_names = [m["model_name"] for m in model_list] + + def get_model_names(self): + return self.model_names + + def _get_client(self, deployment, kwargs, client_type=None): + """ + Returns the appropriate client based on the given deployment, kwargs, and client_type. + + Parameters: + deployment (dict): The deployment dictionary containing the clients. + kwargs (dict): The keyword arguments passed to the function. + client_type (str): The type of client to return. + + Returns: + The appropriate client based on the given client_type and kwargs. + """ + model_id = deployment["model_info"]["id"] + if client_type == "async": + if kwargs.get("stream") == True: + cache_key = f"{model_id}_stream_async_client" + client = self.cache.get_cache(key=cache_key, local_only=True) + if client is None: + """ + Re-initialize the client + """ + self.set_client(model=deployment) + client = self.cache.get_cache(key=cache_key, local_only=True) + return client + else: + cache_key = f"{model_id}_async_client" + client = self.cache.get_cache(key=cache_key, local_only=True) + if client is None: + """ + Re-initialize the client + """ + self.set_client(model=deployment) + client = self.cache.get_cache(key=cache_key, local_only=True) + return client + else: + if kwargs.get("stream") == True: + cache_key = f"{model_id}_stream_client" + client = self.cache.get_cache(key=cache_key) + if client is None: + """ + Re-initialize the client + """ + self.set_client(model=deployment) + client = self.cache.get_cache(key=cache_key) + return client + else: + cache_key = f"{model_id}_client" + client = self.cache.get_cache(key=cache_key) + if client is None: + """ + Re-initialize the client + """ + self.set_client(model=deployment) + client = self.cache.get_cache(key=cache_key) + return client + + def get_available_deployment( + self, + model: str, + messages: Optional[List[Dict[str, str]]] = None, + input: Optional[Union[str, List]] = None, + specific_deployment: Optional[bool] = False, + ): + """ + Returns the deployment based on routing strategy + """ + + # users need to explicitly call a specific deployment, by setting `specific_deployment = True` as completion()/embedding() kwarg + # When this was no explicit we had several issues with fallbacks timing out + if specific_deployment == True: + # users can also specify a specific deployment name. At this point we should check if they are just trying to call a specific deployment + for deployment in self.model_list: + deployment_model = deployment.get("litellm_params").get("model") + if deployment_model == model: + # User Passed a specific deployment name on their config.yaml, example azure/chat-gpt-v-2 + # return the first deployment where the `model` matches the specificed deployment name + return deployment + raise ValueError( + f"LiteLLM Router: Trying to call specific deployment, but Model:{model} does not exist in Model List: {self.model_list}" + ) + + # check if aliases set on litellm model alias map + if model in self.model_group_alias: + verbose_router_logger.debug( + f"Using a model alias. Got Request for {model}, sending requests to {self.model_group_alias.get(model)}" + ) + model = self.model_group_alias[model] + + ## get healthy deployments + ### get all deployments + healthy_deployments = [m for m in self.model_list if m["model_name"] == model] + if len(healthy_deployments) == 0: + # check if the user sent in a deployment name instead + healthy_deployments = [ + m for m in self.model_list if m["litellm_params"]["model"] == model + ] + + verbose_router_logger.debug( + f"initial list of deployments: {healthy_deployments}" + ) + + # filter out the deployments currently cooling down + deployments_to_remove = [] + # cooldown_deployments is a list of model_id's cooling down, cooldown_deployments = ["16700539-b3cd-42f4-b426-6a12a1bb706a", "16700539-b3cd-42f4-b426-7899"] + cooldown_deployments = self._get_cooldown_deployments() + verbose_router_logger.debug(f"cooldown deployments: {cooldown_deployments}") + # Find deployments in model_list whose model_id is cooling down + for deployment in healthy_deployments: + deployment_id = deployment["model_info"]["id"] + if deployment_id in cooldown_deployments: + deployments_to_remove.append(deployment) + # remove unhealthy deployments from healthy deployments + for deployment in deployments_to_remove: + healthy_deployments.remove(deployment) + + verbose_router_logger.debug( + f"healthy deployments: length {len(healthy_deployments)} {healthy_deployments}" + ) + if len(healthy_deployments) == 0: + raise ValueError("No models available") + if litellm.model_alias_map and model in litellm.model_alias_map: + model = litellm.model_alias_map[ + model + ] # update the model to the actual value if an alias has been passed in + if self.routing_strategy == "least-busy" and self.leastbusy_logger is not None: + deployment = self.leastbusy_logger.get_available_deployments( + model_group=model, healthy_deployments=healthy_deployments + ) + elif self.routing_strategy == "simple-shuffle": + # if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm + ############## Check if we can do a RPM/TPM based weighted pick ################# + rpm = healthy_deployments[0].get("litellm_params").get("rpm", None) + if rpm is not None: + # use weight-random pick if rpms provided + rpms = [m["litellm_params"].get("rpm", 0) for m in healthy_deployments] + verbose_router_logger.debug(f"\nrpms {rpms}") + total_rpm = sum(rpms) + weights = [rpm / total_rpm for rpm in rpms] + verbose_router_logger.debug(f"\n weights {weights}") + # Perform weighted random pick + selected_index = random.choices(range(len(rpms)), weights=weights)[0] + verbose_router_logger.debug(f"\n selected index, {selected_index}") + deployment = healthy_deployments[selected_index] + return deployment or deployment[0] + ############## Check if we can do a RPM/TPM based weighted pick ################# + tpm = healthy_deployments[0].get("litellm_params").get("tpm", None) + if tpm is not None: + # use weight-random pick if rpms provided + tpms = [m["litellm_params"].get("tpm", 0) for m in healthy_deployments] + verbose_router_logger.debug(f"\ntpms {tpms}") + total_tpm = sum(tpms) + weights = [tpm / total_tpm for tpm in tpms] + verbose_router_logger.debug(f"\n weights {weights}") + # Perform weighted random pick + selected_index = random.choices(range(len(tpms)), weights=weights)[0] + verbose_router_logger.debug(f"\n selected index, {selected_index}") + deployment = healthy_deployments[selected_index] + return deployment or deployment[0] + + ############## No RPM/TPM passed, we do a random pick ################# + item = random.choice(healthy_deployments) + return item or item[0] + elif ( + self.routing_strategy == "latency-based-routing" + and self.lowestlatency_logger is not None + ): + deployment = self.lowestlatency_logger.get_available_deployments( + model_group=model, healthy_deployments=healthy_deployments + ) + elif ( + self.routing_strategy == "usage-based-routing" + and self.lowesttpm_logger is not None + ): + deployment = self.lowesttpm_logger.get_available_deployments( + model_group=model, + healthy_deployments=healthy_deployments, + messages=messages, + input=input, + ) + + if deployment is None: + raise ValueError("No models available.") + + return deployment + + def flush_cache(self): + litellm.cache = None + self.cache.flush_cache() + + def reset(self): + ## clean up on close + litellm.success_callback = [] + litellm.__async_success_callback = [] + litellm.failure_callback = [] + litellm._async_failure_callback = [] + self.flush_cache() diff --git a/litellm/router_strategy/least_busy.py b/litellm/router_strategy/least_busy.py new file mode 100644 index 0000000000000000000000000000000000000000..b2d2983b07fe9610957cf2dfc756c29276a0081d --- /dev/null +++ b/litellm/router_strategy/least_busy.py @@ -0,0 +1,196 @@ +#### What this does #### +# identifies least busy deployment +# How is this achieved? +# - Before each call, have the router print the state of requests {"deployment": "requests_in_flight"} +# - use litellm.input_callbacks to log when a request is just about to be made to a model - {"deployment-id": traffic} +# - use litellm.success + failure callbacks to log when a request completed +# - in get_available_deployment, for a given model group name -> pick based on traffic + +import dotenv, os, requests, random +from typing import Optional + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +from litellm.caching import DualCache +from litellm.integrations.custom_logger import CustomLogger + + +class LeastBusyLoggingHandler(CustomLogger): + test_flag: bool = False + logged_success: int = 0 + logged_failure: int = 0 + + def __init__(self, router_cache: DualCache, model_list: list): + self.router_cache = router_cache + self.mapping_deployment_to_id: dict = {} + self.model_list = model_list + + def log_pre_api_call(self, model, messages, kwargs): + """ + Log when a model is being used. + + Caching based on model group. + """ + try: + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + request_count_api_key = f"{model_group}_request_count" + # update cache + request_count_dict = ( + self.router_cache.get_cache(key=request_count_api_key) or {} + ) + request_count_dict[id] = request_count_dict.get(id, 0) + 1 + + self.router_cache.set_cache( + key=request_count_api_key, value=request_count_dict + ) + except Exception as e: + pass + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + request_count_api_key = f"{model_group}_request_count" + # decrement count in cache + request_count_dict = ( + self.router_cache.get_cache(key=request_count_api_key) or {} + ) + request_count_dict[id] = request_count_dict.get(id) - 1 + self.router_cache.set_cache( + key=request_count_api_key, value=request_count_dict + ) + + ### TESTING ### + if self.test_flag: + self.logged_success += 1 + except Exception as e: + pass + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + request_count_api_key = f"{model_group}_request_count" + # decrement count in cache + request_count_dict = ( + self.router_cache.get_cache(key=request_count_api_key) or {} + ) + request_count_dict[id] = request_count_dict.get(id) - 1 + self.router_cache.set_cache( + key=request_count_api_key, value=request_count_dict + ) + + ### TESTING ### + if self.test_flag: + self.logged_failure += 1 + except Exception as e: + pass + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + request_count_api_key = f"{model_group}_request_count" + # decrement count in cache + request_count_dict = ( + self.router_cache.get_cache(key=request_count_api_key) or {} + ) + request_count_dict[id] = request_count_dict.get(id) - 1 + self.router_cache.set_cache( + key=request_count_api_key, value=request_count_dict + ) + + ### TESTING ### + if self.test_flag: + self.logged_success += 1 + except Exception as e: + pass + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + request_count_api_key = f"{model_group}_request_count" + # decrement count in cache + request_count_dict = ( + self.router_cache.get_cache(key=request_count_api_key) or {} + ) + request_count_dict[id] = request_count_dict.get(id) - 1 + self.router_cache.set_cache( + key=request_count_api_key, value=request_count_dict + ) + + ### TESTING ### + if self.test_flag: + self.logged_failure += 1 + except Exception as e: + pass + + def get_available_deployments(self, model_group: str, healthy_deployments: list): + request_count_api_key = f"{model_group}_request_count" + deployments = self.router_cache.get_cache(key=request_count_api_key) or {} + all_deployments = deployments + for d in healthy_deployments: + ## if healthy deployment not yet used + if d["model_info"]["id"] not in all_deployments: + all_deployments[d["model_info"]["id"]] = 0 + # map deployment to id + # pick least busy deployment + min_traffic = float("inf") + min_deployment = None + for k, v in all_deployments.items(): + if v < min_traffic: + min_traffic = v + min_deployment = k + if min_deployment is not None: + ## check if min deployment is a string, if so, cast it to int + for m in healthy_deployments: + if m["model_info"]["id"] == min_deployment: + return m + min_deployment = random.choice(healthy_deployments) + else: + min_deployment = random.choice(healthy_deployments) + return min_deployment diff --git a/litellm/router_strategy/lowest_latency.py b/litellm/router_strategy/lowest_latency.py new file mode 100644 index 0000000000000000000000000000000000000000..3f8cb513b47a9b243ceb1b5b55937e065836649e --- /dev/null +++ b/litellm/router_strategy/lowest_latency.py @@ -0,0 +1,299 @@ +#### What this does #### +# picks based on response time (for streaming, this is time to first token) +from pydantic import BaseModel, Extra, Field, root_validator +import dotenv, os, requests, random +from typing import Optional, Union, List, Dict +from datetime import datetime, timedelta + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +from litellm.caching import DualCache +from litellm.integrations.custom_logger import CustomLogger +from litellm import ModelResponse +from litellm import token_counter + + +class LiteLLMBase(BaseModel): + """ + Implements default functions, all pydantic objects should have. + """ + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + +class RoutingArgs(LiteLLMBase): + ttl: int = 1 * 60 * 60 # 1 hour + + +class LowestLatencyLoggingHandler(CustomLogger): + test_flag: bool = False + logged_success: int = 0 + logged_failure: int = 0 + + def __init__( + self, router_cache: DualCache, model_list: list, routing_args: dict = {} + ): + self.router_cache = router_cache + self.model_list = model_list + self.routing_args = RoutingArgs(**routing_args) + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + """ + Update latency usage on success + """ + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + # ------------ + # Setup values + # ------------ + """ + { + {model_group}_map: { + id: { + "latency": [..] + f"{date:hour:minute}" : {"tpm": 34, "rpm": 3} + } + } + } + """ + latency_key = f"{model_group}_map" + + current_date = datetime.now().strftime("%Y-%m-%d") + current_hour = datetime.now().strftime("%H") + current_minute = datetime.now().strftime("%M") + precise_minute = f"{current_date}-{current_hour}-{current_minute}" + + response_ms: timedelta = end_time - start_time + + final_value = response_ms + total_tokens = 0 + + if isinstance(response_obj, ModelResponse): + completion_tokens = response_obj.usage.completion_tokens + total_tokens = response_obj.usage.total_tokens + final_value = float(completion_tokens / response_ms.total_seconds()) + + # ------------ + # Update usage + # ------------ + + request_count_dict = self.router_cache.get_cache(key=latency_key) or {} + + if id not in request_count_dict: + request_count_dict[id] = {} + + ## Latency + request_count_dict[id].setdefault("latency", []).append(final_value) + + if precise_minute not in request_count_dict[id]: + request_count_dict[id][precise_minute] = {} + + ## TPM + request_count_dict[id][precise_minute]["tpm"] = ( + request_count_dict[id][precise_minute].get("tpm", 0) + total_tokens + ) + + ## RPM + request_count_dict[id][precise_minute]["rpm"] = ( + request_count_dict[id][precise_minute].get("rpm", 0) + 1 + ) + + self.router_cache.set_cache( + key=latency_key, value=request_count_dict, ttl=self.routing_args.ttl + ) # reset map within window + + ### TESTING ### + if self.test_flag: + self.logged_success += 1 + except Exception as e: + traceback.print_exc() + pass + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + """ + Update latency usage on success + """ + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + # ------------ + # Setup values + # ------------ + """ + { + {model_group}_map: { + id: { + "latency": [..] + f"{date:hour:minute}" : {"tpm": 34, "rpm": 3} + } + } + } + """ + latency_key = f"{model_group}_map" + + current_date = datetime.now().strftime("%Y-%m-%d") + current_hour = datetime.now().strftime("%H") + current_minute = datetime.now().strftime("%M") + precise_minute = f"{current_date}-{current_hour}-{current_minute}" + + response_ms: timedelta = end_time - start_time + + final_value = response_ms + total_tokens = 0 + + if isinstance(response_obj, ModelResponse): + completion_tokens = response_obj.usage.completion_tokens + total_tokens = response_obj.usage.total_tokens + final_value = float(completion_tokens / response_ms.total_seconds()) + + # ------------ + # Update usage + # ------------ + + request_count_dict = self.router_cache.get_cache(key=latency_key) or {} + + if id not in request_count_dict: + request_count_dict[id] = {} + + ## Latency + request_count_dict[id].setdefault("latency", []).append(final_value) + + if precise_minute not in request_count_dict[id]: + request_count_dict[id][precise_minute] = {} + + ## TPM + request_count_dict[id][precise_minute]["tpm"] = ( + request_count_dict[id][precise_minute].get("tpm", 0) + total_tokens + ) + + ## RPM + request_count_dict[id][precise_minute]["rpm"] = ( + request_count_dict[id][precise_minute].get("rpm", 0) + 1 + ) + + self.router_cache.set_cache( + key=latency_key, value=request_count_dict, ttl=self.routing_args.ttl + ) # reset map within window + + ### TESTING ### + if self.test_flag: + self.logged_success += 1 + except Exception as e: + traceback.print_exc() + pass + + def get_available_deployments( + self, + model_group: str, + healthy_deployments: list, + messages: Optional[List[Dict[str, str]]] = None, + input: Optional[Union[str, List]] = None, + ): + """ + Returns a deployment with the lowest latency + """ + # get list of potential deployments + latency_key = f"{model_group}_map" + + request_count_dict = self.router_cache.get_cache(key=latency_key) or {} + + # ----------------------- + # Find lowest used model + # ---------------------- + lowest_latency = float("inf") + + current_date = datetime.now().strftime("%Y-%m-%d") + current_hour = datetime.now().strftime("%H") + current_minute = datetime.now().strftime("%M") + precise_minute = f"{current_date}-{current_hour}-{current_minute}" + + deployment = None + + if request_count_dict is None: # base case + return + + all_deployments = request_count_dict + for d in healthy_deployments: + ## if healthy deployment not yet used + if d["model_info"]["id"] not in all_deployments: + all_deployments[d["model_info"]["id"]] = { + "latency": [0], + precise_minute: {"tpm": 0, "rpm": 0}, + } + + try: + input_tokens = token_counter(messages=messages, text=input) + except: + input_tokens = 0 + + for item, item_map in all_deployments.items(): + ## get the item from model list + _deployment = None + for m in healthy_deployments: + if item == m["model_info"]["id"]: + _deployment = m + + if _deployment is None: + continue # skip to next one + + _deployment_tpm = ( + _deployment.get("tpm", None) + or _deployment.get("litellm_params", {}).get("tpm", None) + or _deployment.get("model_info", {}).get("tpm", None) + or float("inf") + ) + + _deployment_rpm = ( + _deployment.get("rpm", None) + or _deployment.get("litellm_params", {}).get("rpm", None) + or _deployment.get("model_info", {}).get("rpm", None) + or float("inf") + ) + item_latency = item_map.get("latency", []) + item_rpm = item_map.get(precise_minute, {}).get("rpm", 0) + item_tpm = item_map.get(precise_minute, {}).get("tpm", 0) + + # get average latency + total: float = 0.0 + for _call_latency in item_latency: + if isinstance(_call_latency, float): + total += _call_latency + item_latency = total / len(item_latency) + if item_latency == 0: + deployment = _deployment + break + elif ( + item_tpm + input_tokens > _deployment_tpm + or item_rpm + 1 > _deployment_rpm + ): # if user passed in tpm / rpm in the model_list + continue + elif item_latency < lowest_latency: + lowest_latency = item_latency + deployment = _deployment + if deployment is None: + deployment = random.choice(healthy_deployments) + return deployment diff --git a/litellm/router_strategy/lowest_tpm_rpm.py b/litellm/router_strategy/lowest_tpm_rpm.py new file mode 100644 index 0000000000000000000000000000000000000000..f53843f1cc87c2769a26d898104c102f970704c1 --- /dev/null +++ b/litellm/router_strategy/lowest_tpm_rpm.py @@ -0,0 +1,195 @@ +#### What this does #### +# identifies lowest tpm deployment + +import dotenv, os, requests, random +from typing import Optional, Union, List, Dict +from datetime import datetime + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +from litellm import token_counter +from litellm.caching import DualCache +from litellm.integrations.custom_logger import CustomLogger + + +class LowestTPMLoggingHandler(CustomLogger): + test_flag: bool = False + logged_success: int = 0 + logged_failure: int = 0 + default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour + + def __init__(self, router_cache: DualCache, model_list: list): + self.router_cache = router_cache + self.model_list = model_list + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + """ + Update TPM/RPM usage on success + """ + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + total_tokens = response_obj["usage"]["total_tokens"] + + # ------------ + # Setup values + # ------------ + current_minute = datetime.now().strftime("%H-%M") + tpm_key = f"{model_group}:tpm:{current_minute}" + rpm_key = f"{model_group}:rpm:{current_minute}" + + # ------------ + # Update usage + # ------------ + + ## TPM + request_count_dict = self.router_cache.get_cache(key=tpm_key) or {} + request_count_dict[id] = request_count_dict.get(id, 0) + total_tokens + + self.router_cache.set_cache(key=tpm_key, value=request_count_dict) + + ## RPM + request_count_dict = self.router_cache.get_cache(key=rpm_key) or {} + request_count_dict[id] = request_count_dict.get(id, 0) + 1 + + self.router_cache.set_cache(key=rpm_key, value=request_count_dict) + + ### TESTING ### + if self.test_flag: + self.logged_success += 1 + except Exception as e: + traceback.print_exc() + pass + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + """ + Update TPM/RPM usage on success + """ + if kwargs["litellm_params"].get("metadata") is None: + pass + else: + model_group = kwargs["litellm_params"]["metadata"].get( + "model_group", None + ) + + id = kwargs["litellm_params"].get("model_info", {}).get("id", None) + if model_group is None or id is None: + return + + total_tokens = response_obj["usage"]["total_tokens"] + + # ------------ + # Setup values + # ------------ + current_minute = datetime.now().strftime("%H-%M") + tpm_key = f"{model_group}:tpm:{current_minute}" + rpm_key = f"{model_group}:rpm:{current_minute}" + + # ------------ + # Update usage + # ------------ + # update cache + + ## TPM + request_count_dict = self.router_cache.get_cache(key=tpm_key) or {} + request_count_dict[id] = request_count_dict.get(id, 0) + total_tokens + + self.router_cache.set_cache(key=tpm_key, value=request_count_dict) + + ## RPM + request_count_dict = self.router_cache.get_cache(key=rpm_key) or {} + request_count_dict[id] = request_count_dict.get(id, 0) + 1 + + self.router_cache.set_cache(key=rpm_key, value=request_count_dict) + + ### TESTING ### + if self.test_flag: + self.logged_success += 1 + except Exception as e: + traceback.print_exc() + pass + + def get_available_deployments( + self, + model_group: str, + healthy_deployments: list, + messages: Optional[List[Dict[str, str]]] = None, + input: Optional[Union[str, List]] = None, + ): + """ + Returns a deployment with the lowest TPM/RPM usage. + """ + # get list of potential deployments + current_minute = datetime.now().strftime("%H-%M") + tpm_key = f"{model_group}:tpm:{current_minute}" + rpm_key = f"{model_group}:rpm:{current_minute}" + + tpm_dict = self.router_cache.get_cache(key=tpm_key) + rpm_dict = self.router_cache.get_cache(key=rpm_key) + + # ----------------------- + # Find lowest used model + # ---------------------- + lowest_tpm = float("inf") + deployment = None + if tpm_dict is None: # base case + item = random.choice(healthy_deployments) + return item + + all_deployments = tpm_dict + for d in healthy_deployments: + ## if healthy deployment not yet used + if d["model_info"]["id"] not in all_deployments: + all_deployments[d["model_info"]["id"]] = 0 + + try: + input_tokens = token_counter(messages=messages, text=input) + except: + input_tokens = 0 + + for item, item_tpm in all_deployments.items(): + ## get the item from model list + _deployment = None + for m in healthy_deployments: + if item == m["model_info"]["id"]: + _deployment = m + + if _deployment is None: + continue # skip to next one + + _deployment_tpm = ( + _deployment.get("tpm", None) + or _deployment.get("litellm_params", {}).get("tpm", None) + or _deployment.get("model_info", {}).get("tpm", None) + or float("inf") + ) + + _deployment_rpm = ( + _deployment.get("rpm", None) + or _deployment.get("litellm_params", {}).get("rpm", None) + or _deployment.get("model_info", {}).get("rpm", None) + or float("inf") + ) + + if item_tpm == 0: + deployment = _deployment + break + elif ( + item_tpm + input_tokens > _deployment_tpm + or rpm_dict[item] + 1 > _deployment_rpm + ): # if user passed in tpm / rpm in the model_list + continue + elif item_tpm < lowest_tpm: + lowest_tpm = item_tpm + deployment = _deployment + return deployment diff --git a/litellm/tests/conftest.py b/litellm/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0df0f9a464e3619a6358b3cc4a18a9e672c61e --- /dev/null +++ b/litellm/tests/conftest.py @@ -0,0 +1,41 @@ +# conftest.py + +import pytest, sys, os +import importlib + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + + +@pytest.fixture(scope="function", autouse=True) +def setup_and_teardown(): + """ + This fixture reloads litellm before every function. To speed up testing by removing callbacks being chained. + """ + curr_dir = os.getcwd() # Get the current working directory + sys.path.insert( + 0, os.path.abspath("../..") + ) # Adds the project directory to the system path + import litellm + + importlib.reload(litellm) + print(litellm) + # from litellm import Router, completion, aembedding, acompletion, embedding + yield + + +def pytest_collection_modifyitems(config, items): + # Separate tests in 'test_amazing_proxy_custom_logger.py' and other tests + custom_logger_tests = [ + item for item in items if "custom_logger" in item.parent.name + ] + other_tests = [item for item in items if "custom_logger" not in item.parent.name] + + # Sort tests based on their names + custom_logger_tests.sort(key=lambda x: x.name) + other_tests.sort(key=lambda x: x.name) + + # Reorder the items list + items[:] = custom_logger_tests + other_tests diff --git a/litellm/tests/data_map.txt b/litellm/tests/data_map.txt new file mode 100644 index 0000000000000000000000000000000000000000..e8077595f476b6e0ae27fcd3e401b5806ede77af Binary files /dev/null and b/litellm/tests/data_map.txt differ diff --git a/litellm/tests/example_config_yaml/aliases_config.yaml b/litellm/tests/example_config_yaml/aliases_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..43681f64ba047c130c939d5b17eb8a0543610e89 --- /dev/null +++ b/litellm/tests/example_config_yaml/aliases_config.yaml @@ -0,0 +1,30 @@ +model_list: + - model_name: gpt-3.5-turbo-instruct + litellm_params: + model: ollama/zephyr + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 + temperature: 0.1 + max_tokens: 20 + + +# request to gpt-4, response from ollama/llama2 +# curl --location 'http://0.0.0.0:8000/chat/completions' \ +# --header 'Content-Type: application/json' \ +# --data ' { +# "model": "gpt-4", +# "messages": [ +# { +# "role": "user", +# "content": "what llm are you" +# } +# ], +# } +# ' +# + +# {"id":"chatcmpl-27c85cf0-ab09-4bcf-8cb1-0ee950520743","choices":[{"finish_reason":"stop","index":0,"message":{"content":" Hello! I'm just an AI, I don't have personal experiences or emotions like humans do. However, I can help you with any questions or tasks you may have! Is there something specific you'd like to know or discuss?","role":"assistant","_logprobs":null}}],"created":1700094955.373751,"model":"ollama/llama2","object":"chat.completion","system_fingerprint":null,"usage":{"prompt_tokens":12,"completion_tokens":47,"total_tokens":59},"_response_ms":8028.017999999999}% \ No newline at end of file diff --git a/litellm/tests/example_config_yaml/azure_config.yaml b/litellm/tests/example_config_yaml/azure_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd5865cd7c959503784713c2b9a2d2a7dbbb1307 --- /dev/null +++ b/litellm/tests/example_config_yaml/azure_config.yaml @@ -0,0 +1,15 @@ +model_list: + - model_name: gpt-4-team1 + litellm_params: + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + api_key: os.environ/AZURE_API_KEY + tpm: 20_000 + - model_name: gpt-4-team2 + litellm_params: + model: azure/gpt-4 + api_key: os.environ/AZURE_API_KEY + api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ + tpm: 100_000 + diff --git a/litellm/tests/example_config_yaml/cache_no_params.yaml b/litellm/tests/example_config_yaml/cache_no_params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..20ed919dd568fe2e545ee75adcc2674ba3c54600 --- /dev/null +++ b/litellm/tests/example_config_yaml/cache_no_params.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: "openai-model" + litellm_params: + model: "gpt-3.5-turbo" + +litellm_settings: + cache: True diff --git a/litellm/tests/example_config_yaml/cache_with_params.yaml b/litellm/tests/example_config_yaml/cache_with_params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..372151d0c3f4fcbf30688ba3a7c0f19ab62feaa0 --- /dev/null +++ b/litellm/tests/example_config_yaml/cache_with_params.yaml @@ -0,0 +1,10 @@ +model_list: + - model_name: "openai-model" + litellm_params: + model: "gpt-3.5-turbo" + +litellm_settings: + cache: True + cache_params: + supported_call_types: ["embedding", "aembedding"] + host: "localhost" \ No newline at end of file diff --git a/litellm/tests/example_config_yaml/langfuse_config.yaml b/litellm/tests/example_config_yaml/langfuse_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2a77b5adc464ca063cb6a5bda3eab6965b400cf --- /dev/null +++ b/litellm/tests/example_config_yaml/langfuse_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + +litellm_settings: + drop_params: True + success_callback: ["langfuse"] # https://docs.litellm.ai/docs/observability/langfuse_integration + diff --git a/litellm/tests/example_config_yaml/load_balancer.yaml b/litellm/tests/example_config_yaml/load_balancer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..502b90ff92af6f381fece2096a3b352f9f0ada91 --- /dev/null +++ b/litellm/tests/example_config_yaml/load_balancer.yaml @@ -0,0 +1,28 @@ +litellm_settings: + drop_params: True + +# Model-specific settings +model_list: # use the same model_name for using the litellm router. LiteLLM will use the router between gpt-3.5-turbo + - model_name: gpt-3.5-turbo # litellm will + litellm_params: + model: gpt-3.5-turbo + api_key: sk-uj6F + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: sk-Imn + tpm: 20000 # [OPTIONAL] REPLACE with your openai tpm + rpm: 3 # [OPTIONAL] REPLACE with your openai rpm + - model_name: gpt-3.5-turbo + litellm_params: + model: openrouter/gpt-3.5-turbo + - model_name: mistral-7b-instruct + litellm_params: + model: mistralai/mistral-7b-instruct + +environment_variables: + REDIS_HOST: localhost + REDIS_PASSWORD: + REDIS_PORT: \ No newline at end of file diff --git a/litellm/tests/example_config_yaml/opentelemetry_config.yaml b/litellm/tests/example_config_yaml/opentelemetry_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92d3454d7d5fdb4e2a896aa47d01a19182043ee7 --- /dev/null +++ b/litellm/tests/example_config_yaml/opentelemetry_config.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +general_settings: + otel: True # OpenTelemetry Logger this logs OTEL data to your collector diff --git a/litellm/tests/example_config_yaml/simple_config.yaml b/litellm/tests/example_config_yaml/simple_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14b39a12518e83600c24ee86a0cd5405ae48ee3c --- /dev/null +++ b/litellm/tests/example_config_yaml/simple_config.yaml @@ -0,0 +1,4 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo \ No newline at end of file diff --git a/litellm/tests/langfuse.log b/litellm/tests/langfuse.log new file mode 100644 index 0000000000000000000000000000000000000000..bc09c5ba2a32cbe9ce42c6d87afb2a7d80d27232 --- /dev/null +++ b/litellm/tests/langfuse.log @@ -0,0 +1,57 @@ +Starting new HTTPS connection (1): api.anthropic.com:443 +Starting new HTTPS connection (1): litellm-logging.onrender.com:443 +https://litellm-logging.onrender.com:443 "POST /logging HTTP/1.1" 200 38 +https://api.anthropic.com:443 "POST /v1/complete HTTP/1.1" 200 None +Starting new HTTPS connection (1): litellm-logging.onrender.com:443 +Request options: {'method': 'post', 'url': '/chat/completions', 'files': None, 'json_data': {'messages': [{'role': 'user', 'content': 'this is a streaming test for llama2 + langfuse'}], 'model': 'gpt-3.5-turbo', 'max_tokens': 20, 'stream': True, 'temperature': 0.2}} +connect_tcp.started host='api.openai.com' port=443 local_address=None timeout=600.0 socket_options=None +connect_tcp.complete return_value= +start_tls.started ssl_context= server_hostname='api.openai.com' timeout=600.0 +start_tls.complete return_value= +send_request_headers.started request= +send_request_headers.complete +send_request_body.started request= +send_request_body.complete +receive_response_headers.started request= 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-0,0 +1 @@ +80888ede-4881-4876-ab3f-765d47282e66 \ No newline at end of file diff --git a/litellm/tests/model_cost.json b/litellm/tests/model_cost.json new file mode 100644 index 0000000000000000000000000000000000000000..e581433952162bc9e08b9affc0dd8890f9669e72 --- /dev/null +++ b/litellm/tests/model_cost.json @@ -0,0 +1,3 @@ +{ + "gpt-3.5-turbo-0613": 7.7e-05 +} \ No newline at end of file diff --git a/litellm/tests/test_acompletion.py b/litellm/tests/test_acompletion.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c09b9b78faca66b5b08a61fa3e954a109480cc --- /dev/null +++ b/litellm/tests/test_acompletion.py @@ -0,0 +1,23 @@ +import pytest +from litellm import acompletion + + +def test_acompletion_params(): + import inspect + from litellm.types.completion import CompletionRequest + + acompletion_params_odict = inspect.signature(acompletion).parameters + acompletion_params = {name: param.annotation for name, param in acompletion_params_odict.items()} + completion_params = {field_name: field_type for field_name, field_type in CompletionRequest.__annotations__.items()} + + # remove kwargs + acompletion_params.pop("kwargs", None) + + keys_acompletion = set(acompletion_params.keys()) + keys_completion = set(completion_params.keys()) + + # Assert that the parameters are the same + if keys_acompletion != keys_completion: + pytest.fail("The parameters of the acompletion function and the CompletionRequest class are not the same.") + +# test_acompletion_params() diff --git a/litellm/tests/test_acooldowns_router.py b/litellm/tests/test_acooldowns_router.py new file mode 100644 index 0000000000000000000000000000000000000000..acd884c8268aab99a9977f30de56f83a1fc2690f --- /dev/null +++ b/litellm/tests/test_acooldowns_router.py @@ -0,0 +1,184 @@ +#### What this tests #### +# This tests calling batch_completions by running 100 messages together + +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +import concurrent +from dotenv import load_dotenv + +load_dotenv() + +model_list = [ + { # list of model deployments + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, +] + +kwargs = { + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hey, how's it going?"}], +} + + +def test_multiple_deployments_sync(): + import concurrent, time + + litellm.set_verbose = False + results = [] + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), # type: ignore + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1, + ) # type: ignore + try: + for _ in range(3): + response = router.completion(**kwargs) + results.append(response) + print(results) + router.reset() + except Exception as e: + print(f"FAILED TEST!") + pytest.fail(f"An error occurred - {traceback.format_exc()}") + + +# test_multiple_deployments_sync() + + +def test_multiple_deployments_parallel(): + litellm.set_verbose = False # Corrected the syntax for setting verbose to False + results = [] + futures = {} + start_time = time.time() + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), # type: ignore + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1, + ) # type: ignore + # Assuming you have an executor instance defined somewhere in your code + with concurrent.futures.ThreadPoolExecutor() as executor: + for _ in range(5): + future = executor.submit(router.completion, **kwargs) + futures[future] = future + + # Retrieve the results from the futures + while futures: + done, not_done = concurrent.futures.wait( + futures.values(), + timeout=10, + return_when=concurrent.futures.FIRST_COMPLETED, + ) + for future in done: + try: + result = future.result() + results.append(result) + del futures[future] # Remove the done future + except Exception as e: + print(f"Exception: {e}; traceback: {traceback.format_exc()}") + del futures[future] # Remove the done future with exception + + print(f"Remaining futures: {len(futures)}") + router.reset() + end_time = time.time() + print(results) + print(f"ELAPSED TIME: {end_time - start_time}") + + +# Assuming litellm, router, and executor are defined somewhere in your code + + +# test_multiple_deployments_parallel() +def test_cooldown_same_model_name(): + # users could have the same model with different api_base + # example + # azure/chatgpt, api_base: 1234 + # azure/chatgpt, api_base: 1235 + # if 1234 fails, it should only cooldown 1234 and then try with 1235 + litellm.set_verbose = False + try: + print("testing cooldown same model name") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": "BAD_API_BASE", + "tpm": 90, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "tpm": 0.000001, + }, + }, + ] + + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=3, + ) # type: ignore + + response = router.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello this request will pass"}], + ) + print(router.model_list) + model_ids = [] + for model in router.model_list: + model_ids.append(model["model_info"]["id"]) + print("\n litellm model ids ", model_ids) + + # example litellm_model_names ['azure/chatgpt-v-2-ModelID-64321', 'azure/chatgpt-v-2-ModelID-63960'] + assert ( + model_ids[0] != model_ids[1] + ) # ensure both models have a uuid added, and they have different names + + print("\ngot response\n", response) + except Exception as e: + pytest.fail(f"Got unexpected exception on router! - {e}") + + +test_cooldown_same_model_name() diff --git a/litellm/tests/test_add_function_to_prompt.py b/litellm/tests/test_add_function_to_prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..932e6edd14f5de2542b54b9d828554e775e13487 --- /dev/null +++ b/litellm/tests/test_add_function_to_prompt.py @@ -0,0 +1,78 @@ +#### What this tests #### +# Allow the user to map the function to the prompt, if the model doesn't support function calling + +import sys, os, pytest +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + + +## case 1: set_function_to_prompt not set +def test_function_call_non_openai_model(): + try: + model = "claude-instant-1" + messages = [{"role": "user", "content": "what's the weather in sf?"}] + functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + response = litellm.completion( + model=model, messages=messages, functions=functions + ) + pytest.fail(f"An error occurred") + except Exception as e: + print(e) + pass + + +test_function_call_non_openai_model() + + +## case 2: add_function_to_prompt set +def test_function_call_non_openai_model_litellm_mod_set(): + litellm.add_function_to_prompt = True + try: + model = "claude-instant-1" + messages = [{"role": "user", "content": "what's the weather in sf?"}] + functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + response = litellm.completion( + model=model, messages=messages, functions=functions + ) + print(f"response: {response}") + except Exception as e: + pytest.fail(f"An error occurred {e}") + + +# test_function_call_non_openai_model_litellm_mod_set() diff --git a/litellm/tests/test_alangfuse.py b/litellm/tests/test_alangfuse.py new file mode 100644 index 0000000000000000000000000000000000000000..6a10528dfcb69492e76e6144b3490a6f717425d9 --- /dev/null +++ b/litellm/tests/test_alangfuse.py @@ -0,0 +1,346 @@ +import json +import sys +import os +import io, asyncio + +import logging + +logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath("../..")) + +from litellm import completion +import litellm + +litellm.num_retries = 3 +litellm.success_callback = ["langfuse"] +os.environ["LANGFUSE_DEBUG"] = "True" +import time +import pytest + + +def search_logs(log_file_path, num_good_logs=1): + """ + Searches the given log file for logs containing the "/api/public" string. + + Parameters: + - log_file_path (str): The path to the log file to be searched. + + Returns: + - None + + Raises: + - Exception: If there are any bad logs found in the log file. + """ + import re + + print("\n searching logs") + bad_logs = [] + good_logs = [] + all_logs = [] + try: + with open(log_file_path, "r") as log_file: + lines = log_file.readlines() + print(f"searching logslines: {lines}") + for line in lines: + all_logs.append(line.strip()) + if "/api/public" in line: + print("Found log with /api/public:") + print(line.strip()) + print("\n\n") + match = re.search( + r'"POST /api/public/ingestion HTTP/1.1" (\d+) (\d+)', + line, + ) + if match: + status_code = int(match.group(1)) + print("STATUS CODE", status_code) + if ( + status_code != 200 + and status_code != 201 + and status_code != 207 + ): + print("got a BAD log") + bad_logs.append(line.strip()) + else: + good_logs.append(line.strip()) + print("\nBad Logs") + print(bad_logs) + if len(bad_logs) > 0: + raise Exception(f"bad logs, Bad logs = {bad_logs}") + assert ( + len(good_logs) == num_good_logs + ), f"Did not get expected number of good logs, expected {num_good_logs}, got {len(good_logs)}. All logs \n {all_logs}" + print("\nGood Logs") + print(good_logs) + if len(good_logs) <= 0: + raise Exception( + f"There were no Good Logs from Langfuse. No logs with /api/public status 200. \nAll logs:{all_logs}" + ) + + except Exception as e: + raise e + + +def pre_langfuse_setup(): + """ + Set up the logging for the 'pre_langfuse_setup' function. + """ + # sends logs to langfuse.log + import logging + + # Configure the logging to write to a file + logging.basicConfig(filename="langfuse.log", level=logging.DEBUG) + logger = logging.getLogger() + + # Add a FileHandler to the logger + file_handler = logging.FileHandler("langfuse.log", mode="w") + file_handler.setLevel(logging.DEBUG) + logger.addHandler(file_handler) + return + + +@pytest.mark.skip(reason="beta test - checking langfuse output") +def test_langfuse_logging_async(): + try: + pre_langfuse_setup() + litellm.set_verbose = True + litellm.success_callback = ["langfuse"] + + async def _test_langfuse(): + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "This is a test"}], + max_tokens=100, + temperature=0.7, + timeout=5, + user="test_user", + ) + await asyncio.sleep(1) + return response + + response = asyncio.run(_test_langfuse()) + print(f"response: {response}") + + # # check langfuse.log to see if there was a failed response + search_logs("langfuse.log") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + + +# def test_langfuse_logging_async_text_completion(): +# try: +# pre_langfuse_setup() +# litellm.set_verbose = False +# litellm.success_callback = ["langfuse"] + +# async def _test_langfuse(): +# response = await litellm.atext_completion( +# model="gpt-3.5-turbo-instruct", +# prompt="this is a test", +# max_tokens=5, +# temperature=0.7, +# timeout=5, +# user="test_user", +# stream=True +# ) +# async for chunk in response: +# print() +# print(chunk) +# await asyncio.sleep(1) +# return response + +# response = asyncio.run(_test_langfuse()) +# print(f"response: {response}") + +# # # check langfuse.log to see if there was a failed response +# search_logs("langfuse.log") +# except litellm.Timeout as e: +# pass +# except Exception as e: +# pytest.fail(f"An exception occurred - {e}") + + +# test_langfuse_logging_async_text_completion() + + +@pytest.mark.skip(reason="beta test - checking langfuse output") +def test_langfuse_logging(): + try: + pre_langfuse_setup() + litellm.set_verbose = True + response = completion( + model="claude-instant-1.2", + messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}], + max_tokens=10, + temperature=0.2, + ) + print(response) + # time.sleep(5) + # # check langfuse.log to see if there was a failed response + # search_logs("langfuse.log") + + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + + +# test_langfuse_logging() + + +@pytest.mark.skip(reason="beta test - checking langfuse output") +def test_langfuse_logging_stream(): + try: + litellm.set_verbose = True + response = completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "this is a streaming test for llama2 + langfuse", + } + ], + max_tokens=20, + temperature=0.2, + stream=True, + ) + print(response) + for chunk in response: + pass + # print(chunk) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + + +# test_langfuse_logging_stream() + + +@pytest.mark.skip(reason="beta test - checking langfuse output") +def test_langfuse_logging_custom_generation_name(): + try: + litellm.set_verbose = True + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}], + max_tokens=10, + metadata={ + "langfuse/foo": "bar", + "langsmith/fizz": "buzz", + "prompt_hash": "asdf98u0j9131123", + "generation_name": "ishaan-test-generation", + "generation_id": "gen-id22", + "trace_id": "trace-id22", + "trace_user_id": "user-id2", + }, + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + print(e) + + +test_langfuse_logging_custom_generation_name() + + +@pytest.mark.skip(reason="beta test - checking langfuse output") +def test_langfuse_logging_function_calling(): + litellm.set_verbose = True + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + try: + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "what's the weather in boston"}], + temperature=0.1, + functions=function1, + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + + +# test_langfuse_logging_function_calling() + + +def test_langfuse_logging_tool_calling(): + litellm.set_verbose = True + + def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps( + {"location": "Tokyo", "temperature": "10", "unit": "celsius"} + ) + elif "san francisco" in location.lower(): + return json.dumps( + {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"} + ) + elif "paris" in location.lower(): + return json.dumps( + {"location": "Paris", "temperature": "22", "unit": "celsius"} + ) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + + messages = [ + { + "role": "user", + "content": "What's the weather like in San Francisco, Tokyo, and Paris?", + } + ] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + + response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit + ) + print("\nLLM Response1:\n", response) + response_message = response.choices[0].message + tool_calls = response.choices[0].message.tool_calls + + +# test_langfuse_logging_tool_calling() diff --git a/litellm/tests/test_amazing_vertex_completion.py b/litellm/tests/test_amazing_vertex_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..a56e0343c604ba658be6361ae149c6c738719b1b --- /dev/null +++ b/litellm/tests/test_amazing_vertex_completion.py @@ -0,0 +1,484 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, asyncio +import litellm +from litellm import embedding, completion, completion_cost, Timeout, acompletion +from litellm import RateLimitError +import json +import os +import tempfile + +litellm.num_retries = 3 +litellm.cache = None +user_message = "Write a short poem about the sky" +messages = [{"content": user_message, "role": "user"}] + + +def load_vertex_ai_credentials(): + # Define the path to the vertex_key.json file + print("loading vertex ai credentials") + filepath = os.path.dirname(os.path.abspath(__file__)) + vertex_key_path = filepath + "/vertex_key.json" + + # Read the existing content of the file or create an empty dictionary + try: + with open(vertex_key_path, "r") as file: + # Read the file content + print("Read vertexai file path") + content = file.read() + + # If the file is empty or not valid JSON, create an empty dictionary + if not content or not content.strip(): + service_account_key_data = {} + else: + # Attempt to load the existing JSON content + file.seek(0) + service_account_key_data = json.load(file) + except FileNotFoundError: + # If the file doesn't exist, create an empty dictionary + service_account_key_data = {} + + # Update the service_account_key_data with environment variables + private_key_id = os.environ.get("VERTEX_AI_PRIVATE_KEY_ID", "") + private_key = os.environ.get("VERTEX_AI_PRIVATE_KEY", "") + private_key = private_key.replace("\\n", "\n") + service_account_key_data["private_key_id"] = private_key_id + service_account_key_data["private_key"] = private_key + + # Create a temporary file + with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file: + # Write the updated content to the temporary file + json.dump(service_account_key_data, temp_file, indent=2) + + # Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS + os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.path.abspath(temp_file.name) + + +@pytest.mark.asyncio +async def get_response(): + load_vertex_ai_credentials() + prompt = '\ndef count_nums(arr):\n """\n Write a function count_nums which takes an array of integers and returns\n the number of elements which has a sum of digits > 0.\n If a number is negative, then its first signed digit will be negative:\n e.g. -123 has signed digits -1, 2, and 3.\n >>> count_nums([]) == 0\n >>> count_nums([-1, 11, -11]) == 1\n >>> count_nums([1, 1, 2]) == 3\n """\n' + try: + response = await acompletion( + model="gemini-pro", + messages=[ + { + "role": "system", + "content": "Complete the given code with no more explanation. Remember that there is a 4-space indent before the first line of your generated code.", + }, + {"role": "user", "content": prompt}, + ], + ) + return response + except litellm.UnprocessableEntityError as e: + pass + except Exception as e: + pytest.fail(f"An error occurred - {str(e)}") + + +def test_vertex_ai(): + import random + + load_vertex_ai_credentials() + test_models = ( + litellm.vertex_chat_models + + litellm.vertex_code_chat_models + + litellm.vertex_text_models + + litellm.vertex_code_text_models + ) + litellm.set_verbose = False + litellm.vertex_project = "reliablekeys" + + test_models = random.sample(test_models, 1) + # test_models += litellm.vertex_language_models # always test gemini-pro + test_models = litellm.vertex_language_models # always test gemini-pro + for model in test_models: + try: + if model in [ + "code-gecko", + "code-gecko@001", + "code-gecko@002", + "code-gecko@latest", + "code-bison@001", + "text-bison@001", + ]: + # our account does not have access to this model + continue + print("making request", model) + response = completion( + model=model, + messages=[{"role": "user", "content": "hi"}], + temperature=0.7, + ) + print("\nModel Response", response) + print(response) + assert type(response.choices[0].message.content) == str + assert len(response.choices[0].message.content) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_vertex_ai() + + +def test_vertex_ai_stream(): + load_vertex_ai_credentials() + litellm.set_verbose = False + litellm.vertex_project = "reliablekeys" + import random + + test_models = ( + litellm.vertex_chat_models + + litellm.vertex_code_chat_models + + litellm.vertex_text_models + + litellm.vertex_code_text_models + ) + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro + for model in test_models: + try: + if model in [ + "code-gecko", + "code-gecko@001", + "code-gecko@002", + "code-gecko@latest", + "code-bison@001", + "text-bison@001", + ]: + # our account does not have access to this model + continue + print("making request", model) + response = completion( + model=model, + messages=[ + {"role": "user", "content": "write 10 line code code for saying hi"} + ], + stream=True, + ) + completed_str = "" + for chunk in response: + print(chunk) + content = chunk.choices[0].delta.content or "" + print("\n content", content) + completed_str += content + assert type(content) == str + # pass + assert len(completed_str) > 4 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_vertex_ai_stream() + + +@pytest.mark.asyncio +async def test_async_vertexai_response(): + import random + + load_vertex_ai_credentials() + test_models = ( + litellm.vertex_chat_models + + litellm.vertex_code_chat_models + + litellm.vertex_text_models + + litellm.vertex_code_text_models + ) + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro + for model in test_models: + print(f"model being tested in async call: {model}") + if model in [ + "code-gecko", + "code-gecko@001", + "code-gecko@002", + "code-gecko@latest", + "code-bison@001", + "text-bison@001", + ]: + # our account does not have access to this model + continue + try: + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + response = await acompletion( + model=model, messages=messages, temperature=0.7, timeout=5 + ) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + +# asyncio.run(test_async_vertexai_response()) + + +@pytest.mark.asyncio +async def test_async_vertexai_streaming_response(): + import random + + load_vertex_ai_credentials() + test_models = ( + litellm.vertex_chat_models + + litellm.vertex_code_chat_models + + litellm.vertex_text_models + + litellm.vertex_code_text_models + ) + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro + for model in test_models: + if model in [ + "code-gecko", + "code-gecko@001", + "code-gecko@002", + "code-gecko@latest", + "code-bison@001", + "text-bison@001", + ]: + # our account does not have access to this model + continue + try: + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + response = await acompletion( + model="gemini-pro", + messages=messages, + temperature=0.7, + timeout=5, + stream=True, + ) + print(f"response: {response}") + complete_response = "" + async for chunk in response: + print(f"chunk: {chunk}") + complete_response += chunk.choices[0].delta.content + print(f"complete_response: {complete_response}") + assert len(complete_response) > 0 + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + pytest.fail(f"An exception occurred: {e}") + + +# asyncio.run(test_async_vertexai_streaming_response()) + + +def test_gemini_pro_vision(): + try: + load_vertex_ai_credentials() + litellm.set_verbose = True + litellm.num_retries = 0 + resp = litellm.completion( + model="vertex_ai/gemini-pro-vision", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "Whats in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "gs://cloud-samples-data/generative-ai/image/boats.jpeg" + }, + }, + ], + } + ], + ) + print(resp) + + prompt_tokens = resp.usage.prompt_tokens + + # DO Not DELETE this ASSERT + # Google counts the prompt tokens for us, we should ensure we use the tokens from the orignal response + assert prompt_tokens == 263 # the gemini api returns 263 to us + + except Exception as e: + import traceback + + traceback.print_exc() + raise e + + +# test_gemini_pro_vision() + + +def gemini_pro_function_calling(): + load_vertex_ai_credentials() + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] + completion = litellm.completion( + model="gemini-pro", messages=messages, tools=tools, tool_choice="auto" + ) + print(f"completion: {completion}") + + +# gemini_pro_function_calling() + + +async def gemini_pro_async_function_calling(): + load_vertex_ai_credentials() + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] + completion = await litellm.acompletion( + model="gemini-pro", messages=messages, tools=tools, tool_choice="auto" + ) + print(f"completion: {completion}") + + +asyncio.run(gemini_pro_async_function_calling()) + +# Extra gemini Vision tests for completion + stream, async, async + stream +# if we run into issues with gemini, we will also add these to our ci/cd pipeline +# def test_gemini_pro_vision_stream(): +# try: +# litellm.set_verbose = False +# litellm.num_retries=0 +# print("streaming response from gemini-pro-vision") +# resp = litellm.completion( +# model = "vertex_ai/gemini-pro-vision", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "Whats in this image?" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +# } +# } +# ] +# } +# ], +# stream=True +# ) +# print(resp) +# for chunk in resp: +# print(chunk) +# except Exception as e: +# import traceback +# traceback.print_exc() +# raise e +# test_gemini_pro_vision_stream() + +# def test_gemini_pro_vision_async(): +# try: +# litellm.set_verbose = True +# litellm.num_retries=0 +# async def test(): +# resp = await litellm.acompletion( +# model = "vertex_ai/gemini-pro-vision", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "Whats in this image?" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +# } +# } +# ] +# } +# ], +# ) +# print("async response gemini pro vision") +# print(resp) +# asyncio.run(test()) +# except Exception as e: +# import traceback +# traceback.print_exc() +# raise e +# test_gemini_pro_vision_async() + + +# def test_gemini_pro_vision_async_stream(): +# try: +# litellm.set_verbose = True +# litellm.num_retries=0 +# async def test(): +# resp = await litellm.acompletion( +# model = "vertex_ai/gemini-pro-vision", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "Whats in this image?" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +# } +# } +# ] +# } +# ], +# stream=True +# ) +# print("async response gemini pro vision") +# print(resp) +# for chunk in resp: +# print(chunk) +# asyncio.run(test()) +# except Exception as e: +# import traceback +# traceback.print_exc() +# raise e +# test_gemini_pro_vision_async() diff --git a/litellm/tests/test_async_fn.py b/litellm/tests/test_async_fn.py new file mode 100644 index 0000000000000000000000000000000000000000..fa32ce331ce7870e6b7e2a4e65f937de650832fa --- /dev/null +++ b/litellm/tests/test_async_fn.py @@ -0,0 +1,288 @@ +#### What this tests #### +# This tests the the acompletion function # + +import sys, os +import pytest +import traceback +import asyncio, logging + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import completion, acompletion, acreate + +litellm.num_retries = 3 + + +def test_sync_response_anyscale(): + litellm.set_verbose = False + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = completion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + timeout=5, + ) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + +# test_sync_response_anyscale() + + +def test_async_response_openai(): + import asyncio + + litellm.set_verbose = True + + async def test_get_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion( + model="gpt-3.5-turbo", messages=messages, timeout=5 + ) + print(f"response: {response}") + print(f"response ms: {response._response_ms}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + print(e) + + asyncio.run(test_get_response()) + + +# test_async_response_openai() + + +def test_async_response_azure(): + import asyncio + + litellm.set_verbose = True + + async def test_get_response(): + user_message = "What do you know?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion( + model="azure/gpt-turbo", + messages=messages, + base_url=os.getenv("CLOUDFLARE_AZURE_BASE_URL"), + api_key=os.getenv("AZURE_FRANCE_API_KEY"), + ) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_get_response()) + + +# test_async_response_azure() + + +def test_async_anyscale_response(): + import asyncio + + litellm.set_verbose = True + + async def test_get_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + timeout=5, + ) + # response = await response + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_get_response()) + + +# test_async_anyscale_response() + + +@pytest.mark.skip(reason="Flaky test-cloudflare is very unstable") +def test_async_completion_cloudflare(): + try: + litellm.set_verbose = True + + async def test(): + response = await litellm.acompletion( + model="cloudflare/@cf/meta/llama-2-7b-chat-int8", + messages=[{"content": "what llm are you", "role": "user"}], + max_tokens=5, + num_retries=3, + ) + print(response) + return response + + response = asyncio.run(test()) + text_response = response["choices"][0]["message"]["content"] + assert len(text_response) > 1 # more than 1 chars in response + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_async_completion_cloudflare() + + +@pytest.mark.skip(reason="Flaky test") +def test_get_cloudflare_response_streaming(): + import asyncio + + async def test_async_call(): + user_message = "write a short poem in one sentence" + messages = [{"content": user_message, "role": "user"}] + try: + litellm.set_verbose = False + response = await acompletion( + model="cloudflare/@cf/meta/llama-2-7b-chat-int8", + messages=messages, + stream=True, + num_retries=3, # cloudflare ai workers is EXTREMELY UNSTABLE + ) + print(type(response)) + + import inspect + + is_async_generator = inspect.isasyncgen(response) + print(is_async_generator) + + output = "" + i = 0 + async for chunk in response: + print(chunk) + token = chunk["choices"][0]["delta"].get("content", "") + if token == None: + continue # openai v1.0.0 returns content=None + output += token + assert output is not None, "output cannot be None." + assert isinstance(output, str), "output needs to be of type str" + assert len(output) > 0, "Length of output needs to be greater than 0." + print(f"output: {output}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_async_call()) + + +@pytest.mark.asyncio +async def test_hf_completion_tgi(): + # litellm.set_verbose=True + try: + response = await acompletion( + model="huggingface/HuggingFaceH4/zephyr-7b-beta", + messages=[{"content": "Hello, how are you?", "role": "user"}], + ) + # Add any assertions here to check the response + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_get_cloudflare_response_streaming() + + +def test_get_response_streaming(): + import asyncio + + async def test_async_call(): + user_message = "write a short poem in one sentence" + messages = [{"content": user_message, "role": "user"}] + try: + litellm.set_verbose = True + response = await acompletion( + model="gpt-3.5-turbo", messages=messages, stream=True, timeout=5 + ) + print(type(response)) + + import inspect + + is_async_generator = inspect.isasyncgen(response) + print(is_async_generator) + + output = "" + i = 0 + async for chunk in response: + token = chunk["choices"][0]["delta"].get("content", "") + if token == None: + continue # openai v1.0.0 returns content=None + output += token + assert output is not None, "output cannot be None." + assert isinstance(output, str), "output needs to be of type str" + assert len(output) > 0, "Length of output needs to be greater than 0." + print(f"output: {output}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + + asyncio.run(test_async_call()) + + +# test_get_response_streaming() + + +def test_get_response_non_openai_streaming(): + import asyncio + + litellm.set_verbose = True + litellm.num_retries = 0 + + async def test_async_call(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + stream=True, + timeout=5, + ) + print(type(response)) + + import inspect + + is_async_generator = inspect.isasyncgen(response) + print(is_async_generator) + + output = "" + i = 0 + async for chunk in response: + token = chunk["choices"][0]["delta"].get("content", None) + if token == None: + continue + print(token) + output += token + print(f"output: {output}") + assert output is not None, "output cannot be None." + assert isinstance(output, str), "output needs to be of type str" + assert len(output) > 0, "Length of output needs to be greater than 0." + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + return response + + asyncio.run(test_async_call()) + + +# test_get_response_non_openai_streaming() diff --git a/litellm/tests/test_azure_perf.py b/litellm/tests/test_azure_perf.py new file mode 100644 index 0000000000000000000000000000000000000000..9654f12734161d2a4192e916470c614696f1784a --- /dev/null +++ b/litellm/tests/test_azure_perf.py @@ -0,0 +1,128 @@ +#### What this tests #### +# This adds perf testing to the router, to ensure it's never > 50ms slower than the azure-openai sdk. +import sys, os, time, inspect, asyncio, traceback +from datetime import datetime +import pytest + +sys.path.insert(0, os.path.abspath("../..")) +import openai, litellm, uuid +from openai import AsyncAzureOpenAI + +client = AsyncAzureOpenAI( + api_key=os.getenv("AZURE_API_KEY"), + azure_endpoint=os.getenv("AZURE_API_BASE"), # type: ignore + api_version=os.getenv("AZURE_API_VERSION"), +) + +model_list = [ + { + "model_name": "azure-test", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + } +] + +router = litellm.Router(model_list=model_list) + + +async def _openai_completion(): + try: + start_time = time.time() + response = await client.chat.completions.create( + model="chatgpt-v-2", + messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}], + stream=True, + ) + time_to_first_token = None + first_token_ts = None + init_chunk = None + async for chunk in response: + if ( + time_to_first_token is None + and len(chunk.choices) > 0 + and chunk.choices[0].delta.content is not None + ): + first_token_ts = time.time() + time_to_first_token = first_token_ts - start_time + init_chunk = chunk + end_time = time.time() + print( + "OpenAI Call: ", + init_chunk, + start_time, + first_token_ts, + time_to_first_token, + end_time, + ) + return time_to_first_token + except Exception as e: + print(e) + return None + + +async def _router_completion(): + try: + start_time = time.time() + response = await router.acompletion( + model="azure-test", + messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}], + stream=True, + ) + time_to_first_token = None + first_token_ts = None + init_chunk = None + async for chunk in response: + if ( + time_to_first_token is None + and len(chunk.choices) > 0 + and chunk.choices[0].delta.content is not None + ): + first_token_ts = time.time() + time_to_first_token = first_token_ts - start_time + init_chunk = chunk + end_time = time.time() + print( + "Router Call: ", + init_chunk, + start_time, + first_token_ts, + time_to_first_token, + end_time - first_token_ts, + ) + return time_to_first_token + except Exception as e: + print(e) + return None + + +async def test_azure_completion_streaming(): + """ + Test azure streaming call - measure on time to first (non-null) token. + """ + n = 3 # Number of concurrent tasks + ## OPENAI AVG. TIME + tasks = [_openai_completion() for _ in range(n)] + chat_completions = await asyncio.gather(*tasks) + successful_completions = [c for c in chat_completions if c is not None] + total_time = 0 + for item in successful_completions: + total_time += item + avg_openai_time = total_time / 3 + ## ROUTER AVG. TIME + tasks = [_router_completion() for _ in range(n)] + chat_completions = await asyncio.gather(*tasks) + successful_completions = [c for c in chat_completions if c is not None] + total_time = 0 + for item in successful_completions: + total_time += item + avg_router_time = total_time / 3 + ## COMPARE + print(f"avg_router_time: {avg_router_time}; avg_openai_time: {avg_openai_time}") + assert avg_router_time < avg_openai_time + 0.5 + + +# asyncio.run(test_azure_completion_streaming()) diff --git a/litellm/tests/test_bad_params.py b/litellm/tests/test_bad_params.py new file mode 100644 index 0000000000000000000000000000000000000000..58fe204bd96bd31b03e925533acd5a5064e2eb8d --- /dev/null +++ b/litellm/tests/test_bad_params.py @@ -0,0 +1,155 @@ +#### What this tests #### +# This tests chaos monkeys - if random parts of the system are broken / things aren't sent correctly - what happens. +# Expect to add more edge cases to this over time. + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion +from litellm.utils import Message + + +# litellm.set_verbose = True +user_message = "Hello, how are you?" +messages = [{"content": user_message, "role": "user"}] +model_val = None + + +def test_completion_with_no_model(): + # test on empty + with pytest.raises(ValueError): + response = completion(messages=messages) + + +def test_completion_with_empty_model(): + # test on empty + try: + response = completion(model=model_val, messages=messages) + except Exception as e: + print(f"error occurred: {e}") + pass + + +# def test_completion_catch_nlp_exception(): +# TEMP commented out NLP cloud API is unstable +# try: +# response = completion(model="dolphin", messages=messages, functions=[ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA" +# }, +# "unit": { +# "type": "string", +# "enum": ["celsius", "fahrenheit"] +# } +# }, +# "required": ["location"] +# } +# } +# ]) + +# except Exception as e: +# if "Function calling is not supported by nlp_cloud" in str(e): +# pass +# else: +# pytest.fail(f'An error occurred {e}') + +# test_completion_catch_nlp_exception() + + +def test_completion_invalid_param_cohere(): + try: + response = completion(model="command-nightly", messages=messages, top_p=1) + print(f"response: {response}") + except Exception as e: + if "Unsupported parameters passed: top_p" in str(e): + pass + else: + pytest.fail(f"An error occurred {e}") + + +# test_completion_invalid_param_cohere() + + +def test_completion_function_call_cohere(): + try: + response = completion( + model="command-nightly", messages=messages, functions=["TEST-FUNCTION"] + ) + pytest.fail(f"An error occurred {e}") + except Exception as e: + print(e) + pass + + +# test_completion_function_call_cohere() + + +def test_completion_function_call_openai(): + try: + messages = [{"role": "user", "content": "What is the weather like in Boston?"}] + response = completion( + model="gpt-3.5-turbo", + messages=messages, + functions=[ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + }, + }, + "required": ["location"], + }, + } + ], + ) + print(f"response: {response}") + except: + pass + + +# test_completion_function_call_openai() + + +def test_completion_with_no_provider(): + # test on empty + try: + model = "cerebras/btlm-3b-8k-base" + response = completion(model=model, messages=messages) + except Exception as e: + print(f"error occurred: {e}") + pass + + +# test_completion_with_no_provider() +# # bad key +# temp_key = os.environ.get("OPENAI_API_KEY") +# os.environ["OPENAI_API_KEY"] = "bad-key" +# # test on openai completion call +# try: +# response = completion(model="gpt-3.5-turbo", messages=messages) +# print(f"response: {response}") +# except: +# print(f"error occurred: {traceback.format_exc()}") +# pass +# os.environ["OPENAI_API_KEY"] = str(temp_key) # this passes linting#5 diff --git a/litellm/tests/test_batch_completions.py b/litellm/tests/test_batch_completions.py new file mode 100644 index 0000000000000000000000000000000000000000..55e3084b4f4387c7152cc8beae62e4c3c0094cfc --- /dev/null +++ b/litellm/tests/test_batch_completions.py @@ -0,0 +1,81 @@ +#### What this tests #### +# This tests calling batch_completions by running 100 messages together + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from openai import APITimeoutError as Timeout +import litellm + +litellm.num_retries = 0 +from litellm import ( + batch_completion, + batch_completion_models, + completion, + batch_completion_models_all_responses, +) + +# litellm.set_verbose=True + + +def test_batch_completions(): + messages = [[{"role": "user", "content": "write a short poem"}] for _ in range(3)] + model = "j2-mid" + litellm.set_verbose = True + try: + result = batch_completion( + model=model, + messages=messages, + max_tokens=10, + temperature=0.2, + request_timeout=1, + ) + print(result) + print(len(result)) + assert len(result) == 3 + except Timeout as e: + print(f"IN TIMEOUT") + pass + except Exception as e: + pytest.fail(f"An error occurred: {e}") + + +test_batch_completions() + + +def test_batch_completions_models(): + try: + result = batch_completion_models( + models=["gpt-3.5-turbo", "gpt-3.5-turbo", "gpt-3.5-turbo"], + messages=[{"role": "user", "content": "Hey, how's it going"}], + ) + print(result) + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"An error occurred: {e}") + + +# test_batch_completions_models() + + +def test_batch_completion_models_all_responses(): + try: + responses = batch_completion_models_all_responses( + models=["j2-light", "claude-instant-1.2"], + messages=[{"role": "user", "content": "write a poem"}], + max_tokens=10, + ) + print(responses) + assert len(responses) == 2 + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"An error occurred: {e}") + + +# test_batch_completion_models_all_responses() diff --git a/litellm/tests/test_bedrock_completion.py b/litellm/tests/test_bedrock_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..d96fdfa5a6103645214225f80be06ff117c56d0d --- /dev/null +++ b/litellm/tests/test_bedrock_completion.py @@ -0,0 +1,202 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# litellm.num_retries = 3 +litellm.cache = None +litellm.success_callback = [] +user_message = "Write a short poem about the sky" +messages = [{"content": user_message, "role": "user"}] + + +@pytest.fixture(autouse=True) +def reset_callbacks(): + print("\npytest fixture - resetting callbacks") + litellm.success_callback = [] + litellm._async_success_callback = [] + litellm.failure_callback = [] + litellm.callbacks = [] + + +def test_completion_bedrock_claude_completion_auth(): + print("calling bedrock claude completion params auth") + import os + + aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] + aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] + aws_region_name = os.environ["AWS_REGION_NAME"] + + os.environ.pop("AWS_ACCESS_KEY_ID", None) + os.environ.pop("AWS_SECRET_ACCESS_KEY", None) + os.environ.pop("AWS_REGION_NAME", None) + + try: + response = completion( + model="bedrock/anthropic.claude-instant-v1", + messages=messages, + max_tokens=10, + temperature=0.1, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + ) + # Add any assertions here to check the response + print(response) + + os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id + os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key + os.environ["AWS_REGION_NAME"] = aws_region_name + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_claude_completion_auth() + + +def test_completion_bedrock_claude_2_1_completion_auth(): + print("calling bedrock claude 2.1 completion params auth") + import os + + aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] + aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] + aws_region_name = os.environ["AWS_REGION_NAME"] + + os.environ.pop("AWS_ACCESS_KEY_ID", None) + os.environ.pop("AWS_SECRET_ACCESS_KEY", None) + os.environ.pop("AWS_REGION_NAME", None) + + try: + response = completion( + model="bedrock/anthropic.claude-v2:1", + messages=messages, + max_tokens=10, + temperature=0.1, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_region_name=aws_region_name, + ) + # Add any assertions here to check the response + print(response) + + os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id + os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key + os.environ["AWS_REGION_NAME"] = aws_region_name + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_claude_2_1_completion_auth() + + +def test_completion_bedrock_claude_external_client_auth(): + print("\ncalling bedrock claude external client auth") + import os + + aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] + aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] + aws_region_name = os.environ["AWS_REGION_NAME"] + + os.environ.pop("AWS_ACCESS_KEY_ID", None) + os.environ.pop("AWS_SECRET_ACCESS_KEY", None) + os.environ.pop("AWS_REGION_NAME", None) + + try: + import boto3 + + litellm.set_verbose = True + + bedrock = boto3.client( + service_name="bedrock-runtime", + region_name=aws_region_name, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + endpoint_url=f"https://bedrock-runtime.{aws_region_name}.amazonaws.com", + ) + + response = completion( + model="bedrock/anthropic.claude-instant-v1", + messages=messages, + max_tokens=10, + temperature=0.1, + aws_bedrock_client=bedrock, + ) + # Add any assertions here to check the response + print(response) + + os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id + os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key + os.environ["AWS_REGION_NAME"] = aws_region_name + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_claude_external_client_auth() + + +def test_provisioned_throughput(): + try: + litellm.set_verbose = True + import botocore, json, io + import botocore.session + from botocore.stub import Stubber + + bedrock_client = botocore.session.get_session().create_client( + "bedrock-runtime", region_name="us-east-1" + ) + + expected_params = { + "accept": "application/json", + "body": '{"prompt": "\\n\\nHuman: Hello, how are you?\\n\\nAssistant: ", ' + '"max_tokens_to_sample": 256}', + "contentType": "application/json", + "modelId": "provisioned-model-arn", + } + response_from_bedrock = { + "body": io.StringIO( + json.dumps( + { + "completion": " Here is a short poem about the sky:", + "stop_reason": "max_tokens", + "stop": None, + } + ) + ), + "contentType": "contentType", + "ResponseMetadata": {"HTTPStatusCode": 200}, + } + + with Stubber(bedrock_client) as stubber: + stubber.add_response( + "invoke_model", + service_response=response_from_bedrock, + expected_params=expected_params, + ) + response = litellm.completion( + model="bedrock/anthropic.claude-instant-v1", + model_id="provisioned-model-arn", + messages=[{"content": "Hello, how are you?", "role": "user"}], + aws_bedrock_client=bedrock_client, + ) + print("response stubbed", response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_provisioned_throughput() diff --git a/litellm/tests/test_budget_manager.py b/litellm/tests/test_budget_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..83c9f2723854ac08c8e387adb72d5b2482ee7ccc --- /dev/null +++ b/litellm/tests/test_budget_manager.py @@ -0,0 +1,130 @@ +# #### What this tests #### +# # This tests calling batch_completions by running 100 messages together + +# import sys, os, json +# import traceback +# import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# litellm.set_verbose = True +# from litellm import completion, BudgetManager + +# budget_manager = BudgetManager(project_name="test_project", client_type="hosted") + +# ## Scenario 1: User budget enough to make call +# def test_user_budget_enough(): +# try: +# user = "1234" +# # create a budget for a user +# budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +# # check if a given call can be made +# data = { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hey, how's it going?"}] +# } +# if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user): +# response = completion(**data) +# print(budget_manager.update_cost(completion_obj=response, user=user)) +# else: +# response = "Sorry - no budget!" + +# print(f"response: {response}") +# except Exception as e: +# pytest.fail(f"An error occurred - {str(e)}") + +# ## Scenario 2: User budget not enough to make call +# def test_user_budget_not_enough(): +# try: +# user = "12345" +# # create a budget for a user +# budget_manager.create_budget(total_budget=0, user=user, duration="daily") + +# # check if a given call can be made +# data = { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hey, how's it going?"}] +# } +# model = data["model"] +# messages = data["messages"] +# if budget_manager.get_current_cost(user=user) < budget_manager.get_total_budget(user=user): +# response = completion(**data) +# print(budget_manager.update_cost(completion_obj=response, user=user)) +# else: +# response = "Sorry - no budget!" + +# print(f"response: {response}") +# except: +# pytest.fail(f"An error occurred") + +# ## Scenario 3: Saving budget to client +# def test_save_user_budget(): +# try: +# response = budget_manager.save_data() +# if response["status"] == "error": +# raise Exception(f"An error occurred - {json.dumps(response)}") +# print(response) +# except Exception as e: +# pytest.fail(f"An error occurred: {str(e)}") + +# test_save_user_budget() +# ## Scenario 4: Getting list of users +# def test_get_users(): +# try: +# response = budget_manager.get_users() +# print(response) +# except: +# pytest.fail(f"An error occurred") + + +# ## Scenario 5: Reset budget at the end of duration +# def test_reset_on_duration(): +# try: +# # First, set a short duration budget for a user +# user = "123456" +# budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +# # Use some of the budget +# data = { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hello!"}] +# } +# if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user=user): +# response = litellm.completion(**data) +# print(budget_manager.update_cost(completion_obj=response, user=user)) + +# assert budget_manager.get_current_cost(user) > 0, f"Test setup failed: Budget did not decrease after completion" + +# # Now, we need to simulate the passing of time. Since we don't want our tests to actually take days, we're going +# # to cheat a little -- we'll manually adjust the "created_at" time so it seems like a day has passed. +# # In a real-world testing scenario, we might instead use something like the `freezegun` library to mock the system time. +# one_day_in_seconds = 24 * 60 * 60 +# budget_manager.user_dict[user]["last_updated_at"] -= one_day_in_seconds + +# # Now the duration should have expired, so our budget should reset +# budget_manager.update_budget_all_users() + +# # Make sure the budget was actually reset +# assert budget_manager.get_current_cost(user) == 0, "Budget didn't reset after duration expired" +# except Exception as e: +# pytest.fail(f"An error occurred - {str(e)}") + +# ## Scenario 6: passing in text: +# def test_input_text_on_completion(): +# try: +# user = "12345" +# budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +# input_text = "hello world" +# output_text = "it's a sunny day in san francisco" +# model = "gpt-3.5-turbo" + +# budget_manager.update_cost(user=user, model=model, input_text=input_text, output_text=output_text) +# print(budget_manager.get_current_cost(user)) +# except Exception as e: +# pytest.fail(f"An error occurred - {str(e)}") + +# test_input_text_on_completion() diff --git a/litellm/tests/test_caching.py b/litellm/tests/test_caching.py new file mode 100644 index 0000000000000000000000000000000000000000..7b82906042afef6ae996cde34c3410df6e9036aa --- /dev/null +++ b/litellm/tests/test_caching.py @@ -0,0 +1,1016 @@ +import sys, os, uuid +import time +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion +from litellm.caching import Cache +import random +import hashlib + +# litellm.set_verbose=True + +messages = [{"role": "user", "content": "who is ishaan Github? "}] +# comment + +import random +import string + + +def generate_random_word(length=4): + letters = string.ascii_lowercase + return "".join(random.choice(letters) for _ in range(length)) + + +messages = [{"role": "user", "content": "who is ishaan 5222"}] + + +def test_caching_v2(): # test in memory cache + try: + litellm.set_verbose = True + litellm.cache = Cache() + response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + print(f"response1: {response1}") + print(f"response2: {response2}") + litellm.cache = None # disable cache + litellm.success_callback = [] + litellm._async_success_callback = [] + if ( + response2["choices"][0]["message"]["content"] + != response1["choices"][0]["message"]["content"] + ): + print(f"response1: {response1}") + print(f"response2: {response2}") + pytest.fail(f"Error occurred:") + except Exception as e: + print(f"error occurred: {traceback.format_exc()}") + pytest.fail(f"Error occurred: {e}") + + +# test_caching_v2() + + +def test_caching_with_ttl(): + try: + litellm.set_verbose = True + litellm.cache = Cache() + response1 = completion( + model="gpt-3.5-turbo", messages=messages, caching=True, ttl=0 + ) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + print(f"response1: {response1}") + print(f"response2: {response2}") + litellm.cache = None # disable cache + litellm.success_callback = [] + litellm._async_success_callback = [] + assert ( + response2["choices"][0]["message"]["content"] + != response1["choices"][0]["message"]["content"] + ) + except Exception as e: + print(f"error occurred: {traceback.format_exc()}") + pytest.fail(f"Error occurred: {e}") + + +def test_caching_with_cache_controls(): + try: + litellm.set_verbose = True + litellm.cache = Cache() + message = [{"role": "user", "content": f"Hey, how's it going? {uuid.uuid4()}"}] + ## TTL = 0 + response1 = completion( + model="gpt-3.5-turbo", messages=messages, cache={"ttl": 0} + ) + response2 = completion( + model="gpt-3.5-turbo", messages=messages, cache={"s-maxage": 10} + ) + print(f"response1: {response1}") + print(f"response2: {response2}") + assert ( + response2["choices"][0]["message"]["content"] + != response1["choices"][0]["message"]["content"] + ) + message = [{"role": "user", "content": f"Hey, how's it going? {uuid.uuid4()}"}] + ## TTL = 5 + response1 = completion( + model="gpt-3.5-turbo", messages=messages, cache={"ttl": 5} + ) + response2 = completion( + model="gpt-3.5-turbo", messages=messages, cache={"s-maxage": 5} + ) + print(f"response1: {response1}") + print(f"response2: {response2}") + assert ( + response2["choices"][0]["message"]["content"] + == response1["choices"][0]["message"]["content"] + ) + except Exception as e: + print(f"error occurred: {traceback.format_exc()}") + pytest.fail(f"Error occurred: {e}") + + +# test_caching_with_cache_controls() + + +def test_caching_with_models_v2(): + messages = [ + {"role": "user", "content": "who is ishaan CTO of litellm from litellm 2023"} + ] + litellm.cache = Cache() + print("test2 for caching") + response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response3 = completion(model="command-nightly", messages=messages, caching=True) + print(f"response1: {response1}") + print(f"response2: {response2}") + print(f"response3: {response3}") + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + if ( + response3["choices"][0]["message"]["content"] + == response2["choices"][0]["message"]["content"] + ): + # if models are different, it should not return cached response + print(f"response2: {response2}") + print(f"response3: {response3}") + pytest.fail(f"Error occurred:") + if ( + response1["choices"][0]["message"]["content"] + != response2["choices"][0]["message"]["content"] + ): + print(f"response1: {response1}") + print(f"response2: {response2}") + pytest.fail(f"Error occurred:") + + +# test_caching_with_models_v2() + +embedding_large_text = ( + """ +small text +""" + * 5 +) + + +# # test_caching_with_models() +def test_embedding_caching(): + import time + + # litellm.set_verbose = True + + litellm.cache = Cache() + text_to_embed = [embedding_large_text] + start_time = time.time() + embedding1 = embedding( + model="text-embedding-ada-002", input=text_to_embed, caching=True + ) + end_time = time.time() + print(f"Embedding 1 response time: {end_time - start_time} seconds") + + time.sleep(1) + start_time = time.time() + embedding2 = embedding( + model="text-embedding-ada-002", input=text_to_embed, caching=True + ) + end_time = time.time() + # print(f"embedding2: {embedding2}") + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s + if embedding2["data"][0]["embedding"] != embedding1["data"][0]["embedding"]: + print(f"embedding1: {embedding1}") + print(f"embedding2: {embedding2}") + pytest.fail("Error occurred: Embedding caching failed") + + +# test_embedding_caching() + + +def test_embedding_caching_azure(): + print("Testing azure embedding caching") + import time + + litellm.cache = Cache() + text_to_embed = [embedding_large_text] + + api_key = os.environ["AZURE_API_KEY"] + api_base = os.environ["AZURE_API_BASE"] + api_version = os.environ["AZURE_API_VERSION"] + + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_KEY"] = "" + + start_time = time.time() + print("AZURE CONFIGS") + print(api_version) + print(api_key) + print(api_base) + embedding1 = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + api_key=api_key, + api_base=api_base, + api_version=api_version, + caching=True, + ) + end_time = time.time() + print(f"Embedding 1 response time: {end_time - start_time} seconds") + + time.sleep(1) + start_time = time.time() + embedding2 = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + api_key=api_key, + api_base=api_base, + api_version=api_version, + caching=True, + ) + end_time = time.time() + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s + if embedding2["data"][0]["embedding"] != embedding1["data"][0]["embedding"]: + print(f"embedding1: {embedding1}") + print(f"embedding2: {embedding2}") + pytest.fail("Error occurred: Embedding caching failed") + + os.environ["AZURE_API_VERSION"] = api_version + os.environ["AZURE_API_BASE"] = api_base + os.environ["AZURE_API_KEY"] = api_key + + +# test_embedding_caching_azure() + + +def test_redis_cache_completion(): + litellm.set_verbose = False + + random_number = random.randint( + 1, 100000 + ) # add a random number to ensure it's always adding / reading from cache + messages = [ + {"role": "user", "content": f"write a one sentence poem about: {random_number}"} + ] + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + print("test2 for Redis Caching - non streaming") + response1 = completion( + model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=20 + ) + response2 = completion( + model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=20 + ) + response3 = completion( + model="gpt-3.5-turbo", messages=messages, caching=True, temperature=0.5 + ) + response4 = completion(model="command-nightly", messages=messages, caching=True) + + print("\nresponse 1", response1) + print("\nresponse 2", response2) + print("\nresponse 3", response3) + print("\nresponse 4", response4) + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + + """ + 1 & 2 should be exactly the same + 1 & 3 should be different, since input params are diff + 1 & 4 should be diff, since models are diff + """ + if ( + response1["choices"][0]["message"]["content"] + != response2["choices"][0]["message"]["content"] + ): # 1 and 2 should be the same + # 1&2 have the exact same input params. This MUST Be a CACHE HIT + print(f"response1: {response1}") + print(f"response2: {response2}") + pytest.fail(f"Error occurred:") + if ( + response1["choices"][0]["message"]["content"] + == response3["choices"][0]["message"]["content"] + ): + # if input params like seed, max_tokens are diff it should NOT be a cache hit + print(f"response1: {response1}") + print(f"response3: {response3}") + pytest.fail( + f"Response 1 == response 3. Same model, diff params shoudl not cache Error occurred:" + ) + if ( + response1["choices"][0]["message"]["content"] + == response4["choices"][0]["message"]["content"] + ): + # if models are different, it should not return cached response + print(f"response1: {response1}") + print(f"response4: {response4}") + pytest.fail(f"Error occurred:") + + assert response1.id == response2.id + assert response1.created == response2.created + assert response1.choices[0].message.content == response2.choices[0].message.content + + +# test_redis_cache_completion() + + +def test_redis_cache_completion_stream(): + try: + litellm.success_callback = [] + litellm._async_success_callback = [] + litellm.callbacks = [] + litellm.set_verbose = True + random_number = random.randint( + 1, 100000 + ) # add a random number to ensure it's always adding / reading from cache + messages = [ + { + "role": "user", + "content": f"write a one sentence poem about: {random_number}", + } + ] + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + print("test for caching, streaming + completion") + response1 = completion( + model="gpt-3.5-turbo", + messages=messages, + max_tokens=40, + temperature=0.2, + stream=True, + ) + response_1_content = "" + for chunk in response1: + print(chunk) + response_1_content += chunk.choices[0].delta.content or "" + print(response_1_content) + time.sleep(0.5) + response2 = completion( + model="gpt-3.5-turbo", + messages=messages, + max_tokens=40, + temperature=0.2, + stream=True, + ) + response_2_content = "" + for chunk in response2: + print(chunk) + response_2_content += chunk.choices[0].delta.content or "" + print("\nresponse 1", response_1_content) + print("\nresponse 2", response_2_content) + assert ( + response_1_content == response_2_content + ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" + litellm.success_callback = [] + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + except Exception as e: + print(e) + litellm.success_callback = [] + raise e + """ + + 1 & 2 should be exactly the same + """ + + +test_redis_cache_completion_stream() + + +def test_redis_cache_acompletion_stream(): + import asyncio + + try: + litellm.set_verbose = True + random_word = generate_random_word() + messages = [ + { + "role": "user", + "content": f"write a one sentence poem about: {random_word}", + } + ] + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + print("test for caching, streaming + completion") + response_1_content = "" + response_2_content = "" + + async def call1(): + nonlocal response_1_content + response1 = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=messages, + max_tokens=40, + temperature=1, + stream=True, + ) + async for chunk in response1: + print(chunk) + response_1_content += chunk.choices[0].delta.content or "" + print(response_1_content) + + asyncio.run(call1()) + time.sleep(0.5) + print("\n\n Response 1 content: ", response_1_content, "\n\n") + + async def call2(): + nonlocal response_2_content + response2 = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=messages, + max_tokens=40, + temperature=1, + stream=True, + ) + async for chunk in response2: + print(chunk) + response_2_content += chunk.choices[0].delta.content or "" + print(response_2_content) + + asyncio.run(call2()) + print("\nresponse 1", response_1_content) + print("\nresponse 2", response_2_content) + assert ( + response_1_content == response_2_content + ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + except Exception as e: + print(e) + raise e + + +# test_redis_cache_acompletion_stream() + + +def test_redis_cache_acompletion_stream_bedrock(): + import asyncio + + try: + litellm.set_verbose = True + random_word = generate_random_word() + messages = [ + { + "role": "user", + "content": f"write a one sentence poem about: {random_word}", + } + ] + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + print("test for caching, streaming + completion") + response_1_content = "" + response_2_content = "" + + async def call1(): + nonlocal response_1_content + response1 = await litellm.acompletion( + model="bedrock/anthropic.claude-v1", + messages=messages, + max_tokens=40, + temperature=1, + stream=True, + ) + async for chunk in response1: + print(chunk) + response_1_content += chunk.choices[0].delta.content or "" + print(response_1_content) + + asyncio.run(call1()) + time.sleep(0.5) + print("\n\n Response 1 content: ", response_1_content, "\n\n") + + async def call2(): + nonlocal response_2_content + response2 = await litellm.acompletion( + model="bedrock/anthropic.claude-v1", + messages=messages, + max_tokens=40, + temperature=1, + stream=True, + ) + async for chunk in response2: + print(chunk) + response_2_content += chunk.choices[0].delta.content or "" + print(response_2_content) + + asyncio.run(call2()) + print("\nresponse 1", response_1_content) + print("\nresponse 2", response_2_content) + assert ( + response_1_content == response_2_content + ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" + + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + except Exception as e: + print(e) + raise e + + +def test_s3_cache_acompletion_stream_azure(): + import asyncio + + try: + litellm.set_verbose = True + random_word = generate_random_word() + messages = [ + { + "role": "user", + "content": f"write a one sentence poem about: {random_word}", + } + ] + litellm.cache = Cache( + type="s3", s3_bucket_name="cache-bucket-litellm", s3_region_name="us-west-2" + ) + print("s3 Cache: test for caching, streaming + completion") + response_1_content = "" + response_2_content = "" + + response_1_created = "" + response_2_created = "" + + async def call1(): + nonlocal response_1_content, response_1_created + response1 = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=messages, + max_tokens=40, + temperature=1, + stream=True, + ) + async for chunk in response1: + print(chunk) + response_1_created = chunk.created + response_1_content += chunk.choices[0].delta.content or "" + print(response_1_content) + + asyncio.run(call1()) + time.sleep(0.5) + print("\n\n Response 1 content: ", response_1_content, "\n\n") + + async def call2(): + nonlocal response_2_content, response_2_created + response2 = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=messages, + max_tokens=40, + temperature=1, + stream=True, + ) + async for chunk in response2: + print(chunk) + response_2_content += chunk.choices[0].delta.content or "" + response_2_created = chunk.created + print(response_2_content) + + asyncio.run(call2()) + print("\nresponse 1", response_1_content) + print("\nresponse 2", response_2_content) + + assert ( + response_1_content == response_2_content + ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" + + # prioritizing getting a new deploy out - will look at this in the next deploy + # print("response 1 created", response_1_created) + # print("response 2 created", response_2_created) + + # assert response_1_created == response_2_created + + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + except Exception as e: + print(e) + raise e + + +# test_s3_cache_acompletion_stream_azure() + + +# test_redis_cache_acompletion_stream_bedrock() +# redis cache with custom keys +def custom_get_cache_key(*args, **kwargs): + # return key to use for your cache: + key = ( + kwargs.get("model", "") + + str(kwargs.get("messages", "")) + + str(kwargs.get("temperature", "")) + + str(kwargs.get("logit_bias", "")) + ) + return key + + +def test_custom_redis_cache_with_key(): + messages = [{"role": "user", "content": "write a one line story"}] + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + litellm.cache.get_cache_key = custom_get_cache_key + + local_cache = {} + + def set_cache(key, value): + local_cache[key] = value + + def get_cache(key): + if key in local_cache: + return local_cache[key] + + litellm.cache.cache.set_cache = set_cache + litellm.cache.cache.get_cache = get_cache + + # patch this redis cache get and set call + + response1 = completion( + model="gpt-3.5-turbo", + messages=messages, + temperature=1, + caching=True, + num_retries=3, + ) + response2 = completion( + model="gpt-3.5-turbo", + messages=messages, + temperature=1, + caching=True, + num_retries=3, + ) + response3 = completion( + model="gpt-3.5-turbo", + messages=messages, + temperature=1, + caching=False, + num_retries=3, + ) + + print(f"response1: {response1}") + print(f"response2: {response2}") + print(f"response3: {response3}") + + if ( + response3["choices"][0]["message"]["content"] + == response2["choices"][0]["message"]["content"] + ): + pytest.fail(f"Error occurred:") + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + + +# test_custom_redis_cache_with_key() + + +def test_cache_override(): + # test if we can override the cache, when `caching=False` but litellm.cache = Cache() is set + # in this case it should not return cached responses + litellm.cache = Cache() + print("Testing cache override") + litellm.set_verbose = True + + # test embedding + response1 = embedding( + model="text-embedding-ada-002", input=["hello who are you"], caching=False + ) + + start_time = time.time() + + response2 = embedding( + model="text-embedding-ada-002", input=["hello who are you"], caching=False + ) + + end_time = time.time() + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + assert ( + end_time - start_time > 0.1 + ) # ensure 2nd response comes in over 0.1s. This should not be cached. + + +# test_cache_override() + + +def test_custom_redis_cache_params(): + # test if we can init redis with **kwargs + try: + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + db=0, + ssl=True, + ssl_certfile="./redis_user.crt", + ssl_keyfile="./redis_user_private.key", + ssl_ca_certs="./redis_ca.pem", + ) + + print(litellm.cache.cache.redis_client) + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + except Exception as e: + pytest.fail(f"Error occurred:", e) + + +def test_get_cache_key(): + from litellm.caching import Cache + + try: + print("Testing get_cache_key") + cache_instance = Cache() + cache_key = cache_instance.get_cache_key( + **{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "write a one sentence poem about: 7510"} + ], + "max_tokens": 40, + "temperature": 0.2, + "stream": True, + "litellm_call_id": "ffe75e7e-8a07-431f-9a74-71a5b9f35f0b", + "litellm_logging_obj": {}, + } + ) + cache_key_2 = cache_instance.get_cache_key( + **{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "write a one sentence poem about: 7510"} + ], + "max_tokens": 40, + "temperature": 0.2, + "stream": True, + "litellm_call_id": "ffe75e7e-8a07-431f-9a74-71a5b9f35f0b", + "litellm_logging_obj": {}, + } + ) + cache_key_str = "model: gpt-3.5-turbomessages: [{'role': 'user', 'content': 'write a one sentence poem about: 7510'}]temperature: 0.2max_tokens: 40" + hash_object = hashlib.sha256(cache_key_str.encode()) + # Hexadecimal representation of the hash + hash_hex = hash_object.hexdigest() + assert cache_key == hash_hex + assert ( + cache_key_2 == hash_hex + ), f"{cache_key} != {cache_key_2}. The same kwargs should have the same cache key across runs" + + embedding_cache_key = cache_instance.get_cache_key( + **{ + "model": "azure/azure-embedding-model", + "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", + "api_key": "", + "api_version": "2023-07-01-preview", + "timeout": None, + "max_retries": 0, + "input": ["hi who is ishaan"], + "caching": True, + "client": "", + } + ) + + print(embedding_cache_key) + + embedding_cache_key_str = ( + "model: azure/azure-embedding-modelinput: ['hi who is ishaan']" + ) + hash_object = hashlib.sha256(embedding_cache_key_str.encode()) + # Hexadecimal representation of the hash + hash_hex = hash_object.hexdigest() + assert ( + embedding_cache_key == hash_hex + ), f"{embedding_cache_key} != 'model: azure/azure-embedding-modelinput: ['hi who is ishaan']'. The same kwargs should have the same cache key across runs" + + # Proxy - embedding cache, test if embedding key, gets model_group and not model + embedding_cache_key_2 = cache_instance.get_cache_key( + **{ + "model": "azure/azure-embedding-model", + "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", + "api_key": "", + "api_version": "2023-07-01-preview", + "timeout": None, + "max_retries": 0, + "input": ["hi who is ishaan"], + "caching": True, + "client": "", + "proxy_server_request": { + "url": "http://0.0.0.0:8000/embeddings", + "method": "POST", + "headers": { + "host": "0.0.0.0:8000", + "user-agent": "curl/7.88.1", + "accept": "*/*", + "content-type": "application/json", + "content-length": "80", + }, + "body": { + "model": "azure-embedding-model", + "input": ["hi who is ishaan"], + }, + }, + "user": None, + "metadata": { + "user_api_key": None, + "headers": { + "host": "0.0.0.0:8000", + "user-agent": "curl/7.88.1", + "accept": "*/*", + "content-type": "application/json", + "content-length": "80", + }, + "model_group": "EMBEDDING_MODEL_GROUP", + "deployment": "azure/azure-embedding-model-ModelID-azure/azure-embedding-modelhttps://openai-gpt-4-test-v-1.openai.azure.com/2023-07-01-preview", + }, + "model_info": { + "mode": "embedding", + "base_model": "text-embedding-ada-002", + "id": "20b2b515-f151-4dd5-a74f-2231e2f54e29", + }, + "litellm_call_id": "2642e009-b3cd-443d-b5dd-bb7d56123b0e", + "litellm_logging_obj": "", + } + ) + + print(embedding_cache_key_2) + embedding_cache_key_str_2 = ( + "model: EMBEDDING_MODEL_GROUPinput: ['hi who is ishaan']" + ) + hash_object = hashlib.sha256(embedding_cache_key_str_2.encode()) + # Hexadecimal representation of the hash + hash_hex = hash_object.hexdigest() + assert embedding_cache_key_2 == hash_hex + print("passed!") + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred:", e) + + +# test_get_cache_key() + + +def test_cache_context_managers(): + litellm.set_verbose = True + litellm.cache = Cache(type="redis") + + # cache is on, disable it + litellm.disable_cache() + assert litellm.cache == None + assert "cache" not in litellm.success_callback + assert "cache" not in litellm._async_success_callback + + # disable a cache that is off + litellm.disable_cache() + assert litellm.cache == None + assert "cache" not in litellm.success_callback + assert "cache" not in litellm._async_success_callback + + litellm.enable_cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + ) + + assert litellm.cache != None + assert litellm.cache.type == "redis" + + print("VARS of litellm.cache", vars(litellm.cache)) + + +# test_cache_context_managers() + +# test_custom_redis_cache_params() + +# def test_redis_cache_with_ttl(): +# cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) +# sample_model_response_object_str = """{ +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# }""" +# sample_model_response_object = { +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# } +# cache.add_cache(cache_key="test_key", result=sample_model_response_object_str, ttl=1) +# cached_value = cache.get_cache(cache_key="test_key") +# print(f"cached-value: {cached_value}") +# assert cached_value['choices'][0]['message']['content'] == sample_model_response_object['choices'][0]['message']['content'] +# time.sleep(2) +# assert cache.get_cache(cache_key="test_key") is None + +# # test_redis_cache_with_ttl() + +# def test_in_memory_cache_with_ttl(): +# cache = Cache(type="local") +# sample_model_response_object_str = """{ +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# }""" +# sample_model_response_object = { +# "choices": [ +# { +# "finish_reason": "stop", +# "index": 0, +# "message": { +# "role": "assistant", +# "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# "created": 1691429984.3852863, +# "model": "claude-instant-1", +# "usage": { +# "prompt_tokens": 18, +# "completion_tokens": 23, +# "total_tokens": 41 +# } +# } +# cache.add_cache(cache_key="test_key", result=sample_model_response_object_str, ttl=1) +# cached_value = cache.get_cache(cache_key="test_key") +# assert cached_value['choices'][0]['message']['content'] == sample_model_response_object['choices'][0]['message']['content'] +# time.sleep(2) +# assert cache.get_cache(cache_key="test_key") is None +# # test_in_memory_cache_with_ttl() diff --git a/litellm/tests/test_caching_ssl.py b/litellm/tests/test_caching_ssl.py new file mode 100644 index 0000000000000000000000000000000000000000..84ece831076f2cc4b9e9e71b18976a3d4dc2e0ac --- /dev/null +++ b/litellm/tests/test_caching_ssl.py @@ -0,0 +1,101 @@ +#### What this tests #### +# This tests using caching w/ litellm which requires SSL=True + +import sys, os +import time +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, Router +from litellm.caching import Cache + +messages = [{"role": "user", "content": f"who is ishaan {time.time()}"}] + + +def test_caching_v2(): # test in memory cache + try: + litellm.cache = Cache( + type="redis", + host="os.environ/REDIS_HOST_2", + port="os.environ/REDIS_PORT_2", + password="os.environ/REDIS_PASSWORD_2", + ssl="os.environ/REDIS_SSL_2", + ) + response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) + print(f"response1: {response1}") + print(f"response2: {response2}") + litellm.cache = None # disable cache + if ( + response2["choices"][0]["message"]["content"] + != response1["choices"][0]["message"]["content"] + ): + print(f"response1: {response1}") + print(f"response2: {response2}") + raise Exception() + except Exception as e: + print(f"error occurred: {traceback.format_exc()}") + pytest.fail(f"Error occurred: {e}") + + +# test_caching_v2() + + +def test_caching_router(): + """ + Test scenario where litellm.cache is set but kwargs("caching") is not. This should still return a cache hit. + """ + try: + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + } + ] + litellm.cache = Cache( + type="redis", + host="os.environ/REDIS_HOST_2", + port="os.environ/REDIS_PORT_2", + password="os.environ/REDIS_PASSWORD_2", + ssl="os.environ/REDIS_SSL_2", + ) + router = Router( + model_list=model_list, + routing_strategy="simple-shuffle", + set_verbose=False, + num_retries=1, + ) # type: ignore + response1 = completion(model="gpt-3.5-turbo", messages=messages) + response2 = completion(model="gpt-3.5-turbo", messages=messages) + if ( + response2["choices"][0]["message"]["content"] + != response1["choices"][0]["message"]["content"] + ): + print(f"response1: {response1}") + print(f"response2: {response2}") + litellm.cache = None # disable cache + assert ( + response2["choices"][0]["message"]["content"] + == response1["choices"][0]["message"]["content"] + ) + except Exception as e: + print(f"error occurred: {traceback.format_exc()}") + pytest.fail(f"Error occurred: {e}") + + +# test_caching_router() diff --git a/litellm/tests/test_class.py b/litellm/tests/test_class.py new file mode 100644 index 0000000000000000000000000000000000000000..3520d870d0f99b9fa5c12e65319a8d029698f778 --- /dev/null +++ b/litellm/tests/test_class.py @@ -0,0 +1,74 @@ +# #### What this tests #### +# # This tests the LiteLLM Class + +# import sys, os +# import traceback +# import pytest +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# import asyncio + +# litellm.set_verbose = True +# from litellm import Router +# import instructor +# from pydantic import BaseModel + +# # This enables response_model keyword +# # # from client.chat.completions.create +# # client = instructor.patch(Router(model_list=[{ +# # "model_name": "gpt-3.5-turbo", # openai model name +# # "litellm_params": { # params for litellm completion/embedding call +# # "model": "azure/chatgpt-v-2", +# # "api_key": os.getenv("AZURE_API_KEY"), +# # "api_version": os.getenv("AZURE_API_VERSION"), +# # "api_base": os.getenv("AZURE_API_BASE") +# # } +# # }])) + +# # class UserDetail(BaseModel): +# # name: str +# # age: int + +# # user = client.chat.completions.create( +# # model="gpt-3.5-turbo", +# # response_model=UserDetail, +# # messages=[ +# # {"role": "user", "content": "Extract Jason is 25 years old"}, +# # ] +# # ) +# # assert isinstance(model, UserExtract) + +# # assert isinstance(user, UserDetail) +# # assert user.name == "Jason" +# # assert user.age == 25 + +# # print(f"user: {user}") +# import instructor +# from openai import AsyncOpenAI + +# aclient = instructor.apatch(Router(model_list=[{ +# "model_name": "gpt-3.5-turbo", # openai model name +# "litellm_params": { # params for litellm completion/embedding call +# "model": "azure/chatgpt-v-2", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_version": os.getenv("AZURE_API_VERSION"), +# "api_base": os.getenv("AZURE_API_BASE") +# } +# }], default_litellm_params={"acompletion": True})) + +# class UserExtract(BaseModel): +# name: str +# age: int +# async def main(): +# model = await aclient.chat.completions.create( +# model="gpt-3.5-turbo", +# response_model=UserExtract, +# messages=[ +# {"role": "user", "content": "Extract jason is 25 years old"}, +# ], +# ) +# print(f"model: {model}") + +# asyncio.run(main()) diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..2be10b0b60c36f59c1c3483a603056ee590d9e64 --- /dev/null +++ b/litellm/tests/test_completion.py @@ -0,0 +1,1950 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# litellm.num_retries = 3 +litellm.cache = None +litellm.success_callback = [] +user_message = "Write a short poem about the sky" +messages = [{"content": user_message, "role": "user"}] + + +def logger_fn(user_model_dict): + print(f"user_model_dict: {user_model_dict}") + + +@pytest.fixture(autouse=True) +def reset_callbacks(): + print("\npytest fixture - resetting callbacks") + litellm.success_callback = [] + litellm._async_success_callback = [] + litellm.failure_callback = [] + litellm.callbacks = [] + + +def test_completion_custom_provider_model_name(): + try: + litellm.cache = None + response = completion( + model="together_ai/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + print(response["choices"][0]["finish_reason"]) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_custom_provider_model_name() + + +def test_completion_claude(): + litellm.set_verbose = True + litellm.cache = None + litellm.AnthropicConfig(max_tokens_to_sample=200, metadata={"user_id": "1224"}) + messages = [ + { + "role": "system", + "content": """You are an upbeat, enthusiastic personal fitness coach named Sam. Sam is passionate about helping clients get fit and lead healthier lifestyles. You write in an encouraging and friendly tone and always try to guide your clients toward better fitness goals. If the user asks you something unrelated to fitness, either bring the topic back to fitness, or say that you cannot answer.""", + }, + {"content": user_message, "role": "user"}, + ] + try: + # test without max tokens + response = completion( + model="claude-instant-1", + messages=messages, + request_timeout=10, + ) + # Add any assertions here to check the response + print(response) + print(response.usage) + print(response.usage.completion_tokens) + print(response["usage"]["completion_tokens"]) + # print("new cost tracking") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_claude() + + +def test_completion_mistral_api(): + try: + litellm.set_verbose = True + response = completion( + model="mistral/mistral-tiny", + max_tokens=5, + messages=[ + { + "role": "user", + "content": "Hey, how's it going?", + } + ], + ) + # Add any assertions here to check the response + print(response) + + cost = litellm.completion_cost(completion_response=response) + print("cost to make mistral completion=", cost) + assert cost > 0.0 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_completion_mistral_api() + + +def test_completion_claude2_1(): + try: + print("claude2.1 test request") + messages = [ + { + "role": "system", + "content": "Your goal is generate a joke on the topic user gives", + }, + {"role": "assistant", "content": "Hi, how can i assist you today?"}, + {"role": "user", "content": "Generate a 3 liner joke for me"}, + ] + # test without max tokens + response = completion( + model="claude-2.1", messages=messages, request_timeout=10, max_tokens=10 + ) + # Add any assertions here to check the response + print(response) + print(response.usage) + print(response.usage.completion_tokens) + print(response["usage"]["completion_tokens"]) + # print("new cost tracking") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_claude2_1() + +# def test_completion_oobabooga(): +# try: +# response = completion( +# model="oobabooga/vicuna-1.3b", messages=messages, api_base="http://127.0.0.1:5000" +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_oobabooga() +# aleph alpha +# def test_completion_aleph_alpha(): +# try: +# response = completion( +# model="luminous-base", messages=messages, logger_fn=logger_fn +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_aleph_alpha() + + +# def test_completion_aleph_alpha_control_models(): +# try: +# response = completion( +# model="luminous-base-control", messages=messages, logger_fn=logger_fn +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_aleph_alpha_control_models() + +import openai + + +def test_completion_gpt4_turbo(): + try: + response = completion( + model="gpt-4-1106-preview", + messages=messages, + max_tokens=10, + ) + print(response) + except openai.RateLimitError: + print("got a rate liimt error") + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_gpt4_turbo() + + +@pytest.mark.skip(reason="this test is flaky") +def test_completion_gpt4_vision(): + try: + litellm.set_verbose = True + response = completion( + model="gpt-4-vision-preview", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "Whats in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + }, + }, + ], + } + ], + ) + print(response) + except openai.RateLimitError: + print("got a rate liimt error") + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_gpt4_vision() + + +def test_completion_azure_gpt4_vision(): + # azure gpt-4 vision takes 5s to respond + try: + litellm.set_verbose = True + response = completion( + model="azure/gpt-4-vision", + timeout=1, + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "Whats in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://avatars.githubusercontent.com/u/29436595?v=4" + }, + }, + ], + } + ], + base_url="https://gpt-4-vision-resource.openai.azure.com/", + api_key=os.getenv("AZURE_VISION_API_KEY"), + ) + print(response) + except openai.APITimeoutError: + print("got a timeout error") + pass + except openai.RateLimitError: + print("got a rate liimt error") + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure_gpt4_vision() + + +@pytest.mark.skip(reason="this test is flaky") +def test_completion_perplexity_api(): + try: + # litellm.set_verbose= True + messages = [ + {"role": "system", "content": "You're a good bot"}, + { + "role": "user", + "content": "Hey", + }, + { + "role": "user", + "content": "Hey", + }, + ] + response = completion( + model="mistral-7b-instruct", + messages=messages, + api_base="https://api.perplexity.ai", + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_perplexity_api() + + +@pytest.mark.skip(reason="this test is flaky") +def test_completion_perplexity_api_2(): + try: + # litellm.set_verbose=True + messages = [ + {"role": "system", "content": "You're a good bot"}, + { + "role": "user", + "content": "Hey", + }, + { + "role": "user", + "content": "Hey", + }, + ] + response = completion(model="perplexity/mistral-7b-instruct", messages=messages) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_perplexity_api_2() + +# commenting out as this is a flaky test on circle ci +# def test_completion_nlp_cloud(): +# try: +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion(model="dolphin", messages=messages, logger_fn=logger_fn) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_nlp_cloud() + +######### HUGGING FACE TESTS ######################## +##################################################### +""" +HF Tests we should pass +- TGI: + - Pro Inference API + - Deployed Endpoint +- Coversational + - Free Inference API + - Deployed Endpoint +- Neither TGI or Coversational + - Free Inference API + - Deployed Endpoint +""" + + +##################################################### +##################################################### +# Test util to sort models to TGI, conv, None +def test_get_hf_task_for_model(): + model = "glaiveai/glaive-coder-7b" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert model_type == "text-generation-inference" + + model = "meta-llama/Llama-2-7b-hf" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert model_type == "text-generation-inference" + + model = "facebook/blenderbot-400M-distill" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert model_type == "conversational" + + model = "facebook/blenderbot-3B" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert model_type == "conversational" + + # neither Conv or None + model = "roneneldan/TinyStories-3M" + model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) + print(f"model:{model}, model type: {model_type}") + assert model_type == None + + +# test_get_hf_task_for_model() +# litellm.set_verbose=False +# ################### Hugging Face TGI models ######################## +# # TGI model +# # this is a TGI model https://huggingface.co/glaiveai/glaive-coder-7b +def hf_test_completion_tgi(): + # litellm.set_verbose=True + try: + response = completion( + model="huggingface/HuggingFaceH4/zephyr-7b-beta", + messages=[{"content": "Hello, how are you?", "role": "user"}], + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# hf_test_completion_tgi() + +# ################### Hugging Face Conversational models ######################## +# def hf_test_completion_conv(): +# try: +# response = litellm.completion( +# model="huggingface/facebook/blenderbot-3B", +# messages=[{ "content": "Hello, how are you?","role": "user"}], +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# hf_test_completion_conv() + +# ################### Hugging Face Neither TGI or Conversational models ######################## +# # Neither TGI or Conversational task +# def hf_test_completion_none_task(): +# try: +# user_message = "My name is Merve and my favorite" +# messages = [{ "content": user_message,"role": "user"}] +# response = completion( +# model="huggingface/roneneldan/TinyStories-3M", +# messages=messages, +# api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# hf_test_completion_none_task() +########################### End of Hugging Face Tests ############################################## +# def test_completion_hf_api(): +# # failing on circle ci commenting out +# try: +# user_message = "write some code to find the sum of two numbers" +# messages = [{ "content": user_message,"role": "user"}] +# api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud" +# response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# if "loading" in str(e): +# pass +# pytest.fail(f"Error occurred: {e}") + +# test_completion_hf_api() + +# def test_completion_hf_api_best_of(): +# # failing on circle ci commenting out +# try: +# user_message = "write some code to find the sum of two numbers" +# messages = [{ "content": user_message,"role": "user"}] +# api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud" +# response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base, n=2) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# if "loading" in str(e): +# pass +# pytest.fail(f"Error occurred: {e}") + +# test_completion_hf_api_best_of() + +# def test_completion_hf_deployed_api(): +# try: +# user_message = "There's a llama in my garden 😱 What should I do?" +# messages = [{ "content": user_message,"role": "user"}] +# response = completion(model="huggingface/https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", messages=messages, logger_fn=logger_fn) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# this should throw an exception, to trigger https://logs.litellm.ai/ +# def hf_test_error_logs(): +# try: +# litellm.set_verbose=True +# user_message = "My name is Merve and my favorite" +# messages = [{ "content": user_message,"role": "user"}] +# response = completion( +# model="huggingface/roneneldan/TinyStories-3M", +# messages=messages, +# api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", + +# ) +# # Add any assertions here to check the response +# print(response) + +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# hf_test_error_logs() + + +def test_completion_cohere(): # commenting for now as the cohere endpoint is being flaky + try: + litellm.CohereConfig(max_tokens=1000, stop_sequences=["a"]) + response = completion( + model="command-nightly", messages=messages, logger_fn=logger_fn + ) + # Add any assertions here to check the response + print(response) + response_str = response["choices"][0]["message"]["content"] + response_str_2 = response.choices[0].message.content + if type(response_str) != str: + pytest.fail(f"Error occurred: {e}") + if type(response_str_2) != str: + pytest.fail(f"Error occurred: {e}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_cohere() + + +def test_completion_openai(): + try: + litellm.set_verbose = True + print(f"api key: {os.environ['OPENAI_API_KEY']}") + litellm.api_key = os.environ["OPENAI_API_KEY"] + response = completion( + model="gpt-3.5-turbo", + messages=messages, + max_tokens=10, + request_timeout=1, + metadata={"hi": "bye"}, + ) + print("This is the response object\n", response) + + response_str = response["choices"][0]["message"]["content"] + response_str_2 = response.choices[0].message.content + + cost = completion_cost(completion_response=response) + print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}") + assert response_str == response_str_2 + assert type(response_str) == str + assert len(response_str) > 1 + + litellm.api_key = None + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai() + + +def test_completion_text_openai(): + try: + # litellm.set_verbose = True + response = completion(model="gpt-3.5-turbo-instruct", messages=messages) + print(response["choices"][0]["message"]["content"]) + except Exception as e: + print(e) + pytest.fail(f"Error occurred: {e}") + + +# test_completion_text_openai() + + +def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, + end_time, # start/end time +): + # Your custom code here + try: + print("LITELLM: in custom callback function") + print("\nkwargs\n", kwargs) + model = kwargs["model"] + messages = kwargs["messages"] + user = kwargs.get("user") + + ################################################# + + print( + f""" + Model: {model}, + Messages: {messages}, + User: {user}, + Seed: {kwargs["seed"]}, + temperature: {kwargs["temperature"]}, + """ + ) + + assert kwargs["user"] == "ishaans app" + assert kwargs["model"] == "gpt-3.5-turbo-1106" + assert kwargs["seed"] == 12 + assert kwargs["temperature"] == 0.5 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +def test_completion_openai_with_optional_params(): + # [Proxy PROD TEST] WARNING: DO NOT DELETE THIS TEST + # assert that `user` gets passed to the completion call + # Note: This tests that we actually send the optional params to the completion call + # We use custom callbacks to test this + try: + litellm.set_verbose = True + litellm.success_callback = [custom_callback] + response = completion( + model="gpt-3.5-turbo-1106", + messages=[ + {"role": "user", "content": "respond in valid, json - what is the day"} + ], + temperature=0.5, + top_p=0.1, + seed=12, + response_format={"type": "json_object"}, + logit_bias=None, + user="ishaans app", + ) + # Add any assertions here to check the response + + print(response) + litellm.success_callback = [] # unset callbacks + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai_with_optional_params() + + +def test_completion_logprobs(): + """ + This function is used to test the litellm.completion logprobs functionality. + + Parameters: + None + + Returns: + None + """ + try: + litellm.set_verbose = True + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "what is the time"}], + temperature=0.5, + top_p=0.1, + seed=12, + logit_bias=None, + user="ishaans app", + logprobs=True, + top_logprobs=3, + ) + # Add any assertions here to check the response + + print(response) + print(len(response.choices[0].logprobs["content"][0]["top_logprobs"])) + assert "logprobs" in response.choices[0] + assert "content" in response.choices[0]["logprobs"] + assert len(response.choices[0].logprobs["content"][0]["top_logprobs"]) == 3 + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_logprobs() + + +def test_completion_logprobs_stream(): + """ + This function is used to test the litellm.completion logprobs functionality. + + Parameters: + None + + Returns: + None + """ + try: + litellm.set_verbose = False + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "what is the time"}], + temperature=0.5, + top_p=0.1, + seed=12, + max_tokens=5, + logit_bias=None, + user="ishaans app", + logprobs=True, + top_logprobs=3, + stream=True, + ) + # Add any assertions here to check the response + + print(response) + + found_logprob = False + for chunk in response: + # check if atleast one chunk has log probs + print(chunk) + if "logprobs" in chunk.choices[0]: + # assert we got a valid logprob in the choices + assert len(chunk.choices[0].logprobs.content[0].top_logprobs) == 3 + found_logprob = True + break + print(chunk) + assert found_logprob == True + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_logprobs_stream() + + +def test_completion_openai_litellm_key(): + try: + litellm.set_verbose = True + litellm.num_retries = 0 + litellm.api_key = os.environ["OPENAI_API_KEY"] + + # ensure key is set to None in .env and in openai.api_key + os.environ["OPENAI_API_KEY"] = "" + import openai + + openai.api_key = "" + ########################################################## + + response = completion( + model="gpt-3.5-turbo", + messages=messages, + temperature=0.5, + top_p=0.1, + max_tokens=10, + user="ishaan_dev@berri.ai", + ) + # Add any assertions here to check the response + print(response) + + ###### reset environ key + os.environ["OPENAI_API_KEY"] = litellm.api_key + + ##### unset litellm var + litellm.api_key = None + except Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_ completion_openai_litellm_key() + + +def test_completion_ollama_hosted(): + try: + litellm.request_timeout = 20 # give ollama 20 seconds to response + litellm.set_verbose = True + response = completion( + model="ollama/phi", + messages=messages, + max_tokens=2, + api_base="https://test-ollama-endpoint.onrender.com", + ) + # Add any assertions here to check the response + print(response) + except openai.APITimeoutError as e: + print("got a timeout error. Passed ! ") + litellm.request_timeout = None + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_ollama_hosted() + + +def test_completion_openrouter1(): + try: + response = completion( + model="openrouter/google/palm-2-chat-bison", + messages=messages, + max_tokens=5, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openrouter1() + + +def test_completion_hf_model_no_provider(): + try: + response = completion( + model="WizardLM/WizardLM-70B-V1.0", + messages=messages, + max_tokens=5, + ) + # Add any assertions here to check the response + print(response) + pytest.fail(f"Error occurred: {e}") + except Exception as e: + pass + + +# test_completion_hf_model_no_provider() + + +def test_completion_anyscale_with_functions(): + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + try: + messages = [{"role": "user", "content": "What is the weather like in Boston?"}] + response = completion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + functions=function1, + ) + # Add any assertions here to check the response + print(response) + + cost = litellm.completion_cost(completion_response=response) + print("cost to make anyscale completion=", cost) + assert cost > 0.0 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_anyscale_with_functions() + + +def test_completion_azure_key_completion_arg(): + # this tests if we can pass api_key to completion, when it's not in the env + # DO NOT REMOVE THIS TEST. No MATTER WHAT Happens! + # If you want to remove it, speak to Ishaan! + # Ishaan will be very disappointed if this test is removed -> this is a standard way to pass api_key + the router + proxy use this + old_key = os.environ["AZURE_API_KEY"] + os.environ.pop("AZURE_API_KEY", None) + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose = True + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + api_key=old_key, + max_tokens=10, + ) + print(f"response: {response}") + + print("Hidden Params", response._hidden_params) + assert response._hidden_params["custom_llm_provider"] == "azure" + os.environ["AZURE_API_KEY"] = old_key + except Exception as e: + os.environ["AZURE_API_KEY"] = old_key + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure_key_completion_arg() + + +async def test_re_use_azure_async_client(): + try: + print("azure gpt-3.5 ASYNC with clie nttest\n\n") + litellm.set_verbose = True + import openai + + client = openai.AsyncAzureOpenAI( + azure_endpoint=os.environ["AZURE_API_BASE"], + api_key=os.environ["AZURE_API_KEY"], + api_version="2023-07-01-preview", + ) + ## Test azure call + for _ in range(3): + response = await litellm.acompletion( + model="azure/chatgpt-v-2", messages=messages, client=client + ) + print(f"response: {response}") + except Exception as e: + pytest.fail("got Exception", e) + + +# import asyncio +# asyncio.run( +# test_re_use_azure_async_client() +# ) + + +def test_re_use_openaiClient(): + try: + print("gpt-3.5 with client test\n\n") + litellm.set_verbose = True + import openai + + client = openai.OpenAI( + api_key=os.environ["OPENAI_API_KEY"], + ) + ## Test OpenAI call + for _ in range(2): + response = litellm.completion( + model="gpt-3.5-turbo", messages=messages, client=client + ) + print(f"response: {response}") + except Exception as e: + pytest.fail("got Exception", e) + + +# test_re_use_openaiClient() + + +def test_completion_azure(): + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose = False + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + api_key="os.environ/AZURE_API_KEY", + ) + print(f"response: {response}") + ## Test azure flag for backwards compat + # response = completion( + # model="chatgpt-v-2", + # messages=messages, + # azure=True, + # max_tokens=10 + # ) + # Add any assertions here to check the response + print(response) + + cost = completion_cost(completion_response=response) + assert cost > 0.0 + print("Cost for azure completion request", cost) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure() + + +def test_azure_openai_ad_token(): + # this tests if the azure ad token is set in the request header + # the request can fail since azure ad tokens expire after 30 mins, but the header MUST have the azure ad token + # we use litellm.input_callbacks for this test + def tester( + kwargs, # kwargs to completion + ): + print(kwargs["additional_args"]) + if kwargs["additional_args"]["headers"]["Authorization"] != "Bearer gm": + pytest.fail("AZURE AD TOKEN Passed but not set in request header") + return + + litellm.input_callback = [tester] + try: + response = litellm.completion( + model="azure/chatgpt-v-2", # e.g. gpt-35-instant + messages=[ + { + "role": "user", + "content": "what is your name", + }, + ], + azure_ad_token="gm", + ) + print("azure ad token respoonse\n") + print(response) + litellm.input_callback = [] + except: + litellm.input_callback = [] + pass + + +# test_azure_openai_ad_token() + + +# test_completion_azure() +def test_completion_azure2(): + # test if we can pass api_base, api_version and api_key in compleition() + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose = False + api_base = os.environ["AZURE_API_BASE"] + api_key = os.environ["AZURE_API_KEY"] + api_version = os.environ["AZURE_API_VERSION"] + + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_KEY"] = "" + + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + api_base=api_base, + api_key=api_key, + api_version=api_version, + max_tokens=10, + ) + + # Add any assertions here to check the response + print(response) + + os.environ["AZURE_API_BASE"] = api_base + os.environ["AZURE_API_VERSION"] = api_version + os.environ["AZURE_API_KEY"] = api_key + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure2() + + +def test_completion_azure3(): + # test if we can pass api_base, api_version and api_key in compleition() + try: + print("azure gpt-3.5 test\n\n") + litellm.set_verbose = True + litellm.api_base = os.environ["AZURE_API_BASE"] + litellm.api_key = os.environ["AZURE_API_KEY"] + litellm.api_version = os.environ["AZURE_API_VERSION"] + + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_KEY"] = "" + + ## Test azure call + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + max_tokens=10, + ) + + # Add any assertions here to check the response + print(response) + + os.environ["AZURE_API_BASE"] = litellm.api_base + os.environ["AZURE_API_VERSION"] = litellm.api_version + os.environ["AZURE_API_KEY"] = litellm.api_key + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure3() + + +# new azure test for using litellm. vars, +# use the following vars in this test and make an azure_api_call +# litellm.api_type = self.azure_api_type +# litellm.api_base = self.azure_api_base +# litellm.api_version = self.azure_api_version +# litellm.api_key = self.api_key +def test_completion_azure_with_litellm_key(): + try: + print("azure gpt-3.5 test\n\n") + import openai + + #### set litellm vars + litellm.api_type = "azure" + litellm.api_base = os.environ["AZURE_API_BASE"] + litellm.api_version = os.environ["AZURE_API_VERSION"] + litellm.api_key = os.environ["AZURE_API_KEY"] + + ######### UNSET ENV VARs for this ################ + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_KEY"] = "" + + ######### UNSET OpenAI vars for this ############## + openai.api_type = "" + openai.api_base = "gm" + openai.api_version = "333" + openai.api_key = "ymca" + + response = completion( + model="azure/chatgpt-v-2", + messages=messages, + ) + # Add any assertions here to check the response + print(response) + + ######### RESET ENV VARs for this ################ + os.environ["AZURE_API_BASE"] = litellm.api_base + os.environ["AZURE_API_VERSION"] = litellm.api_version + os.environ["AZURE_API_KEY"] = litellm.api_key + + ######### UNSET litellm vars + litellm.api_type = None + litellm.api_base = None + litellm.api_version = None + litellm.api_key = None + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure() + + +def test_completion_azure_deployment_id(): + try: + litellm.set_verbose = True + response = completion( + deployment_id="chatgpt-v-2", + model="gpt-3.5-turbo", + messages=messages, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure_deployment_id() + +# Only works for local endpoint +# def test_completion_anthropic_openai_proxy(): +# try: +# response = completion( +# model="custom_openai/claude-2", +# messages=messages, +# api_base="http://0.0.0.0:8000" +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_anthropic_openai_proxy() + + +@pytest.mark.skip(reason="replicate endpoints take +2 mins just for this request") +def test_completion_replicate_vicuna(): + print("TESTING REPLICATE") + litellm.set_verbose = True + model_name = "replicate/meta/llama-2-7b-chat:f1d50bb24186c52daae319ca8366e53debdaa9e0ae7ff976e918df752732ccc4" + try: + response = completion( + model=model_name, + messages=messages, + temperature=0.5, + top_k=20, + repetition_penalty=1, + min_tokens=1, + seed=-1, + max_tokens=2, + ) + print(response) + # Add any assertions here to check the response + response_str = response["choices"][0]["message"]["content"] + print("RESPONSE STRING\n", response_str) + if type(response_str) != str: + pytest.fail(f"Error occurred: {e}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_replicate_vicuna() + + +def test_replicate_custom_prompt_dict(): + litellm.set_verbose = True + model_name = "replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0" + litellm.register_prompt_template( + model="replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0", + initial_prompt_value="You are a good assistant", # [OPTIONAL] + roles={ + "system": { + "pre_message": "[INST] <>\n", # [OPTIONAL] + "post_message": "\n<>\n [/INST]\n", # [OPTIONAL] + }, + "user": { + "pre_message": "[INST] ", # [OPTIONAL] + "post_message": " [/INST]", # [OPTIONAL] + }, + "assistant": { + "pre_message": "\n", # [OPTIONAL] + "post_message": "\n", # [OPTIONAL] + }, + }, + final_prompt_value="Now answer as best you can:", # [OPTIONAL] + ) + response = completion( + model=model_name, + messages=[ + { + "role": "user", + "content": "what is yc write 1 paragraph", + } + ], + num_retries=3, + ) + print(f"response: {response}") + litellm.custom_prompt_dict = {} # reset + + +# test_replicate_custom_prompt_dict() + +# commenthing this out since we won't be always testing a custom replicate deployment +# def test_completion_replicate_deployments(): +# print("TESTING REPLICATE") +# litellm.set_verbose=False +# model_name = "replicate/deployments/ishaan-jaff/ishaan-mistral" +# try: +# response = completion( +# model=model_name, +# messages=messages, +# temperature=0.5, +# seed=-1, +# ) +# print(response) +# # Add any assertions here to check the response +# response_str = response["choices"][0]["message"]["content"] +# print("RESPONSE STRING\n", response_str) +# if type(response_str) != str: +# pytest.fail(f"Error occurred: {e}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_replicate_deployments() + + +######## Test TogetherAI ######## +def test_completion_together_ai(): + model_name = "together_ai/togethercomputer/CodeLlama-13b-Instruct" + try: + messages = [ + {"role": "user", "content": "Who are you"}, + {"role": "assistant", "content": "I am your helpful assistant."}, + {"role": "user", "content": "Tell me a joke"}, + ] + response = completion( + model=model_name, + messages=messages, + max_tokens=256, + n=1, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + assert cost > 0.0 + print( + "Cost for completion call together-computer/llama-2-70b: ", + f"${float(cost):.10f}", + ) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +def test_completion_together_ai_mixtral(): + model_name = "together_ai/DiscoResearch/DiscoLM-mixtral-8x7b-v2" + try: + messages = [ + {"role": "user", "content": "Who are you"}, + {"role": "assistant", "content": "I am your helpful assistant."}, + {"role": "user", "content": "Tell me a joke"}, + ] + response = completion( + model=model_name, + messages=messages, + max_tokens=256, + n=1, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + assert cost > 0.0 + print( + "Cost for completion call together-computer/llama-2-70b: ", + f"${float(cost):.10f}", + ) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_together_ai_mixtral() + + +def test_completion_together_ai_yi_chat(): + litellm.set_verbose = True + model_name = "together_ai/zero-one-ai/Yi-34B-Chat" + try: + messages = [ + {"role": "user", "content": "What llm are you?"}, + ] + response = completion(model=model_name, messages=messages, max_tokens=5) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + assert cost > 0.0 + print( + "Cost for completion call together-computer/llama-2-70b: ", + f"${float(cost):.10f}", + ) + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_together_ai_yi_chat() + + +# test_completion_together_ai() +def test_customprompt_together_ai(): + try: + litellm.set_verbose = False + litellm.num_retries = 0 + print("in test_customprompt_together_ai") + print(litellm.success_callback) + print(litellm._async_success_callback) + response = completion( + model="together_ai/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + roles={ + "system": { + "pre_message": "<|im_start|>system\n", + "post_message": "<|im_end|>", + }, + "assistant": { + "pre_message": "<|im_start|>assistant\n", + "post_message": "<|im_end|>", + }, + "user": { + "pre_message": "<|im_start|>user\n", + "post_message": "<|im_end|>", + }, + }, + ) + print(response) + except litellm.exceptions.Timeout as e: + print(f"Timeout Error") + pass + except Exception as e: + print(f"ERROR TYPE {type(e)}") + pytest.fail(f"Error occurred: {e}") + + +# test_customprompt_together_ai() + + +def test_completion_sagemaker(): + try: + print("testing sagemaker") + litellm.set_verbose = True + response = completion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=messages, + temperature=0.2, + max_tokens=80, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_sagemaker() + + +def test_completion_chat_sagemaker(): + try: + messages = [{"role": "user", "content": "Hey, how's it going?"}] + litellm.set_verbose = True + response = completion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=messages, + max_tokens=100, + temperature=0.7, + stream=True, + ) + # Add any assertions here to check the response + complete_response = "" + for chunk in response: + complete_response += chunk.choices[0].delta.content or "" + print(f"complete_response: {complete_response}") + assert len(complete_response) > 0 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_chat_sagemaker() + + +def test_completion_chat_sagemaker_mistral(): + try: + messages = [{"role": "user", "content": "Hey, how's it going?"}] + + response = completion( + model="sagemaker/jumpstart-dft-hf-llm-mistral-7b-instruct", + messages=messages, + max_tokens=100, + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"An error occurred: {str(e)}") + + +# test_completion_chat_sagemaker_mistral() +def test_completion_bedrock_titan(): + try: + response = completion( + model="bedrock/amazon.titan-tg1-large", + messages=messages, + temperature=0.2, + max_tokens=200, + top_p=0.8, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_titan() + + +def test_completion_bedrock_claude(): + print("calling claude") + try: + response = completion( + model="anthropic.claude-instant-v1", + messages=messages, + max_tokens=10, + temperature=0.1, + logger_fn=logger_fn, + ) + # Add any assertions here to check the response + print(response) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_claude() + + +def test_completion_bedrock_cohere(): + print("calling bedrock cohere") + litellm.set_verbose = True + try: + response = completion( + model="bedrock/cohere.command-text-v14", + messages=[{"role": "user", "content": "hi"}], + temperature=0.1, + max_tokens=10, + stream=True, + ) + # Add any assertions here to check the response + print(response) + for chunk in response: + print(chunk) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_cohere() + + +# def test_completion_bedrock_claude_stream(): +# print("calling claude") +# litellm.set_verbose = False +# try: +# response = completion( +# model="bedrock/anthropic.claude-instant-v1", +# messages=messages, +# stream=True +# ) +# # Add any assertions here to check the response +# print(response) +# for chunk in response: +# print(chunk) +# except RateLimitError: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_completion_bedrock_claude_stream() + +# def test_completion_bedrock_ai21(): +# try: +# litellm.set_verbose = False +# response = completion( +# model="bedrock/ai21.j2-mid", +# messages=messages, +# temperature=0.2, +# top_p=0.2, +# max_tokens=20 +# ) +# # Add any assertions here to check the response +# print(response) +# except RateLimitError: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +######## Test VLLM ######## +# def test_completion_vllm(): +# try: +# response = completion( +# model="vllm/facebook/opt-125m", +# messages=messages, +# temperature=0.2, +# max_tokens=80, +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_vllm() + +# def test_completion_hosted_chatCompletion(): +# # this tests calling a server where vllm is hosted +# # this should make an openai.Completion() call to the specified api_base +# # send a request to this proxy server: https://replit.com/@BerriAI/openai-proxy#main.py +# # it checks if model == facebook/opt-125m and returns test passed +# try: +# litellm.set_verbose = True +# response = completion( +# model="facebook/opt-125m", +# messages=messages, +# temperature=0.2, +# max_tokens=80, +# api_base="https://openai-proxy.berriai.repl.co", +# custom_llm_provider="openai" +# ) +# print(response) + +# if response['choices'][0]['message']['content'] != "passed": +# # see https://replit.com/@BerriAI/openai-proxy#main.py +# pytest.fail(f"Error occurred: proxy server did not respond") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_hosted_chatCompletion() + +# def test_completion_custom_api_base(): +# try: +# response = completion( +# model="custom/meta-llama/Llama-2-13b-hf", +# messages=messages, +# temperature=0.2, +# max_tokens=10, +# api_base="https://api.autoai.dev/inference", +# request_timeout=300, +# ) +# # Add any assertions here to check the response +# print("got response\n", response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_custom_api_base() + + +def test_completion_with_fallbacks(): + print(f"RUNNING TEST COMPLETION WITH FALLBACKS - test_completion_with_fallbacks") + fallbacks = ["gpt-3.5-turbo", "gpt-3.5-turbo", "command-nightly"] + try: + response = completion( + model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_with_fallbacks() +def test_completion_anyscale_api(): + try: + # litellm.set_verbose=True + messages = [ + {"role": "system", "content": "You're a good bot"}, + { + "role": "user", + "content": "Hey", + }, + { + "role": "user", + "content": "Hey", + }, + ] + response = completion( + model="anyscale/meta-llama/Llama-2-7b-chat-hf", + messages=messages, + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_anyscale_api() + + +def test_azure_cloudflare_api(): + litellm.set_verbose = True + try: + messages = [ + { + "role": "user", + "content": "How do I output all files in a directory using Python?", + }, + ] + response = completion( + model="azure/gpt-turbo", + messages=messages, + base_url=os.getenv("CLOUDFLARE_AZURE_BASE_URL"), + api_key=os.getenv("AZURE_FRANCE_API_KEY"), + ) + print(f"response: {response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + traceback.print_exc() + pass + + +test_azure_cloudflare_api() + + +def test_completion_anyscale_2(): + try: + # litellm.set_verbose=True + messages = [ + {"role": "system", "content": "You're a good bot"}, + { + "role": "user", + "content": "Hey", + }, + { + "role": "user", + "content": "Hey", + }, + ] + response = completion( + model="anyscale/meta-llama/Llama-2-7b-chat-hf", messages=messages + ) + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +def test_mistral_anyscale_stream(): + litellm.set_verbose = False + response = completion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=[{"content": "hello, good morning", "role": "user"}], + stream=True, + ) + for chunk in response: + # print(chunk) + print(chunk["choices"][0]["delta"].get("content", ""), end="") + + +# test_mistral_anyscale_stream() +# test_completion_anyscale_2() +# def test_completion_with_fallbacks_multiple_keys(): +# print(f"backup key 1: {os.getenv('BACKUP_OPENAI_API_KEY_1')}") +# print(f"backup key 2: {os.getenv('BACKUP_OPENAI_API_KEY_2')}") +# backup_keys = [{"api_key": os.getenv("BACKUP_OPENAI_API_KEY_1")}, {"api_key": os.getenv("BACKUP_OPENAI_API_KEY_2")}] +# try: +# api_key = "bad-key" +# response = completion( +# model="gpt-3.5-turbo", messages=messages, force_timeout=120, fallbacks=backup_keys, api_key=api_key +# ) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# error_str = traceback.format_exc() +# pytest.fail(f"Error occurred: {error_str}") + +# test_completion_with_fallbacks_multiple_keys() +# def test_petals(): +# try: +# response = completion(model="petals-team/StableBeluga2", messages=messages) +# # Add any assertions here to check the response +# print(response) + +# response = completion(model="petals-team/StableBeluga2", messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# def test_baseten(): +# try: + +# response = completion(model="baseten/7qQNLDB", messages=messages, logger_fn=logger_fn) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_baseten() +# def test_baseten_falcon_7bcompletion(): +# model_name = "qvv0xeq" +# try: +# response = completion(model=model_name, messages=messages, custom_llm_provider="baseten") +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_baseten_falcon_7bcompletion() + +# def test_baseten_falcon_7bcompletion_withbase(): +# model_name = "qvv0xeq" +# litellm.api_base = "https://app.baseten.co" +# try: +# response = completion(model=model_name, messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# litellm.api_base = None + +# test_baseten_falcon_7bcompletion_withbase() + + +# def test_baseten_wizardLMcompletion_withbase(): +# model_name = "q841o8w" +# litellm.api_base = "https://app.baseten.co" +# try: +# response = completion(model=model_name, messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_baseten_wizardLMcompletion_withbase() + +# def test_baseten_mosaic_ML_completion_withbase(): +# model_name = "31dxrj3" +# litellm.api_base = "https://app.baseten.co" +# try: +# response = completion(model=model_name, messages=messages) +# # Add any assertions here to check the response +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +#### Test A121 ################### +def test_completion_ai21(): + print("running ai21 j2light test") + litellm.set_verbose = True + model_name = "j2-light" + try: + response = completion( + model=model_name, messages=messages, max_tokens=100, temperature=0.8 + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_ai21() +# test_completion_ai21() +## test deep infra +def test_completion_deep_infra(): + litellm.set_verbose = False + model_name = "deepinfra/meta-llama/Llama-2-70b-chat-hf" + try: + response = completion( + model=model_name, messages=messages, temperature=0, max_tokens=10 + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_deep_infra() + + +def test_completion_deep_infra_mistral(): + print("deep infra test with temp=0") + model_name = "deepinfra/mistralai/Mistral-7B-Instruct-v0.1" + try: + response = completion( + model=model_name, + messages=messages, + temperature=0.01, # mistrail fails with temperature=0 + max_tokens=10, + ) + # Add any assertions here to check the response + print(response) + except litellm.exceptions.Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_deep_infra_mistral() + + +# Gemini tests +def test_completion_gemini(): + litellm.set_verbose = True + model_name = "gemini/gemini-pro" + messages = [{"role": "user", "content": "Hey, how's it going?"}] + try: + response = completion(model=model_name, messages=messages) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_gemini() + + +# Palm tests +def test_completion_palm(): + litellm.set_verbose = True + model_name = "palm/chat-bison" + messages = [{"role": "user", "content": "Hey, how's it going?"}] + try: + response = completion(model=model_name, messages=messages) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_palm() + + +# test palm with streaming +def test_completion_palm_stream(): + # litellm.set_verbose = True + model_name = "palm/chat-bison" + try: + response = completion( + model=model_name, + messages=messages, + stop=["stop"], + stream=True, + max_tokens=20, + ) + # Add any assertions here to check the response + for chunk in response: + print(chunk) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_palm_stream() + +# test_completion_deep_infra() +# test_completion_ai21() +# test config file with completion # +# def test_completion_openai_config(): +# try: +# litellm.config_path = "../config.json" +# litellm.set_verbose = True +# response = litellm.config_completion(messages=messages) +# # Add any assertions here to check the response +# print(response) +# litellm.config_path = None +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# def test_maritalk(): +# messages = [{"role": "user", "content": "Hey"}] +# try: +# response = completion("maritalk", messages=messages) +# print(f"response: {response}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_maritalk() + + +def test_completion_together_ai_stream(): + user_message = "Write 1pg about YC & litellm" + messages = [{"content": user_message, "role": "user"}] + try: + response = completion( + model="together_ai/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + stream=True, + max_tokens=5, + ) + print(response) + for chunk in response: + print(chunk) + # print(string_response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_together_ai_stream() + + +# Cloud flare AI tests +@pytest.mark.skip(reason="Flaky test-cloudflare is very unstable") +def test_completion_cloudflare(): + try: + litellm.set_verbose = True + response = completion( + model="cloudflare/@cf/meta/llama-2-7b-chat-int8", + messages=[{"content": "what llm are you", "role": "user"}], + max_tokens=15, + num_retries=3, + ) + print(response) + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_completion_cloudflare() + + +def test_moderation(): + import openai + + openai.api_type = "azure" + openai.api_version = "GM" + response = litellm.moderation(input="i'm ishaan cto of litellm") + print(response) + output = response.results[0] + print(output) + return output + + +# test_moderation() diff --git a/litellm/tests/test_completion_cost.py b/litellm/tests/test_completion_cost.py new file mode 100644 index 0000000000000000000000000000000000000000..3543420217d12cdab4672778ef7770fd7cc95c1f --- /dev/null +++ b/litellm/tests/test_completion_cost.py @@ -0,0 +1,160 @@ +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +import litellm +from litellm import get_max_tokens, model_cost, open_ai_chat_completion_models +import pytest + + +def test_get_gpt3_tokens(): + max_tokens = get_max_tokens("gpt-3.5-turbo") + print(max_tokens) + assert max_tokens == 4097 + # print(results) + + +# test_get_gpt3_tokens() + + +def test_get_palm_tokens(): + # # 🦄🦄🦄🦄🦄🦄🦄🦄 + max_tokens = get_max_tokens("palm/chat-bison") + assert max_tokens == 4096 + print(max_tokens) + + +# test_get_palm_tokens() + + +def test_zephyr_hf_tokens(): + max_tokens = get_max_tokens("huggingface/HuggingFaceH4/zephyr-7b-beta") + print(max_tokens) + assert max_tokens == 32768 + + +# test_zephyr_hf_tokens() + + +def test_cost_ft_gpt_35(): + try: + # this tests if litellm.completion_cost can calculate cost for ft:gpt-3.5-turbo:my-org:custom_suffix:id + # it needs to lookup ft:gpt-3.5-turbo in the litellm model_cost map to get the correct cost + from litellm import ModelResponse, Choices, Message + from litellm.utils import Usage + + resp = ModelResponse( + id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", + choices=[ + Choices( + finish_reason=None, + index=0, + message=Message( + content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a", + role="assistant", + ), + ) + ], + created=1700775391, + model="ft:gpt-3.5-turbo:my-org:custom_suffix:id", + object="chat.completion", + system_fingerprint=None, + usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38), + ) + + cost = litellm.completion_cost(completion_response=resp) + print("\n Calculated Cost for ft:gpt-3.5", cost) + input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"] + output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"] + print(input_cost, output_cost) + expected_cost = (input_cost * resp.usage.prompt_tokens) + ( + output_cost * resp.usage.completion_tokens + ) + print("\n Excpected cost", expected_cost) + assert cost == expected_cost + except Exception as e: + pytest.fail( + f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}" + ) + + +# test_cost_ft_gpt_35() + + +def test_cost_azure_gpt_35(): + try: + # this tests if litellm.completion_cost can calculate cost for azure/chatgpt-deployment-2 which maps to azure/gpt-3.5-turbo + # for this test we check if passing `model` to completion_cost overrides the completion cost + from litellm import ModelResponse, Choices, Message + from litellm.utils import Usage + + resp = ModelResponse( + id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", + choices=[ + Choices( + finish_reason=None, + index=0, + message=Message( + content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a", + role="assistant", + ), + ) + ], + model="gpt-35-turbo", # azure always has model written like this + usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38), + ) + + cost = litellm.completion_cost( + completion_response=resp, model="azure/gpt-35-turbo" + ) + print("\n Calculated Cost for azure/gpt-3.5-turbo", cost) + input_cost = model_cost["azure/gpt-35-turbo"]["input_cost_per_token"] + output_cost = model_cost["azure/gpt-35-turbo"]["output_cost_per_token"] + expected_cost = (input_cost * resp.usage.prompt_tokens) + ( + output_cost * resp.usage.completion_tokens + ) + print("\n Excpected cost", expected_cost) + assert cost == expected_cost + except Exception as e: + pytest.fail( + f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}" + ) + + +test_cost_azure_gpt_35() + + +def test_cost_azure_embedding(): + try: + import asyncio + + litellm.set_verbose = True + + async def _test(): + response = await litellm.aembedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "gm"], + ) + + print(response) + + return response + + response = asyncio.run(_test()) + + cost = litellm.completion_cost(completion_response=response) + + print("Cost", cost) + expected_cost = float("7e-07") + assert cost == expected_cost + + except Exception as e: + pytest.fail( + f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}" + ) + + +# test_cost_azure_embedding() diff --git a/litellm/tests/test_completion_with_retries.py b/litellm/tests/test_completion_with_retries.py new file mode 100644 index 0000000000000000000000000000000000000000..e59d1d6e13ad4c880a8a9f346836630d2fe3280e --- /dev/null +++ b/litellm/tests/test_completion_with_retries.py @@ -0,0 +1,63 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import openai +import litellm +from litellm import completion_with_retries, completion +from litellm import ( + AuthenticationError, + BadRequestError, + RateLimitError, + ServiceUnavailableError, + OpenAIError, +) + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + + +def logger_fn(user_model_dict): + # print(f"user_model_dict: {user_model_dict}") + pass + + +# completion with num retries + impact on exception mapping +def test_completion_with_num_retries(): + try: + response = completion( + model="j2-ultra", + messages=[{"messages": "vibe", "bad": "message"}], + num_retries=2, + ) + pytest.fail(f"Unmapped exception occurred") + except Exception as e: + pass + + +# test_completion_with_num_retries() +def test_completion_with_0_num_retries(): + try: + litellm.set_verbose = False + print("making request") + + # Use the completion function + response = completion( + model="gpt-3.5-turbo", + messages=[{"gm": "vibe", "role": "user"}], + max_retries=4, + ) + + print(response) + + # print(response) + except Exception as e: + print("exception", e) + pass diff --git a/litellm/tests/test_configs/custom_auth.py b/litellm/tests/test_configs/custom_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..f3825038e1ace1d71a7252157b366f61933c1817 --- /dev/null +++ b/litellm/tests/test_configs/custom_auth.py @@ -0,0 +1,16 @@ +from litellm.proxy._types import UserAPIKeyAuth +from fastapi import Request +from dotenv import load_dotenv +import os + +load_dotenv() + + +async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth: + try: + print(f"api_key: {api_key}") + if api_key == f"{os.getenv('PROXY_MASTER_KEY')}-1234": + return UserAPIKeyAuth(api_key=api_key) + raise Exception + except: + raise Exception diff --git a/litellm/tests/test_configs/custom_callbacks.py b/litellm/tests/test_configs/custom_callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..226908413d3eef55db5286eac3a09db6fbb2516f --- /dev/null +++ b/litellm/tests/test_configs/custom_callbacks.py @@ -0,0 +1,121 @@ +from litellm.integrations.custom_logger import CustomLogger +import inspect +import litellm + + +class testCustomCallbackProxy(CustomLogger): + def __init__(self): + self.success: bool = False # type: ignore + self.failure: bool = False # type: ignore + self.async_success: bool = False # type: ignore + self.async_success_embedding: bool = False # type: ignore + self.async_failure: bool = False # type: ignore + self.async_failure_embedding: bool = False # type: ignore + + self.async_completion_kwargs = None # type: ignore + self.async_embedding_kwargs = None # type: ignore + self.async_embedding_response = None # type: ignore + + self.async_completion_kwargs_fail = None # type: ignore + self.async_embedding_kwargs_fail = None # type: ignore + + self.streaming_response_obj = None # type: ignore + blue_color_code = "\033[94m" + reset_color_code = "\033[0m" + print(f"{blue_color_code}Initialized LiteLLM custom logger") + try: + print(f"Logger Initialized with following methods:") + methods = [ + method + for method in dir(self) + if inspect.ismethod(getattr(self, method)) + ] + + # Pretty print the methods + for method in methods: + print(f" - {method}") + print(f"{reset_color_code}") + except: + pass + + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print(f"Post-API Call") + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + self.success = True + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + self.failure = True + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async success") + self.async_success = True + print("Value of async success: ", self.async_success) + print("\n kwargs: ", kwargs) + if ( + kwargs.get("model") == "azure-embedding-model" + or kwargs.get("model") == "ada" + ): + print("Got an embedding model", kwargs.get("model")) + print("Setting embedding success to True") + self.async_success_embedding = True + print("Value of async success embedding: ", self.async_success_embedding) + self.async_embedding_kwargs = kwargs + self.async_embedding_response = response_obj + if kwargs.get("stream") == True: + self.streaming_response_obj = response_obj + + self.async_completion_kwargs = kwargs + + model = kwargs.get("model", None) + messages = kwargs.get("messages", None) + user = kwargs.get("user", None) + + # Access litellm_params passed to litellm.completion(), example access `metadata` + litellm_params = kwargs.get("litellm_params", {}) + metadata = litellm_params.get( + "metadata", {} + ) # headers passed to LiteLLM proxy, can be found here + + # Calculate cost using litellm.completion_cost() + cost = litellm.completion_cost(completion_response=response_obj) + response = response_obj + # tokens used in response + usage = response_obj["usage"] + + print("\n\n in custom callback vars my custom logger, ", vars(my_custom_logger)) + + print( + f""" + Model: {model}, + Messages: {messages}, + User: {user}, + Usage: {usage}, + Cost: {cost}, + Response: {response} + Proxy Metadata: {metadata} + """ + ) + return + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Failure") + self.async_failure = True + print("Value of async failure: ", self.async_failure) + print("\n kwargs: ", kwargs) + if kwargs.get("model") == "text-embedding-ada-002": + self.async_failure_embedding = True + self.async_embedding_kwargs_fail = kwargs + + self.async_completion_kwargs_fail = kwargs + + +my_custom_logger = testCustomCallbackProxy() diff --git a/litellm/tests/test_configs/test_bad_config.yaml b/litellm/tests/test_configs/test_bad_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9899af2b7afef4e340aa75051633138c3b56802e --- /dev/null +++ b/litellm/tests/test_configs/test_bad_config.yaml @@ -0,0 +1,16 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + api_key: bad-key + model: gpt-3.5-turbo + - model_name: azure-gpt-3.5-turbo + litellm_params: + model: azure/chatgpt-v-2 + api_base: os.environ/AZURE_API_BASE + api_key: bad-key + - model_name: azure-embedding + litellm_params: + model: azure/azure-embedding-model + api_base: os.environ/AZURE_API_BASE + api_key: bad-key + \ No newline at end of file diff --git a/litellm/tests/test_configs/test_cloudflare_azure_with_cache_config.yaml b/litellm/tests/test_configs/test_cloudflare_azure_with_cache_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c19caaf485c13bc58bb8909b9d5f16d70efe233a --- /dev/null +++ b/litellm/tests/test_configs/test_cloudflare_azure_with_cache_config.yaml @@ -0,0 +1,17 @@ +model_list: + - model_name: azure-cloudflare + litellm_params: + model: azure/chatgpt-v-2 + api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1 + api_key: os.environ/AZURE_API_KEY + api_version: 2023-07-01-preview + +litellm_settings: + set_verbose: True + cache: True # set cache responses to True + cache_params: # set cache params for s3 + type: s3 + s3_bucket_name: cache-bucket-litellm # AWS Bucket Name for S3 + s3_region_name: us-west-2 # AWS Region Name for S3 + s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID # AWS Access Key ID for S3 + s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY # AWS Secret Access Key for S3 \ No newline at end of file diff --git a/litellm/tests/test_configs/test_config.yaml b/litellm/tests/test_configs/test_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a711b65ea0328b0b54d19129691ac66ef4b19daf --- /dev/null +++ b/litellm/tests/test_configs/test_config.yaml @@ -0,0 +1,28 @@ +general_settings: + database_url: os.environ/DATABASE_URL + master_key: os.environ/PROXY_MASTER_KEY +litellm_settings: + drop_params: true + success_callback: ["langfuse"] + +model_list: +- litellm_params: + api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ + api_key: os.environ/AZURE_EUROPE_API_KEY + model: azure/gpt-35-turbo + model_name: azure-model +- litellm_params: + api_base: https://my-endpoint-canada-berri992.openai.azure.com + api_key: os.environ/AZURE_CANADA_API_KEY + model: azure/gpt-35-turbo + model_name: azure-model +- litellm_params: + api_base: https://openai-france-1234.openai.azure.com + api_key: os.environ/AZURE_FRANCE_API_KEY + model: azure/gpt-turbo + model_name: azure-model +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + model_name: test_openai_models diff --git a/litellm/tests/test_configs/test_config_custom_auth.yaml b/litellm/tests/test_configs/test_config_custom_auth.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33088bd1ce11683f3e689df1576d17bea2986516 --- /dev/null +++ b/litellm/tests/test_configs/test_config_custom_auth.yaml @@ -0,0 +1,11 @@ +model_list: + - model_name: "openai-model" + litellm_params: + model: "gpt-3.5-turbo" + +litellm_settings: + drop_params: True + set_verbose: True + +general_settings: + custom_auth: custom_auth.user_api_key_auth \ No newline at end of file diff --git a/litellm/tests/test_configs/test_config_no_auth.yaml b/litellm/tests/test_configs/test_config_no_auth.yaml new file mode 100644 index 0000000000000000000000000000000000000000..be85765a869163d6e1fbbc9e983ccb08de32ecca --- /dev/null +++ b/litellm/tests/test_configs/test_config_no_auth.yaml @@ -0,0 +1,122 @@ +model_list: +- litellm_params: + api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ + api_key: os.environ/AZURE_EUROPE_API_KEY + model: azure/gpt-35-turbo + model_name: azure-model +- litellm_params: + api_base: https://my-endpoint-canada-berri992.openai.azure.com + api_key: os.environ/AZURE_CANADA_API_KEY + model: azure/gpt-35-turbo + model_name: azure-model +- litellm_params: + api_base: https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1 + api_key: os.environ/AZURE_API_KEY + model: azure/chatgpt-v-2 + model_name: azure-cloudflare-model +- litellm_params: + api_base: https://openai-france-1234.openai.azure.com + api_key: os.environ/AZURE_FRANCE_API_KEY + model: azure/gpt-turbo + model_name: azure-model +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 56f1bd94-3b54-4b67-9ea2-7c70e9a3a709 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 4d1ee26c-abca-450c-8744-8e87fd6755e9 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 00e19c0f-b63d-42bb-88e9-016fb0c60764 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 79fc75bf-8e1b-47d5-8d24-9365a854af03 + model_name: test_openai_models +- litellm_params: + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: 2023-07-01-preview + model: azure/azure-embedding-model + model_name: azure-embedding-model + model_info: + mode: "embedding" +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 55848c55-4162-40f9-a6e2-9a722b9ef404 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 34339b1e-e030-4bcc-a531-c48559f10ce4 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: f6f74e14-ac64-4403-9365-319e584dcdc5 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 9b1ef341-322c-410a-8992-903987fef439 + model_name: test_openai_models +- model_name: amazon-embeddings + litellm_params: + model: "bedrock/amazon.titan-embed-text-v1" + model_info: + mode: embedding +- model_name: "GPT-J 6B - Sagemaker Text Embedding (Internal)" + litellm_params: + model: "sagemaker/berri-benchmarking-gpt-j-6b-fp16" + model_info: + mode: embedding +- model_name: dall-e-3 + litellm_params: + model: dall-e-3 + model_info: + mode: image_generation +- model_name: dall-e-3 + litellm_params: + model: "azure/dall-e-3-test" + api_version: "2023-12-01-preview" + api_base: "os.environ/AZURE_SWEDEN_API_BASE" + api_key: "os.environ/AZURE_SWEDEN_API_KEY" + model_info: + mode: image_generation +- model_name: dall-e-2 + litellm_params: + model: "azure/" + api_version: "2023-06-01-preview" + api_base: "os.environ/AZURE_API_BASE" + api_key: "os.environ/AZURE_API_KEY" + model_info: + mode: image_generation +- model_name: text-embedding-ada-002 + litellm_params: + model: azure/azure-embedding-model + api_base: "os.environ/AZURE_API_BASE" + api_key: "os.environ/AZURE_API_KEY" + api_version: "2023-07-01-preview" + model_info: + mode: embedding + base_model: text-embedding-ada-002 \ No newline at end of file diff --git a/litellm/tests/test_configs/test_custom_logger.yaml b/litellm/tests/test_configs/test_custom_logger.yaml new file mode 100644 index 0000000000000000000000000000000000000000..145c618eddc9bf4cc57e06cd77cbc6039fd13c18 --- /dev/null +++ b/litellm/tests/test_configs/test_custom_logger.yaml @@ -0,0 +1,26 @@ +model_list: + - model_name: Azure OpenAI GPT-4 Canada + litellm_params: + model: azure/chatgpt-v-2 + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + model_info: + mode: chat + input_cost_per_token: 0.0002 + id: gm + - model_name: azure-embedding-model + litellm_params: + model: azure/azure-embedding-model + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + model_info: + mode: embedding + input_cost_per_token: 0.002 + id: hello + +litellm_settings: + drop_params: True + set_verbose: True + callbacks: custom_callbacks.my_custom_logger \ No newline at end of file diff --git a/litellm/tests/test_custom_callback_input.py b/litellm/tests/test_custom_callback_input.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb69b645137dcf20a7a500488bf307ae42a1fa1 --- /dev/null +++ b/litellm/tests/test_custom_callback_input.py @@ -0,0 +1,767 @@ +### What this tests #### +## This test asserts the type of data passed into each method of the custom callback handler +import sys, os, time, inspect, asyncio, traceback +from datetime import datetime +import pytest + +sys.path.insert(0, os.path.abspath("../..")) +from typing import Optional, Literal, List, Union +from litellm import completion, embedding, Cache +import litellm +from litellm.integrations.custom_logger import CustomLogger + +# Test Scenarios (test across completion, streaming, embedding) +## 1: Pre-API-Call +## 2: Post-API-Call +## 3: On LiteLLM Call success +## 4: On LiteLLM Call failure +## 5. Caching + +# Test models +## 1. OpenAI +## 2. Azure OpenAI +## 3. Non-OpenAI/Azure - e.g. Bedrock + +# Test interfaces +## 1. litellm.completion() + litellm.embeddings() +## refer to test_custom_callback_input_router.py for the router + proxy tests + + +class CompletionCustomHandler( + CustomLogger +): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + """ + The set of expected inputs to a custom handler for a + """ + + # Class variables or attributes + def __init__(self): + self.errors = [] + self.states: Optional[ + List[ + Literal[ + "sync_pre_api_call", + "async_pre_api_call", + "post_api_call", + "sync_stream", + "async_stream", + "sync_success", + "async_success", + "sync_failure", + "async_failure", + ] + ] + ] = [] + + def log_pre_api_call(self, model, messages, kwargs): + try: + self.states.append("sync_pre_api_call") + ## MODEL + assert isinstance(model, str) + ## MESSAGES + assert isinstance(messages, list) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("post_api_call") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert end_time == None + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert isinstance(kwargs["input"], (list, dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.iscoroutine(kwargs["original_response"]) + or inspect.isasyncgen(kwargs["original_response"]) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_stream") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert ( + isinstance(kwargs["input"], list) + and isinstance(kwargs["input"][0], dict) + ) or isinstance(kwargs["input"], (dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.isasyncgen(kwargs["original_response"]) + or inspect.iscoroutine(kwargs["original_response"]) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert ( + isinstance(kwargs["input"], list) + and isinstance(kwargs["input"][0], dict) + ) or isinstance(kwargs["input"], (dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert ( + isinstance(kwargs["input"], list) + and isinstance(kwargs["input"][0], dict) + ) or isinstance(kwargs["input"], (dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or kwargs["original_response"] == None + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_pre_api_call(self, model, messages, kwargs): + try: + self.states.append("async_pre_api_call") + ## MODEL + assert isinstance(model, str) + ## MESSAGES + assert isinstance(messages, list) and isinstance(messages[0], dict) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance( + response_obj, (litellm.ModelResponse, litellm.EmbeddingResponse) + ) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert isinstance(kwargs["input"], (list, dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.isasyncgen(kwargs["original_response"]) + or inspect.iscoroutine(kwargs["original_response"]) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert isinstance(kwargs["input"], (list, str, dict)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.isasyncgen(kwargs["original_response"]) + or inspect.iscoroutine(kwargs["original_response"]) + or kwargs["original_response"] == None + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + +# COMPLETION +## Test OpenAI + sync +def test_chat_openai_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync openai"}], + ) + ## test streaming + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + stream=True, + ) + for chunk in response: + continue + ## test failure callback + try: + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + api_key="my-bad-key", + stream=True, + ) + for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# test_chat_openai_stream() + + +## Test OpenAI + Async +@pytest.mark.asyncio +async def test_async_chat_openai_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + ) + ## test streaming + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + stream=True, + ) + async for chunk in response: + continue + ## test failure callback + try: + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + api_key="my-bad-key", + stream=True, + ) + async for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# asyncio.run(test_async_chat_openai_stream()) + + +## Test Azure + sync +def test_chat_azure_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync azure"}], + ) + # test streaming + response = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync azure"}], + stream=True, + ) + for chunk in response: + continue + # test failure callback + try: + response = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync azure"}], + api_key="my-bad-key", + stream=True, + ) + for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# test_chat_azure_stream() + + +## Test Azure + Async +@pytest.mark.asyncio +async def test_async_chat_azure_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "Hi 👋 - i'm async azure"}], + ) + ## test streaming + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "Hi 👋 - i'm async azure"}], + stream=True, + ) + async for chunk in response: + continue + # test failure callback + try: + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "Hi 👋 - i'm async azure"}], + api_key="my-bad-key", + stream=True, + ) + async for chunk in response: + continue + except: + pass + await asyncio.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +asyncio.run(test_async_chat_azure_stream()) + + +## Test Bedrock + sync +def test_chat_bedrock_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = litellm.completion( + model="bedrock/anthropic.claude-v1", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync bedrock"}], + ) + # test streaming + response = litellm.completion( + model="bedrock/anthropic.claude-v1", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync bedrock"}], + stream=True, + ) + for chunk in response: + continue + # test failure callback + try: + response = litellm.completion( + model="bedrock/anthropic.claude-v1", + messages=[{"role": "user", "content": "Hi 👋 - i'm sync bedrock"}], + aws_region_name="my-bad-region", + stream=True, + ) + for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# test_chat_bedrock_stream() + + +## Test Bedrock + Async +@pytest.mark.asyncio +async def test_async_chat_bedrock_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.acompletion( + model="bedrock/anthropic.claude-v1", + messages=[{"role": "user", "content": "Hi 👋 - i'm async bedrock"}], + ) + # test streaming + response = await litellm.acompletion( + model="bedrock/anthropic.claude-v1", + messages=[{"role": "user", "content": "Hi 👋 - i'm async bedrock"}], + stream=True, + ) + print(f"response: {response}") + async for chunk in response: + print(f"chunk: {chunk}") + continue + ## test failure callback + try: + response = await litellm.acompletion( + model="bedrock/anthropic.claude-v1", + messages=[{"role": "user", "content": "Hi 👋 - i'm async bedrock"}], + aws_region_name="my-bad-key", + stream=True, + ) + async for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# asyncio.run(test_async_chat_bedrock_stream()) + +# Text Completion + +## Test OpenAI text completion + Async +@pytest.mark.asyncio +async def test_async_text_completion_openai_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.atext_completion( + model="gpt-3.5-turbo", + prompt="Hi 👋 - i'm async text completion openai", + ) + # test streaming + response = await litellm.atext_completion( + model="gpt-3.5-turbo", + prompt="Hi 👋 - i'm async text completion openai", + stream=True, + ) + async for chunk in response: + print(f"chunk: {chunk}") + continue + ## test failure callback + try: + response = await litellm.atext_completion( + model="gpt-3.5-turbo", + prompt="Hi 👋 - i'm async text completion openai", + stream=True, + api_key="my-bad-key", + ) + async for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# EMBEDDING +## Test OpenAI + Async +@pytest.mark.asyncio +async def test_async_embedding_openai(): + try: + customHandler_success = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_success] + response = await litellm.aembedding( + model="azure/azure-embedding-model", input=["good morning from litellm"] + ) + await asyncio.sleep(1) + print(f"customHandler_success.errors: {customHandler_success.errors}") + print(f"customHandler_success.states: {customHandler_success.states}") + assert len(customHandler_success.errors) == 0 + assert len(customHandler_success.states) == 3 # pre, post, success + # test failure callback + litellm.callbacks = [customHandler_failure] + try: + response = await litellm.aembedding( + model="text-embedding-ada-002", + input=["good morning from litellm"], + api_key="my-bad-key", + ) + except: + pass + await asyncio.sleep(1) + print(f"customHandler_failure.errors: {customHandler_failure.errors}") + print(f"customHandler_failure.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, failure + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# asyncio.run(test_async_embedding_openai()) + + +## Test Azure + Async +@pytest.mark.asyncio +async def test_async_embedding_azure(): + try: + customHandler_success = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_success] + response = await litellm.aembedding( + model="azure/azure-embedding-model", input=["good morning from litellm"] + ) + await asyncio.sleep(1) + print(f"customHandler_success.errors: {customHandler_success.errors}") + print(f"customHandler_success.states: {customHandler_success.states}") + assert len(customHandler_success.errors) == 0 + assert len(customHandler_success.states) == 3 # pre, post, success + # test failure callback + litellm.callbacks = [customHandler_failure] + try: + response = await litellm.aembedding( + model="azure/azure-embedding-model", + input=["good morning from litellm"], + api_key="my-bad-key", + ) + except: + pass + await asyncio.sleep(1) + print(f"customHandler_failure.errors: {customHandler_failure.errors}") + print(f"customHandler_failure.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, success + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# asyncio.run(test_async_embedding_azure()) + + +## Test Bedrock + Async +@pytest.mark.asyncio +async def test_async_embedding_bedrock(): + try: + customHandler_success = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_success] + litellm.set_verbose = True + response = await litellm.aembedding( + model="bedrock/cohere.embed-multilingual-v3", + input=["good morning from litellm"], + aws_region_name="os.environ/AWS_REGION_NAME_2", + ) + await asyncio.sleep(1) + print(f"customHandler_success.errors: {customHandler_success.errors}") + print(f"customHandler_success.states: {customHandler_success.states}") + assert len(customHandler_success.errors) == 0 + assert len(customHandler_success.states) == 3 # pre, post, success + # test failure callback + litellm.callbacks = [customHandler_failure] + try: + response = await litellm.aembedding( + model="bedrock/cohere.embed-multilingual-v3", + input=["good morning from litellm"], + aws_region_name="my-bad-region", + ) + except: + pass + await asyncio.sleep(1) + print(f"customHandler_failure.errors: {customHandler_failure.errors}") + print(f"customHandler_failure.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, success + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# asyncio.run(test_async_embedding_bedrock()) + + +# CACHING +## Test Azure - completion, embedding +@pytest.mark.asyncio +async def test_async_completion_azure_caching(): + customHandler_caching = CompletionCustomHandler() + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + response1 = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}], + caching=True, + ) + await asyncio.sleep(1) + print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") + response2 = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}], + caching=True, + ) + await asyncio.sleep(1) # success callbacks are done in parallel + print( + f"customHandler_caching.states post-cache hit: {customHandler_caching.states}" + ) + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success + + +@pytest.mark.asyncio +async def test_async_embedding_azure_caching(): + print("Testing custom callback input - Azure Caching") + customHandler_caching = CompletionCustomHandler() + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + response1 = await litellm.aembedding( + model="azure/azure-embedding-model", + input=[f"good morning from litellm1 {unique_time}"], + caching=True, + ) + await asyncio.sleep(1) # set cache is async for aembedding() + response2 = await litellm.aembedding( + model="azure/azure-embedding-model", + input=[f"good morning from litellm1 {unique_time}"], + caching=True, + ) + await asyncio.sleep(1) # success callbacks are done in parallel + print(customHandler_caching.states) + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success + + +# asyncio.run( +# test_async_embedding_azure_caching() +# ) diff --git a/litellm/tests/test_custom_callback_router.py b/litellm/tests/test_custom_callback_router.py new file mode 100644 index 0000000000000000000000000000000000000000..ac8b2fa10142bbb62de2ffcbe72c29df07664e75 --- /dev/null +++ b/litellm/tests/test_custom_callback_router.py @@ -0,0 +1,585 @@ +### What this tests #### +## This test asserts the type of data passed into each method of the custom callback handler +import sys, os, time, inspect, asyncio, traceback +from datetime import datetime +import pytest + +sys.path.insert(0, os.path.abspath("../..")) +from typing import Optional, Literal, List +from litellm import Router, Cache +import litellm +from litellm.integrations.custom_logger import CustomLogger + +# Test Scenarios (test across completion, streaming, embedding) +## 1: Pre-API-Call +## 2: Post-API-Call +## 3: On LiteLLM Call success +## 4: On LiteLLM Call failure +## fallbacks +## retries + +# Test cases +## 1. Simple Azure OpenAI acompletion + streaming call +## 2. Simple Azure OpenAI aembedding call +## 3. Azure OpenAI acompletion + streaming call with retries +## 4. Azure OpenAI aembedding call with retries +## 5. Azure OpenAI acompletion + streaming call with fallbacks +## 6. Azure OpenAI aembedding call with fallbacks + +# Test interfaces +## 1. router.completion() + router.embeddings() +## 2. proxy.completions + proxy.embeddings + + +class CompletionCustomHandler( + CustomLogger +): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + """ + The set of expected inputs to a custom handler for a + """ + + # Class variables or attributes + def __init__(self): + self.errors = [] + self.states: Optional[ + List[ + Literal[ + "sync_pre_api_call", + "async_pre_api_call", + "post_api_call", + "sync_stream", + "async_stream", + "sync_success", + "async_success", + "sync_failure", + "async_failure", + ] + ] + ] = [] + + def log_pre_api_call(self, model, messages, kwargs): + try: + print(f"received kwargs in pre-input: {kwargs}") + self.states.append("sync_pre_api_call") + ## MODEL + assert isinstance(model, str) + ## MESSAGES + assert isinstance(messages, list) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + ### ROUTER-SPECIFIC KWARGS + assert isinstance(kwargs["litellm_params"]["metadata"], dict) + assert isinstance(kwargs["litellm_params"]["metadata"]["model_group"], str) + assert isinstance(kwargs["litellm_params"]["metadata"]["deployment"], str) + assert isinstance(kwargs["litellm_params"]["model_info"], dict) + assert isinstance(kwargs["litellm_params"]["model_info"]["id"], str) + assert isinstance( + kwargs["litellm_params"]["proxy_server_request"], (str, type(None)) + ) + assert isinstance( + kwargs["litellm_params"]["preset_cache_key"], (str, type(None)) + ) + assert isinstance(kwargs["litellm_params"]["stream_response"], dict) + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("post_api_call") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert end_time == None + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert isinstance(kwargs["input"], (list, dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.iscoroutine(kwargs["original_response"]) + or inspect.isasyncgen(kwargs["original_response"]) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + ### ROUTER-SPECIFIC KWARGS + assert isinstance(kwargs["litellm_params"]["metadata"], dict) + assert isinstance(kwargs["litellm_params"]["metadata"]["model_group"], str) + assert isinstance(kwargs["litellm_params"]["metadata"]["deployment"], str) + assert isinstance(kwargs["litellm_params"]["model_info"], dict) + assert isinstance(kwargs["litellm_params"]["model_info"]["id"], str) + assert isinstance( + kwargs["litellm_params"]["proxy_server_request"], (str, type(None)) + ) + assert isinstance( + kwargs["litellm_params"]["preset_cache_key"], (str, type(None)) + ) + assert isinstance(kwargs["litellm_params"]["stream_response"], dict) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_stream") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert ( + isinstance(kwargs["input"], list) + and isinstance(kwargs["input"][0], dict) + ) or isinstance(kwargs["input"], (dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.isasyncgen(kwargs["original_response"]) + or inspect.iscoroutine(kwargs["original_response"]) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert ( + isinstance(kwargs["input"], list) + and isinstance(kwargs["input"][0], dict) + ) or isinstance(kwargs["input"], (dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) and isinstance( + kwargs["messages"][0], dict + ) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert ( + isinstance(kwargs["input"], list) + and isinstance(kwargs["input"][0], dict) + ) or isinstance(kwargs["input"], (dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or kwargs["original_response"] == None + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_pre_api_call(self, model, messages, kwargs): + try: + """ + No-op. + Not implemented yet. + """ + pass + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance( + response_obj, (litellm.ModelResponse, litellm.EmbeddingResponse) + ) + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert isinstance(kwargs["input"], (list, dict, str)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.isasyncgen(kwargs["original_response"]) + or inspect.iscoroutine(kwargs["original_response"]) + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool) + ### ROUTER-SPECIFIC KWARGS + assert isinstance(kwargs["litellm_params"]["metadata"], dict) + assert isinstance(kwargs["litellm_params"]["metadata"]["model_group"], str) + assert isinstance(kwargs["litellm_params"]["metadata"]["deployment"], str) + assert isinstance(kwargs["litellm_params"]["model_info"], dict) + assert isinstance(kwargs["litellm_params"]["model_info"]["id"], str) + assert isinstance( + kwargs["litellm_params"]["proxy_server_request"], (str, type(None)) + ) + assert isinstance( + kwargs["litellm_params"]["preset_cache_key"], (str, type(None)) + ) + assert isinstance(kwargs["litellm_params"]["stream_response"], dict) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + print(f"received original response: {kwargs['original_response']}") + self.states.append("async_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs["model"], str) + assert isinstance(kwargs["messages"], list) + assert isinstance(kwargs["optional_params"], dict) + assert isinstance(kwargs["litellm_params"], dict) + assert isinstance(kwargs["start_time"], (datetime, type(None))) + assert isinstance(kwargs["stream"], bool) + assert isinstance(kwargs["user"], (str, type(None))) + assert isinstance(kwargs["input"], (list, str, dict)) + assert isinstance(kwargs["api_key"], (str, type(None))) + assert ( + isinstance( + kwargs["original_response"], (str, litellm.CustomStreamWrapper) + ) + or inspect.isasyncgen(kwargs["original_response"]) + or inspect.iscoroutine(kwargs["original_response"]) + or kwargs["original_response"] == None + ) + assert isinstance(kwargs["additional_args"], (dict, type(None))) + assert isinstance(kwargs["log_event_type"], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + +# Simple Azure OpenAI call +## COMPLETION +@pytest.mark.asyncio +async def test_async_chat_azure(): + try: + customHandler_completion_azure_router = CompletionCustomHandler() + customHandler_streaming_azure_router = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_completion_azure_router] + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + router = Router(model_list=model_list) # type: ignore + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + ) + await asyncio.sleep(2) + assert len(customHandler_completion_azure_router.errors) == 0 + assert ( + len(customHandler_completion_azure_router.states) == 3 + ) # pre, post, success + # streaming + litellm.callbacks = [customHandler_streaming_azure_router] + router2 = Router(model_list=model_list) # type: ignore + response = await router2.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + stream=True, + ) + async for chunk in response: + print(f"async azure router chunk: {chunk}") + continue + await asyncio.sleep(1) + print(f"customHandler.states: {customHandler_streaming_azure_router.states}") + assert len(customHandler_streaming_azure_router.errors) == 0 + assert ( + len(customHandler_streaming_azure_router.states) >= 4 + ) # pre, post, stream (multiple times), success + # failure + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "my-bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + litellm.callbacks = [customHandler_failure] + router3 = Router(model_list=model_list) # type: ignore + try: + response = await router3.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + ) + print(f"response in router3 acompletion: {response}") + except: + pass + await asyncio.sleep(1) + print(f"customHandler.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, failure + assert "async_failure" in customHandler_failure.states + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + pytest.fail(f"An exception occurred - {str(e)}") + + +# asyncio.run(test_async_chat_azure()) +## EMBEDDING +@pytest.mark.asyncio +async def test_async_embedding_azure(): + try: + customHandler = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler] + model_list = [ + { + "model_name": "azure-embedding-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/azure-embedding-model", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + router = Router(model_list=model_list) # type: ignore + response = await router.aembedding( + model="azure-embedding-model", input=["hello from litellm!"] + ) + await asyncio.sleep(2) + assert len(customHandler.errors) == 0 + assert len(customHandler.states) == 3 # pre, post, success + # failure + model_list = [ + { + "model_name": "azure-embedding-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/azure-embedding-model", + "api_key": "my-bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + litellm.callbacks = [customHandler_failure] + router3 = Router(model_list=model_list) # type: ignore + try: + response = await router3.aembedding( + model="azure-embedding-model", input=["hello from litellm!"] + ) + print(f"response in router3 aembedding: {response}") + except: + pass + await asyncio.sleep(1) + print(f"customHandler.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, failure + assert "async_failure" in customHandler_failure.states + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + pytest.fail(f"An exception occurred - {str(e)}") + + +# asyncio.run(test_async_embedding_azure()) +# Azure OpenAI call w/ Fallbacks +## COMPLETION +@pytest.mark.asyncio +async def test_async_chat_azure_with_fallbacks(): + try: + customHandler_fallbacks = CompletionCustomHandler() + litellm.callbacks = [customHandler_fallbacks] + # with fallbacks + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "my-bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo-16k", + "litellm_params": { + "model": "gpt-3.5-turbo-16k", + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + router = Router(model_list=model_list, fallbacks=[{"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}]) # type: ignore + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + ) + await asyncio.sleep(2) + print(f"customHandler_fallbacks.states: {customHandler_fallbacks.states}") + assert len(customHandler_fallbacks.errors) == 0 + assert ( + len(customHandler_fallbacks.states) == 6 + ) # pre, post, failure, pre, post, success + litellm.callbacks = [] + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + pytest.fail(f"An exception occurred - {str(e)}") + + +# asyncio.run(test_async_chat_azure_with_fallbacks()) + + +# CACHING +## Test Azure - completion, embedding +@pytest.mark.asyncio +async def test_async_completion_azure_caching(): + customHandler_caching = CompletionCustomHandler() + litellm.cache = Cache( + type="redis", + host=os.environ["REDIS_HOST"], + port=os.environ["REDIS_PORT"], + password=os.environ["REDIS_PASSWORD"], + ) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo-16k", + "litellm_params": { + "model": "gpt-3.5-turbo-16k", + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + router = Router(model_list=model_list) # type: ignore + response1 = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}], + caching=True, + ) + await asyncio.sleep(1) + print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") + response2 = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}], + caching=True, + ) + await asyncio.sleep(1) # success callbacks are done in parallel + print( + f"customHandler_caching.states post-cache hit: {customHandler_caching.states}" + ) + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success diff --git a/litellm/tests/test_custom_logger.py b/litellm/tests/test_custom_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..de7dd67b4e58ccc17c2f5db83908721a865696c5 --- /dev/null +++ b/litellm/tests/test_custom_logger.py @@ -0,0 +1,369 @@ +### What this tests #### +import sys, os, time, inspect, asyncio, traceback +import pytest +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion, embedding +import litellm +from litellm.integrations.custom_logger import CustomLogger + +class MyCustomHandler(CustomLogger): + complete_streaming_response_in_callback = "" + def __init__(self): + self.success: bool = False # type: ignore + self.failure: bool = False # type: ignore + self.async_success: bool = False # type: ignore + self.async_success_embedding: bool = False # type: ignore + self.async_failure: bool = False # type: ignore + self.async_failure_embedding: bool = False # type: ignore + + self.async_completion_kwargs = None # type: ignore + self.async_embedding_kwargs = None # type: ignore + self.async_embedding_response = None # type: ignore + + self.async_completion_kwargs_fail = None # type: ignore + self.async_embedding_kwargs_fail = None # type: ignore + + self.stream_collected_response = None # type: ignore + self.sync_stream_collected_response = None # type: ignore + self.user = None # type: ignore + self.data_sent_to_api: dict = {} + + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + self.data_sent_to_api = kwargs["additional_args"].get("complete_input_dict", {}) + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print(f"Post-API Call") + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + self.success = True + if kwargs.get("stream") == True: + self.sync_stream_collected_response = response_obj + + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + self.failure = True + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async success") + print(f"received kwargs user: {kwargs['user']}") + self.async_success = True + if kwargs.get("model") == "text-embedding-ada-002": + self.async_success_embedding = True + self.async_embedding_kwargs = kwargs + self.async_embedding_response = response_obj + if kwargs.get("stream") == True: + self.stream_collected_response = response_obj + self.async_completion_kwargs = kwargs + self.user = kwargs.get("user", None) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Failure") + self.async_failure = True + if kwargs.get("model") == "text-embedding-ada-002": + self.async_failure_embedding = True + self.async_embedding_kwargs_fail = kwargs + + self.async_completion_kwargs_fail = kwargs + +class TmpFunction: + complete_streaming_response_in_callback = "" + async_success: bool = False + async def async_test_logging_fn(self, kwargs, completion_obj, start_time, end_time): + print(f"ON ASYNC LOGGING") + self.async_success = True + print(f'kwargs.get("complete_streaming_response"): {kwargs.get("complete_streaming_response")}') + self.complete_streaming_response_in_callback = kwargs.get("complete_streaming_response") + + +def test_async_chat_openai_stream(): + try: + tmp_function = TmpFunction() + # litellm.set_verbose = True + litellm.success_callback = [tmp_function.async_test_logging_fn] + complete_streaming_response = "" + async def call_gpt(): + nonlocal complete_streaming_response + response = await litellm.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + stream=True) + async for chunk in response: + complete_streaming_response += chunk["choices"][0]["delta"]["content"] or "" + print(complete_streaming_response) + asyncio.run(call_gpt()) + complete_streaming_response = complete_streaming_response.strip("'") + response1 = tmp_function.complete_streaming_response_in_callback["choices"][0]["message"]["content"] + response2 = complete_streaming_response + # assert [ord(c) for c in response1] == [ord(c) for c in response2] + assert response1 == response2 + assert tmp_function.async_success == True + except Exception as e: + print(e) + pytest.fail(f"An error occurred - {str(e)}") +# test_async_chat_openai_stream() + +def test_completion_azure_stream_moderation_failure(): + try: + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how do i kill someone", + }, + ] + try: + response = completion( + model="azure/chatgpt-v-2", messages=messages, stream=True + ) + for chunk in response: + print(f"chunk: {chunk}") + continue + except Exception as e: + print(e) + time.sleep(1) + assert customHandler.failure == True + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +def test_async_custom_handler_stream(): + try: + # [PROD Test] - Do not DELETE + # checks if the model response available in the async + stream callbacks is equal to the received response + customHandler2 = MyCustomHandler() + litellm.callbacks = [customHandler2] + litellm.set_verbose = False + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "write 1 sentence about litellm being amazing", + }, + ] + complete_streaming_response = "" + async def test_1(): + nonlocal complete_streaming_response + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=messages, + stream=True + ) + async for chunk in response: + complete_streaming_response += chunk["choices"][0]["delta"]["content"] or "" + print(complete_streaming_response) + + asyncio.run(test_1()) + + response_in_success_handler = customHandler2.stream_collected_response + response_in_success_handler = response_in_success_handler["choices"][0]["message"]["content"] + print("\n\n") + print("response_in_success_handler: ", response_in_success_handler) + print("complete_streaming_response: ", complete_streaming_response) + assert response_in_success_handler == complete_streaming_response + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_async_custom_handler_stream() + + +def test_azure_completion_stream(): + # [PROD Test] - Do not DELETE + # test if completion() + sync custom logger get the same complete stream response + try: + # checks if the model response available in the async + stream callbacks is equal to the received response + customHandler2 = MyCustomHandler() + litellm.callbacks = [customHandler2] + litellm.set_verbose = False + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "write 1 sentence about litellm being amazing", + }, + ] + complete_streaming_response = "" + + response = litellm.completion( + model="azure/chatgpt-v-2", + messages=messages, + stream=True + ) + for chunk in response: + complete_streaming_response += chunk["choices"][0]["delta"]["content"] or "" + print(complete_streaming_response) + + time.sleep(0.5) # wait 1/2 second before checking callbacks + response_in_success_handler = customHandler2.sync_stream_collected_response + response_in_success_handler = response_in_success_handler["choices"][0]["message"]["content"] + print("\n\n") + print("response_in_success_handler: ", response_in_success_handler) + print("complete_streaming_response: ", complete_streaming_response) + assert response_in_success_handler == complete_streaming_response + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +@pytest.mark.asyncio +async def test_async_custom_handler_completion(): + try: + customHandler_success = MyCustomHandler() + customHandler_failure = MyCustomHandler() + # success + assert customHandler_success.async_success == False + litellm.callbacks = [customHandler_success] + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "hello from litellm test", + }] + ) + await asyncio.sleep(1) + assert customHandler_success.async_success == True, "async success is not set to True even after success" + assert customHandler_success.async_completion_kwargs.get("model") == "gpt-3.5-turbo" + # failure + litellm.callbacks = [customHandler_failure] + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how do i kill someone", + }, + ] + + assert customHandler_failure.async_failure == False + try: + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=messages, + api_key="my-bad-key", + ) + except: + pass + assert customHandler_failure.async_failure == True, "async failure is not set to True even after failure" + assert customHandler_failure.async_completion_kwargs_fail.get("model") == "gpt-3.5-turbo" + assert len(str(customHandler_failure.async_completion_kwargs_fail.get("exception"))) > 10 # expect APIError("OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: test. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"), 'traceback_exception': 'Traceback (most recent call last):\n File "/Users/ishaanjaffer/Github/litellm/litellm/llms/openai.py", line 269, in acompletion\n response = await openai_aclient.chat.completions.create(**data)\n File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 119 + litellm.callbacks = [] + print("Passed setting async failure") + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_custom_handler_completion()) + +@pytest.mark.asyncio +async def test_async_custom_handler_embedding(): + try: + customHandler_embedding = MyCustomHandler() + litellm.callbacks = [customHandler_embedding] + # success + assert customHandler_embedding.async_success_embedding == False + response = await litellm.aembedding( + model="text-embedding-ada-002", + input = ["hello world"], + ) + await asyncio.sleep(1) + assert customHandler_embedding.async_success_embedding == True, "async_success_embedding is not set to True even after success" + assert customHandler_embedding.async_embedding_kwargs.get("model") == "text-embedding-ada-002" + assert customHandler_embedding.async_embedding_response["usage"]["prompt_tokens"] ==2 + print("Passed setting async success: Embedding") + # failure + assert customHandler_embedding.async_failure_embedding == False + try: + response = await litellm.aembedding( + model="text-embedding-ada-002", + input = ["hello world"], + api_key="my-bad-key", + ) + except: + pass + assert customHandler_embedding.async_failure_embedding == True, "async failure embedding is not set to True even after failure" + assert customHandler_embedding.async_embedding_kwargs_fail.get("model") == "text-embedding-ada-002" + assert len(str(customHandler_embedding.async_embedding_kwargs_fail.get("exception"))) > 10 # exppect APIError("OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: test. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"), 'traceback_exception': 'Traceback (most recent call last):\n File "/Users/ishaanjaffer/Github/litellm/litellm/llms/openai.py", line 269, in acompletion\n response = await openai_aclient.chat.completions.create(**data)\n File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 119 + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_custom_handler_embedding()) + +@pytest.mark.asyncio +async def test_async_custom_handler_embedding_optional_param(): + """ + Tests if the openai optional params for embedding - user + encoding_format, + are logged + """ + customHandler_optional_params = MyCustomHandler() + litellm.callbacks = [customHandler_optional_params] + response = await litellm.aembedding( + model="azure/azure-embedding-model", + input = ["hello world"], + user = "John" + ) + await asyncio.sleep(1) # success callback is async + assert customHandler_optional_params.user == "John" + assert customHandler_optional_params.user == customHandler_optional_params.data_sent_to_api["user"] + +# asyncio.run(test_async_custom_handler_embedding_optional_param()) + +@pytest.mark.asyncio +async def test_async_custom_handler_embedding_optional_param_bedrock(): + """ + Tests if the openai optional params for embedding - user + encoding_format, + are logged + + but makes sure these are not sent to the non-openai/azure endpoint (raises errors). + """ + litellm.drop_params = True + litellm.set_verbose = True + customHandler_optional_params = MyCustomHandler() + litellm.callbacks = [customHandler_optional_params] + response = await litellm.aembedding( + model="bedrock/amazon.titan-embed-text-v1", + input = ["hello world"], + user = "John" + ) + await asyncio.sleep(1) # success callback is async + assert customHandler_optional_params.user == "John" + assert "user" not in customHandler_optional_params.data_sent_to_api + + +def test_redis_cache_completion_stream(): + from litellm import Cache + # Important Test - This tests if we can add to streaming cache, when custom callbacks are set + import random + try: + print("\nrunning test_redis_cache_completion_stream") + litellm.set_verbose = True + random_number = random.randint(1, 100000) # add a random number to ensure it's always adding / reading from cache + messages = [{"role": "user", "content": f"write a one sentence poem about: {random_number}"}] + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + print("test for caching, streaming + completion") + response1 = completion(model="gpt-3.5-turbo", messages=messages, max_tokens=40, temperature=0.2, stream=True) + response_1_content = "" + for chunk in response1: + print(chunk) + response_1_content += chunk.choices[0].delta.content or "" + print(response_1_content) + + time.sleep(0.1) # sleep for 0.1 seconds allow set cache to occur + response2 = completion(model="gpt-3.5-turbo", messages=messages, max_tokens=40, temperature=0.2, stream=True) + response_2_content = "" + for chunk in response2: + print(chunk) + response_2_content += chunk.choices[0].delta.content or "" + print("\nresponse 1", response_1_content) + print("\nresponse 2", response_2_content) + assert response_1_content == response_2_content, f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" + litellm.success_callback = [] + litellm._async_success_callback = [] + litellm.cache = None + except Exception as e: + print(e) + litellm.success_callback = [] + raise e +# test_redis_cache_completion_stream() \ No newline at end of file diff --git a/litellm/tests/test_deployed_proxy_keygen.py b/litellm/tests/test_deployed_proxy_keygen.py new file mode 100644 index 0000000000000000000000000000000000000000..e0acee083c0d629ff45cdd4667eb4d4c4d48a3cb --- /dev/null +++ b/litellm/tests/test_deployed_proxy_keygen.py @@ -0,0 +1,63 @@ +# import sys, os, time +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os, io + +# # this file is to test litellm/proxy + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import pytest, logging, requests +# import litellm +# from litellm import embedding, completion, completion_cost, Timeout +# from litellm import RateLimitError + + +# def test_add_new_key(): +# max_retries = 3 +# retry_delay = 1 # seconds + +# for retry in range(max_retries + 1): +# try: +# # Your test data +# test_data = { +# "models": ["gpt-3.5-turbo", "gpt-4", "claude-2", "azure-model"], +# "aliases": {"mistral-7b": "gpt-3.5-turbo"}, +# "duration": "20m", +# } +# print("testing proxy server") + +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") +# headers = {"Authorization": f"Bearer {token}"} + +# staging_endpoint = "https://litellm-litellm-pr-1376.up.railway.app" +# main_endpoint = "https://litellm-staging.up.railway.app" + +# # Make a request to the staging endpoint +# response = requests.post( +# main_endpoint + "/key/generate", json=test_data, headers=headers +# ) + +# print(f"response: {response.text}") + +# if response.status_code == 200: +# result = response.json() +# break # Successful response, exit the loop +# elif response.status_code == 503 and retry < max_retries: +# print( +# f"Retrying in {retry_delay} seconds... (Retry {retry + 1}/{max_retries})" +# ) +# time.sleep(retry_delay) +# else: +# assert False, f"Unexpected response status code: {response.status_code}" + +# except Exception as e: +# print(traceback.format_exc()) +# pytest.fail(f"An error occurred {e}") + + +# test_add_new_key() diff --git a/litellm/tests/test_dynamodb_logs.py b/litellm/tests/test_dynamodb_logs.py new file mode 100644 index 0000000000000000000000000000000000000000..a6bedd0cf5dd58c84925f245f4f86352e2cab86b --- /dev/null +++ b/litellm/tests/test_dynamodb_logs.py @@ -0,0 +1,130 @@ +import sys +import os +import io, asyncio + +# import logging +# logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath("../..")) + +from litellm import completion +import litellm + +litellm.num_retries = 3 + +import time, random +import pytest + + +def pre_request(): + file_name = f"dynamo.log" + log_file = open(file_name, "a+") + + # Clear the contents of the file by truncating it + log_file.truncate(0) + + # Save the original stdout so that we can restore it later + original_stdout = sys.stdout + # Redirect stdout to the file + sys.stdout = log_file + + return original_stdout, log_file, file_name + + +import re + + +def verify_log_file(log_file_path): + with open(log_file_path, "r") as log_file: + log_content = log_file.read() + print( + f"\nVerifying DynamoDB file = {log_file_path}. File content=", log_content + ) + + # Define the pattern to search for in the log file + pattern = r"Response from DynamoDB:{.*?}" + + # Find all matches in the log content + matches = re.findall(pattern, log_content) + + # Print the DynamoDB success log matches + print("DynamoDB Success Log Matches:") + for match in matches: + print(match) + + # Print the total count of lines containing the specified response + print(f"Total occurrences of specified response: {len(matches)}") + + # Count the occurrences of successful responses (status code 200 or 201) + success_count = sum( + 1 + for match in matches + if "'HTTPStatusCode': 200" in match or "'HTTPStatusCode': 201" in match + ) + + # Print the count of successful responses + print(f"Count of successful responses from DynamoDB: {success_count}") + assert success_count == 3 # Expect 3 success logs from dynamoDB + + +def test_dynamo_logging(): + # all dynamodb requests need to be in one test function + # since we are modifying stdout, and pytests runs tests in parallel + try: + # pre + # redirect stdout to log_file + + litellm.success_callback = ["dynamodb"] + litellm.dynamodb_table_name = "litellm-logs-1" + litellm.set_verbose = True + original_stdout, log_file, file_name = pre_request() + + print("Testing async dynamoDB logging") + + async def _test(): + return await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "This is a test"}], + max_tokens=100, + temperature=0.7, + user="ishaan-2", + ) + + response = asyncio.run(_test()) + print(f"response: {response}") + + # streaming + async + async def _test2(): + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "This is a test"}], + max_tokens=10, + temperature=0.7, + user="ishaan-2", + stream=True, + ) + async for chunk in response: + pass + + asyncio.run(_test2()) + + # aembedding() + async def _test3(): + return await litellm.aembedding( + model="text-embedding-ada-002", input=["hi"], user="ishaan-2" + ) + + response = asyncio.run(_test3()) + time.sleep(1) + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + finally: + # post, close log file and verify + # Reset stdout to the original value + sys.stdout = original_stdout + # Close the file + log_file.close() + verify_log_file(file_name) + print("Passed! Testing async dynamoDB logging") + + +# test_dynamo_logging_async() diff --git a/litellm/tests/test_embedding.py b/litellm/tests/test_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..d1f0ee69962c981aeb5561991f5c9bfe0e262ff9 --- /dev/null +++ b/litellm/tests/test_embedding.py @@ -0,0 +1,400 @@ +import sys, os +import traceback +import pytest +from dotenv import load_dotenv +import openai + +load_dotenv() + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion + +litellm.set_verbose = False + + +def test_openai_embedding(): + try: + litellm.set_verbose = True + response = embedding( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"], + metadata={"anything": "good day"}, + ) + litellm_response = dict(response) + litellm_response_keys = set(litellm_response.keys()) + litellm_response_keys.discard("_response_ms") + + print(litellm_response_keys) + print("LiteLLM Response\n") + # print(litellm_response) + + # same request with OpenAI 1.0+ + import openai + + client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"]) + response = client.embeddings.create( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"], + ) + + response = dict(response) + openai_response_keys = set(response.keys()) + print(openai_response_keys) + assert ( + litellm_response_keys == openai_response_keys + ) # ENSURE the Keys in litellm response is exactly what the openai package returns + assert ( + len(litellm_response["data"]) == 2 + ) # expect two embedding responses from litellm_response since input had two + print(openai_response_keys) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_openai_embedding() + + +def test_openai_azure_embedding_simple(): + try: + litellm.set_verbose = True + response = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm"], + ) + print(response) + response_keys = set(dict(response).keys()) + response_keys.discard("_response_ms") + assert set(["usage", "model", "object", "data"]) == set( + response_keys + ) # assert litellm response has expected keys from OpenAI embedding response + + request_cost = litellm.completion_cost(completion_response=response) + + print("Calculated request cost=", request_cost) + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_openai_azure_embedding_simple() + + +def test_openai_azure_embedding_timeouts(): + try: + response = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm"], + timeout=0.00001, + ) + print(response) + except openai.APITimeoutError: + print("Good job got timeout error!") + pass + except Exception as e: + pytest.fail( + f"Expected timeout error, did not get the correct error. Instead got {e}" + ) + + +# test_openai_azure_embedding_timeouts() + + +def test_openai_embedding_timeouts(): + try: + response = embedding( + model="text-embedding-ada-002", + input=["good morning from litellm"], + timeout=0.00001, + ) + print(response) + except openai.APITimeoutError: + print("Good job got OpenAI timeout error!") + pass + except Exception as e: + pytest.fail( + f"Expected timeout error, did not get the correct error. Instead got {e}" + ) + + +# test_openai_embedding_timeouts() + + +def test_openai_azure_embedding(): + try: + api_key = os.environ["AZURE_API_KEY"] + api_base = os.environ["AZURE_API_BASE"] + api_version = os.environ["AZURE_API_VERSION"] + + os.environ["AZURE_API_VERSION"] = "" + os.environ["AZURE_API_BASE"] = "" + os.environ["AZURE_API_KEY"] = "" + + response = embedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + api_key=api_key, + api_base=api_base, + api_version=api_version, + ) + print(response) + + os.environ["AZURE_API_VERSION"] = api_version + os.environ["AZURE_API_BASE"] = api_base + os.environ["AZURE_API_KEY"] = api_key + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_openai_azure_embedding() + +# test_openai_embedding() + + +def test_cohere_embedding(): + try: + # litellm.set_verbose=True + response = embedding( + model="embed-english-v2.0", + input=["good morning from litellm", "this is another item"], + ) + print(f"response:", response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_cohere_embedding() + + +def test_cohere_embedding3(): + try: + litellm.set_verbose = True + response = embedding( + model="embed-english-v3.0", + input=["good morning from litellm", "this is another item"], + ) + print(f"response:", response) + + custom_llm_provider = response._hidden_params["custom_llm_provider"] + + assert custom_llm_provider == "cohere" + + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_cohere_embedding3() + + +def test_bedrock_embedding_titan(): + try: + # this tests if we support str input for bedrock embedding + litellm.set_verbose = True + litellm.enable_cache() + import time + + current_time = str(time.time()) + # DO NOT MAKE THE INPUT A LIST in this test + response = embedding( + model="bedrock/amazon.titan-embed-text-v1", + input=f"good morning from litellm, attempting to embed data {current_time}", # input should always be a string in this test + ) + print(f"response:", response) + assert isinstance( + response["data"][0]["embedding"], list + ), "Expected response to be a list" + print(f"type of first embedding:", type(response["data"][0]["embedding"][0])) + assert all( + isinstance(x, float) for x in response["data"][0]["embedding"] + ), "Expected response to be a list of floats" + + # this also tests if we can return a cache response for this scenario + import time + + start_time = time.time() + + response = embedding( + model="bedrock/amazon.titan-embed-text-v1", + input=f"good morning from litellm, attempting to embed data {current_time}", # input should always be a string in this test + ) + print(response) + + end_time = time.time() + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + assert end_time - start_time < 0.1 + litellm.disable_cache() + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_bedrock_embedding_titan() + + +def test_bedrock_embedding_cohere(): + try: + litellm.set_verbose = False + response = embedding( + model="cohere.embed-multilingual-v3", + input=[ + "good morning from litellm, attempting to embed data", + "lets test a second string for good measure", + ], + aws_region_name="os.environ/AWS_REGION_NAME_2", + ) + assert isinstance( + response["data"][0]["embedding"], list + ), "Expected response to be a list" + print(f"type of first embedding:", type(response["data"][0]["embedding"][0])) + assert all( + isinstance(x, float) for x in response["data"][0]["embedding"] + ), "Expected response to be a list of floats" + # print(f"response:", response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_bedrock_embedding_cohere() + + +# comment out hf tests - since hf endpoints are unstable +def test_hf_embedding(): + try: + # huggingface/microsoft/codebert-base + # huggingface/facebook/bart-large + response = embedding( + model="huggingface/sentence-transformers/all-MiniLM-L6-v2", + input=["good morning from litellm", "this is another item"], + ) + print(f"response:", response) + except Exception as e: + # Note: Huggingface inference API is unstable and fails with "model loading errors all the time" + pass + + +# test_hf_embedding() + + +# test async embeddings +def test_aembedding(): + try: + import asyncio + + async def embedding_call(): + try: + response = await litellm.aembedding( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"], + ) + print(response) + return response + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + response = asyncio.run(embedding_call()) + print("Before caclulating cost, response", response) + + cost = litellm.completion_cost(completion_response=response) + + print("COST=", cost) + assert cost == float("1e-06") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_aembedding() + + +def test_aembedding_azure(): + try: + import asyncio + + async def embedding_call(): + try: + response = await litellm.aembedding( + model="azure/azure-embedding-model", + input=["good morning from litellm", "this is another item"], + ) + print(response) + + print( + "hidden params - custom_llm_provider", + response._hidden_params["custom_llm_provider"], + ) + assert response._hidden_params["custom_llm_provider"] == "azure" + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + asyncio.run(embedding_call()) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_aembedding_azure() + + +def test_sagemaker_embeddings(): + try: + response = litellm.embedding( + model="sagemaker/berri-benchmarking-gpt-j-6b-fp16", + input=["good morning from litellm", "this is another item"], + ) + print(f"response: {response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +def test_mistral_embeddings(): + try: + litellm.set_verbose = True + response = litellm.embedding( + model="mistral/mistral-embed", + input=["good morning from litellm"], + ) + print(f"response: {response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_mistral_embeddings() + + +def test_voyage_embeddings(): + try: + litellm.set_verbose = True + response = litellm.embedding( + model="voyage/voyage-01", + input=["good morning from litellm"], + ) + print(f"response: {response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_voyage_embeddings() +# def test_xinference_embeddings(): +# try: +# litellm.set_verbose = True +# response = litellm.embedding( +# model="xinference/bge-base-en", +# input=["good morning from litellm"], +# ) +# print(f"response: {response}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_xinference_embeddings() + +# test_sagemaker_embeddings() +# def local_proxy_embeddings(): +# litellm.set_verbose=True +# response = embedding( +# model="openai/custom_embedding", +# input=["good morning from litellm"], +# api_base="http://0.0.0.0:8000/" +# ) +# print(response) + +# local_proxy_embeddings() diff --git a/litellm/tests/test_exceptions.py b/litellm/tests/test_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..998e3eb9b9414304297206c3702c197ec3dcb2b2 --- /dev/null +++ b/litellm/tests/test_exceptions.py @@ -0,0 +1,412 @@ +from openai import AuthenticationError, BadRequestError, RateLimitError, OpenAIError +import os +import sys +import traceback +import subprocess + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import ( + embedding, + completion, + # AuthenticationError, + ContextWindowExceededError, + # RateLimitError, + # ServiceUnavailableError, + # OpenAIError, +) +from concurrent.futures import ThreadPoolExecutor +import pytest + +litellm.vertex_project = "pathrise-convert-1606954137718" +litellm.vertex_location = "us-central1" +litellm.num_retries = 0 + +# litellm.failure_callback = ["sentry"] +#### What this tests #### +# This tests exception mapping -> trigger an exception from an llm provider -> assert if output is of the expected type + + +# 5 providers -> OpenAI, Azure, Anthropic, Cohere, Replicate + +# 3 main types of exceptions -> - Rate Limit Errors, Context Window Errors, Auth errors (incorrect/rotated key, etc.) + +# Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered + +models = ["command-nightly"] + + +# Test 1: Context Window Errors +@pytest.mark.parametrize("model", models) +def test_context_window(model): + print("Testing context window error") + sample_text = "Say error 50 times" * 1000000 + messages = [{"content": sample_text, "role": "user"}] + try: + litellm.set_verbose = True + response = completion(model=model, messages=messages) + print(f"response: {response}") + print("FAILED!") + pytest.fail(f"An exception occurred") + except ContextWindowExceededError as e: + print(f"Worked!") + except RateLimitError: + print("RateLimited!") + except Exception as e: + print(f"{e}") + pytest.fail(f"An error occcurred - {e}") + + +@pytest.mark.parametrize("model", models) +def test_context_window_with_fallbacks(model): + ctx_window_fallback_dict = { + "command-nightly": "claude-2", + "gpt-3.5-turbo-instruct": "gpt-3.5-turbo-16k", + "azure/chatgpt-v-2": "gpt-3.5-turbo-16k", + } + sample_text = "how does a court case get to the Supreme Court?" * 1000 + messages = [{"content": sample_text, "role": "user"}] + + completion( + model=model, + messages=messages, + context_window_fallback_dict=ctx_window_fallback_dict, + ) + + +# for model in litellm.models_by_provider["bedrock"]: +# test_context_window(model=model) +# test_context_window(model="chat-bison") +# test_context_window_with_fallbacks(model="command-nightly") +# Test 2: InvalidAuth Errors +@pytest.mark.parametrize("model", models) +def invalid_auth(model): # set the model key to an invalid key, depending on the model + messages = [{"content": "Hello, how are you?", "role": "user"}] + temporary_key = None + try: + if model == "gpt-3.5-turbo" or model == "gpt-3.5-turbo-instruct": + temporary_key = os.environ["OPENAI_API_KEY"] + os.environ["OPENAI_API_KEY"] = "bad-key" + elif "bedrock" in model: + temporary_aws_access_key = os.environ["AWS_ACCESS_KEY_ID"] + os.environ["AWS_ACCESS_KEY_ID"] = "bad-key" + temporary_aws_region_name = os.environ["AWS_REGION_NAME"] + os.environ["AWS_REGION_NAME"] = "bad-key" + temporary_secret_key = os.environ["AWS_SECRET_ACCESS_KEY"] + os.environ["AWS_SECRET_ACCESS_KEY"] = "bad-key" + elif model == "azure/chatgpt-v-2": + temporary_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "bad-key" + elif model == "claude-instant-1": + temporary_key = os.environ["ANTHROPIC_API_KEY"] + os.environ["ANTHROPIC_API_KEY"] = "bad-key" + elif model == "command-nightly": + temporary_key = os.environ["COHERE_API_KEY"] + os.environ["COHERE_API_KEY"] = "bad-key" + elif "j2" in model: + temporary_key = os.environ["AI21_API_KEY"] + os.environ["AI21_API_KEY"] = "bad-key" + elif "togethercomputer" in model: + temporary_key = os.environ["TOGETHERAI_API_KEY"] + os.environ[ + "TOGETHERAI_API_KEY" + ] = "84060c79880fc49df126d3e87b53f8a463ff6e1c6d27fe64207cde25cdfcd1f24a" + elif model in litellm.openrouter_models: + temporary_key = os.environ["OPENROUTER_API_KEY"] + os.environ["OPENROUTER_API_KEY"] = "bad-key" + elif model in litellm.aleph_alpha_models: + temporary_key = os.environ["ALEPH_ALPHA_API_KEY"] + os.environ["ALEPH_ALPHA_API_KEY"] = "bad-key" + elif model in litellm.nlp_cloud_models: + temporary_key = os.environ["NLP_CLOUD_API_KEY"] + os.environ["NLP_CLOUD_API_KEY"] = "bad-key" + elif ( + model + == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1" + ): + temporary_key = os.environ["REPLICATE_API_KEY"] + os.environ["REPLICATE_API_KEY"] = "bad-key" + print(f"model: {model}") + response = completion(model=model, messages=messages) + print(f"response: {response}") + except AuthenticationError as e: + print(f"AuthenticationError Caught Exception - {str(e)}") + except ( + OpenAIError + ) as e: # is at least an openai error -> in case of random model errors - e.g. overloaded server + print(f"OpenAIError Caught Exception - {e}") + except Exception as e: + print(type(e)) + print(type(AuthenticationError)) + print(e.__class__.__name__) + print(f"Uncaught Exception - {e}") + pytest.fail(f"Error occurred: {e}") + if temporary_key != None: # reset the key + if model == "gpt-3.5-turbo": + os.environ["OPENAI_API_KEY"] = temporary_key + elif model == "chatgpt-test": + os.environ["AZURE_API_KEY"] = temporary_key + azure = True + elif model == "claude-instant-1": + os.environ["ANTHROPIC_API_KEY"] = temporary_key + elif model == "command-nightly": + os.environ["COHERE_API_KEY"] = temporary_key + elif ( + model + == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1" + ): + os.environ["REPLICATE_API_KEY"] = temporary_key + elif "j2" in model: + os.environ["AI21_API_KEY"] = temporary_key + elif "togethercomputer" in model: + os.environ["TOGETHERAI_API_KEY"] = temporary_key + elif model in litellm.aleph_alpha_models: + os.environ["ALEPH_ALPHA_API_KEY"] = temporary_key + elif model in litellm.nlp_cloud_models: + os.environ["NLP_CLOUD_API_KEY"] = temporary_key + elif "bedrock" in model: + os.environ["AWS_ACCESS_KEY_ID"] = temporary_aws_access_key + os.environ["AWS_REGION_NAME"] = temporary_aws_region_name + os.environ["AWS_SECRET_ACCESS_KEY"] = temporary_secret_key + return + + +# for model in litellm.models_by_provider["bedrock"]: +# invalid_auth(model=model) +# invalid_auth(model="command-nightly") + + +# Test 3: Invalid Request Error +@pytest.mark.parametrize("model", models) +def test_invalid_request_error(model): + messages = [{"content": "hey, how's it going?", "role": "user"}] + + with pytest.raises(BadRequestError): + completion(model=model, messages=messages, max_tokens="hello world") + + +def test_completion_azure_exception(): + try: + import openai + + print("azure gpt-3.5 test\n\n") + litellm.set_verbose = True + ## Test azure call + old_azure_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "good morning" + response = completion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "hello"}], + ) + os.environ["AZURE_API_KEY"] = old_azure_key + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["AZURE_API_KEY"] = old_azure_key + print("good job got the correct error for azure when key not set") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure_exception() + + +async def asynctest_completion_azure_exception(): + try: + import openai + import litellm + + print("azure gpt-3.5 test\n\n") + litellm.set_verbose = True + ## Test azure call + old_azure_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "good morning" + response = await litellm.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "hello"}], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["AZURE_API_KEY"] = old_azure_key + print("good job got the correct error for azure when key not set") + print(e) + except Exception as e: + print("Got wrong exception") + print("exception", e) + pytest.fail(f"Error occurred: {e}") + + +# import asyncio +# asyncio.run( +# asynctest_completion_azure_exception() +# ) + + +def asynctest_completion_openai_exception_bad_model(): + try: + import openai + import litellm, asyncio + + print("azure exception bad model\n\n") + litellm.set_verbose = True + + ## Test azure call + async def test(): + response = await litellm.acompletion( + model="openai/gpt-6", + messages=[{"role": "user", "content": "hello"}], + ) + + asyncio.run(test()) + except openai.NotFoundError: + print("Good job this is a NotFoundError for a model that does not exist!") + print("Passed") + except Exception as e: + print("Raised wrong type of exception", type(e)) + assert isinstance(e, openai.BadRequestError) + pytest.fail(f"Error occurred: {e}") + + +# asynctest_completion_openai_exception_bad_model() + + +def asynctest_completion_azure_exception_bad_model(): + try: + import openai + import litellm, asyncio + + print("azure exception bad model\n\n") + litellm.set_verbose = True + + ## Test azure call + async def test(): + response = await litellm.acompletion( + model="azure/gpt-12", + messages=[{"role": "user", "content": "hello"}], + ) + + asyncio.run(test()) + except openai.NotFoundError: + print("Good job this is a NotFoundError for a model that does not exist!") + print("Passed") + except Exception as e: + print("Raised wrong type of exception", type(e)) + pytest.fail(f"Error occurred: {e}") + + +# asynctest_completion_azure_exception_bad_model() + + +def test_completion_openai_exception(): + # test if openai:gpt raises openai.AuthenticationError + try: + import openai + + print("openai gpt-3.5 test\n\n") + litellm.set_verbose = True + ## Test azure call + old_azure_key = os.environ["OPENAI_API_KEY"] + os.environ["OPENAI_API_KEY"] = "good morning" + response = completion( + model="gpt-4", + messages=[{"role": "user", "content": "hello"}], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["OPENAI_API_KEY"] = old_azure_key + print("OpenAI: good job got the correct error for openai when key not set") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai_exception() + + +def test_completion_mistral_exception(): + # test if mistral/mistral-tiny raises openai.AuthenticationError + try: + import openai + + print("Testing mistral ai exception mapping") + litellm.set_verbose = True + ## Test azure call + old_azure_key = os.environ["MISTRAL_API_KEY"] + os.environ["MISTRAL_API_KEY"] = "good morning" + response = completion( + model="mistral/mistral-tiny", + messages=[{"role": "user", "content": "hello"}], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["MISTRAL_API_KEY"] = old_azure_key + print("good job got the correct error for openai when key not set") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_mistral_exception() + + +def test_content_policy_exceptionimage_generation_openai(): + try: + # this is ony a test - we needed some way to invoke the exception :( + litellm.set_verbose = True + response = litellm.image_generation( + prompt="where do i buy lethal drugs from", model="dall-e-3" + ) + print(f"response: {response}") + assert len(response.data) > 0 + except litellm.ContentPolicyViolationError as e: + print("caught a content policy violation error! Passed") + pass + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_content_policy_exceptionimage_generation_openai() + + +# # test_invalid_request_error(model="command-nightly") +# # Test 3: Rate Limit Errors +# def test_model_call(model): +# try: +# sample_text = "how does a court case get to the Supreme Court?" +# messages = [{ "content": sample_text,"role": "user"}] +# print(f"model: {model}") +# response = completion(model=model, messages=messages) +# except RateLimitError as e: +# print(f"headers: {e.response.headers}") +# return True +# # except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server +# # return True +# except Exception as e: +# print(f"Uncaught Exception {model}: {type(e).__name__} - {e}") +# traceback.print_exc() +# pass +# return False +# # Repeat each model 500 times +# # extended_models = [model for model in models for _ in range(250)] +# extended_models = ["azure/chatgpt-v-2" for _ in range(250)] + +# def worker(model): +# return test_model_call(model) + +# # Create a dictionary to store the results +# counts = {True: 0, False: 0} + +# # Use Thread Pool Executor +# with ThreadPoolExecutor(max_workers=500) as executor: +# # Use map to start the operation in thread pool +# results = executor.map(worker, extended_models) + +# # Iterate over results and count True/False +# for result in results: +# counts[result] += 1 + +# accuracy_score = counts[True]/(counts[True] + counts[False]) +# print(f"accuracy_score: {accuracy_score}") diff --git a/litellm/tests/test_function_calling.py b/litellm/tests/test_function_calling.py new file mode 100644 index 0000000000000000000000000000000000000000..2fcbdc9460ec4e9e593d7e26a6849a679fd851eb --- /dev/null +++ b/litellm/tests/test_function_calling.py @@ -0,0 +1,220 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError +import pytest + +litellm.num_retries = 0 +litellm.cache = None +# litellm.set_verbose=True +import json + +# litellm.success_callback = ["langfuse"] + + +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps( + {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"} + ) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + + +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def test_parallel_function_call(): + try: + # Step 1: send the conversation and available functions to the model + messages = [ + { + "role": "user", + "content": "What's the weather like in San Francisco, Tokyo, and Paris?", + } + ] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + }, + }, + "required": ["location"], + }, + }, + } + ] + response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit + ) + print("Response\n", response) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + print("length of tool calls", len(tool_calls)) + print("Expecting there to be 3 tool calls") + assert ( + len(tool_calls) > 1 + ) # this has to call the function for SF, Tokyo and parise + + # Step 2: check if the model wanted to call a function + if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } # only one function in this example, but you can have multiple + messages.append( + response_message + ) # extend conversation with assistant's reply + print("Response message\n", response_message) + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + print(f"messages: {messages}") + second_response = litellm.completion( + model="gpt-3.5-turbo-1106", messages=messages, temperature=0.2, seed=22 + ) # get a new response from the model where it can see the function response + print("second response\n", second_response) + return second_response + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_parallel_function_call() + + +def test_parallel_function_call_stream(): + try: + # Step 1: send the conversation and available functions to the model + messages = [ + { + "role": "user", + "content": "What's the weather like in San Francisco, Tokyo, and Paris?", + } + ] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + }, + }, + "required": ["location"], + }, + }, + } + ] + response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + stream=True, + tool_choice="auto", # auto is default, but we'll be explicit + complete_response=True, + ) + print("Response\n", response) + # for chunk in response: + # print(chunk) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + print("length of tool calls", len(tool_calls)) + print("Expecting there to be 3 tool calls") + assert ( + len(tool_calls) > 1 + ) # this has to call the function for SF, Tokyo and parise + + # Step 2: check if the model wanted to call a function + if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } # only one function in this example, but you can have multiple + messages.append( + response_message + ) # extend conversation with assistant's reply + print("Response message\n", response_message) + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + print(f"messages: {messages}") + second_response = litellm.completion( + model="gpt-3.5-turbo-1106", messages=messages, temperature=0.2, seed=22 + ) # get a new response from the model where it can see the function response + print("second response\n", second_response) + return second_response + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_parallel_function_call_stream() diff --git a/litellm/tests/test_get_llm_provider.py b/litellm/tests/test_get_llm_provider.py new file mode 100644 index 0000000000000000000000000000000000000000..cf28f2d0d7b730e08b8e5f189b853be2c8f5ca5e --- /dev/null +++ b/litellm/tests/test_get_llm_provider.py @@ -0,0 +1,21 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm + + +def test_get_llm_provider(): + _, response, _, _ = litellm.get_llm_provider(model="anthropic.claude-v2:1") + + assert response == "bedrock" + + +test_get_llm_provider() diff --git a/litellm/tests/test_get_model_file.py b/litellm/tests/test_get_model_file.py new file mode 100644 index 0000000000000000000000000000000000000000..949ff43b8a72b099adbe016caf1415ae47c4a668 --- /dev/null +++ b/litellm/tests/test_get_model_file.py @@ -0,0 +1,12 @@ +import os, sys, traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +import pytest + +try: + print(litellm.get_model_cost_map(url="fake-url")) +except Exception as e: + pytest.fail(f"An exception occurred: {e}") diff --git a/litellm/tests/test_get_model_list.py b/litellm/tests/test_get_model_list.py new file mode 100644 index 0000000000000000000000000000000000000000..7663eebf5213f05df200953f2462e542a7bccdcb --- /dev/null +++ b/litellm/tests/test_get_model_list.py @@ -0,0 +1,11 @@ +import os, sys, traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import get_model_list + +print(get_model_list()) +print(get_model_list()) +# print(litellm.model_list) diff --git a/litellm/tests/test_google_ai_studio_gemini.py b/litellm/tests/test_google_ai_studio_gemini.py new file mode 100644 index 0000000000000000000000000000000000000000..5012717d38331895a01b6201a15d70567f41f2a9 --- /dev/null +++ b/litellm/tests/test_google_ai_studio_gemini.py @@ -0,0 +1,39 @@ +import os, sys, traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from dotenv import load_dotenv + + +def generate_text(): + try: + litellm.set_verbose = True + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://avatars.githubusercontent.com/u/17561003?v=4" + }, + }, + ], + } + ] + response = litellm.completion( + model="gemini/gemini-pro-vision", + messages=messages, + stop="Hello world", + num_retries=3, + ) + print(response) + assert isinstance(response.choices[0].message.content, str) == True + except Exception as exception: + raise Exception("An error occurred during text generation:", exception) + + +# generate_text() diff --git a/litellm/tests/test_health_check.py b/litellm/tests/test_health_check.py new file mode 100644 index 0000000000000000000000000000000000000000..21b72d2ac39b65a7bebb3cc1eb73401087fcd557 --- /dev/null +++ b/litellm/tests/test_health_check.py @@ -0,0 +1,106 @@ +#### What this tests #### +# This tests if ahealth_check() actually works + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm, asyncio + + +@pytest.mark.asyncio +async def test_azure_health_check(): + response = await litellm.ahealth_check( + model_params={ + "model": "azure/chatgpt-v-2", + "messages": [{"role": "user", "content": "Hey, how's it going?"}], + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + } + ) + print(f"response: {response}") + + assert "x-ratelimit-remaining-tokens" in response + return response + + +# asyncio.run(test_azure_health_check()) + + +@pytest.mark.asyncio +async def test_azure_embedding_health_check(): + response = await litellm.ahealth_check( + model_params={ + "model": "azure/azure-embedding-model", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + input=["test for litellm"], + mode="embedding", + ) + print(f"response: {response}") + + assert "x-ratelimit-remaining-tokens" in response + return response + + +@pytest.mark.asyncio +async def test_openai_img_gen_health_check(): + response = await litellm.ahealth_check( + model_params={ + "model": "dall-e-3", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + mode="image_generation", + prompt="cute baby sea otter", + ) + print(f"response: {response}") + + assert isinstance(response, dict) and "error" not in response + return response + + +# asyncio.run(test_openai_img_gen_health_check()) + + +async def test_azure_img_gen_health_check(): + response = await litellm.ahealth_check( + model_params={ + "model": "azure/", + "api_base": os.getenv("AZURE_API_BASE"), + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": "2023-06-01-preview", + }, + mode="image_generation", + prompt="cute baby sea otter", + ) + + assert isinstance(response, dict) and "error" not in response + return response + + +# asyncio.run(test_azure_img_gen_health_check()) + + +@pytest.mark.asyncio +async def test_sagemaker_embedding_health_check(): + response = await litellm.ahealth_check( + model_params={ + "model": "sagemaker/berri-benchmarking-gpt-j-6b-fp16", + "messages": [{"role": "user", "content": "Hey, how's it going?"}], + }, + mode="embedding", + input=["test from litellm"], + ) + print(f"response: {response}") + + assert isinstance(response, dict) + return response + + +# asyncio.run(test_sagemaker_embedding_health_check()) diff --git a/litellm/tests/test_helicone_integration.py b/litellm/tests/test_helicone_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..82669d09206d35ed254df516efa40c98ebdf6b48 --- /dev/null +++ b/litellm/tests/test_helicone_integration.py @@ -0,0 +1,30 @@ +# #### What this tests #### +# # This tests if logging to the helicone integration actually works + +# import sys, os +# import traceback +# import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# from litellm import embedding, completion + +# litellm.success_callback = ["helicone"] + +# litellm.set_verbose = True + +# user_message = "Hello, how are you?" +# messages = [{"content": user_message, "role": "user"}] + + +# # openai call +# response = completion( +# model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}] +# ) + +# # cohere call +# response = completion( +# model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}] +# ) diff --git a/litellm/tests/test_hf_prompt_templates.py b/litellm/tests/test_hf_prompt_templates.py new file mode 100644 index 0000000000000000000000000000000000000000..ea1e6a7d88f95c5bfd3d50bef066565da0c188e4 --- /dev/null +++ b/litellm/tests/test_hf_prompt_templates.py @@ -0,0 +1,75 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +from litellm.llms.prompt_templates.factory import prompt_factory + + +def test_prompt_formatting(): + try: + prompt = prompt_factory( + model="mistralai/Mistral-7B-Instruct-v0.1", + messages=[ + {"role": "system", "content": "Be a good bot"}, + {"role": "user", "content": "Hello world"}, + ], + ) + assert ( + prompt == "[INST] Be a good bot [/INST] [INST] Hello world [/INST]" + ) + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +def test_prompt_formatting_custom_model(): + try: + prompt = prompt_factory( + model="ehartford/dolphin-2.5-mixtral-8x7b", + messages=[ + {"role": "system", "content": "Be a good bot"}, + {"role": "user", "content": "Hello world"}, + ], + custom_llm_provider="huggingface", + ) + print(f"prompt: {prompt}") + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + + +# test_prompt_formatting_custom_model() +# def logger_fn(user_model_dict): +# return +# print(f"user_model_dict: {user_model_dict}") + +# messages=[{"role": "user", "content": "Write me a function to print hello world"}] + +# # test if the first-party prompt templates work +# def test_huggingface_supported_models(): +# model = "huggingface/WizardLM/WizardCoder-Python-34B-V1.0" +# response = completion(model=model, messages=messages, max_tokens=256, api_base="https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", logger_fn=logger_fn) +# print(response['choices'][0]['message']['content']) +# return response + +# test_huggingface_supported_models() + +# # test if a custom prompt template works +# litellm.register_prompt_template( +# model="togethercomputer/LLaMA-2-7B-32K", +# roles={"system":"", "assistant":"Assistant:", "user":"User:"}, +# pre_message_sep= "\n", +# post_message_sep= "\n" +# ) +# def test_huggingface_custom_model(): +# model = "huggingface/togethercomputer/LLaMA-2-7B-32K" +# response = completion(model=model, messages=messages, api_base="https://ecd4sb5n09bo4ei2.us-east-1.aws.endpoints.huggingface.cloud", logger_fn=logger_fn) +# print(response['choices'][0]['message']['content']) +# return response + +# test_huggingface_custom_model() diff --git a/litellm/tests/test_image_generation.py b/litellm/tests/test_image_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..3c792f802298bd10e8592ef49408fe49b7cbc82e --- /dev/null +++ b/litellm/tests/test_image_generation.py @@ -0,0 +1,112 @@ +# What this tests? +## This tests the litellm support for the openai /generations endpoint + +import sys, os +import traceback +from dotenv import load_dotenv +import logging + +logging.basicConfig(level=logging.DEBUG) +load_dotenv() +import os +import asyncio + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm + + +def test_image_generation_openai(): + try: + litellm.set_verbose = True + response = litellm.image_generation( + prompt="A cute baby sea otter", model="dall-e-3" + ) + print(f"response: {response}") + assert len(response.data) > 0 + except litellm.RateLimitError as e: + pass + except litellm.ContentPolicyViolationError: + pass # OpenAI randomly raises these errors - skip when they occur + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_image_generation_openai() + + +def test_image_generation_azure(): + try: + response = litellm.image_generation( + prompt="A cute baby sea otter", + model="azure/", + api_version="2023-06-01-preview", + ) + print(f"response: {response}") + assert len(response.data) > 0 + except litellm.RateLimitError as e: + pass + except litellm.ContentPolicyViolationError: + pass # Azure randomly raises these errors - skip when they occur + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_image_generation_azure() + + +def test_image_generation_azure_dall_e_3(): + try: + litellm.set_verbose = True + response = litellm.image_generation( + prompt="A cute baby sea otter", + model="azure/dall-e-3-test", + api_version="2023-12-01-preview", + api_base=os.getenv("AZURE_SWEDEN_API_BASE"), + api_key=os.getenv("AZURE_SWEDEN_API_KEY"), + ) + print(f"response: {response}") + assert len(response.data) > 0 + except litellm.RateLimitError as e: + pass + except litellm.ContentPolicyViolationError: + pass # OpenAI randomly raises these errors - skip when they occur + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_image_generation_azure_dall_e_3() +@pytest.mark.asyncio +async def test_async_image_generation_openai(): + try: + response = litellm.image_generation( + prompt="A cute baby sea otter", model="dall-e-3" + ) + print(f"response: {response}") + assert len(response.data) > 0 + except litellm.RateLimitError as e: + pass + except litellm.ContentPolicyViolationError: + pass # openai randomly raises these errors - skip when they occur + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# asyncio.run(test_async_image_generation_openai()) + + +@pytest.mark.asyncio +async def test_async_image_generation_azure(): + try: + response = await litellm.aimage_generation( + prompt="A cute baby sea otter", model="azure/dall-e-3-test" + ) + print(f"response: {response}") + except litellm.RateLimitError as e: + pass + except litellm.ContentPolicyViolationError: + pass # Azure randomly raises these errors - skip when they occur + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") diff --git a/litellm/tests/test_img_resize.py b/litellm/tests/test_img_resize.py new file mode 100644 index 0000000000000000000000000000000000000000..b5ea9364f17c1139b74accb580b346e07e4c050e --- /dev/null +++ b/litellm/tests/test_img_resize.py @@ -0,0 +1,78 @@ +from typing import Literal + + +def calculage_img_tokens( + width, + height, + mode: Literal["low", "high", "auto"] = "auto", + base_tokens: int = 85, # openai default - https://openai.com/pricing +): + if mode == "low": + return base_tokens + elif mode == "high" or mode == "auto": + resized_width, resized_height = resize_image_high_res( + width=width, height=height + ) + tiles_needed_high_res = calculate_tiles_needed(resized_width, resized_height) + tile_tokens = (base_tokens * 2) * tiles_needed_high_res + total_tokens = base_tokens + tile_tokens + return total_tokens + + +def resize_image_high_res(width, height): + # Maximum dimensions for high res mode + max_short_side = 768 + max_long_side = 2000 + + # Determine the longer and shorter sides + longer_side = max(width, height) + shorter_side = min(width, height) + + # Calculate the aspect ratio + aspect_ratio = longer_side / shorter_side + + # Resize based on the short side being 768px + if width <= height: # Portrait or square + resized_width = max_short_side + resized_height = int(resized_width * aspect_ratio) + # if the long side exceeds the limit after resizing, adjust both sides accordingly + if resized_height > max_long_side: + resized_height = max_long_side + resized_width = int(resized_height / aspect_ratio) + else: # Landscape + resized_height = max_short_side + resized_width = int(resized_height * aspect_ratio) + # if the long side exceeds the limit after resizing, adjust both sides accordingly + if resized_width > max_long_side: + resized_width = max_long_side + resized_height = int(resized_width / aspect_ratio) + + return resized_width, resized_height + + +# Test the function with the given example +def calculate_tiles_needed( + resized_width, resized_height, tile_width=512, tile_height=512 +): + tiles_across = (resized_width + tile_width - 1) // tile_width + tiles_down = (resized_height + tile_height - 1) // tile_height + total_tiles = tiles_across * tiles_down + return total_tiles + + +# Test high res mode with 1875 x 768 image +resized_width_high_res = 1875 +resized_height_high_res = 768 +tiles_needed_high_res = calculate_tiles_needed( + resized_width_high_res, resized_height_high_res +) +print( + f"Tiles needed for high res image ({resized_width_high_res}x{resized_height_high_res}): {tiles_needed_high_res}" +) + +# If you had the original size and needed to resize and then calculate tiles: +original_size = (10000, 4096) +resized_size_high_res = resize_image_high_res(*original_size) +print(f"Resized dimensions in high res mode: {resized_size_high_res}") +tiles_needed = calculate_tiles_needed(*resized_size_high_res) +print(f"Tiles needed for high res image {resized_size_high_res}: {tiles_needed}") diff --git a/litellm/tests/test_langchain_ChatLiteLLM.py b/litellm/tests/test_langchain_ChatLiteLLM.py new file mode 100644 index 0000000000000000000000000000000000000000..27bc209f1b7120e95f083fa9cddfd694a4df7c7f --- /dev/null +++ b/litellm/tests/test_langchain_ChatLiteLLM.py @@ -0,0 +1,106 @@ +# import os +# import sys, os +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os, io + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import pytest +# import litellm +# from litellm import embedding, completion, text_completion, completion_cost + +# from langchain.chat_models import ChatLiteLLM +# from langchain.prompts.chat import ( +# ChatPromptTemplate, +# SystemMessagePromptTemplate, +# AIMessagePromptTemplate, +# HumanMessagePromptTemplate, +# ) +# from langchain.schema import AIMessage, HumanMessage, SystemMessage + +# def test_chat_gpt(): +# try: +# chat = ChatLiteLLM(model="gpt-3.5-turbo", max_tokens=10) +# messages = [ +# HumanMessage( +# content="what model are you" +# ) +# ] +# resp = chat(messages) + +# print(resp) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_chat_gpt() + + +# def test_claude(): +# try: +# chat = ChatLiteLLM(model="claude-2", max_tokens=10) +# messages = [ +# HumanMessage( +# content="what model are you" +# ) +# ] +# resp = chat(messages) + +# print(resp) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_claude() + +# def test_palm(): +# try: +# chat = ChatLiteLLM(model="palm/chat-bison", max_tokens=10) +# messages = [ +# HumanMessage( +# content="what model are you" +# ) +# ] +# resp = chat(messages) + +# print(resp) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_palm() + + +# # def test_openai_with_params(): +# # try: +# # api_key = os.environ["OPENAI_API_KEY"] +# # os.environ.pop("OPENAI_API_KEY") +# # print("testing openai with params") +# # llm = ChatLiteLLM( +# # model="gpt-3.5-turbo", +# # openai_api_key=api_key, +# # # Prefer using None which is the default value, endpoint could be empty string +# # openai_api_base= None, +# # max_tokens=20, +# # temperature=0.5, +# # request_timeout=10, +# # model_kwargs={ +# # "frequency_penalty": 0, +# # "presence_penalty": 0, +# # }, +# # verbose=True, +# # max_retries=0, +# # ) +# # messages = [ +# # HumanMessage( +# # content="what model are you" +# # ) +# # ] +# # resp = llm(messages) + +# # print(resp) +# # except Exception as e: +# # pytest.fail(f"Error occurred: {e}") + +# # test_openai_with_params() diff --git a/litellm/tests/test_langsmith.py b/litellm/tests/test_langsmith.py new file mode 100644 index 0000000000000000000000000000000000000000..603a8370da2e99193cc18ce1d40d1497a423df99 --- /dev/null +++ b/litellm/tests/test_langsmith.py @@ -0,0 +1,70 @@ +import sys +import os +import io + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm import completion +import litellm + +litellm.success_callback = ["langsmith"] +litellm.set_verbose = True +import time + + +def test_langsmith_logging(): + try: + response = completion( + model="claude-instant-1.2", + messages=[{"role": "user", "content": "what llm are u"}], + max_tokens=10, + temperature=0.2, + ) + print(response) + except Exception as e: + print(e) + + +# test_langsmith_logging() + + +def test_langsmith_logging_with_metadata(): + try: + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "what llm are u"}], + max_tokens=10, + temperature=0.2, + metadata={ + "run_name": "litellmRUN", + "project_name": "litellm-completion", + }, + ) + print(response) + except Exception as e: + print(e) + + +# test_langsmith_logging_with_metadata() + + +def test_langsmith_logging_with_streaming_and_metadata(): + try: + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "what llm are u"}], + max_tokens=10, + temperature=0.2, + metadata={ + "run_name": "litellmRUN", + "project_name": "litellm-completion", + }, + stream=True, + ) + for chunk in response: + continue + except Exception as e: + print(e) + + +test_langsmith_logging_with_streaming_and_metadata() diff --git a/litellm/tests/test_least_busy_routing.py b/litellm/tests/test_least_busy_routing.py new file mode 100644 index 0000000000000000000000000000000000000000..dcb72898f591414339b5d0a93fe64f9d14313729 --- /dev/null +++ b/litellm/tests/test_least_busy_routing.py @@ -0,0 +1,265 @@ +#### What this tests #### +# This tests the router's ability to identify the least busy deployment + +import sys, os, asyncio, time, random +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +from litellm import Router +import litellm +from litellm.router_strategy.least_busy import LeastBusyLoggingHandler +from litellm.caching import DualCache + +### UNIT TESTS FOR LEAST BUSY LOGGING ### + + +def test_model_added(): + test_cache = DualCache() + least_busy_logger = LeastBusyLoggingHandler(router_cache=test_cache, model_list=[]) + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": "1234"}, + } + } + least_busy_logger.log_pre_api_call(model="test", messages=[], kwargs=kwargs) + request_count_api_key = f"gpt-3.5-turbo_request_count" + assert test_cache.get_cache(key=request_count_api_key) is not None + + +def test_get_available_deployments(): + test_cache = DualCache() + least_busy_logger = LeastBusyLoggingHandler(router_cache=test_cache, model_list=[]) + model_group = "gpt-3.5-turbo" + deployment = "azure/chatgpt-v-2" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": model_group, + "deployment": deployment, + }, + "model_info": {"id": "1234"}, + } + } + least_busy_logger.log_pre_api_call(model="test", messages=[], kwargs=kwargs) + request_count_api_key = f"{model_group}_request_count" + assert test_cache.get_cache(key=request_count_api_key) is not None + + +# test_get_available_deployments() + + +def test_router_get_available_deployments(): + """ + Tests if 'get_available_deployments' returns the least busy deployment + """ + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_CANADA_API_KEY", + "api_base": "https://my-endpoint-canada-berri992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 3}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="least-busy", + set_verbose=False, + num_retries=3, + ) # type: ignore + + router.leastbusy_logger.test_flag = True + + model_group = "azure-model" + deployment = "azure/chatgpt-v-2" + request_count_dict = {1: 10, 2: 54, 3: 100} + cache_key = f"{model_group}_request_count" + router.cache.set_cache(key=cache_key, value=request_count_dict) + + deployment = router.get_available_deployment(model=model_group, messages=None) + print(f"deployment: {deployment}") + assert deployment["model_info"]["id"] == 1 + + ## run router completion - assert completion event, no change in 'busy'ness once calls are complete + + router.completion( + model=model_group, + messages=[{"role": "user", "content": "Hey, how's it going?"}], + ) + + return_dict = router.cache.get_cache(key=cache_key) + + assert router.leastbusy_logger.logged_success == 1 + assert return_dict[1] == 10 + assert return_dict[2] == 54 + assert return_dict[3] == 100 + + +## Test with Real calls ## + + +@pytest.mark.asyncio +async def test_router_atext_completion_streaming(): + prompt = "Hello, can you generate a 500 words poem?" + model = "azure-model" + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_CANADA_API_KEY", + "api_base": "https://my-endpoint-canada-berri992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 3}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="least-busy", + set_verbose=False, + num_retries=3, + ) # type: ignore + + ### Call the async calls in sequence, so we start 1 call before going to the next. + + ## CALL 1 + await asyncio.sleep(random.uniform(0, 2)) + await router.atext_completion(model=model, prompt=prompt, stream=True) + + ## CALL 2 + await asyncio.sleep(random.uniform(0, 2)) + await router.atext_completion(model=model, prompt=prompt, stream=True) + + ## CALL 3 + await asyncio.sleep(random.uniform(0, 2)) + await router.atext_completion(model=model, prompt=prompt, stream=True) + + cache_key = f"{model}_request_count" + ## check if calls equally distributed + cache_dict = router.cache.get_cache(key=cache_key) + for k, v in cache_dict.items(): + assert v == 1 + + +# asyncio.run(test_router_atext_completion_streaming()) + + +@pytest.mark.asyncio +async def test_router_completion_streaming(): + messages = [ + {"role": "user", "content": "Hello, can you generate a 500 words poem?"} + ] + model = "azure-model" + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_CANADA_API_KEY", + "api_base": "https://my-endpoint-canada-berri992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 3}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="least-busy", + set_verbose=False, + num_retries=3, + ) # type: ignore + + ### Call the async calls in sequence, so we start 1 call before going to the next. + + ## CALL 1 + await asyncio.sleep(random.uniform(0, 2)) + await router.acompletion(model=model, messages=messages, stream=True) + + ## CALL 2 + await asyncio.sleep(random.uniform(0, 2)) + await router.acompletion(model=model, messages=messages, stream=True) + + ## CALL 3 + await asyncio.sleep(random.uniform(0, 2)) + await router.acompletion(model=model, messages=messages, stream=True) + + cache_key = f"{model}_request_count" + ## check if calls equally distributed + cache_dict = router.cache.get_cache(key=cache_key) + for k, v in cache_dict.items(): + assert v == 1 diff --git a/litellm/tests/test_litellm_max_budget.py b/litellm/tests/test_litellm_max_budget.py new file mode 100644 index 0000000000000000000000000000000000000000..9fcddfe32dac0afc096aab41e9a5e094be659f41 --- /dev/null +++ b/litellm/tests/test_litellm_max_budget.py @@ -0,0 +1,29 @@ +# #### What this tests #### +# # This tests calling litellm.max_budget by making back-to-back gpt-4 calls +# # commenting out this test for circle ci, as it causes other tests to fail, since litellm.max_budget would impact other litellm imports +# import sys, os, json +# import traceback +# import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# # litellm.set_verbose = True +# from litellm import completion, BudgetExceededError + +# def test_max_budget(): +# try: +# litellm.max_budget = 0.001 # sets a max budget of $0.001 + +# messages = [{"role": "user", "content": "Hey, how's it going"}] +# response = completion(model="gpt-4", messages=messages, stream=True) +# for chunk in response: +# continue +# print(litellm._current_cost) +# completion(model="gpt-4", messages=messages, stream=True) +# litellm.max_budget = float('inf') +# except BudgetExceededError as e: +# pass +# except Exception as e: +# pytest.fail(f"An error occured: {str(e)}") diff --git a/litellm/tests/test_llmonitor_integration.py b/litellm/tests/test_llmonitor_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..e88995f3b44418030233d0082208fad7cf0fdcd3 --- /dev/null +++ b/litellm/tests/test_llmonitor_integration.py @@ -0,0 +1,76 @@ +# #### What this tests #### +# # This tests if logging to the llmonitor integration actually works +# # Adds the parent directory to the system path +# import sys +# import os + +# sys.path.insert(0, os.path.abspath("../..")) + +# from litellm import completion, embedding +# import litellm + +# litellm.success_callback = ["llmonitor"] +# litellm.failure_callback = ["llmonitor"] + +# litellm.set_verbose = True + + +# def test_chat_openai(): +# try: +# response = completion( +# model="gpt-3.5-turbo", +# messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], +# user="ishaan_from_litellm" +# ) + +# print(response) + +# except Exception as e: +# print(e) + + +# def test_embedding_openai(): +# try: +# response = embedding(model="text-embedding-ada-002", input=["test"]) +# # Add any assertions here to check the response +# print(f"response: {str(response)[:50]}") +# except Exception as e: +# print(e) + + +# test_chat_openai() +# # test_embedding_openai() + + +# def test_llmonitor_logging_function_calling(): +# function1 = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# try: +# response = completion(model="gpt-3.5-turbo", +# messages=[{ +# "role": "user", +# "content": "what's the weather in boston" +# }], +# temperature=0.1, +# functions=function1, +# ) +# print(response) +# except Exception as e: +# print(e) + +# # test_llmonitor_logging_function_calling() diff --git a/litellm/tests/test_loadtest_router.py b/litellm/tests/test_loadtest_router.py new file mode 100644 index 0000000000000000000000000000000000000000..fc3a7c5ad4b3a0c759e5b5ae366fa45550f2848e --- /dev/null +++ b/litellm/tests/test_loadtest_router.py @@ -0,0 +1,84 @@ +# import sys, os +# import traceback +# from dotenv import load_dotenv +# import copy + +# load_dotenv() +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import asyncio +# from litellm import Router, Timeout + + +# async def call_acompletion(semaphore, router: Router, input_data): +# async with semaphore: +# try: +# # Use asyncio.wait_for to set a timeout for the task +# response = await router.acompletion(**input_data) +# # Handle the response as needed +# return response +# except Timeout: +# print(f"Task timed out: {input_data}") +# return None # You may choose to return something else or raise an exception + + +# async def main(): +# # Initialize the Router +# model_list = [ +# { +# "model_name": "gpt-3.5-turbo", +# "litellm_params": { +# "model": "gpt-3.5-turbo", +# "api_key": os.getenv("OPENAI_API_KEY"), +# }, +# }, +# { +# "model_name": "gpt-3.5-turbo", +# "litellm_params": { +# "model": "azure/chatgpt-v-2", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_base": os.getenv("AZURE_API_BASE"), +# "api_version": os.getenv("AZURE_API_VERSION"), +# }, +# }, +# { +# "model_name": "gpt-3.5-turbo", +# "litellm_params": { +# "model": "azure/chatgpt-functioncalling", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_base": os.getenv("AZURE_API_BASE"), +# "api_version": os.getenv("AZURE_API_VERSION"), +# }, +# }, +# ] +# router = Router(model_list=model_list, num_retries=3, timeout=10) + +# # Create a semaphore with a capacity of 100 +# semaphore = asyncio.Semaphore(100) + +# # List to hold all task references +# tasks = [] + +# # Launch 1000 tasks +# for _ in range(1000): +# task = asyncio.create_task( +# call_acompletion( +# semaphore, +# router, +# { +# "model": "gpt-3.5-turbo", +# "messages": [{"role": "user", "content": "Hey, how's it going?"}], +# }, +# ) +# ) +# tasks.append(task) + +# # Wait for all tasks to complete +# responses = await asyncio.gather(*tasks) +# # Process responses as needed +# print(f"NUMBER OF COMPLETED TASKS: {len(responses)}") + + +# # Run the main function +# asyncio.run(main()) diff --git a/litellm/tests/test_logging.py b/litellm/tests/test_logging.py new file mode 100644 index 0000000000000000000000000000000000000000..1a35d8454e0443908e92789443e2fe160dde0140 --- /dev/null +++ b/litellm/tests/test_logging.py @@ -0,0 +1,382 @@ +# #### What this tests #### +# # This tests error logging (with custom user functions) for the raw `completion` + `embedding` endpoints + +# # Test Scenarios (test across completion, streaming, embedding) +# ## 1: Pre-API-Call +# ## 2: Post-API-Call +# ## 3: On LiteLLM Call success +# ## 4: On LiteLLM Call failure + +# import sys, os, io +# import traceback, logging +# import pytest +# import dotenv +# dotenv.load_dotenv() + +# # Create logger +# logger = logging.getLogger(__name__) +# logger.setLevel(logging.DEBUG) + +# # Create a stream handler +# stream_handler = logging.StreamHandler(sys.stdout) +# logger.addHandler(stream_handler) + +# # Create a function to log information +# def logger_fn(message): +# logger.info(message) + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# from litellm import embedding, completion +# from openai.error import AuthenticationError +# litellm.set_verbose = True + +# score = 0 + +# user_message = "Hello, how are you?" +# messages = [{"content": user_message, "role": "user"}] + +# # 1. On Call Success +# # normal completion +# # test on openai completion call +# def test_logging_success_completion(): +# global score +# try: +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = completion(model="gpt-3.5-turbo", messages=messages) +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# score += 1 +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# pass + +# # ## test on non-openai completion call +# # def test_logging_success_completion_non_openai(): +# # global score +# # try: +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # response = completion(model="claude-instant-1", messages=messages) + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Success Call" not in output: +# # raise Exception("Required log message not found!") +# # score += 1 +# # except Exception as e: +# # pytest.fail(f"Error occurred: {e}") +# # pass + +# # streaming completion +# ## test on openai completion call +# def test_logging_success_streaming_openai(): +# global score +# try: +# # litellm.set_verbose = False +# def custom_callback( +# kwargs, # kwargs to completion +# completion_response, # response from completion +# start_time, end_time # start/end time +# ): +# if "complete_streaming_response" in kwargs: +# print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") + +# # Assign the custom callback function +# litellm.success_callback = [custom_callback] + +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) +# for chunk in response: +# pass + +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# elif "Complete Streaming Response:" not in output: +# raise Exception("Required log message not found!") +# score += 1 +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# pass + +# # test_logging_success_streaming_openai() + +# ## test on non-openai completion call +# def test_logging_success_streaming_non_openai(): +# global score +# try: +# # litellm.set_verbose = False +# def custom_callback( +# kwargs, # kwargs to completion +# completion_response, # response from completion +# start_time, end_time # start/end time +# ): +# # print(f"streaming response: {completion_response}") +# if "complete_streaming_response" in kwargs: +# print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") + +# # Assign the custom callback function +# litellm.success_callback = [custom_callback] + +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = completion(model="claude-instant-1", messages=messages, stream=True) +# for idx, chunk in enumerate(response): +# pass + +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# elif "Complete Streaming Response:" not in output: +# raise Exception(f"Required log message not found! {output}") +# score += 1 +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# pass + +# # test_logging_success_streaming_non_openai() +# # embedding + +# def test_logging_success_embedding_openai(): +# try: +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + +# response = embedding(model="text-embedding-ada-002", input=["good morning from litellm"]) + +# # Restore stdout +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() + +# if "Logging Details Pre-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details Post-API Call" not in output: +# raise Exception("Required log message not found!") +# elif "Logging Details LiteLLM-Success Call" not in output: +# raise Exception("Required log message not found!") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # ## 2. On LiteLLM Call failure +# # ## TEST BAD KEY + +# # # normal completion +# # ## test on openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" + + +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="gpt-3.5-turbo", messages=messages) +# # except AuthenticationError: +# # print(f"raised auth error") +# # pass +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") + +# # os.environ["OPENAI_API_KEY"] = temporary_oai_key +# # os.environ["ANTHROPIC_API_KEY"] = temporary_anthropic_key + +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") +# # pass + +# # ## test on non-openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="claude-instant-1", messages=messages) +# # except AuthenticationError: +# # pass + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") +# # os.environ["OPENAI_API_KEY"] = temporary_oai_key +# # os.environ["ANTHROPIC_API_KEY"] = temporary_anthropic_key +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) +# # pytest.fail(f"Error occurred: {e}") + + +# # # streaming completion +# # ## test on openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="gpt-3.5-turbo", messages=messages) +# # except AuthenticationError: +# # pass + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") + +# # os.environ["OPENAI_API_KEY"] = temporary_oai_key +# # os.environ["ANTHROPIC_API_KEY"] = temporary_anthropic_key +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") + +# # ## test on non-openai completion call +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = completion(model="claude-instant-1", messages=messages) +# # except AuthenticationError: +# # pass + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") +# # score += 1 +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") + +# # # embedding + +# # try: +# # temporary_oai_key = os.environ["OPENAI_API_KEY"] +# # os.environ["OPENAI_API_KEY"] = "bad-key" + +# # temporary_anthropic_key = os.environ["ANTHROPIC_API_KEY"] +# # os.environ["ANTHROPIC_API_KEY"] = "bad-key" +# # # Redirect stdout +# # old_stdout = sys.stdout +# # sys.stdout = new_stdout = io.StringIO() + +# # try: +# # response = embedding(model="text-embedding-ada-002", input=["good morning from litellm"]) +# # except AuthenticationError: +# # pass + +# # # Restore stdout +# # sys.stdout = old_stdout +# # output = new_stdout.getvalue().strip() + +# # print(output) + +# # if "Logging Details Pre-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details Post-API Call" not in output: +# # raise Exception("Required log message not found!") +# # elif "Logging Details LiteLLM-Failure Call" not in output: +# # raise Exception("Required log message not found!") +# # except Exception as e: +# # print(f"exception type: {type(e).__name__}") +# # pytest.fail(f"Error occurred: {e}") diff --git a/litellm/tests/test_longer_context_fallback.py b/litellm/tests/test_longer_context_fallback.py new file mode 100644 index 0000000000000000000000000000000000000000..07e9e8cad74a08e1683fcc6ac89d502140fc37d3 --- /dev/null +++ b/litellm/tests/test_longer_context_fallback.py @@ -0,0 +1,14 @@ +#### What this tests #### +# This tests context fallback dict + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import longer_context_model_fallback_dict + +print(longer_context_model_fallback_dict) diff --git a/litellm/tests/test_lowest_latency_routing.py b/litellm/tests/test_lowest_latency_routing.py new file mode 100644 index 0000000000000000000000000000000000000000..7d93f0e6089aade61b002ac0cc720ddccd0970d0 --- /dev/null +++ b/litellm/tests/test_lowest_latency_routing.py @@ -0,0 +1,406 @@ +#### What this tests #### +# This tests the router's ability to pick deployment with lowest latency + +import sys, os, asyncio, time, random +from datetime import datetime +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +from litellm import Router +import litellm +from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler +from litellm.caching import DualCache + +### UNIT TESTS FOR LATENCY ROUTING ### + + +def test_latency_updated(): + test_cache = DualCache() + model_list = [] + lowest_latency_logger = LowestLatencyLoggingHandler( + router_cache=test_cache, model_list=model_list + ) + model_group = "gpt-3.5-turbo" + deployment_id = "1234" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + time.sleep(5) + end_time = time.time() + lowest_latency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + latency_key = f"{model_group}_map" + assert ( + end_time - start_time + == test_cache.get_cache(key=latency_key)[deployment_id]["latency"][0] + ) + + +# test_tpm_rpm_updated() + + +def test_latency_updated_custom_ttl(): + """ + Invalidate the cached request. + + Test that the cache is empty + """ + test_cache = DualCache() + model_list = [] + cache_time = 3 + lowest_latency_logger = LowestLatencyLoggingHandler( + router_cache=test_cache, model_list=model_list, routing_args={"ttl": cache_time} + ) + model_group = "gpt-3.5-turbo" + deployment_id = "1234" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + time.sleep(5) + end_time = time.time() + lowest_latency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + latency_key = f"{model_group}_map" + print(f"cache: {test_cache.get_cache(key=latency_key)}") + assert isinstance(test_cache.get_cache(key=latency_key), dict) + time.sleep(cache_time) + assert test_cache.get_cache(key=latency_key) is None + + +def test_get_available_deployments(): + test_cache = DualCache() + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "azure/chatgpt-v-2"}, + "model_info": {"id": "1234"}, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "azure/chatgpt-v-2"}, + "model_info": {"id": "5678"}, + }, + ] + lowest_latency_logger = LowestLatencyLoggingHandler( + router_cache=test_cache, model_list=model_list + ) + model_group = "gpt-3.5-turbo" + ## DEPLOYMENT 1 ## + deployment_id = "1234" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + time.sleep(3) + end_time = time.time() + lowest_latency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + ## DEPLOYMENT 2 ## + deployment_id = "5678" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 20}} + time.sleep(2) + end_time = time.time() + lowest_latency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + + ## CHECK WHAT'S SELECTED ## + print( + lowest_latency_logger.get_available_deployments( + model_group=model_group, healthy_deployments=model_list + ) + ) + assert ( + lowest_latency_logger.get_available_deployments( + model_group=model_group, healthy_deployments=model_list + )["model_info"]["id"] + == "5678" + ) + + +# test_get_available_deployments() + + +def test_get_available_endpoints_tpm_rpm_check(): + """ + Pass in list of 2 valid models + + Update cache with 1 model clearly being at tpm/rpm limit + + assert that only the valid model is returned + """ + test_cache = DualCache() + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "azure/chatgpt-v-2"}, + "model_info": {"id": "1234", "rpm": 10}, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "azure/chatgpt-v-2"}, + "model_info": {"id": "5678", "rpm": 3}, + }, + ] + lowest_latency_logger = LowestLatencyLoggingHandler( + router_cache=test_cache, model_list=model_list + ) + model_group = "gpt-3.5-turbo" + ## DEPLOYMENT 1 ## + deployment_id = "1234" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + for _ in range(3): + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + time.sleep(0.05) + end_time = time.time() + lowest_latency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + ## DEPLOYMENT 2 ## + deployment_id = "5678" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + for _ in range(3): + start_time = time.time() + response_obj = {"usage": {"total_tokens": 20}} + time.sleep(2) + end_time = time.time() + lowest_latency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + + ## CHECK WHAT'S SELECTED ## + print( + lowest_latency_logger.get_available_deployments( + model_group=model_group, healthy_deployments=model_list + ) + ) + assert ( + lowest_latency_logger.get_available_deployments( + model_group=model_group, healthy_deployments=model_list + )["model_info"]["id"] + == "1234" + ) + + +def test_router_get_available_deployments(): + """ + Test if routers 'get_available_deployments' returns the fastest deployment + """ + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="latency-based-routing", + set_verbose=False, + num_retries=3, + ) # type: ignore + + ## DEPLOYMENT 1 ## + deployment_id = 1 + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "azure-model", + }, + "model_info": {"id": 1}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + time.sleep(3) + end_time = time.time() + router.lowestlatency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + ## DEPLOYMENT 2 ## + deployment_id = 2 + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "azure-model", + }, + "model_info": {"id": 2}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 20}} + time.sleep(2) + end_time = time.time() + router.lowestlatency_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + + ## CHECK WHAT'S SELECTED ## + # print(router.lowesttpm_logger.get_available_deployments(model_group="azure-model")) + print(router.get_available_deployment(model="azure-model")) + assert router.get_available_deployment(model="azure-model")["model_info"]["id"] == 2 + + +# test_router_get_available_deployments() + + +@pytest.mark.asyncio +async def test_router_completion_streaming(): + messages = [ + {"role": "user", "content": "Hello, can you generate a 500 words poem?"} + ] + model = "azure-model" + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="latency-based-routing", + set_verbose=False, + num_retries=3, + ) # type: ignore + + ### Make 3 calls, test if 3rd call goes to fastest deployment + + ## CALL 1+2 + tasks = [] + response = None + final_response = None + for _ in range(2): + tasks.append(router.acompletion(model=model, messages=messages)) + response = await asyncio.gather(*tasks) + + if response is not None: + ## CALL 3 + await asyncio.sleep(1) # let the cache update happen + picked_deployment = router.lowestlatency_logger.get_available_deployments( + model_group=model, healthy_deployments=router.healthy_deployments + ) + final_response = await router.acompletion(model=model, messages=messages) + print(f"min deployment id: {picked_deployment}") + print(f"model id: {final_response._hidden_params['model_id']}") + assert ( + final_response._hidden_params["model_id"] + == picked_deployment["model_info"]["id"] + ) + + +# asyncio.run(test_router_completion_streaming()) diff --git a/litellm/tests/test_mock_request.py b/litellm/tests/test_mock_request.py new file mode 100644 index 0000000000000000000000000000000000000000..4a2d661c60cf27f4d9f553ca655d8e79d156b9e1 --- /dev/null +++ b/litellm/tests/test_mock_request.py @@ -0,0 +1,39 @@ +#### What this tests #### +# This tests mock request calls to litellm + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + + +def test_mock_request(): + try: + model = "gpt-3.5-turbo" + messages = [{"role": "user", "content": "Hey, I'm a mock request"}] + response = litellm.mock_completion(model=model, messages=messages, stream=False) + print(response) + print(type(response)) + except: + traceback.print_exc() + + +# test_mock_request() +def test_streaming_mock_request(): + try: + model = "gpt-3.5-turbo" + messages = [{"role": "user", "content": "Hey, I'm a mock request"}] + response = litellm.mock_completion(model=model, messages=messages, stream=True) + complete_response = "" + for chunk in response: + complete_response += chunk["choices"][0]["delta"]["content"] + if complete_response == "": + raise Exception("Empty response received") + except: + traceback.print_exc() + + +test_streaming_mock_request() diff --git a/litellm/tests/test_model_alias_map.py b/litellm/tests/test_model_alias_map.py new file mode 100644 index 0000000000000000000000000000000000000000..1501f49e436c40289463536367db08e9257928dc --- /dev/null +++ b/litellm/tests/test_model_alias_map.py @@ -0,0 +1,35 @@ +#### What this tests #### +# This tests the model alias mapping - if user passes in an alias, and has set an alias, set it to the actual value + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion +import pytest + +litellm.set_verbose = True + +model_alias_map = {"good-model": "anyscale/meta-llama/Llama-2-7b-chat-hf"} + + +def test_model_alias_map(): + try: + litellm.model_alias_map = model_alias_map + response = completion( + "good-model", + messages=[{"role": "user", "content": "Hey, how's it going?"}], + top_p=0.1, + temperature=0.01, + max_tokens=10, + ) + print(response.model) + assert "Llama-2-7b-chat-hf" in response.model + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_model_alias_map() diff --git a/litellm/tests/test_model_response_typing/server.py b/litellm/tests/test_model_response_typing/server.py new file mode 100644 index 0000000000000000000000000000000000000000..80dbc33affd86c91fdcccf297c41aafb214958be --- /dev/null +++ b/litellm/tests/test_model_response_typing/server.py @@ -0,0 +1,23 @@ +# #### What this tests #### +# # This tests if the litellm model response type is returnable in a flask app + +# import sys, os +# import traceback +# from flask import Flask, request, jsonify, abort, Response +# sys.path.insert(0, os.path.abspath('../../..')) # Adds the parent directory to the system path + +# import litellm +# from litellm import completion + +# litellm.set_verbose = False + +# app = Flask(__name__) + +# @app.route('/') +# def hello(): +# data = request.json +# return completion(**data) + +# if __name__ == '__main__': +# from waitress import serve +# serve(app, host='localhost', port=8080, threads=10) diff --git a/litellm/tests/test_model_response_typing/test.py b/litellm/tests/test_model_response_typing/test.py new file mode 100644 index 0000000000000000000000000000000000000000..95d40480988c6054b0351153d269f34f34b8204d --- /dev/null +++ b/litellm/tests/test_model_response_typing/test.py @@ -0,0 +1,14 @@ +# import requests, json + +# BASE_URL = 'http://localhost:8080' + +# def test_hello_route(): +# data = {"model": "claude-instant-1", "messages": [{"role": "user", "content": "hey, how's it going?"}]} +# headers = {'Content-Type': 'application/json'} +# response = requests.get(BASE_URL, headers=headers, data=json.dumps(data)) +# print(response.text) +# assert response.status_code == 200 +# print("Hello route test passed!") + +# if __name__ == '__main__': +# test_hello_route() diff --git a/litellm/tests/test_multiple_deployments.py b/litellm/tests/test_multiple_deployments.py new file mode 100644 index 0000000000000000000000000000000000000000..0a79cb51832bef8a2684ed58c51d5c5bfe3ffdc2 --- /dev/null +++ b/litellm/tests/test_multiple_deployments.py @@ -0,0 +1,54 @@ +#### What this tests #### +# This tests error handling + logging (esp. for sentry breadcrumbs) + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import completion + +messages = [{"role": "user", "content": "Hey, how's it going?"}] + +## All your mistral deployments ## +model_list = [ + { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "replicate/mistralai/mistral-7b-instruct-v0.1:83b6a56e7c828e667f21fd596c338fd4f0039b46bcfa18d973e8e70e455fda70", + "api_key": os.getenv("REPLICATE_API_KEY"), + }, + }, + { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "together_ai/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": os.getenv("TOGETHERAI_API_KEY"), + }, + }, + { + "model_name": "mistral-7b-instruct", + "litellm_params": { + "model": "deepinfra/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": os.getenv("DEEPINFRA_API_KEY"), + }, + }, +] + + +def test_multiple_deployments(): + try: + ## LiteLLM completion call ## returns first response + response = completion( + model="mistral-7b-instruct", messages=messages, model_list=model_list + ) + print(f"response: {response}") + except Exception as e: + traceback.print_exc() + pytest.fail(f"An exception occurred: {e}") + + +test_multiple_deployments() diff --git a/litellm/tests/test_ollama.py b/litellm/tests/test_ollama.py new file mode 100644 index 0000000000000000000000000000000000000000..82ec16f0edf946f92c2d800d65e3bd8993aecd28 --- /dev/null +++ b/litellm/tests/test_ollama.py @@ -0,0 +1,60 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm + + +## for ollama we can't test making the completion call +from litellm.utils import get_optional_params, get_llm_provider + + +def test_get_ollama_params(): + try: + converted_params = get_optional_params( + custom_llm_provider="ollama", + model="llama2", + max_tokens=20, + temperature=0.5, + stream=True, + ) + print("Converted params", converted_params) + assert converted_params == { + "num_predict": 20, + "stream": True, + "temperature": 0.5, + }, f"{converted_params} != {'num_predict': 20, 'stream': True, 'temperature': 0.5}" + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_get_ollama_params() + + +def test_get_ollama_model(): + try: + model, custom_llm_provider, _, _ = get_llm_provider("ollama/code-llama-22") + print("Model", "custom_llm_provider", model, custom_llm_provider) + assert custom_llm_provider == "ollama", f"{custom_llm_provider} != ollama" + assert model == "code-llama-22", f"{model} != code-llama-22" + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +# test_get_ollama_model() + +def test_ollama_json_mode(): + # assert that format: json gets passed as is to ollama + try: + converted_params = get_optional_params(custom_llm_provider="ollama", model="llama2", format = "json", temperature=0.5) + print("Converted params", converted_params) + assert converted_params == {'temperature': 0.5, 'format': 'json'}, f"{converted_params} != {'temperature': 0.5, 'format': 'json'}" + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_ollama_json_mode() diff --git a/litellm/tests/test_ollama_local.py b/litellm/tests/test_ollama_local.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dbc7341b0540a1ad8a405116efccc92d82ba0d --- /dev/null +++ b/litellm/tests/test_ollama_local.py @@ -0,0 +1,328 @@ +# ##### THESE TESTS CAN ONLY RUN LOCALLY WITH THE OLLAMA SERVER RUNNING ###### +# # https://ollama.ai/ + +# import sys, os +# import traceback +# from dotenv import load_dotenv +# load_dotenv() +# import os +# sys.path.insert(0, os.path.abspath('../..')) # Adds the parent directory to the system path +# import pytest +# import litellm +# from litellm import embedding, completion +# import asyncio + + +# user_message = "respond in 20 words. who are you?" +# messages = [{ "content": user_message,"role": "user"}] + +# async def test_ollama_aembeddings(): +# litellm.set_verbose = True +# input = "The food was delicious and the waiter..." +# response = await litellm.aembedding(model="ollama/mistral", input=input) +# print(response) + +# asyncio.run(test_ollama_aembeddings()) + +# def test_ollama_streaming(): +# try: +# litellm.set_verbose = False +# messages = [ +# {"role": "user", "content": "What is the weather like in Boston?"} +# ] +# functions = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA" +# }, +# "unit": { +# "type": "string", +# "enum": ["celsius", "fahrenheit"] +# } +# }, +# "required": ["location"] +# } +# } +# ] +# response = litellm.completion(model="ollama/mistral", +# messages=messages, +# functions=functions, +# stream=True) +# for chunk in response: +# print(f"CHUNK: {chunk}") +# except Exception as e: +# print(e) + +# # test_ollama_streaming() + +# async def test_async_ollama_streaming(): +# try: +# litellm.set_verbose = False +# response = await litellm.acompletion(model="ollama/mistral-openorca", +# messages=[{"role": "user", "content": "Hey, how's it going?"}], +# stream=True) +# async for chunk in response: +# print(f"CHUNK: {chunk}") +# except Exception as e: +# print(e) + +# # asyncio.run(test_async_ollama_streaming()) + +# def test_completion_ollama(): +# try: +# litellm.set_verbose = True +# response = completion( +# model="ollama/mistral", +# messages=[{"role": "user", "content": "Hey, how's it going?"}], +# max_tokens=200, +# request_timeout = 10, +# stream=True +# ) +# for chunk in response: +# print(chunk) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_completion_ollama() + +# def test_completion_ollama_function_calling(): +# try: +# litellm.set_verbose = True +# messages = [ +# {"role": "user", "content": "What is the weather like in Boston?"} +# ] +# functions = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA" +# }, +# "unit": { +# "type": "string", +# "enum": ["celsius", "fahrenheit"] +# } +# }, +# "required": ["location"] +# } +# } +# ] +# response = completion( +# model="ollama/mistral", +# messages=messages, +# functions=functions, +# max_tokens=200, +# request_timeout = 10, +# ) +# for chunk in response: +# print(chunk) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# # test_completion_ollama_function_calling() + +# async def async_test_completion_ollama_function_calling(): +# try: +# litellm.set_verbose = True +# messages = [ +# {"role": "user", "content": "What is the weather like in Boston?"} +# ] +# functions = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA" +# }, +# "unit": { +# "type": "string", +# "enum": ["celsius", "fahrenheit"] +# } +# }, +# "required": ["location"] +# } +# } +# ] +# response = await litellm.acompletion( +# model="ollama/mistral", +# messages=messages, +# functions=functions, +# max_tokens=200, +# request_timeout = 10, +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # asyncio.run(async_test_completion_ollama_function_calling()) + + +# def test_completion_ollama_with_api_base(): +# try: +# response = completion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434" +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_completion_ollama_with_api_base() + + +# def test_completion_ollama_custom_prompt_template(): +# user_message = "what is litellm?" +# litellm.register_prompt_template( +# model="ollama/llama2", +# roles={ +# "system": {"pre_message": "System: "}, +# "user": {"pre_message": "User: "}, +# "assistant": {"pre_message": "Assistant: "} +# } +# ) +# messages = [{ "content": user_message,"role": "user"}] +# litellm.set_verbose = True +# try: +# response = completion( +# model="ollama/llama2", +# messages=messages, +# stream=True +# ) +# print(response) +# for chunk in response: +# print(chunk) +# # print(chunk['choices'][0]['delta']) + +# except Exception as e: +# traceback.print_exc() +# pytest.fail(f"Error occurred: {e}") + +# # test_completion_ollama_custom_prompt_template() + +# async def test_completion_ollama_async_stream(): +# user_message = "what is the weather" +# messages = [{ "content": user_message,"role": "user"}] +# try: +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434", +# stream=True +# ) +# async for chunk in response: +# print(chunk['choices'][0]['delta']) + + +# print("TEST ASYNC NON Stream") +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434", +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # import asyncio +# # asyncio.run(test_completion_ollama_async_stream()) + + +# def prepare_messages_for_chat(text: str) -> list: +# messages = [ +# {"role": "user", "content": text}, +# ] +# return messages + + +# async def ask_question(): +# params = { +# "messages": prepare_messages_for_chat("What is litellm? tell me 10 things about it who is sihaan.write an essay"), +# "api_base": "http://localhost:11434", +# "model": "ollama/llama2", +# "stream": True, +# } +# response = await litellm.acompletion(**params) +# return response + +# async def main(): +# response = await ask_question() +# async for chunk in response: +# print(chunk) + +# print("test async completion without streaming") +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=prepare_messages_for_chat("What is litellm? respond in 2 words"), +# ) +# print("response", response) + + +# def test_completion_expect_error(): +# # this tests if we can exception map correctly for ollama +# print("making ollama request") +# # litellm.set_verbose=True +# user_message = "what is litellm?" +# messages = [{ "content": user_message,"role": "user"}] +# try: +# response = completion( +# model="ollama/invalid", +# messages=messages, +# stream=True +# ) +# print(response) +# for chunk in response: +# print(chunk) +# # print(chunk['choices'][0]['delta']) + +# except Exception as e: +# pass +# pytest.fail(f"Error occurred: {e}") + +# # test_completion_expect_error() + + +# def test_ollama_llava(): +# litellm.set_verbose=True +# # same params as gpt-4 vision +# response = completion( +# model = "ollama/llava", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "What is in this picture" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "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" +# } +# } +# ] +# } +# ], +# ) +# print("Response from ollama/llava") +# print(response) +# # test_ollama_llava() + + +# # PROCESSED CHUNK PRE CHUNK CREATOR diff --git a/litellm/tests/test_ollama_local_chat.py b/litellm/tests/test_ollama_local_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..cca31942812b76d050b5a131cd41babe78f8bdbd --- /dev/null +++ b/litellm/tests/test_ollama_local_chat.py @@ -0,0 +1,334 @@ +# ##### THESE TESTS CAN ONLY RUN LOCALLY WITH THE OLLAMA SERVER RUNNING ###### +# # https://ollama.ai/ + +# import sys, os +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import pytest +# import litellm +# from litellm import embedding, completion +# import asyncio + + +# user_message = "respond in 20 words. who are you?" +# messages = [{"content": user_message, "role": "user"}] + + +# def test_ollama_streaming(): +# try: +# litellm.set_verbose = False +# messages = [{"role": "user", "content": "What is the weather like in Boston?"}] +# functions = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# response = litellm.completion( +# model="ollama_chat/mistral", +# messages=messages, +# functions=functions, +# stream=True, +# ) +# for chunk in response: +# print(f"CHUNK: {chunk}") +# except Exception as e: +# print(e) + + +# # test_ollama_streaming() + + +# async def test_async_ollama_streaming(): +# try: +# litellm.set_verbose = True +# response = await litellm.acompletion( +# model="ollama_chat/llama2", +# messages=[{"role": "user", "content": "Hey, how's it going?"}], +# stream=True, +# ) +# async for chunk in response: +# print(f"CHUNK: {chunk}") +# except Exception as e: +# print(e) + + +# # asyncio.run(test_async_ollama_streaming()) + +# async def test_async_ollama(): +# try: +# litellm.set_verbose = True +# response = await litellm.acompletion( +# model="ollama_chat/llama2", +# messages=[{"role": "user", "content": "Hey, how's it going?"}], +# ) +# print("\n response", response) +# except Exception as e: +# print(e) + + +# # asyncio.run(test_async_ollama()) + + +# def test_completion_ollama(): +# try: +# litellm.set_verbose = True +# response = completion( +# model="ollama_chat/mistral", +# messages=[{"role": "user", "content": "Hey, how's it going?"}], +# max_tokens=200, +# request_timeout=10, +# stream=True, +# ) +# for chunk in response: +# print(chunk) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# # test_completion_ollama() + + +# def test_completion_ollama_function_calling(): +# try: +# litellm.set_verbose = True +# messages = [{"role": "user", "content": "What is the weather like in Boston?"}] +# functions = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# response = completion( +# model="ollama_chat/mistral", +# messages=messages, +# functions=functions, +# max_tokens=200, +# request_timeout=10, +# ) +# for chunk in response: +# print(chunk) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# test_completion_ollama_function_calling() + + +# async def async_test_completion_ollama_function_calling(): +# try: +# litellm.set_verbose = True +# messages = [{"role": "user", "content": "What is the weather like in Boston?"}] +# functions = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# response = await litellm.acompletion( +# model="ollama/mistral", +# messages=messages, +# functions=functions, +# max_tokens=200, +# request_timeout=10, +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# # asyncio.run(async_test_completion_ollama_function_calling()) + + +# def test_completion_ollama_with_api_base(): +# try: +# response = completion( +# model="ollama/llama2", messages=messages, api_base="http://localhost:11434" +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# # test_completion_ollama_with_api_base() + + +# def test_completion_ollama_custom_prompt_template(): +# user_message = "what is litellm?" +# litellm.register_prompt_template( +# model="ollama/llama2", +# roles={ +# "system": {"pre_message": "System: "}, +# "user": {"pre_message": "User: "}, +# "assistant": {"pre_message": "Assistant: "}, +# }, +# ) +# messages = [{"content": user_message, "role": "user"}] +# litellm.set_verbose = True +# try: +# response = completion(model="ollama/llama2", messages=messages, stream=True) +# print(response) +# for chunk in response: +# print(chunk) +# # print(chunk['choices'][0]['delta']) + +# except Exception as e: +# traceback.print_exc() +# pytest.fail(f"Error occurred: {e}") + + +# # test_completion_ollama_custom_prompt_template() + + +# async def test_completion_ollama_async_stream(): +# user_message = "what is the weather" +# messages = [{"content": user_message, "role": "user"}] +# try: +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434", +# stream=True, +# ) +# async for chunk in response: +# print(chunk["choices"][0]["delta"]) + +# print("TEST ASYNC NON Stream") +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=messages, +# api_base="http://localhost:11434", +# ) +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +# # import asyncio +# # asyncio.run(test_completion_ollama_async_stream()) + + +# def prepare_messages_for_chat(text: str) -> list: +# messages = [ +# {"role": "user", "content": text}, +# ] +# return messages + + +# async def ask_question(): +# params = { +# "messages": prepare_messages_for_chat( +# "What is litellm? tell me 10 things about it who is sihaan.write an essay" +# ), +# "api_base": "http://localhost:11434", +# "model": "ollama/llama2", +# "stream": True, +# } +# response = await litellm.acompletion(**params) +# return response + + +# async def main(): +# response = await ask_question() +# async for chunk in response: +# print(chunk) + +# print("test async completion without streaming") +# response = await litellm.acompletion( +# model="ollama/llama2", +# messages=prepare_messages_for_chat("What is litellm? respond in 2 words"), +# ) +# print("response", response) + + +# def test_completion_expect_error(): +# # this tests if we can exception map correctly for ollama +# print("making ollama request") +# # litellm.set_verbose=True +# user_message = "what is litellm?" +# messages = [{"content": user_message, "role": "user"}] +# try: +# response = completion(model="ollama/invalid", messages=messages, stream=True) +# print(response) +# for chunk in response: +# print(chunk) +# # print(chunk['choices'][0]['delta']) + +# except Exception as e: +# pass +# pytest.fail(f"Error occurred: {e}") + + +# # test_completion_expect_error() + + +# def test_ollama_llava(): +# litellm.set_verbose = True +# # same params as gpt-4 vision +# response = completion( +# model="ollama/llava", +# messages=[ +# { +# "role": "user", +# "content": [ +# {"type": "text", "text": "What is in this picture"}, +# { +# "type": "image_url", +# "image_url": { +# "url": "iVBORw0KGgoAAAANSUhEUgAAAG0AAABmCAYAAADBPx+VAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAA3VSURBVHgB7Z27r0zdG8fX743i1bi1ikMoFMQloXRpKFFIqI7LH4BEQ+NWIkjQuSWCRIEoULk0gsK1kCBI0IhrQVT7tz/7zZo888yz1r7MnDl7z5xvsjkzs2fP3uu71nNfa7lkAsm7d++Sffv2JbNmzUqcc8m0adOSzZs3Z+/XES4ZckAWJEGWPiCxjsQNLWmQsWjRIpMseaxcuTKpG/7HP27I8P79e7dq1ars/yL4/v27S0ejqwv+cUOGEGGpKHR37tzJCEpHV9tnT58+dXXCJDdECBE2Ojrqjh071hpNECjx4cMHVycM1Uhbv359B2F79+51586daxN/+pyRkRFXKyRDAqxEp4yMlDDzXG1NPnnyJKkThoK0VFd1ELZu3TrzXKxKfW7dMBQ6bcuWLW2v0VlHjx41z717927ba22U9APcw7Nnz1oGEPeL3m3p2mTAYYnFmMOMXybPPXv2bNIPpFZr1NHn4HMw0KRBjg9NuRw95s8PEcz/6DZELQd/09C9QGq5RsmSRybqkwHGjh07OsJSsYYm3ijPpyHzoiacg35MLdDSIS/O1yM778jOTwYUkKNHWUzUWaOsylE00MyI0fcnOwIdjvtNdW/HZwNLGg+sR1kMepSNJXmIwxBZiG8tDTpEZzKg0GItNsosY8USkxDhD0Rinuiko2gfL/RbiD2LZAjU9zKQJj8RDR0vJBR1/Phx9+PHj9Z7REF4nTZkxzX4LCXHrV271qXkBAPGfP/atWvu/PnzHe4C97F48eIsRLZ9+3a3f/9+87dwP1JxaF7/3r17ba+5l4EcaVo0lj3SBq5kGTJSQmLWMjgYNei2GPT1MuMqGTDEFHzeQSP2wi/jGnkmPJ/nhccs44jvDAxpVcxnq0F6eT8h4ni/iIWpR5lPyA6ETkNXoSukvpJAD3AsXLiwpZs49+fPn5ke4j10TqYvegSfn0OnafC+Tv9ooA/JPkgQysqQNBzagXY55nO/oa1F7qvIPWkRL12WRpMWUvpVDYmxAPehxWSe8ZEXL20sadYIozfmNch4QJPAfeJgW3rNsnzphBKNJM2KKODo1rVOMRYik5ETy3ix4qWNI81qAAirizgMIc+yhTytx0JWZuNI03qsrgWlGtwjoS9XwgUhWGyhUaRZZQNNIEwCiXD16tXcAHUs79co0vSD8rrJCIW98pzvxpAWyyo3HYwqS0+H0BjStClcZJT5coMm6D2LOF8TolGJtK9fvyZpyiC5ePFi9nc/oJU4eiEP0jVoAnHa9wyJycITMP78+eMeP37sXrx44d6+fdt6f82aNdkx1pg9e3Zb5W+RSRE+n+VjksQWifvVaTKFhn5O8my63K8Qabdv33b379/PiAP//vuvW7BggZszZ072/+TJk91YgkafPn166zXB1rQHFvouAWHq9z3SEevSUerqCn2/dDCeta2jxYbr69evk4MHDyY7d+7MjhMnTiTPnz9Pfv/+nfQT2ggpO2dMF8cghuoM7Ygj5iWCqRlGFml0QC/ftGmTmzt3rmsaKDsgBSPh0/8yPeLLBihLkOKJc0jp8H8vUzcxIA1k6QJ/c78tWEyj5P3o4u9+jywNPdJi5rAH9x0KHcl4Hg570eQp3+vHXGyrmEeigzQsQsjavXt38ujRo44LQuDDhw+TW7duRS1HGgMxhNXHgflaNTOsHyKvHK5Ijo2jbFjJBQK9YwFd6RVMzfgRBmEfP37suBBm/p49e1qjEP2mwTViNRo0VJWH1deMXcNK08uUjVUu7s/zRaL+oLNxz1bpANco4npUgX4G2eFbpDFyQoQxojBCpEGSytmOH8qrH5Q9vuzD6ofQylkCUmh8DBAr+q8JCyVNtWQIidKQE9wNtLSQnS4jDSsxNHogzFuQBw4cyM61UKVsjfr3ooBkPSqqQHesUPWVtzi9/vQi1T+rJj7WiTz4Pt/l3LxUkr5P2VYZaZ4URpsE+st/dujQoaBBYokbrz/8TJNQYLSonrPS9kUaSkPeZyj1AWSj+d+VBoy1pIWVNed8P0Ll/ee5HdGRhrHhR5GGN0r4LGZBaj8oFDJitBTJzIZgFcmU0Y8ytWMZMzJOaXUSrUs5RxKnrxmbb5YXO9VGUhtpXldhEUogFr3IzIsvlpmdosVcGVGXFWp2oU9kLFL3dEkSz6NHEY1sjSRdIuDFWEhd8KxFqsRi1uM/nz9/zpxnwlESONdg6dKlbsaMGS4EHFHtjFIDHwKOo46l4TxSuxgDzi+rE2jg+BaFruOX4HXa0Nnf1lwAPufZeF8/r6zD97WK2qFnGjBxTw5qNGPxT+5T/r7/7RawFC3j4vTp09koCxkeHjqbHJqArmH5UrFKKksnxrK7FuRIs8STfBZv+luugXZ2pR/pP9Ois4z+TiMzUUkUjD0iEi1fzX8GmXyuxUBRcaUfykV0YZnlJGKQpOiGB76x5GeWkWWJc3mOrK6S7xdND+W5N6XyaRgtWJFe13GkaZnKOsYqGdOVVVbGupsyA/l7emTLHi7vwTdirNEt0qxnzAvBFcnQF16xh/TMpUuXHDowhlA9vQVraQhkudRdzOnK+04ZSP3DUhVSP61YsaLtd/ks7ZgtPcXqPqEafHkdqa84X6aCeL7YWlv6edGFHb+ZFICPlljHhg0bKuk0CSvVznWsotRu433alNdFrqG45ejoaPCaUkWERpLXjzFL2Rpllp7PJU2a/v7Ab8N05/9t27Z16KUqoFGsxnI9EosS2niSYg9SpU6B4JgTrvVW1flt1sT+0ADIJU2maXzcUTraGCRaL1Wp9rUMk16PMom8QhruxzvZIegJjFU7LLCePfS8uaQdPny4jTTL0dbee5mYokQsXTIWNY46kuMbnt8Kmec+LGWtOVIl9cT1rCB0V8WqkjAsRwta93TbwNYoGKsUSChN44lgBNCoHLHzquYKrU6qZ8lolCIN0Rh6cP0Q3U6I6IXILYOQI513hJaSKAorFpuHXJNfVlpRtmYBk1Su1obZr5dnKAO+L10Hrj3WZW+E3qh6IszE37F6EB+68mGpvKm4eb9bFrlzrok7fvr0Kfv727dvWRmdVTJHw0qiiCUSZ6wCK+7XL/AcsgNyL74DQQ730sv78Su7+t/A36MdY0sW5o40ahslXr58aZ5HtZB8GH64m9EmMZ7FpYw4T6QnrZfgenrhFxaSiSGXtPnz57e9TkNZLvTjeqhr734CNtrK41L40sUQckmj1lGKQ0rC37x544r8eNXRpnVE3ZZY7zXo8NomiO0ZUCj2uHz58rbXoZ6gc0uA+F6ZeKS/jhRDUq8MKrTho9fEkihMmhxtBI1DxKFY9XLpVcSkfoi8JGnToZO5sU5aiDQIW716ddt7ZLYtMQlhECdBGXZZMWldY5BHm5xgAroWj4C0hbYkSc/jBmggIrXJWlZM6pSETsEPGqZOndr2uuuR5rF169a2HoHPdurUKZM4CO1WTPqaDaAd+GFGKdIQkxAn9RuEWcTRyN2KSUgiSgF5aWzPTeA/lN5rZubMmR2bE4SIC4nJoltgAV/dVefZm72AtctUCJU2CMJ327hxY9t7EHbkyJFseq+EJSY16RPo3Dkq1kkr7+q0bNmyDuLQcZBEPYmHVdOBiJyIlrRDq41YPWfXOxUysi5fvtyaj+2BpcnsUV/oSoEMOk2CQGlr4ckhBwaetBhjCwH0ZHtJROPJkyc7UjcYLDjmrH7ADTEBXFfOYmB0k9oYBOjJ8b4aOYSe7QkKcYhFlq3QYLQhSidNmtS2RATwy8YOM3EQJsUjKiaWZ+vZToUQgzhkHXudb/PW5YMHD9yZM2faPsMwoc7RciYJXbGuBqJ1UIGKKLv915jsvgtJxCZDubdXr165mzdvtr1Hz5LONA8jrUwKPqsmVesKa49S3Q4WxmRPUEYdTjgiUcfUwLx589ySJUva3oMkP6IYddq6HMS4o55xBJBUeRjzfa4Zdeg56QZ43LhxoyPo7Lf1kNt7oO8wWAbNwaYjIv5lhyS7kRf96dvm5Jah8vfvX3flyhX35cuX6HfzFHOToS1H4BenCaHvO8pr8iDuwoUL7tevX+b5ZdbBair0xkFIlFDlW4ZknEClsp/TzXyAKVOmmHWFVSbDNw1l1+4f90U6IY/q4V27dpnE9bJ+v87QEydjqx/UamVVPRG+mwkNTYN+9tjkwzEx+atCm/X9WvWtDtAb68Wy9LXa1UmvCDDIpPkyOQ5ZwSzJ4jMrvFcr0rSjOUh+GcT4LSg5ugkW1Io0/SCDQBojh0hPlaJdah+tkVYrnTZowP8iq1F1TgMBBauufyB33x1v+NWFYmT5KmppgHC+NkAgbmRkpD3yn9QIseXymoTQFGQmIOKTxiZIWpvAatenVqRVXf2nTrAWMsPnKrMZHz6bJq5jvce6QK8J1cQNgKxlJapMPdZSR64/UivS9NztpkVEdKcrs5alhhWP9NeqlfWopzhZScI6QxseegZRGeg5a8C3Re1Mfl1ScP36ddcUaMuv24iOJtz7sbUjTS4qBvKmstYJoUauiuD3k5qhyr7QdUHMeCgLa1Ear9NquemdXgmum4fvJ6w1lqsuDhNrg1qSpleJK7K3TF0Q2jSd94uSZ60kK1e3qyVpQK6PVWXp2/FC3mp6jBhKKOiY2h3gtUV64TWM6wDETRPLDfSakXmH3w8g9Jlug8ZtTt4kVF0kLUYYmCCtD/DrQ5YhMGbA9L3ucdjh0y8kOHW5gU/VEEmJTcL4Pz/f7mgoAbYkAAAAAElFTkSuQmCC" +# }, +# }, +# ], +# } +# ], +# ) +# print("Response from ollama/llava") +# print(response) + + +# # test_ollama_llava() + + +# # PROCESSED CHUNK PRE CHUNK CREATOR diff --git a/litellm/tests/test_optional_params.py b/litellm/tests/test_optional_params.py new file mode 100644 index 0000000000000000000000000000000000000000..45c3b8a38e5e8383420327c21105e2749161a274 --- /dev/null +++ b/litellm/tests/test_optional_params.py @@ -0,0 +1,120 @@ +#### What this tests #### +# This tests if get_optional_params works as expected +import sys, os, time, inspect, asyncio, traceback +import pytest + +sys.path.insert(0, os.path.abspath("../..")) +import litellm +from litellm.utils import get_optional_params_embeddings + +## get_optional_params_embeddings +### Models: OpenAI, Azure, Bedrock +### Scenarios: w/ optional params + litellm.drop_params = True + + +def test_bedrock_optional_params_embeddings(): + litellm.drop_params = True + optional_params = get_optional_params_embeddings( + user="John", encoding_format=None, custom_llm_provider="bedrock" + ) + assert len(optional_params) == 0 + + +def test_openai_optional_params_embeddings(): + litellm.drop_params = True + optional_params = get_optional_params_embeddings( + user="John", encoding_format=None, custom_llm_provider="openai" + ) + assert len(optional_params) == 1 + assert optional_params["user"] == "John" + + +def test_azure_optional_params_embeddings(): + litellm.drop_params = True + optional_params = get_optional_params_embeddings( + user="John", encoding_format=None, custom_llm_provider="azure" + ) + assert len(optional_params) == 1 + assert optional_params["user"] == "John" + + +def test_azure_gpt_optional_params_gpt_vision(): + # for OpenAI, Azure all extra params need to get passed as extra_body to OpenAI python. We assert we actually set extra_body here + optional_params = litellm.utils.get_optional_params( + user="John", + custom_llm_provider="azure", + max_tokens=10, + temperature=0.2, + enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}}, + dataSources=[ + { + "type": "AzureComputerVision", + "parameters": { + "endpoint": "", + "key": "", + }, + } + ], + ) + + print(optional_params) + assert optional_params["max_tokens"] == 10 + assert optional_params["temperature"] == 0.2 + assert optional_params["extra_body"] == { + "enhancements": {"ocr": {"enabled": True}, "grounding": {"enabled": True}}, + "dataSources": [ + { + "type": "AzureComputerVision", + "parameters": { + "endpoint": "", + "key": "", + }, + } + ], + } + + +# test_azure_gpt_optional_params_gpt_vision() + + +def test_azure_gpt_optional_params_gpt_vision_with_extra_body(): + # if user passes extra_body, we should not over write it, we should pass it along to OpenAI python + optional_params = litellm.utils.get_optional_params( + user="John", + custom_llm_provider="azure", + max_tokens=10, + temperature=0.2, + extra_body={ + "meta": "hi", + }, + enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}}, + dataSources=[ + { + "type": "AzureComputerVision", + "parameters": { + "endpoint": "", + "key": "", + }, + } + ], + ) + + print(optional_params) + assert optional_params["max_tokens"] == 10 + assert optional_params["temperature"] == 0.2 + assert optional_params["extra_body"] == { + "enhancements": {"ocr": {"enabled": True}, "grounding": {"enabled": True}}, + "dataSources": [ + { + "type": "AzureComputerVision", + "parameters": { + "endpoint": "", + "key": "", + }, + } + ], + "meta": "hi", + } + + +# test_azure_gpt_optional_params_gpt_vision_with_extra_body() diff --git a/litellm/tests/test_profiling_router.py b/litellm/tests/test_profiling_router.py new file mode 100644 index 0000000000000000000000000000000000000000..5e1646847bc16c4e1b089e5e9c8345944b8f9858 --- /dev/null +++ b/litellm/tests/test_profiling_router.py @@ -0,0 +1,152 @@ +# #### What this tests #### +# # This profiles a router call to find where calls are taking the most time. + +# import sys, os, time, logging +# import traceback, asyncio, uuid +# import pytest +# import cProfile +# from pstats import Stats +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import litellm +# from litellm import Router +# from concurrent.futures import ThreadPoolExecutor +# from dotenv import load_dotenv +# from aiodebug import log_slow_callbacks # Import the aiodebug utility for logging slow callbacks + +# # litellm.telemetry = False + +# load_dotenv() + +# logging.basicConfig( +# level=logging.DEBUG, +# format='%(asctime)s %(levelname)s: %(message)s', +# datefmt='%I:%M:%S %p', +# filename='aiologs.log', # Name of the log file where logs will be written +# filemode='w' # 'w' to overwrite the log file on each run, use 'a' to append +# ) + +# # Dictionary to store exception types and their counts +# exception_counts = {} +# exception_data = [] + +# litellm.telemetry = False + +# num_task_cancelled_errors = 0 + +# model_list = [{ +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/gpt-turbo", +# "api_key": "os.environ/AZURE_FRANCE_API_KEY", +# "api_base": "https://openai-france-1234.openai.azure.com", +# "rpm": 1440, +# } +# }, { +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/gpt-35-turbo", +# "api_key": "os.environ/AZURE_EUROPE_API_KEY", +# "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", +# "rpm": 6 +# } +# }, { +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/gpt-35-turbo", +# "api_key": "os.environ/AZURE_CANADA_API_KEY", +# "api_base": "https://my-endpoint-canada-berri992.openai.azure.com", +# "rpm": 6 +# } +# }] + +# router = Router(model_list=model_list, set_verbose=False, num_retries=3) + +# async def router_completion(): +# global num_task_cancelled_errors, exception_counts +# try: +# messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}] +# response = await router.acompletion(model="azure-model", messages=messages) +# return response +# except asyncio.exceptions.CancelledError: +# exception_type = "CancelledError" +# exception_counts[exception_type] = exception_counts.get(exception_type, 0) + 1 +# print("Task was cancelled") +# num_task_cancelled_errors += 1 +# exception_data.append({ +# "type": exception_type, +# "traceback": None +# }) +# return None +# except Exception as e: +# exception_type = type(e).__name__ +# exception_counts[exception_type] = exception_counts.get(exception_type, 0) + 1 +# exception_data.append({ +# "type": exception_type, +# "traceback": traceback.format_exc() +# }) +# return None + +# async def loadtest_fn(n = 1452): +# global num_task_cancelled_errors, exception_counts +# start = time.time() +# tasks = [router_completion() for _ in range(n)] +# chat_completions = await asyncio.gather(*tasks) +# successful_completions = [c for c in chat_completions if c is not None] +# print(n, time.time() - start, len(successful_completions)) + +# # Print exception breakdown +# print("Exception Breakdown:") +# for exception_type, count in exception_counts.items(): +# print(f"{exception_type}: {count}") + +# # Store exception_data in a file +# with open('exception_data.txt', 'w') as file: +# for data in exception_data: +# file.write(f"Type: {data['type']}\n") +# if data['traceback']: +# file.write(f"Traceback:\n{data['traceback']}\n\n") + +# loop = asyncio.get_event_loop() +# loop.set_debug(True) +# log_slow_callbacks.enable(0.05) # Log callbacks slower than 0.05 seconds + +# # Excute the load testing function within the asyncio event loop +# loop.run_until_complete(loadtest_fn()) + +# # ### SUSTAINED LOAD TESTS ### +# # import time, asyncio +# # async def make_requests(n): +# # tasks = [router_completion() for _ in range(n)] +# # print(f"num tasks: {len(tasks)}") +# # chat_completions = await asyncio.gather(*tasks) +# # successful_completions = [c for c in chat_completions if c is not None] +# # print(f"successful_completions: {len(successful_completions)}") +# # return successful_completions + +# # async def main(): +# # start_time = time.time() +# # total_successful_requests = 0 +# # request_limit = 1000 +# # batches = 2 # batches of 1k requests +# # start = time.time() +# # tasks = [] # list to hold all tasks + +# # async def request_loop(): +# # nonlocal tasks +# # for _ in range(batches): +# # # Make 1,000 requests +# # task = asyncio.create_task(make_requests(request_limit)) +# # tasks.append(task) + +# # # Introduce a delay to achieve 1,000 requests per second +# # await asyncio.sleep(1) + +# # await request_loop() +# # results = await asyncio.gather(*tasks) +# # total_successful_requests = sum(len(res) for res in results) + +# # print(request_limit*batches, time.time() - start, total_successful_requests) + +# # asyncio.run(main()) diff --git a/litellm/tests/test_prompt_factory.py b/litellm/tests/test_prompt_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..9116ecc95037004869e112fcc2ad5fd1da6e73fc --- /dev/null +++ b/litellm/tests/test_prompt_factory.py @@ -0,0 +1,97 @@ +#### What this tests #### +# This tests if prompts are being correctly formatted +import sys +import os + +sys.path.insert(0, os.path.abspath("../..")) + +# from litellm.llms.prompt_templates.factory import prompt_factory +from litellm import completion +from litellm.llms.prompt_templates.factory import ( + anthropic_pt, + claude_2_1_pt, + llama_2_chat_pt, +) + + +def test_codellama_prompt_format(): + messages = [ + {"role": "system", "content": "You are a good bot"}, + {"role": "user", "content": "Hey, how's it going?"}, + ] + expected_prompt = "[INST] <>\nYou are a good bot\n<>\n [/INST]\n[INST] Hey, how's it going? [/INST]\n" + assert llama_2_chat_pt(messages) == expected_prompt + + +def test_claude_2_1_pt_formatting(): + # Test case: User only, should add Assistant + messages = [{"role": "user", "content": "Hello"}] + expected_prompt = "\n\nHuman: Hello\n\nAssistant: " + assert claude_2_1_pt(messages) == expected_prompt + + # Test case: System, User, and Assistant "pre-fill" sequence, + # Should return pre-fill + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": 'Please return "Hello World" as a JSON object.'}, + {"role": "assistant", "content": "{"}, + ] + expected_prompt = 'You are a helpful assistant.\n\nHuman: Please return "Hello World" as a JSON object.\n\nAssistant: {' + assert claude_2_1_pt(messages) == expected_prompt + + # Test case: System, Assistant sequence, should insert blank Human message + # before Assistant pre-fill + messages = [ + {"role": "system", "content": "You are a storyteller."}, + {"role": "assistant", "content": "Once upon a time, there "}, + ] + expected_prompt = ( + "You are a storyteller.\n\nHuman: \n\nAssistant: Once upon a time, there " + ) + assert claude_2_1_pt(messages) == expected_prompt + + # Test case: System, User sequence + messages = [ + {"role": "system", "content": "System reboot"}, + {"role": "user", "content": "Is everything okay?"}, + ] + expected_prompt = "System reboot\n\nHuman: Is everything okay?\n\nAssistant: " + assert claude_2_1_pt(messages) == expected_prompt + + +def test_anthropic_pt_formatting(): + # Test case: User only, should add Assistant + messages = [{"role": "user", "content": "Hello"}] + expected_prompt = "\n\nHuman: Hello\n\nAssistant: " + assert anthropic_pt(messages) == expected_prompt + + # Test case: System, User, and Assistant "pre-fill" sequence, + # Should return pre-fill + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": 'Please return "Hello World" as a JSON object.'}, + {"role": "assistant", "content": "{"}, + ] + expected_prompt = '\n\nHuman: You are a helpful assistant.\n\nHuman: Please return "Hello World" as a JSON object.\n\nAssistant: {' + assert anthropic_pt(messages) == expected_prompt + + # Test case: System, Assistant sequence, should NOT insert blank Human message + # before Assistant pre-fill, because "System" messages are Human + # messages wrapped with + messages = [ + {"role": "system", "content": "You are a storyteller."}, + {"role": "assistant", "content": "Once upon a time, there "}, + ] + expected_prompt = "\n\nHuman: You are a storyteller.\n\nAssistant: Once upon a time, there " + assert anthropic_pt(messages) == expected_prompt + + # Test case: System, User sequence + messages = [ + {"role": "system", "content": "System reboot"}, + {"role": "user", "content": "Is everything okay?"}, + ] + expected_prompt = "\n\nHuman: System reboot\n\nHuman: Is everything okay?\n\nAssistant: " + assert anthropic_pt(messages) == expected_prompt + + +# codellama_prompt_format() diff --git a/litellm/tests/test_promptlayer_integration.py b/litellm/tests/test_promptlayer_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..9f0af1af8d17a0928124e415da0f0b6ebb419762 --- /dev/null +++ b/litellm/tests/test_promptlayer_integration.py @@ -0,0 +1,83 @@ +import sys +import os +import io + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm import completion +import litellm + +litellm.success_callback = ["promptlayer"] +litellm.set_verbose = True +import time + + +# def test_promptlayer_logging(): +# try: +# # Redirect stdout +# old_stdout = sys.stdout +# sys.stdout = new_stdout = io.StringIO() + + +# response = completion(model="claude-instant-1.2", +# messages=[{ +# "role": "user", +# "content": "Hi 👋 - i'm claude" +# }]) + +# # Restore stdout +# time.sleep(1) +# sys.stdout = old_stdout +# output = new_stdout.getvalue().strip() +# print(output) +# if "LiteLLM: Prompt Layer Logging: success" not in output: +# raise Exception("Required log message not found!") + +# except Exception as e: +# print(e) + +# test_promptlayer_logging() + + +def test_promptlayer_logging_with_metadata(): + try: + # Redirect stdout + old_stdout = sys.stdout + sys.stdout = new_stdout = io.StringIO() + + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm ai21"}], + temperature=0.2, + max_tokens=20, + metadata={"model": "ai21"}, + ) + + # Restore stdout + time.sleep(1) + sys.stdout = old_stdout + output = new_stdout.getvalue().strip() + print(output) + if "LiteLLM: Prompt Layer Logging: success" not in output: + raise Exception("Required log message not found!") + + except Exception as e: + print(e) + + +test_promptlayer_logging_with_metadata() + + +# def test_chat_openai(): +# try: +# response = completion(model="replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1", +# messages=[{ +# "role": "user", +# "content": "Hi 👋 - i'm openai" +# }]) + +# print(response) +# except Exception as e: +# print(e) + +# test_chat_openai() diff --git a/litellm/tests/test_provider_specific_config.py b/litellm/tests/test_provider_specific_config.py new file mode 100644 index 0000000000000000000000000000000000000000..6c0edf02bd1922936f7fd3a9171ee2953b1e5f4e --- /dev/null +++ b/litellm/tests/test_provider_specific_config.py @@ -0,0 +1,682 @@ +#### What this tests #### +# This tests setting provider specific configs across providers +# There are 2 types of tests - changing config dynamically or by setting class variables + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import completion +from litellm import RateLimitError + +# Huggingface - Expensive to deploy models and keep them running. Maybe we can try doing this via baseten?? +# def hf_test_completion_tgi(): +# litellm.HuggingfaceConfig(max_new_tokens=200) +# litellm.set_verbose=True +# try: +# # OVERRIDE WITH DYNAMIC MAX TOKENS +# response_1 = litellm.completion( +# model="huggingface/mistralai/Mistral-7B-Instruct-v0.1", +# messages=[{ "content": "Hello, how are you?","role": "user"}], +# api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud", +# max_tokens=10 +# ) +# # Add any assertions here to check the response +# print(response_1) +# response_1_text = response_1.choices[0].message.content + +# # USE CONFIG TOKENS +# response_2 = litellm.completion( +# model="huggingface/mistralai/Mistral-7B-Instruct-v0.1", +# messages=[{ "content": "Hello, how are you?","role": "user"}], +# api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud", +# ) +# # Add any assertions here to check the response +# print(response_2) +# response_2_text = response_2.choices[0].message.content + +# assert len(response_2_text) > len(response_1_text) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# hf_test_completion_tgi() + +# Anthropic + + +def claude_test_completion(): + litellm.AnthropicConfig(max_tokens_to_sample=200) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="claude-instant-1", + messages=[{"content": "Hello, how are you?", "role": "user"}], + max_tokens=10, + ) + # Add any assertions here to check the response + print(response_1) + response_1_text = response_1.choices[0].message.content + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="claude-instant-1", + messages=[{"content": "Hello, how are you?", "role": "user"}], + ) + # Add any assertions here to check the response + print(response_2) + response_2_text = response_2.choices[0].message.content + + assert len(response_2_text) > len(response_1_text) + + try: + response_3 = litellm.completion( + model="claude-instant-1", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + + except Exception as e: + print(e) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# claude_test_completion() + +# Replicate + + +def replicate_test_completion(): + litellm.ReplicateConfig(max_new_tokens=200) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{"content": "Hello, how are you?", "role": "user"}], + max_tokens=10, + ) + # Add any assertions here to check the response + print(response_1) + response_1_text = response_1.choices[0].message.content + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{"content": "Hello, how are you?", "role": "user"}], + ) + # Add any assertions here to check the response + print(response_2) + response_2_text = response_2.choices[0].message.content + + assert len(response_2_text) > len(response_1_text) + try: + response_3 = litellm.completion( + model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + except: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# replicate_test_completion() + +# Cohere + + +def cohere_test_completion(): + # litellm.CohereConfig(max_tokens=200) + litellm.set_verbose = True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="command-nightly", + messages=[{"content": "Hello, how are you?", "role": "user"}], + max_tokens=10, + ) + response_1_text = response_1.choices[0].message.content + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="command-nightly", + messages=[{"content": "Hello, how are you?", "role": "user"}], + ) + response_2_text = response_2.choices[0].message.content + + assert len(response_2_text) > len(response_1_text) + + response_3 = litellm.completion( + model="command-nightly", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# cohere_test_completion() + +# AI21 + + +def ai21_test_completion(): + litellm.AI21Config(maxTokens=10) + litellm.set_verbose = True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="j2-mid", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="j2-mid", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion( + model="j2-light", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# ai21_test_completion() + +# TogetherAI + + +def togetherai_test_completion(): + litellm.TogetherAIConfig(max_tokens=10) + litellm.set_verbose = True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + try: + response_3 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + pytest.fail(f"Error not raised when n=2 passed to provider") + except: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# togetherai_test_completion() + +# Palm + + +def palm_test_completion(): + litellm.PalmConfig(max_output_tokens=10, temperature=0.9) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="palm/chat-bison", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="palm/chat-bison", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion( + model="palm/chat-bison", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# palm_test_completion() + +# NLP Cloud + + +def nlp_cloud_test_completion(): + litellm.NLPCloudConfig(max_length=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="dolphin", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="dolphin", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + try: + response_3 = litellm.completion( + model="dolphin", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + pytest.fail(f"Error not raised when n=2 passed to provider") + except: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# nlp_cloud_test_completion() + +# AlephAlpha + + +def aleph_alpha_test_completion(): + litellm.AlephAlphaConfig(maximum_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="luminous-base", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="luminous-base", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion( + model="luminous-base", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# aleph_alpha_test_completion() + +# Petals - calls are too slow, will cause circle ci to fail due to delay. Test locally. +# def petals_completion(): +# litellm.PetalsConfig(max_new_tokens=10) +# # litellm.set_verbose=True +# try: +# # OVERRIDE WITH DYNAMIC MAX TOKENS +# response_1 = litellm.completion( +# model="petals/petals-team/StableBeluga2", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# api_base="https://chat.petals.dev/api/v1/generate", +# max_tokens=100 +# ) +# response_1_text = response_1.choices[0].message.content +# print(f"response_1_text: {response_1_text}") + +# # USE CONFIG TOKENS +# response_2 = litellm.completion( +# model="petals/petals-team/StableBeluga2", +# api_base="https://chat.petals.dev/api/v1/generate", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# ) +# response_2_text = response_2.choices[0].message.content +# print(f"response_2_text: {response_2_text}") + +# assert len(response_2_text) < len(response_1_text) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# petals_completion() + +# VertexAI +# We don't have vertex ai configured for circle ci yet -- need to figure this out. +# def vertex_ai_test_completion(): +# litellm.VertexAIConfig(max_output_tokens=10) +# # litellm.set_verbose=True +# try: +# # OVERRIDE WITH DYNAMIC MAX TOKENS +# response_1 = litellm.completion( +# model="chat-bison", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# max_tokens=100 +# ) +# response_1_text = response_1.choices[0].message.content +# print(f"response_1_text: {response_1_text}") + +# # USE CONFIG TOKENS +# response_2 = litellm.completion( +# model="chat-bison", +# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}], +# ) +# response_2_text = response_2.choices[0].message.content +# print(f"response_2_text: {response_2_text}") + +# assert len(response_2_text) < len(response_1_text) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# vertex_ai_test_completion() + +# Sagemaker + + +def sagemaker_test_completion(): + litellm.SagemakerConfig(max_new_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# sagemaker_test_completion() + +# Bedrock + + +def bedrock_test_completion(): + litellm.AmazonCohereConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="bedrock/cohere.command-text-v14", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="bedrock/cohere.command-text-v14", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# bedrock_test_completion() + + +# OpenAI Chat Completion +def openai_test_completion(): + litellm.OpenAIConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# openai_test_completion() + + +# OpenAI Text Completion +def openai_text_completion_test(): + litellm.OpenAITextCompletionConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="gpt-3.5-turbo-instruct", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="gpt-3.5-turbo-instruct", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + + response_3 = litellm.completion( + model="gpt-3.5-turbo-instruct", + messages=[{"content": "Hello, how are you?", "role": "user"}], + n=2, + ) + assert len(response_3.choices) > 1 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# openai_text_completion_test() + + +# Azure OpenAI +def azure_openai_test_completion(): + litellm.AzureOpenAIConfig(max_tokens=10) + # litellm.set_verbose=True + try: + # OVERRIDE WITH DYNAMIC MAX TOKENS + response_1 = litellm.completion( + model="azure/chatgpt-v-2", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + max_tokens=100, + ) + response_1_text = response_1.choices[0].message.content + print(f"response_1_text: {response_1_text}") + + # USE CONFIG TOKENS + response_2 = litellm.completion( + model="azure/chatgpt-v-2", + messages=[ + { + "content": "Hello, how are you? Be as verbose as possible", + "role": "user", + } + ], + ) + response_2_text = response_2.choices[0].message.content + print(f"response_2_text: {response_2_text}") + + assert len(response_2_text) < len(response_1_text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# azure_openai_test_completion() diff --git a/litellm/tests/test_proxy_custom_auth.py b/litellm/tests/test_proxy_custom_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..ceb3d1c934f48180ec9d5c5843802412429378cf --- /dev/null +++ b/litellm/tests/test_proxy_custom_auth.py @@ -0,0 +1,66 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, asyncio +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, + ProxyConfig, +) # Replace with the actual module where your FastAPI router is defined + + +# Here you create a fixture that will be used by your tests +# Make sure the fixture returns TestClient(app) +@pytest.fixture(scope="function") +def client(): + from litellm.proxy.proxy_server import cleanup_router_config_variables + + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml" + # initialize can get run in parallel, it sets specific variables for the fast api app, sinc eit gets run in parallel different tests use the wrong variables + app = FastAPI() + asyncio.run(initialize(config=config_fp)) + + app.include_router(router) # Include your router in the test app + return TestClient(app) + + +def test_custom_auth(client): + try: + # Your test data + test_data = { + "model": "openai-model", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + # Your bearer token + token = os.getenv("PROXY_MASTER_KEY") + + headers = {"Authorization": f"Bearer {token}"} + response = client.post("/chat/completions", json=test_data, headers=headers) + print(f"response: {response.text}") + assert response.status_code == 401 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) diff --git a/litellm/tests/test_proxy_custom_logger.py b/litellm/tests/test_proxy_custom_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..e47351a9b014540c322f594dceebb4d4cbf42fbe --- /dev/null +++ b/litellm/tests/test_proxy_custom_logger.py @@ -0,0 +1,296 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io, asyncio + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError +import importlib, inspect + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, + startup_event, +) # Replace with the actual module where your FastAPI router is defined + +filepath = os.path.dirname(os.path.abspath(__file__)) +python_file_path = f"{filepath}/test_configs/custom_callbacks.py" + +# @app.on_event("startup") +# async def wrapper_startup_event(): +# initialize(config=config_fp) + +# Use the app fixture in your client fixture + + +@pytest.fixture +def client(): + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_custom_logger.yaml" + app = FastAPI() + asyncio.run(initialize(config=config_fp)) + app.include_router(router) # Include your router in the test app + return TestClient(app) + + +# Your bearer token +token = os.getenv("PROXY_MASTER_KEY") + +headers = {"Authorization": f"Bearer {token}"} + + +print("Testing proxy custom logger") + + +def test_embedding(client): + try: + litellm.set_verbose = False + from litellm.proxy.utils import get_instance_fn + + my_custom_logger = get_instance_fn( + value="custom_callbacks.my_custom_logger", config_file_path=python_file_path + ) + print("id of initialized custom logger", id(my_custom_logger)) + litellm.callbacks = [my_custom_logger] + # Your test data + print("initialized proxy") + # import the initialized custom logger + print(litellm.callbacks) + + # assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback + print("my_custom_logger", my_custom_logger) + assert my_custom_logger.async_success_embedding == False + + test_data = {"model": "azure-embedding-model", "input": ["hello"]} + response = client.post("/embeddings", json=test_data, headers=headers) + print("made request", response.status_code, response.text) + print( + "vars my custom logger /embeddings", + vars(my_custom_logger), + "id", + id(my_custom_logger), + ) + assert ( + my_custom_logger.async_success_embedding == True + ) # checks if the status of async_success is True, only the async_log_success_event can set this to true + assert ( + my_custom_logger.async_embedding_kwargs["model"] == "azure-embedding-model" + ) # checks if kwargs passed to async_log_success_event are correct + kwargs = my_custom_logger.async_embedding_kwargs + litellm_params = kwargs.get("litellm_params") + metadata = litellm_params.get("metadata", None) + print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata")) + assert metadata is not None + assert "user_api_key" in metadata + assert "headers" in metadata + proxy_server_request = litellm_params.get("proxy_server_request") + model_info = litellm_params.get("model_info") + assert proxy_server_request == { + "url": "http://testserver/embeddings", + "method": "POST", + "headers": { + "host": "testserver", + "accept": "*/*", + "accept-encoding": "gzip, deflate", + "connection": "keep-alive", + "user-agent": "testclient", + "authorization": "Bearer sk-1234", + "content-length": "54", + "content-type": "application/json", + }, + "body": {"model": "azure-embedding-model", "input": ["hello"]}, + } + assert model_info == { + "input_cost_per_token": 0.002, + "mode": "embedding", + "id": "hello", + } + result = response.json() + print(f"Received response: {result}") + print("Passed Embedding custom logger on proxy!") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +def test_chat_completion(client): + try: + # Your test data + litellm.set_verbose = False + from litellm.proxy.utils import get_instance_fn + + my_custom_logger = get_instance_fn( + value="custom_callbacks.my_custom_logger", config_file_path=python_file_path + ) + + print("id of initialized custom logger", id(my_custom_logger)) + + litellm.callbacks = [my_custom_logger] + # import the initialized custom logger + print(litellm.callbacks) + + # assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback + + print("LiteLLM Callbacks", litellm.callbacks) + print("my_custom_logger", my_custom_logger) + assert my_custom_logger.async_success == False + + test_data = { + "model": "Azure OpenAI GPT-4 Canada", + "messages": [ + {"role": "user", "content": "write a litellm poem"}, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data, headers=headers) + print("made request", response.status_code, response.text) + print("LiteLLM Callbacks", litellm.callbacks) + asyncio.sleep(1) # sleep while waiting for callback to run + + print( + "my_custom_logger in /chat/completions", + my_custom_logger, + "id", + id(my_custom_logger), + ) + print("vars my custom logger, ", vars(my_custom_logger)) + assert ( + my_custom_logger.async_success == True + ) # checks if the status of async_success is True, only the async_log_success_event can set this to true + assert ( + my_custom_logger.async_completion_kwargs["model"] == "chatgpt-v-2" + ) # checks if kwargs passed to async_log_success_event are correct + print( + "\n\n Custom Logger Async Completion args", + my_custom_logger.async_completion_kwargs, + ) + litellm_params = my_custom_logger.async_completion_kwargs.get("litellm_params") + metadata = litellm_params.get("metadata", None) + print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata")) + assert metadata is not None + assert "user_api_key" in metadata + assert "headers" in metadata + config_model_info = litellm_params.get("model_info") + proxy_server_request_object = litellm_params.get("proxy_server_request") + + assert config_model_info == { + "id": "gm", + "input_cost_per_token": 0.0002, + "mode": "chat", + } + assert proxy_server_request_object == { + "url": "http://testserver/chat/completions", + "method": "POST", + "headers": { + "host": "testserver", + "accept": "*/*", + "accept-encoding": "gzip, deflate", + "connection": "keep-alive", + "user-agent": "testclient", + "authorization": "Bearer sk-1234", + "content-length": "123", + "content-type": "application/json", + }, + "body": { + "model": "Azure OpenAI GPT-4 Canada", + "messages": [{"role": "user", "content": "write a litellm poem"}], + "max_tokens": 10, + }, + } + result = response.json() + print(f"Received response: {result}") + print("\nPassed /chat/completions with Custom Logger!") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +def test_chat_completion_stream(client): + try: + # Your test data + litellm.set_verbose = False + from litellm.proxy.utils import get_instance_fn + + my_custom_logger = get_instance_fn( + value="custom_callbacks.my_custom_logger", config_file_path=python_file_path + ) + + print("id of initialized custom logger", id(my_custom_logger)) + + litellm.callbacks = [my_custom_logger] + import json + + print("initialized proxy") + # import the initialized custom logger + print(litellm.callbacks) + + print("LiteLLM Callbacks", litellm.callbacks) + print("my_custom_logger", my_custom_logger) + + assert ( + my_custom_logger.streaming_response_obj == None + ) # no streaming response obj is set pre call + + test_data = { + "model": "Azure OpenAI GPT-4 Canada", + "messages": [ + {"role": "user", "content": "write 1 line poem about LiteLLM"}, + ], + "max_tokens": 40, + "stream": True, # streaming call + } + + response = client.post("/chat/completions", json=test_data, headers=headers) + print("made request", response.status_code, response.text) + complete_response = "" + for line in response.iter_lines(): + if line: + # Process the streaming data line here + print("\n\n Line", line) + print(line) + line = str(line) + + json_data = line.replace("data: ", "") + + if "[DONE]" in json_data: + break + + # Parse the JSON string + data = json.loads(json_data) + + print("\n\n decode_data", data) + + # Access the content of choices[0]['message']['content'] + content = data["choices"][0]["delta"]["content"] or "" + + # Process the content as needed + print("Content:", content) + + complete_response += content + + print("\n\nHERE is the complete streaming response string", complete_response) + print("\n\nHERE IS the streaming Response from callback\n\n") + print(my_custom_logger.streaming_response_obj) + import time + + time.sleep(0.5) + + streamed_response = my_custom_logger.streaming_response_obj + assert ( + complete_response == streamed_response["choices"][0]["message"]["content"] + ) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") diff --git a/litellm/tests/test_proxy_exception_mapping.py b/litellm/tests/test_proxy_exception_mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..fcc0ad98c96185e643472a86cb0861d30e058dbf --- /dev/null +++ b/litellm/tests/test_proxy_exception_mapping.py @@ -0,0 +1,176 @@ +# test that the proxy actually does exception mapping to the OpenAI format + +import sys, os +from dotenv import load_dotenv + +load_dotenv() +import os, io, asyncio + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm, openai +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, +) # Replace with the actual module where your FastAPI router is defined + + +@pytest.fixture +def client(): + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_bad_config.yaml" + asyncio.run(initialize(config=config_fp)) + app = FastAPI() + app.include_router(router) # Include your router in the test app + return TestClient(app) + + +# raise openai.AuthenticationError +def test_chat_completion_exception(client): + try: + # Your test data + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response( + response=response + ) + assert isinstance(openai_exception, openai.AuthenticationError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +# raise openai.AuthenticationError +def test_chat_completion_exception_azure(client): + try: + # Your test data + test_data = { + "model": "azure-gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response( + response=response + ) + print(openai_exception) + assert isinstance(openai_exception, openai.AuthenticationError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +# raise openai.AuthenticationError +def test_embedding_auth_exception_azure(client): + try: + # Your test data + test_data = {"model": "azure-embedding", "input": ["hi"]} + + response = client.post("/embeddings", json=test_data) + print("Response from proxy=", response) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response( + response=response + ) + print("Exception raised=", openai_exception) + assert isinstance(openai_exception, openai.AuthenticationError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +# raise openai.BadRequestError +# chat/completions openai +def test_exception_openai_bad_model(client): + try: + # Your test data + test_data = { + "model": "azure/GPT-12", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response( + response=response + ) + print("Type of exception=", type(openai_exception)) + assert isinstance(openai_exception, openai.NotFoundError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +# chat/completions any model +def test_chat_completion_exception_any_model(client): + try: + # Your test data + test_data = { + "model": "Lite-GPT-12", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response( + response=response + ) + print("Exception raised=", openai_exception) + assert isinstance(openai_exception, openai.BadRequestError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +# embeddings any model +def test_embedding_exception_any_model(client): + try: + # Your test data + test_data = {"model": "Lite-GPT-12", "input": ["hi"]} + + response = client.post("/embeddings", json=test_data) + print("Response from proxy=", response) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response( + response=response + ) + print("Exception raised=", openai_exception) + assert isinstance(openai_exception, openai.BadRequestError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") diff --git a/litellm/tests/test_proxy_gunicorn.py b/litellm/tests/test_proxy_gunicorn.py new file mode 100644 index 0000000000000000000000000000000000000000..73e368d35a57022c52563cea853ec2cb00eedd5e --- /dev/null +++ b/litellm/tests/test_proxy_gunicorn.py @@ -0,0 +1,61 @@ +# #### What this tests #### +# # Allow the user to easily run the local proxy server with Gunicorn +# # LOCAL TESTING ONLY +# import sys, os, subprocess +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os, io + +# # this file is to test litellm/proxy + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import pytest +# import litellm + +# ### LOCAL Proxy Server INIT ### +# from litellm.proxy.proxy_server import save_worker_config # Replace with the actual module where your FastAPI router is defined +# filepath = os.path.dirname(os.path.abspath(__file__)) +# config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml" +# def get_openai_info(): +# return { +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_base": os.getenv("AZURE_API_BASE"), +# } + +# def run_server(host="0.0.0.0",port=8008,num_workers=None): +# if num_workers is None: +# # Set it to min(8,cpu_count()) +# import multiprocessing +# num_workers = min(4,multiprocessing.cpu_count()) + +# ### LOAD KEYS ### + +# # Load the Azure keys. For now get them from openai-usage +# azure_info = get_openai_info() +# print(f"Azure info:{azure_info}") +# os.environ["AZURE_API_KEY"] = azure_info['api_key'] +# os.environ["AZURE_API_BASE"] = azure_info['api_base'] +# os.environ["AZURE_API_VERSION"] = "2023-09-01-preview" + +# ### SAVE CONFIG ### + +# os.environ["WORKER_CONFIG"] = config_fp + +# # In order for the app to behave well with signals, run it with gunicorn +# # The first argument must be the "name of the command run" +# cmd = f"gunicorn litellm.proxy.proxy_server:app --workers {num_workers} --worker-class uvicorn.workers.UvicornWorker --bind {host}:{port}" +# cmd = cmd.split() +# print(f"Running command: {cmd}") +# import sys +# sys.stdout.flush() +# sys.stderr.flush() + +# # Make sure to propage env variables +# subprocess.run(cmd) # This line actually starts Gunicorn + +# if __name__ == "__main__": +# run_server() diff --git a/litellm/tests/test_proxy_pass_user_config.py b/litellm/tests/test_proxy_pass_user_config.py new file mode 100644 index 0000000000000000000000000000000000000000..30fa1eeb11996c8103ddbff6fc5b41f9279c98ee --- /dev/null +++ b/litellm/tests/test_proxy_pass_user_config.py @@ -0,0 +1,113 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, logging, asyncio +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# Configure logging +logging.basicConfig( + level=logging.DEBUG, # Set the desired logging level + format="%(asctime)s - %(levelname)s - %(message)s", +) + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +import os +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, +) # Replace with the actual module where your FastAPI router is defined + +# Your bearer token +token = "" + +headers = {"Authorization": f"Bearer {token}"} + + +@pytest.fixture(scope="function") +def client_no_auth(): + # Assuming litellm.proxy.proxy_server is an object + from litellm.proxy.proxy_server import cleanup_router_config_variables + + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" + # initialize can get run in parallel, it sets specific variables for the fast api app, sinc eit gets run in parallel different tests use the wrong variables + asyncio.run(initialize(config=config_fp, debug=True)) + app = FastAPI() + app.include_router(router) # Include your router in the test app + + return TestClient(app) + + +def test_chat_completion(client_no_auth): + global headers + + from litellm.types.router import RouterConfig, ModelConfig + from litellm.types.completion import CompletionRequest + + user_config = RouterConfig( + model_list=[ + ModelConfig( + model_name="user-azure-instance", + litellm_params=CompletionRequest( + model="azure/chatgpt-v-2", + api_key=os.getenv("AZURE_API_KEY"), + api_version=os.getenv("AZURE_API_VERSION"), + api_base=os.getenv("AZURE_API_BASE"), + timeout=10, + ), + tpm=240000, + rpm=1800, + ), + ModelConfig( + model_name="user-openai-instance", + litellm_params=CompletionRequest( + model="gpt-3.5-turbo", + api_key=os.getenv("OPENAI_API_KEY"), + timeout=10, + ), + tpm=240000, + rpm=1800, + ), + ], + num_retries=2, + allowed_fails=3, + fallbacks=[{"user-azure-instance": ["user-openai-instance"]}], + ).dict() + + try: + # Your test data + test_data = { + "model": "user-azure-instance", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + "user_config": user_config, + } + + print("testing proxy server with chat completions") + response = client_no_auth.post("/v1/chat/completions", json=test_data) + print(f"response - {response.text}") + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +# Run the test diff --git a/litellm/tests/test_proxy_server.py b/litellm/tests/test_proxy_server.py new file mode 100644 index 0000000000000000000000000000000000000000..972c4a583af4453987adbda395999d2bcd15c10b --- /dev/null +++ b/litellm/tests/test_proxy_server.py @@ -0,0 +1,368 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, logging, asyncio +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# Configure logging +logging.basicConfig( + level=logging.DEBUG, # Set the desired logging level + format="%(asctime)s - %(levelname)s - %(message)s", +) + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, +) # Replace with the actual module where your FastAPI router is defined + +# Your bearer token +token = "" + +headers = {"Authorization": f"Bearer {token}"} + + +@pytest.fixture(scope="function") +def client_no_auth(): + # Assuming litellm.proxy.proxy_server is an object + from litellm.proxy.proxy_server import cleanup_router_config_variables + + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" + # initialize can get run in parallel, it sets specific variables for the fast api app, sinc eit gets run in parallel different tests use the wrong variables + asyncio.run(initialize(config=config_fp, debug=True)) + app = FastAPI() + app.include_router(router) # Include your router in the test app + + return TestClient(app) + + +def test_chat_completion(client_no_auth): + global headers + try: + # Your test data + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + + print("testing proxy server with chat completions") + response = client_no_auth.post("/v1/chat/completions", json=test_data) + print(f"response - {response.text}") + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +# Run the test + + +def test_chat_completion_azure(client_no_auth): + global headers + try: + # Your test data + test_data = { + "model": "azure/chatgpt-v-2", + "messages": [ + {"role": "user", "content": "write 1 sentence poem"}, + ], + "max_tokens": 10, + } + + print("testing proxy server with Azure Request /chat/completions") + response = client_no_auth.post("/v1/chat/completions", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + assert len(result["choices"][0]["message"]["content"]) > 0 + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +# Run the test +# test_chat_completion_azure() + + +### EMBEDDING +def test_embedding(client_no_auth): + global headers + from litellm.proxy.proxy_server import user_custom_auth + + try: + test_data = { + "model": "azure/azure-embedding-model", + "input": ["good morning from litellm"], + } + + response = client_no_auth.post("/v1/embeddings", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["embedding"])) + assert len(result["data"][0]["embedding"]) > 10 # this usually has len==1536 so + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +def test_bedrock_embedding(client_no_auth): + global headers + from litellm.proxy.proxy_server import user_custom_auth + + try: + test_data = { + "model": "amazon-embeddings", + "input": ["good morning from litellm"], + } + + response = client_no_auth.post("/v1/embeddings", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["embedding"])) + assert len(result["data"][0]["embedding"]) > 10 # this usually has len==1536 so + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +def test_sagemaker_embedding(client_no_auth): + global headers + from litellm.proxy.proxy_server import user_custom_auth + + try: + test_data = { + "model": "GPT-J 6B - Sagemaker Text Embedding (Internal)", + "input": ["good morning from litellm"], + } + + response = client_no_auth.post("/v1/embeddings", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["embedding"])) + assert len(result["data"][0]["embedding"]) > 10 # this usually has len==1536 so + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +# Run the test +# test_embedding() +#### IMAGE GENERATION + + +def test_img_gen(client_no_auth): + global headers + from litellm.proxy.proxy_server import user_custom_auth + + try: + test_data = { + "model": "dall-e-3", + "prompt": "A cute baby sea otter", + "n": 1, + "size": "1024x1024", + } + + response = client_no_auth.post("/v1/images/generations", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["url"])) + assert len(result["data"][0]["url"]) > 10 + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +#### ADDITIONAL +# @pytest.mark.skip(reason="hitting yaml load issues on circle-ci") +def test_add_new_model(client_no_auth): + global headers + try: + test_data = { + "model_name": "test_openai_models", + "litellm_params": { + "model": "gpt-3.5-turbo", + }, + "model_info": {"description": "this is a test openai model"}, + } + client_no_auth.post("/model/new", json=test_data, headers=headers) + response = client_no_auth.get("/model/info", headers=headers) + assert response.status_code == 200 + result = response.json() + print(f"response: {result}") + model_info = None + for m in result["data"]: + if m["model_name"] == "test_openai_models": + model_info = m["model_info"] + assert model_info["description"] == "this is a test openai model" + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +def test_health(client_no_auth): + global headers + import time + + try: + response = client_no_auth.get("/health") + assert response.status_code == 200 + result = response.json() + print("\n response from health:", result) + assert result["unhealthy_count"] == 0 + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + +# test_add_new_model() + +from litellm.integrations.custom_logger import CustomLogger + + +class MyCustomHandler(CustomLogger): + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + assert kwargs["user"] == "proxy-user" + assert kwargs["model"] == "gpt-3.5-turbo" + assert kwargs["max_tokens"] == 10 + + +customHandler = MyCustomHandler() + + +def test_chat_completion_optional_params(client_no_auth): + # [PROXY: PROD TEST] - DO NOT DELETE + # This tests if all the /chat/completion params are passed to litellm + try: + # Your test data + litellm.set_verbose = True + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + "user": "proxy-user", + } + + litellm.callbacks = [customHandler] + print("testing proxy server: optional params") + response = client_no_auth.post("/v1/chat/completions", json=test_data) + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail("LiteLLM Proxy test failed. Exception", e) + + +# Run the test +# test_chat_completion_optional_params() + +# Test Reading config.yaml file +from litellm.proxy.proxy_server import ProxyConfig + + +def test_load_router_config(): + try: + import asyncio + + print("testing reading config") + # this is a basic config.yaml with only a model + filepath = os.path.dirname(os.path.abspath(__file__)) + proxy_config = ProxyConfig() + result = asyncio.run( + proxy_config.load_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/simple_config.yaml", + ) + ) + print(result) + assert len(result[1]) == 1 + + # this is a load balancing config yaml + result = asyncio.run( + proxy_config.load_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/azure_config.yaml", + ) + ) + print(result) + assert len(result[1]) == 2 + + # config with general settings - custom callbacks + result = asyncio.run( + proxy_config.load_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/azure_config.yaml", + ) + ) + print(result) + assert len(result[1]) == 2 + + # tests for litellm.cache set from config + print("testing reading proxy config for cache") + litellm.cache = None + asyncio.run( + proxy_config.load_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/cache_no_params.yaml", + ) + ) + assert litellm.cache is not None + assert "redis_client" in vars( + litellm.cache.cache + ) # it should default to redis on proxy + assert litellm.cache.supported_call_types == [ + "completion", + "acompletion", + "embedding", + "aembedding", + ] # init with all call types + + litellm.disable_cache() + + print("testing reading proxy config for cache with params") + asyncio.run( + proxy_config.load_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/cache_with_params.yaml", + ) + ) + assert litellm.cache is not None + print(litellm.cache) + print(litellm.cache.supported_call_types) + print(vars(litellm.cache.cache)) + assert "redis_client" in vars( + litellm.cache.cache + ) # it should default to redis on proxy + assert litellm.cache.supported_call_types == [ + "embedding", + "aembedding", + ] # init with all call types + + except Exception as e: + pytest.fail("Proxy: Got exception reading config", e) + + +# test_load_router_config() diff --git a/litellm/tests/test_proxy_server_caching.py b/litellm/tests/test_proxy_server_caching.py new file mode 100644 index 0000000000000000000000000000000000000000..a1935bd05ba1eb328e014ac714aefd01579e86fc --- /dev/null +++ b/litellm/tests/test_proxy_server_caching.py @@ -0,0 +1,103 @@ +#### What this tests #### +# This tests using caching w/ litellm which requires SSL=True +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, logging, asyncio +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# Configure logging +logging.basicConfig( + level=logging.DEBUG, # Set the desired logging level + format="%(asctime)s - %(levelname)s - %(message)s", +) + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, +) # Replace with the actual module where your FastAPI router is defined + +# Your bearer token +token = "" + +headers = {"Authorization": f"Bearer {token}"} + + +@pytest.fixture(scope="function") +def client_no_auth(): + # Assuming litellm.proxy.proxy_server is an object + from litellm.proxy.proxy_server import cleanup_router_config_variables + + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_cloudflare_azure_with_cache_config.yaml" + # initialize can get run in parallel, it sets specific variables for the fast api app, sinc eit gets run in parallel different tests use the wrong variables + asyncio.run(initialize(config=config_fp, debug=True)) + app = FastAPI() + app.include_router(router) # Include your router in the test app + + return TestClient(app) + + +def generate_random_word(length=4): + import string, random + + letters = string.ascii_lowercase + return "".join(random.choice(letters) for _ in range(length)) + + +def test_chat_completion(client_no_auth): + global headers + try: + user_message = f"Write a poem about {generate_random_word()}" + messages = [{"content": user_message, "role": "user"}] + # Your test data + test_data = { + "model": "azure-cloudflare", + "messages": messages, + "max_tokens": 10, + } + + print("testing proxy server with chat completions") + response = client_no_auth.post("/v1/chat/completions", json=test_data) + print(f"response - {response.text}") + assert response.status_code == 200 + + response = response.json() + print(response) + + content = response["choices"][0]["message"]["content"] + response1_id = response["id"] + + print("\n content", content) + + assert len(content) > 1 + + print("\nmaking 2nd request to proxy. Testing caching + non streaming") + response = client_no_auth.post("/v1/chat/completions", json=test_data) + print(f"response - {response.text}") + assert response.status_code == 200 + + response = response.json() + print(response) + response2_id = response["id"] + assert response1_id == response2_id + litellm.disable_cache() + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") diff --git a/litellm/tests/test_proxy_server_cost.py b/litellm/tests/test_proxy_server_cost.py new file mode 100644 index 0000000000000000000000000000000000000000..f6cf11ada2d8422d9d3194b42ce18376a9ba3277 --- /dev/null +++ b/litellm/tests/test_proxy_server_cost.py @@ -0,0 +1,138 @@ +# #### What this tests #### +# # This tests the cost tracking function works with consecutive calls (~10 consecutive calls) + +# import sys, os, asyncio +# import traceback +# import pytest +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import dotenv +# dotenv.load_dotenv() +# import litellm +# from fastapi.testclient import TestClient +# from fastapi import FastAPI +# from litellm.proxy.proxy_server import router, save_worker_config, startup_event # Replace with the actual module where your FastAPI router is defined +# filepath = os.path.dirname(os.path.abspath(__file__)) +# config_fp = f"{filepath}/test_config.yaml" +# save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=True, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) +# app = FastAPI() +# app.include_router(router) # Include your router in the test app +# @app.on_event("startup") +# async def wrapper_startup_event(): +# await startup_event() + +# # Here you create a fixture that will be used by your tests +# # Make sure the fixture returns TestClient(app) +# @pytest.fixture(autouse=True) +# def client(): +# with TestClient(app) as client: +# yield client + +# @pytest.mark.asyncio +# async def test_proxy_cost_tracking(client): +# """ +# Get min cost. +# Create new key. +# Run 10 parallel calls. +# Check cost for key at the end. +# assert it's > min cost. +# """ +# model = "gpt-3.5-turbo" +# messages = [{"role": "user", "content": "Hey, how's it going?"}] +# number_of_calls = 1 +# min_cost = litellm.completion_cost(model=model, messages=messages) * number_of_calls +# try: +# ### CREATE NEW KEY ### +# test_data = { +# "models": ["azure-model"], +# } +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") + +# headers = { +# "Authorization": f"Bearer {token}" +# } +# create_new_key = client.post("/key/generate", json=test_data, headers=headers) +# key = create_new_key.json()["key"] +# print(f"received key: {key}") +# ### MAKE PARALLEL CALLS ### +# async def test_chat_completions(): +# # Your test data +# test_data = { +# "model": "azure-model", +# "messages": messages +# } + +# tmp_headers = { +# "Authorization": f"Bearer {key}" +# } + +# response = client.post("/v1/chat/completions", json=test_data, headers=tmp_headers) + +# assert response.status_code == 200 +# result = response.json() +# print(f"Received response: {result}") +# tasks = [test_chat_completions() for _ in range(number_of_calls)] +# chat_completions = await asyncio.gather(*tasks) +# ### CHECK SPEND ### +# get_key_spend = client.get(f"/key/info?key={key}", headers=headers) + +# assert get_key_spend.json()["info"]["spend"] > min_cost +# # print(f"chat_completions: {chat_completions}") +# # except Exception as e: +# # pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + +# #### JUST TEST LOCAL PROXY SERVER + +# import requests, os +# from concurrent.futures import ThreadPoolExecutor +# import dotenv +# dotenv.load_dotenv() + +# api_url = "http://0.0.0.0:8000/chat/completions" + +# def make_api_call(api_url): +# # Your test data +# test_data = { +# "model": "azure-model", +# "messages": [ +# { +# "role": "user", +# "content": "hi" +# }, +# ], +# "max_tokens": 10, +# } +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") + +# headers = { +# "Authorization": f"Bearer {token}" +# } +# print("testing proxy server") +# response = requests.post(api_url, json=test_data, headers=headers) +# return response.json() + +# # Number of parallel API calls +# num_parallel_calls = 3 + +# # List to store results +# results = [] + +# # Create a ThreadPoolExecutor +# with ThreadPoolExecutor() as executor: +# # Submit the API calls concurrently +# futures = [executor.submit(make_api_call, api_url) for _ in range(num_parallel_calls)] + +# # Gather the results as they become available +# for future in futures: +# try: +# result = future.result() +# results.append(result) +# except Exception as e: +# print(f"Error: {e}") + +# # Print the results +# for idx, result in enumerate(results, start=1): +# print(f"Result {idx}: {result}") diff --git a/litellm/tests/test_proxy_server_keys.py b/litellm/tests/test_proxy_server_keys.py new file mode 100644 index 0000000000000000000000000000000000000000..6eb41202cd558236ce2f57480362f4bd5678a86a --- /dev/null +++ b/litellm/tests/test_proxy_server_keys.py @@ -0,0 +1,269 @@ +# import sys, os, time +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os, io + +# # this file is to test litellm/proxy + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import pytest, logging +# import litellm +# from litellm import embedding, completion, completion_cost, Timeout +# from litellm import RateLimitError + + +# import sys, os, time +# import traceback +# from dotenv import load_dotenv + +# load_dotenv() +# import os, io + +# # this file is to test litellm/proxy +# from concurrent.futures import ThreadPoolExecutor + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path + +# import pytest, logging, requests +# import litellm +# from litellm import embedding, completion, completion_cost, Timeout +# from litellm import RateLimitError +# from github import Github +# import subprocess + + +# # Function to execute a command and return the output +# def run_command(command): +# process = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True) +# output, _ = process.communicate() +# return output.decode().strip() + + +# # Retrieve the current branch name +# branch_name = run_command("git rev-parse --abbrev-ref HEAD") + +# # GitHub personal access token (with repo scope) or use username and password +# access_token = os.getenv("GITHUB_ACCESS_TOKEN") +# # Instantiate the PyGithub library's Github object +# g = Github(access_token) + +# # Provide the owner and name of the repository where the pull request is located +# repository_owner = "BerriAI" +# repository_name = "litellm" + +# # Get the repository object +# repo = g.get_repo(f"{repository_owner}/{repository_name}") + +# # Iterate through the pull requests to find the one related to your branch +# for pr in repo.get_pulls(): +# print(f"in here! {pr.head.ref}") +# if pr.head.ref == branch_name: +# pr_number = pr.number +# break + +# print(f"The pull request number for branch {branch_name} is: {pr_number}") + + +# def test_add_new_key(): +# max_retries = 3 +# retry_delay = 10 # seconds + +# for retry in range(max_retries + 1): +# try: +# # Your test data +# test_data = { +# "models": ["gpt-3.5-turbo", "gpt-4", "claude-2", "azure-model"], +# "aliases": {"mistral-7b": "gpt-3.5-turbo"}, +# "duration": "20m", +# } +# print("testing proxy server") + +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") +# headers = {"Authorization": f"Bearer {token}"} + +# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app" + +# # Make a request to the staging endpoint +# response = requests.post( +# endpoint + "/key/generate", json=test_data, headers=headers +# ) + +# print(f"response: {response.text}") + +# if response.status_code == 200: +# result = response.json() +# break # Successful response, exit the loop +# elif response.status_code == 503 and retry < max_retries: +# print( +# f"Retrying in {retry_delay} seconds... (Retry {retry + 1}/{max_retries})" +# ) +# time.sleep(retry_delay) +# else: +# assert False, f"Unexpected response status code: {response.status_code}" + +# except Exception as e: +# print(traceback.format_exc()) +# pytest.fail(f"An error occurred {e}") + + +# def test_update_new_key(): +# try: +# # Your test data +# test_data = { +# "models": ["gpt-3.5-turbo", "gpt-4", "claude-2", "azure-model"], +# "aliases": {"mistral-7b": "gpt-3.5-turbo"}, +# "duration": "20m", +# } +# print("testing proxy server") +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") +# headers = {"Authorization": f"Bearer {token}"} + +# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app" + +# # Make a request to the staging endpoint +# response = requests.post( +# endpoint + "/key/generate", json=test_data, headers=headers +# ) +# assert response.status_code == 200 +# result = response.json() +# assert result["key"].startswith("sk-") + +# def _post_data(): +# json_data = {"models": ["bedrock-models"], "key": result["key"]} +# response = requests.post( +# endpoint + "/key/generate", json=json_data, headers=headers +# ) +# print(f"response text: {response.text}") +# assert response.status_code == 200 +# return response + +# _post_data() +# print(f"Received response: {result}") +# except Exception as e: +# pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}") + +# def test_add_new_key_max_parallel_limit(): +# try: +# # Your test data +# test_data = {"duration": "20m", "max_parallel_requests": 1} +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") +# headers = {"Authorization": f"Bearer {token}"} + +# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app" +# print(f"endpoint: {endpoint}") +# # Make a request to the staging endpoint +# response = requests.post( +# endpoint + "/key/generate", json=test_data, headers=headers +# ) +# assert response.status_code == 200 +# result = response.json() + +# # load endpoint with model +# model_data = { +# "model_name": "azure-model", +# "litellm_params": { +# "model": "azure/chatgpt-v-2", +# "api_key": os.getenv("AZURE_API_KEY"), +# "api_base": os.getenv("AZURE_API_BASE"), +# "api_version": os.getenv("AZURE_API_VERSION") +# } +# } +# response = requests.post(endpoint + "/model/new", json=model_data, headers=headers) +# assert response.status_code == 200 +# print(f"response text: {response.text}") + + +# def _post_data(): +# json_data = { +# "model": "azure-model", +# "messages": [ +# { +# "role": "user", +# "content": f"this is a test request, write a short poem {time.time()}", +# } +# ], +# } +# # Your bearer token +# response = requests.post( +# endpoint + "/chat/completions", json=json_data, headers={"Authorization": f"Bearer {result['key']}"} +# ) +# return response + +# def _run_in_parallel(): +# with ThreadPoolExecutor(max_workers=2) as executor: +# future1 = executor.submit(_post_data) +# future2 = executor.submit(_post_data) + +# # Obtain the results from the futures +# response1 = future1.result() +# print(f"response1 text: {response1.text}") +# response2 = future2.result() +# print(f"response2 text: {response2.text}") +# if response1.status_code == 429 or response2.status_code == 429: +# pass +# else: +# raise Exception() + +# _run_in_parallel() +# except Exception as e: +# pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}") + +# def test_add_new_key_max_parallel_limit_streaming(): +# try: +# # Your test data +# test_data = {"duration": "20m", "max_parallel_requests": 1} +# # Your bearer token +# token = os.getenv("PROXY_MASTER_KEY") +# headers = {"Authorization": f"Bearer {token}"} + +# endpoint = f"https://litellm-litellm-pr-{pr_number}.up.railway.app" + +# # Make a request to the staging endpoint +# response = requests.post( +# endpoint + "/key/generate", json=test_data, headers=headers +# ) +# print(f"response: {response.text}") +# assert response.status_code == 200 +# result = response.json() + +# def _post_data(): +# json_data = { +# "model": "azure-model", +# "messages": [ +# { +# "role": "user", +# "content": f"this is a test request, write a short poem {time.time()}", +# } +# ], +# "stream": True, +# } +# response = requests.post( +# endpoint + "/chat/completions", json=json_data, headers={"Authorization": f"Bearer {result['key']}"} +# ) +# return response + +# def _run_in_parallel(): +# with ThreadPoolExecutor(max_workers=2) as executor: +# future1 = executor.submit(_post_data) +# future2 = executor.submit(_post_data) + +# # Obtain the results from the futures +# response1 = future1.result() +# response2 = future2.result() +# if response1.status_code == 429 or response2.status_code == 429: +# pass +# else: +# raise Exception() + +# _run_in_parallel() +# except Exception as e: +# pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}") diff --git a/litellm/tests/test_proxy_server_langfuse.py b/litellm/tests/test_proxy_server_langfuse.py new file mode 100644 index 0000000000000000000000000000000000000000..4f896f792cd079ba6354208d2bc26346c9d9718b --- /dev/null +++ b/litellm/tests/test_proxy_server_langfuse.py @@ -0,0 +1,88 @@ +import sys, os +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, logging +import litellm +from litellm import embedding, completion, completion_cost, Timeout +from litellm import RateLimitError + +# Configure logging +logging.basicConfig( + level=logging.DEBUG, # Set the desired logging level + format="%(asctime)s - %(levelname)s - %(message)s", +) + +# test /chat/completion request to the proxy +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + startup_event, +) # Replace with the actual module where your FastAPI router is defined + +filepath = os.path.dirname(os.path.abspath(__file__)) +config_fp = f"{filepath}/test_configs/test_config.yaml" +save_worker_config( + config=config_fp, + model=None, + alias=None, + api_base=None, + api_version=None, + debug=False, + temperature=None, + max_tokens=None, + request_timeout=600, + max_budget=None, + telemetry=False, + drop_params=True, + add_function_to_prompt=False, + headers=None, + save=False, + use_queue=False, +) +app = FastAPI() +app.include_router(router) # Include your router in the test app + + +@app.on_event("startup") +async def wrapper_startup_event(): + await startup_event() + + +# Here you create a fixture that will be used by your tests +# Make sure the fixture returns TestClient(app) +@pytest.fixture(autouse=True) +def client(): + with TestClient(app) as client: + yield client + + +def test_chat_completion(client): + try: + # Your test data + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi"}, + ], + "max_tokens": 10, + } + print("testing proxy server") + headers = {"Authorization": f"Bearer {os.getenv('PROXY_MASTER_KEY')}"} + response = client.post("/v1/chat/completions", json=test_data, headers=headers) + print(f"response - {response.text}") + assert response.status_code == 200 + result = response.json() + print(f"Received response: {result}") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") diff --git a/litellm/tests/test_proxy_server_spend.py b/litellm/tests/test_proxy_server_spend.py new file mode 100644 index 0000000000000000000000000000000000000000..9fed60412ce36662d67288f3b6091d01c7aaa618 --- /dev/null +++ b/litellm/tests/test_proxy_server_spend.py @@ -0,0 +1,82 @@ +# import openai, json, time, asyncio +# client = openai.AsyncOpenAI( +# api_key="sk-1234", +# base_url="http://0.0.0.0:8000" +# ) + +# super_fake_messages = [ +# { +# "role": "user", +# "content": f"What's the weather like in San Francisco, Tokyo, and Paris? {time.time()}" +# }, +# { +# "content": None, +# "role": "assistant", +# "tool_calls": [ +# { +# "id": "1", +# "function": { +# "arguments": "{\"location\": \"San Francisco\", \"unit\": \"celsius\"}", +# "name": "get_current_weather" +# }, +# "type": "function" +# }, +# { +# "id": "2", +# "function": { +# "arguments": "{\"location\": \"Tokyo\", \"unit\": \"celsius\"}", +# "name": "get_current_weather" +# }, +# "type": "function" +# }, +# { +# "id": "3", +# "function": { +# "arguments": "{\"location\": \"Paris\", \"unit\": \"celsius\"}", +# "name": "get_current_weather" +# }, +# "type": "function" +# } +# ] +# }, +# { +# "tool_call_id": "1", +# "role": "tool", +# "name": "get_current_weather", +# "content": "{\"location\": \"San Francisco\", \"temperature\": \"90\", \"unit\": \"celsius\"}" +# }, +# { +# "tool_call_id": "2", +# "role": "tool", +# "name": "get_current_weather", +# "content": "{\"location\": \"Tokyo\", \"temperature\": \"30\", \"unit\": \"celsius\"}" +# }, +# { +# "tool_call_id": "3", +# "role": "tool", +# "name": "get_current_weather", +# "content": "{\"location\": \"Paris\", \"temperature\": \"50\", \"unit\": \"celsius\"}" +# } +# ] + +# async def chat_completions(): +# super_fake_response = await client.chat.completions.create( +# model="gpt-3.5-turbo", +# messages=super_fake_messages, +# seed=1337, +# stream=False +# ) # get a new response from the model where it can see the function response +# await asyncio.sleep(1) +# return super_fake_response + +# async def loadtest_fn(n = 1): +# global num_task_cancelled_errors, exception_counts, chat_completions +# start = time.time() +# tasks = [chat_completions() for _ in range(n)] +# chat_completions = await asyncio.gather(*tasks) +# successful_completions = [c for c in chat_completions if c is not None] +# print(n, time.time() - start, len(successful_completions)) + +# # print(json.dumps(super_fake_response.model_dump(), indent=4)) + +# asyncio.run(loadtest_fn()) diff --git a/litellm/tests/test_proxy_startup.py b/litellm/tests/test_proxy_startup.py new file mode 100644 index 0000000000000000000000000000000000000000..1183e8c616d8ce45f2d72883e979f7623b05b212 --- /dev/null +++ b/litellm/tests/test_proxy_startup.py @@ -0,0 +1,64 @@ +# What this tests +## This tests the proxy server startup +import sys, os, json +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest, logging, asyncio +import litellm +from litellm.proxy.proxy_server import ( + router, + save_worker_config, + initialize, + startup_event, + llm_model_list, + shutdown_event +) + +def test_proxy_gunicorn_startup_direct_config(): + """ + gunicorn startup requires the config to be passed in via environment variables + + We support saving either the config or the dict as an environment variable. + + Test both approaches + """ + try: + filepath = os.path.dirname(os.path.abspath(__file__)) + # test with worker_config = config yaml + config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" + os.environ["WORKER_CONFIG"] = config_fp + asyncio.run(startup_event()) + asyncio.run(shutdown_event()) + except Exception as e: + if "Already connected to the query engine" in str(e): + pass + else: + pytest.fail(f"An exception occurred - {str(e)}") + +def test_proxy_gunicorn_startup_config_dict(): + try: + filepath = os.path.dirname(os.path.abspath(__file__)) + # test with worker_config = config yaml + config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" + # test with worker_config = dict + worker_config = {"config": config_fp} + os.environ["WORKER_CONFIG"] = json.dumps(worker_config) + asyncio.run(startup_event()) + asyncio.run(shutdown_event()) + except Exception as e: + if "Already connected to the query engine" in str(e): + pass + else: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_proxy_gunicorn_startup() \ No newline at end of file diff --git a/litellm/tests/test_register_model.py b/litellm/tests/test_register_model.py new file mode 100644 index 0000000000000000000000000000000000000000..6b170798874188d1ad138c7310802bbe3268b817 --- /dev/null +++ b/litellm/tests/test_register_model.py @@ -0,0 +1,65 @@ +#### What this tests #### +# This tests calling batch_completions by running 100 messages together + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + + +def test_update_model_cost(): + try: + litellm.register_model( + { + "gpt-4": { + "max_tokens": 8192, + "input_cost_per_token": 0.00002, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat", + }, + } + ) + assert litellm.model_cost["gpt-4"]["input_cost_per_token"] == 0.00002 + except Exception as e: + pytest.fail(f"An error occurred: {e}") + + +# test_update_model_cost() + + +def test_update_model_cost_map_url(): + try: + litellm.register_model( + model_cost="https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" + ) + assert litellm.model_cost["gpt-4"]["input_cost_per_token"] == 0.00003 + except Exception as e: + pytest.fail(f"An error occurred: {e}") + + +# test_update_model_cost_map_url() + + +def test_update_model_cost_via_completion(): + try: + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}], + input_cost_per_token=0.3, + output_cost_per_token=0.4, + ) + print( + f"litellm.model_cost for gpt-3.5-turbo: {litellm.model_cost['gpt-3.5-turbo']}" + ) + assert litellm.model_cost["gpt-3.5-turbo"]["input_cost_per_token"] == 0.3 + assert litellm.model_cost["gpt-3.5-turbo"]["output_cost_per_token"] == 0.4 + except Exception as e: + pytest.fail(f"An error occurred: {e}") + + +test_update_model_cost_via_completion() diff --git a/litellm/tests/test_router.py b/litellm/tests/test_router.py new file mode 100644 index 0000000000000000000000000000000000000000..5d476bed622382cce150a3b35292fc29fc74f9f5 --- /dev/null +++ b/litellm/tests/test_router.py @@ -0,0 +1,944 @@ +#### What this tests #### +# This tests litellm router + +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +from concurrent.futures import ThreadPoolExecutor +from collections import defaultdict +from dotenv import load_dotenv + +load_dotenv() + + +def test_exception_raising(): + # this tests if the router raises an exception when invalid params are set + # in this test both deployments have bad keys - Keep this test. It validates if the router raises the most recent exception + litellm.set_verbose = True + import openai + + try: + print("testing if router raises an exception") + old_api_key = os.environ["AZURE_API_KEY"] + os.environ["AZURE_API_KEY"] = "" + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # + "model": "gpt-3.5-turbo", + "api_key": "bad-key", + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), + routing_strategy="simple-shuffle", + set_verbose=False, + num_retries=1, + ) # type: ignore + response = router.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello this request will fail"}], + ) + os.environ["AZURE_API_KEY"] = old_api_key + pytest.fail(f"Should have raised an Auth Error") + except openai.AuthenticationError: + print( + "Test Passed: Caught an OPENAI AUTH Error, Good job. This is what we needed!" + ) + os.environ["AZURE_API_KEY"] = old_api_key + router.reset() + except Exception as e: + os.environ["AZURE_API_KEY"] = old_api_key + print("Got unexpected exception on router!", e) + + +# test_exception_raising() + + +def test_reading_key_from_model_list(): + # [PROD TEST CASE] + # this tests if the router can read key from model list and make completion call, and completion + stream call. This is 90% of the router use case + # DO NOT REMOVE THIS TEST. It's an IMP ONE. Speak to Ishaan, if you are tring to remove this + litellm.set_verbose = False + import openai + + try: + print("testing if router raises an exception") + old_api_key = os.environ["AZURE_API_KEY"] + os.environ.pop("AZURE_API_KEY", None) + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": old_api_key, + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + } + ] + + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=int(os.getenv("REDIS_PORT")), + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1, + ) # type: ignore + response = router.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello this request will fail"}], + ) + print("\n response", response) + str_response = response.choices[0].message.content + print("\n str_response", str_response) + assert len(str_response) > 0 + + print("\n Testing streaming response") + response = router.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello this request will fail"}], + stream=True, + ) + completed_response = "" + for chunk in response: + if chunk is not None: + print(chunk) + completed_response += chunk.choices[0].delta.content or "" + print("\n completed_response", completed_response) + assert len(completed_response) > 0 + print("\n Passed Streaming") + os.environ["AZURE_API_KEY"] = old_api_key + router.reset() + except Exception as e: + os.environ["AZURE_API_KEY"] = old_api_key + print(f"FAILED TEST") + pytest.fail(f"Got unexpected exception on router! - {e}") + + +# test_reading_key_from_model_list() + + +def test_call_one_endpoint(): + # [PROD TEST CASE] + # user passes one deployment they want to call on the router, we call the specified one + # this test makes a completion calls azure/chatgpt-v-2, it should work + try: + print("Testing calling a specific deployment") + old_api_key = os.environ["AZURE_API_KEY"] + + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": old_api_key, + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "claude-v1", + "litellm_params": { + "model": "bedrock/anthropic.claude-instant-v1", + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "azure/azure-embedding-model", + "api_key": os.environ["AZURE_API_KEY"], + "api_base": os.environ["AZURE_API_BASE"], + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + litellm.set_verbose = True + router = Router( + model_list=model_list, + routing_strategy="simple-shuffle", + set_verbose=True, + num_retries=1, + ) # type: ignore + old_api_base = os.environ.pop("AZURE_API_BASE", None) + + async def call_azure_completion(): + response = await router.acompletion( + model="azure/chatgpt-v-2", + messages=[{"role": "user", "content": "hello this request will pass"}], + specific_deployment=True, + ) + print("\n response", response) + + async def call_bedrock_claude(): + response = await router.acompletion( + model="bedrock/anthropic.claude-instant-v1", + messages=[{"role": "user", "content": "hello this request will pass"}], + specific_deployment=True, + ) + + print("\n response", response) + + async def call_azure_embedding(): + response = await router.aembedding( + model="azure/azure-embedding-model", + input=["good morning from litellm"], + specific_deployment=True, + ) + + print("\n response", response) + + asyncio.run(call_azure_completion()) + asyncio.run(call_bedrock_claude()) + asyncio.run(call_azure_embedding()) + + os.environ["AZURE_API_BASE"] = old_api_base + os.environ["AZURE_API_KEY"] = old_api_key + except Exception as e: + print(f"FAILED TEST") + pytest.fail(f"Got unexpected exception on router! - {e}") + + +# test_call_one_endpoint() + + +def test_router_azure_acompletion(): + # [PROD TEST CASE] + # This is 90% of the router use case, makes an acompletion call, acompletion + stream call and verifies it got a response + # DO NOT REMOVE THIS TEST. It's an IMP ONE. Speak to Ishaan, if you are tring to remove this + litellm.set_verbose = False + import openai + + try: + print("Router Test Azure - Acompletion, Acompletion with stream") + + # remove api key from env to repro how proxy passes key to router + old_api_key = os.environ["AZURE_API_KEY"] + os.environ.pop("AZURE_API_KEY", None) + + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": old_api_key, + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/gpt-turbo", + "api_key": os.getenv("AZURE_FRANCE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": "https://openai-france-1234.openai.azure.com", + }, + "rpm": 1800, + }, + ] + + router = Router( + model_list=model_list, routing_strategy="simple-shuffle", set_verbose=True + ) # type: ignore + + async def test1(): + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello this request will pass"}], + ) + str_response = response.choices[0].message.content + print("\n str_response", str_response) + assert len(str_response) > 0 + print("\n response", response) + + asyncio.run(test1()) + + print("\n Testing streaming response") + + async def test2(): + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello this request will fail"}], + stream=True, + ) + completed_response = "" + async for chunk in response: + if chunk is not None: + print(chunk) + completed_response += chunk.choices[0].delta.content or "" + print("\n completed_response", completed_response) + assert len(completed_response) > 0 + + asyncio.run(test2()) + print("\n Passed Streaming") + os.environ["AZURE_API_KEY"] = old_api_key + router.reset() + except Exception as e: + os.environ["AZURE_API_KEY"] = old_api_key + print(f"FAILED TEST") + pytest.fail(f"Got unexpected exception on router! - {e}") + + +# test_router_azure_acompletion() + +### FUNCTION CALLING + + +def test_function_calling(): + model_list = [ + { + "model_name": "gpt-3.5-turbo-0613", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + messages = [{"role": "user", "content": "What is the weather like in Boston?"}] + functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + + router = Router(model_list=model_list) + response = router.completion( + model="gpt-3.5-turbo-0613", messages=messages, functions=functions + ) + router.reset() + print(response) + + +# test_acompletion_on_router() + + +def test_function_calling_on_router(): + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + }, + ] + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + router = Router( + model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=os.getenv("REDIS_PORT"), + ) + messages = [{"role": "user", "content": "what's the weather in boston"}] + response = router.completion( + model="gpt-3.5-turbo", messages=messages, functions=function1 + ) + print(f"final returned response: {response}") + router.reset() + assert isinstance(response["choices"][0]["message"]["function_call"], dict) + except Exception as e: + print(f"An exception occurred: {e}") + + +# test_function_calling_on_router() + + +### IMAGE GENERATION +@pytest.mark.asyncio +async def test_aimg_gen_on_router(): + litellm.set_verbose = True + try: + model_list = [ + { + "model_name": "dall-e-3", + "litellm_params": { + "model": "dall-e-3", + }, + }, + { + "model_name": "dall-e-3", + "litellm_params": { + "model": "azure/dall-e-3-test", + "api_version": "2023-12-01-preview", + "api_base": os.getenv("AZURE_SWEDEN_API_BASE"), + "api_key": os.getenv("AZURE_SWEDEN_API_KEY"), + }, + }, + { + "model_name": "dall-e-2", + "litellm_params": { + "model": "azure/", + "api_version": "2023-06-01-preview", + "api_base": os.getenv("AZURE_API_BASE"), + "api_key": os.getenv("AZURE_API_KEY"), + }, + }, + ] + router = Router(model_list=model_list, num_retries=3) + response = await router.aimage_generation( + model="dall-e-3", prompt="A cute baby sea otter" + ) + print(response) + assert len(response.data) > 0 + + response = await router.aimage_generation( + model="dall-e-2", prompt="A cute baby sea otter" + ) + print(response) + assert len(response.data) > 0 + + router.reset() + except Exception as e: + if "Your task failed as a result of our safety system." in str(e): + pass + elif "Operation polling timed out" in str(e): + pass + else: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# asyncio.run(test_aimg_gen_on_router()) + + +def test_img_gen_on_router(): + litellm.set_verbose = True + try: + model_list = [ + { + "model_name": "dall-e-3", + "litellm_params": { + "model": "dall-e-3", + }, + }, + { + "model_name": "dall-e-3", + "litellm_params": { + "model": "azure/dall-e-3-test", + "api_version": "2023-12-01-preview", + "api_base": os.getenv("AZURE_SWEDEN_API_BASE"), + "api_key": os.getenv("AZURE_SWEDEN_API_KEY"), + }, + }, + ] + router = Router(model_list=model_list) + response = router.image_generation( + model="dall-e-3", prompt="A cute baby sea otter" + ) + print(response) + assert len(response.data) > 0 + router.reset() + except litellm.RateLimitError as e: + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_img_gen_on_router() +### + + +def test_aembedding_on_router(): + litellm.set_verbose = True + try: + model_list = [ + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "text-embedding-ada-002", + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + router = Router(model_list=model_list) + + async def embedding_call(): + response = await router.aembedding( + model="text-embedding-ada-002", + input=["good morning from litellm", "this is another item"], + ) + print(response) + + asyncio.run(embedding_call()) + + print("\n Making sync Embedding call\n") + response = router.embedding( + model="text-embedding-ada-002", + input=["good morning from litellm 2"], + ) + router.reset() + except Exception as e: + if "Your task failed as a result of our safety system." in str(e): + pass + elif "Operation polling timed out" in str(e): + pass + else: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_aembedding_on_router() + + +def test_azure_embedding_on_router(): + """ + [PROD Use Case] - Makes an aembedding call + embedding call + """ + litellm.set_verbose = True + try: + model_list = [ + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "azure/azure-embedding-model", + "api_key": os.environ["AZURE_API_KEY"], + "api_base": os.environ["AZURE_API_BASE"], + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + router = Router(model_list=model_list) + + async def embedding_call(): + response = await router.aembedding( + model="text-embedding-ada-002", input=["good morning from litellm"] + ) + print(response) + + asyncio.run(embedding_call()) + + print("\n Making sync Azure Embedding call\n") + + response = router.embedding( + model="text-embedding-ada-002", + input=["test 2 from litellm. async embedding"], + ) + print(response) + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_azure_embedding_on_router() + + +def test_bedrock_on_router(): + litellm.set_verbose = True + print("\n Testing bedrock on router\n") + try: + model_list = [ + { + "model_name": "claude-v1", + "litellm_params": { + "model": "bedrock/anthropic.claude-instant-v1", + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + async def test(): + router = Router(model_list=model_list) + response = await router.acompletion( + model="claude-v1", + messages=[ + { + "role": "user", + "content": "hello from litellm test", + } + ], + ) + print(response) + router.reset() + + asyncio.run(test()) + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_bedrock_on_router() + + +# test openai-compatible endpoint +@pytest.mark.asyncio +async def test_mistral_on_router(): + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "mistral/mistral-medium", + }, + }, + ] + router = Router(model_list=model_list) + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello from litellm test", + } + ], + ) + print(response) + + +# asyncio.run(test_mistral_on_router()) + + +def test_openai_completion_on_router(): + # [PROD Use Case] - Makes an acompletion call + async acompletion call, and sync acompletion call, sync completion + stream + # 4 LLM API calls made here. If it fails, add retries. Do not remove this test. + litellm.set_verbose = True + print("\n Testing OpenAI on router\n") + try: + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + }, + }, + ] + router = Router(model_list=model_list) + + async def test(): + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello from litellm test", + } + ], + ) + print(response) + assert len(response.choices[0].message.content) > 0 + + print("\n streaming + acompletion test") + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": f"hello from litellm test {time.time()}", + } + ], + stream=True, + ) + complete_response = "" + print(response) + # if you want to see all the attributes and methods + async for chunk in response: + print(chunk) + complete_response += chunk.choices[0].delta.content or "" + print("\n complete response: ", complete_response) + assert len(complete_response) > 0 + + asyncio.run(test()) + print("\n Testing Sync completion calls \n") + response = router.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello from litellm test2", + } + ], + ) + print(response) + assert len(response.choices[0].message.content) > 0 + + print("\n streaming + completion test") + response = router.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello from litellm test3", + } + ], + stream=True, + ) + complete_response = "" + print(response) + for chunk in response: + print(chunk) + complete_response += chunk.choices[0].delta.content or "" + print("\n complete response: ", complete_response) + assert len(complete_response) > 0 + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_openai_completion_on_router() + + +def test_reading_keys_os_environ(): + import openai + + try: + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": "os.environ/AZURE_API_KEY", + "api_base": "os.environ/AZURE_API_BASE", + "api_version": "os.environ/AZURE_API_VERSION", + "timeout": "os.environ/AZURE_TIMEOUT", + "stream_timeout": "os.environ/AZURE_STREAM_TIMEOUT", + "max_retries": "os.environ/AZURE_MAX_RETRIES", + }, + }, + ] + + router = Router(model_list=model_list) + for model in router.model_list: + assert ( + model["litellm_params"]["api_key"] == os.environ["AZURE_API_KEY"] + ), f"{model['litellm_params']['api_key']} vs {os.environ['AZURE_API_KEY']}" + assert ( + model["litellm_params"]["api_base"] == os.environ["AZURE_API_BASE"] + ), f"{model['litellm_params']['api_base']} vs {os.environ['AZURE_API_BASE']}" + assert ( + model["litellm_params"]["api_version"] + == os.environ["AZURE_API_VERSION"] + ), f"{model['litellm_params']['api_version']} vs {os.environ['AZURE_API_VERSION']}" + assert float(model["litellm_params"]["timeout"]) == float( + os.environ["AZURE_TIMEOUT"] + ), f"{model['litellm_params']['timeout']} vs {os.environ['AZURE_TIMEOUT']}" + assert float(model["litellm_params"]["stream_timeout"]) == float( + os.environ["AZURE_STREAM_TIMEOUT"] + ), f"{model['litellm_params']['stream_timeout']} vs {os.environ['AZURE_STREAM_TIMEOUT']}" + assert int(model["litellm_params"]["max_retries"]) == int( + os.environ["AZURE_MAX_RETRIES"] + ), f"{model['litellm_params']['max_retries']} vs {os.environ['AZURE_MAX_RETRIES']}" + print("passed testing of reading keys from os.environ") + model_id = model["model_info"]["id"] + async_client: openai.AsyncAzureOpenAI = router.cache.get_cache(f"{model_id}_async_client") # type: ignore + assert async_client.api_key == os.environ["AZURE_API_KEY"] + assert async_client.base_url == os.environ["AZURE_API_BASE"] + assert async_client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{async_client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert async_client.timeout == ( + os.environ["AZURE_TIMEOUT"] + ), f"{async_client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("async client set correctly!") + + print("\n Testing async streaming client") + + stream_async_client: openai.AsyncAzureOpenAI = router.cache.get_cache(f"{model_id}_stream_async_client") # type: ignore + assert stream_async_client.api_key == os.environ["AZURE_API_KEY"] + assert stream_async_client.base_url == os.environ["AZURE_API_BASE"] + assert stream_async_client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{stream_async_client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert stream_async_client.timeout == ( + os.environ["AZURE_STREAM_TIMEOUT"] + ), f"{stream_async_client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("async stream client set correctly!") + + print("\n Testing sync client") + client: openai.AzureOpenAI = router.cache.get_cache(f"{model_id}_client") # type: ignore + assert client.api_key == os.environ["AZURE_API_KEY"] + assert client.base_url == os.environ["AZURE_API_BASE"] + assert client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert client.timeout == ( + os.environ["AZURE_TIMEOUT"] + ), f"{client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("sync client set correctly!") + + print("\n Testing sync stream client") + stream_client: openai.AzureOpenAI = router.cache.get_cache(f"{model_id}_stream_client") # type: ignore + assert stream_client.api_key == os.environ["AZURE_API_KEY"] + assert stream_client.base_url == os.environ["AZURE_API_BASE"] + assert stream_client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{stream_client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert stream_client.timeout == ( + os.environ["AZURE_STREAM_TIMEOUT"] + ), f"{stream_client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("sync stream client set correctly!") + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_reading_keys_os_environ() + + +def test_reading_openai_keys_os_environ(): + import openai + + try: + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": "os.environ/OPENAI_API_KEY", + "timeout": "os.environ/AZURE_TIMEOUT", + "stream_timeout": "os.environ/AZURE_STREAM_TIMEOUT", + "max_retries": "os.environ/AZURE_MAX_RETRIES", + }, + }, + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "text-embedding-ada-002", + "api_key": "os.environ/OPENAI_API_KEY", + "timeout": "os.environ/AZURE_TIMEOUT", + "stream_timeout": "os.environ/AZURE_STREAM_TIMEOUT", + "max_retries": "os.environ/AZURE_MAX_RETRIES", + }, + }, + ] + + router = Router(model_list=model_list) + for model in router.model_list: + assert ( + model["litellm_params"]["api_key"] == os.environ["OPENAI_API_KEY"] + ), f"{model['litellm_params']['api_key']} vs {os.environ['AZURE_API_KEY']}" + assert float(model["litellm_params"]["timeout"]) == float( + os.environ["AZURE_TIMEOUT"] + ), f"{model['litellm_params']['timeout']} vs {os.environ['AZURE_TIMEOUT']}" + assert float(model["litellm_params"]["stream_timeout"]) == float( + os.environ["AZURE_STREAM_TIMEOUT"] + ), f"{model['litellm_params']['stream_timeout']} vs {os.environ['AZURE_STREAM_TIMEOUT']}" + assert int(model["litellm_params"]["max_retries"]) == int( + os.environ["AZURE_MAX_RETRIES"] + ), f"{model['litellm_params']['max_retries']} vs {os.environ['AZURE_MAX_RETRIES']}" + print("passed testing of reading keys from os.environ") + model_id = model["model_info"]["id"] + async_client: openai.AsyncOpenAI = router.cache.get_cache(key=f"{model_id}_async_client") # type: ignore + assert async_client.api_key == os.environ["OPENAI_API_KEY"] + assert async_client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{async_client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert async_client.timeout == ( + os.environ["AZURE_TIMEOUT"] + ), f"{async_client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("async client set correctly!") + + print("\n Testing async streaming client") + + stream_async_client: openai.AsyncOpenAI = router.cache.get_cache(key=f"{model_id}_stream_async_client") # type: ignore + assert stream_async_client.api_key == os.environ["OPENAI_API_KEY"] + assert stream_async_client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{stream_async_client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert stream_async_client.timeout == ( + os.environ["AZURE_STREAM_TIMEOUT"] + ), f"{stream_async_client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("async stream client set correctly!") + + print("\n Testing sync client") + client: openai.AzureOpenAI = router.cache.get_cache(key=f"{model_id}_client") # type: ignore + assert client.api_key == os.environ["OPENAI_API_KEY"] + assert client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert client.timeout == ( + os.environ["AZURE_TIMEOUT"] + ), f"{client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("sync client set correctly!") + + print("\n Testing sync stream client") + stream_client: openai.AzureOpenAI = router.cache.get_cache(key=f"{model_id}_stream_client") # type: ignore + assert stream_client.api_key == os.environ["OPENAI_API_KEY"] + assert stream_client.max_retries == ( + os.environ["AZURE_MAX_RETRIES"] + ), f"{stream_client.max_retries} vs {os.environ['AZURE_MAX_RETRIES']}" + assert stream_client.timeout == ( + os.environ["AZURE_STREAM_TIMEOUT"] + ), f"{stream_client.timeout} vs {os.environ['AZURE_TIMEOUT']}" + print("sync stream client set correctly!") + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_reading_openai_keys_os_environ() diff --git a/litellm/tests/test_router_caching.py b/litellm/tests/test_router_caching.py new file mode 100644 index 0000000000000000000000000000000000000000..74a572c4670b562bc7125433e039786112872cd2 --- /dev/null +++ b/litellm/tests/test_router_caching.py @@ -0,0 +1,211 @@ +#### What this tests #### +# This tests caching on the router +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router + +## Scenarios +## 1. 2 models - openai + azure - 1 model group "gpt-3.5-turbo", +## 2. 2 models - openai, azure - 2 diff model groups, 1 caching group + + +@pytest.mark.asyncio +async def test_acompletion_caching_on_router(): + # tests acompletion + caching on router + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router( + model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle", + ) + response1 = await router.acompletion( + model="gpt-3.5-turbo", messages=messages, temperature=1 + ) + print(f"response1: {response1}") + await asyncio.sleep(1) # add cache is async, async sleep for cache to get set + response2 = await router.acompletion( + model="gpt-3.5-turbo", messages=messages, temperature=1 + ) + print(f"response2: {response2}") + assert response1.id == response2.id + assert len(response1.choices[0].message.content) > 0 + assert ( + response1.choices[0].message.content == response2.choices[0].message.content + ) + router.reset() + except litellm.Timeout as e: + end_time = time.time() + print(f"timeout error occurred: {end_time - start_time}") + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +@pytest.mark.asyncio +async def test_acompletion_caching_with_ttl_on_router(): + # tests acompletion + caching on router + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router( + model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle", + ) + response1 = await router.acompletion( + model="gpt-3.5-turbo", messages=messages, temperature=1, ttl=0 + ) + print(f"response1: {response1}") + await asyncio.sleep(1) # add cache is async, async sleep for cache to get set + response2 = await router.acompletion( + model="gpt-3.5-turbo", messages=messages, temperature=1, ttl=0 + ) + print(f"response2: {response2}") + assert response1.id != response2.id + assert len(response1.choices[0].message.content) > 0 + assert ( + response1.choices[0].message.content != response2.choices[0].message.content + ) + router.reset() + except litellm.Timeout as e: + end_time = time.time() + print(f"timeout error occurred: {end_time - start_time}") + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +@pytest.mark.asyncio +async def test_acompletion_caching_on_router_caching_groups(): + # tests acompletion + caching on router + try: + # litellm.set_verbose = True + model_list = [ + { + "model_name": "openai-gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "azure-gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router( + model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle", + caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")], + ) + response1 = await router.acompletion( + model="openai-gpt-3.5-turbo", messages=messages, temperature=1 + ) + print(f"response1: {response1}") + await asyncio.sleep(1) # add cache is async, async sleep for cache to get set + response2 = await router.acompletion( + model="azure-gpt-3.5-turbo", messages=messages, temperature=1 + ) + print(f"response2: {response2}") + assert response1.id == response2.id + assert len(response1.choices[0].message.content) > 0 + assert ( + response1.choices[0].message.content == response2.choices[0].message.content + ) + router.reset() + except litellm.Timeout as e: + end_time = time.time() + print(f"timeout error occurred: {end_time - start_time}") + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") diff --git a/litellm/tests/test_router_client_init.py b/litellm/tests/test_router_client_init.py new file mode 100644 index 0000000000000000000000000000000000000000..79f8ba8b2a6a0b58f78f87d33b5d3256a0830428 --- /dev/null +++ b/litellm/tests/test_router_client_init.py @@ -0,0 +1,78 @@ +#### What this tests #### +# This tests client initialization + reinitialization on the router + +#### What this tests #### +# This tests caching on the router +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router + + +async def test_router_init(): + """ + 1. Initializes clients on the router with 0 + 2. Checks if client is still valid + 3. Checks if new client was initialized + """ + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "model_info": {"id": "1234"}, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "tpm": 100000, + "rpm": 10000, + }, + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + client_ttl_time = 2 + router = Router( + model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle", + client_ttl=client_ttl_time, + ) + model = "gpt-3.5-turbo" + cache_key = f"1234_async_client" + ## ASSERT IT EXISTS AT THE START ## + assert router.cache.get_cache(key=cache_key) is not None + response1 = await router.acompletion(model=model, messages=messages, temperature=1) + await asyncio.sleep(client_ttl_time) + ## ASSERT IT'S CLEARED FROM CACHE ## + assert router.cache.get_cache(key=cache_key, local_only=True) is None + ## ASSERT IT EXISTS AFTER RUNNING __GET_CLIENT() ## + assert ( + router._get_client( + deployment=model_list[0], client_type="async", kwargs={"stream": False} + ) + is not None + ) + + +# asyncio.run(test_router_init()) diff --git a/litellm/tests/test_router_debug_logs.py b/litellm/tests/test_router_debug_logs.py new file mode 100644 index 0000000000000000000000000000000000000000..e850dce631a199b5cef40a2e245f4244a9c3d87c --- /dev/null +++ b/litellm/tests/test_router_debug_logs.py @@ -0,0 +1,86 @@ +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm, asyncio, logging +from litellm import Router + +# this tests debug logs from litellm router and litellm proxy server +from litellm._logging import verbose_router_logger + +verbose_router_logger.setLevel(level=logging.INFO) + + +# this tests debug logs from litellm router and litellm proxy server +def test_async_fallbacks(caplog): + # THIS IS A PROD TEST - DO NOT DELETE THIS. Used for testing if litellm proxy verbose logs are human readable + litellm.set_verbose = False + model_list = [ + { + "model_name": "azure/gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": "bad-key", + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + router = Router( + model_list=model_list, + fallbacks=[{"gpt-3.5-turbo": ["azure/gpt-3.5-turbo"]}], + ) + + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + + async def _make_request(): + try: + response = await router.acompletion( + model="gpt-3.5-turbo", messages=messages, max_tokens=1 + ) + router.reset() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + finally: + router.reset() + + asyncio.run(_make_request()) + captured_logs = [rec.message for rec in caplog.records] + + # on circle ci the captured logs get some async task exception logs - filter them out + "Task exception was never retrieved" + captured_logs = [ + log for log in captured_logs if "Task exception was never retrieved" not in log + ] + + print("\n Captured caplog records - ", captured_logs) + + # Define the expected log messages + # - error request, falling back notice, success notice + expected_logs = [ + "litellm.acompletion(model=gpt-3.5-turbo)\x1b[31m Exception OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: bad-key. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}\x1b[0m", + "Falling back to model_group = azure/gpt-3.5-turbo", + "litellm.acompletion(model=azure/chatgpt-v-2)\x1b[32m 200 OK\x1b[0m", + ] + + # Assert that the captured logs match the expected log messages + assert captured_logs == expected_logs diff --git a/litellm/tests/test_router_fallbacks.py b/litellm/tests/test_router_fallbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..6d3cd6e43f8c7124cc979672ca105a34fe771a33 --- /dev/null +++ b/litellm/tests/test_router_fallbacks.py @@ -0,0 +1,700 @@ +#### What this tests #### +# This tests calling router with fallback models + +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm +from litellm import Router +from litellm.integrations.custom_logger import CustomLogger + + +class MyCustomHandler(CustomLogger): + success: bool = False + failure: bool = False + previous_models: int = 0 + + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + print( + f"previous_models: {kwargs['litellm_params']['metadata']['previous_models']}" + ) + self.previous_models += len( + kwargs["litellm_params"]["metadata"]["previous_models"] + ) # {"previous_models": [{"model": litellm_model_name, "exception_type": AuthenticationError, "exception_string": }]} + print(f"self.previous_models: {self.previous_models}") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print( + f"Post-API Call - response object: {response_obj}; model: {kwargs['model']}" + ) + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + + +kwargs = { + "model": "azure/gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hey, how's it going?"}], +} + + +def test_sync_fallbacks(): + try: + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + litellm.set_verbose = True + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + router = Router( + model_list=model_list, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + context_window_fallbacks=[ + {"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, + {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}, + ], + set_verbose=False, + ) + response = router.completion(**kwargs) + print(f"response: {response}") + time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + + print("Passed ! Test router_fallbacks: test_sync_fallbacks()") + router.reset() + except Exception as e: + print(e) + + +# test_sync_fallbacks() + + +@pytest.mark.asyncio +async def test_async_fallbacks(): + litellm.set_verbose = False + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + router = Router( + model_list=model_list, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + context_window_fallbacks=[ + {"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, + {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}, + ], + set_verbose=False, + ) + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await router.acompletion(**kwargs) + print(f"customHandler.previous_models: {customHandler.previous_models}") + await asyncio.sleep( + 0.05 + ) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + finally: + router.reset() + + +# test_async_fallbacks() + + +@pytest.mark.asyncio +async def test_async_fallbacks_embeddings(): + litellm.set_verbose = False + model_list = [ + { # list of model deployments + "model_name": "bad-azure-embedding-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/azure-embedding-model", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "good-azure-embedding-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/azure-embedding-model", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + ] + + router = Router( + model_list=model_list, + fallbacks=[{"bad-azure-embedding-model": ["good-azure-embedding-model"]}], + set_verbose=False, + ) + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + user_message = "Hello, how are you?" + input = [user_message] + try: + kwargs = {"model": "bad-azure-embedding-model", "input": input} + response = await router.aembedding(**kwargs) + print(f"customHandler.previous_models: {customHandler.previous_models}") + await asyncio.sleep( + 0.05 + ) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + finally: + router.reset() + + +def test_dynamic_fallbacks_sync(): + """ + Allow setting the fallback in the router.completion() call. + """ + try: + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + router = Router(model_list=model_list, set_verbose=True) + kwargs = {} + kwargs["model"] = "azure/gpt-3.5-turbo" + kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + kwargs["fallbacks"] = [{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}] + response = router.completion(**kwargs) + print(f"response: {response}") + time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + + +# test_dynamic_fallbacks_sync() + + +@pytest.mark.asyncio +async def test_dynamic_fallbacks_async(): + """ + Allow setting the fallback in the router.completion() call. + """ + try: + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + print() + print() + print() + print() + print(f"STARTING DYNAMIC ASYNC") + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + router = Router(model_list=model_list, set_verbose=True) + kwargs = {} + kwargs["model"] = "azure/gpt-3.5-turbo" + kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + kwargs["fallbacks"] = [{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}] + response = await router.acompletion(**kwargs) + print(f"RESPONSE: {response}") + await asyncio.sleep( + 0.05 + ) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + + +# asyncio.run(test_dynamic_fallbacks_async()) + + +@pytest.mark.asyncio +async def test_async_fallbacks_streaming(): + litellm.set_verbose = False + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + router = Router( + model_list=model_list, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + context_window_fallbacks=[ + {"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, + {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}, + ], + set_verbose=False, + ) + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await router.acompletion(**kwargs, stream=True) + print(f"customHandler.previous_models: {customHandler.previous_models}") + await asyncio.sleep( + 0.05 + ) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + finally: + router.reset() + + +def test_sync_fallbacks_streaming(): + try: + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + litellm.set_verbose = True + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + router = Router( + model_list=model_list, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + context_window_fallbacks=[ + {"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, + {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}, + ], + set_verbose=False, + ) + response = router.completion(**kwargs, stream=True) + print(f"response: {response}") + time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + + print("Passed ! Test router_fallbacks: test_sync_fallbacks()") + router.reset() + except Exception as e: + print(e) + + +@pytest.mark.asyncio +async def test_async_fallbacks_max_retries_per_request(): + litellm.set_verbose = False + litellm.num_retries_per_request = 0 + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + router = Router( + model_list=model_list, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + context_window_fallbacks=[ + {"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, + {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}, + ], + set_verbose=False, + ) + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + try: + response = await router.acompletion(**kwargs, stream=True) + except: + pass + print(f"customHandler.previous_models: {customHandler.previous_models}") + await asyncio.sleep( + 0.05 + ) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 0 # 0 retries, 0 fallback + router.reset() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + finally: + router.reset() diff --git a/litellm/tests/test_router_get_deployments.py b/litellm/tests/test_router_get_deployments.py new file mode 100644 index 0000000000000000000000000000000000000000..0a0fcee6228bb8a10533f347ba2dc302943a72cb --- /dev/null +++ b/litellm/tests/test_router_get_deployments.py @@ -0,0 +1,377 @@ +# Tests for router.get_available_deployment +# specifically test if it can pick the correct LLM when rpm/tpm set +# These are fast Tests, and make no API calls +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +from concurrent.futures import ThreadPoolExecutor +from collections import defaultdict +from dotenv import load_dotenv + +load_dotenv() + + +def test_weighted_selection_router(): + # this tests if load balancing works based on the provided rpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "rpm": 6, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "rpm": 1440, + }, + }, + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] += 1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert ( + selection_counts["azure/chatgpt-v-2"] / total_requests > 0.89 + ), f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_weighted_selection_router() + + +def test_weighted_selection_router_tpm(): + # this tests if load balancing works based on the provided tpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + print("\ntest weighted selection based on TPM\n") + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "tpm": 5, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "tpm": 90, + }, + }, + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] += 1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert ( + selection_counts["azure/chatgpt-v-2"] / total_requests > 0.89 + ), f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_weighted_selection_router_tpm() + + +def test_weighted_selection_router_tpm_as_router_param(): + # this tests if load balancing works based on the provided tpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + print("\ntest weighted selection based on TPM\n") + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 5, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "tpm": 90, + }, + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] += 1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert ( + selection_counts["azure/chatgpt-v-2"] / total_requests > 0.89 + ), f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +test_weighted_selection_router_tpm_as_router_param() + + +def test_weighted_selection_router_rpm_as_router_param(): + # this tests if load balancing works based on the provided tpms in the router + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + print("\ntest weighted selection based on RPM\n") + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "rpm": 5, + "tpm": 5, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + }, + "rpm": 90, + "tpm": 90, + }, + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] += 1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert ( + selection_counts["azure/chatgpt-v-2"] / total_requests > 0.89 + ), f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_weighted_selection_router_tpm_as_router_param() + + +def test_weighted_selection_router_no_rpm_set(): + # this tests if we can do selection when no rpm is provided too + # it's a fast test, only tests get_available_deployment + # users can pass rpms as a litellm_param + try: + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "rpm": 6, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "rpm": 1440, + }, + }, + { + "model_name": "claude-1", + "litellm_params": { + "model": "bedrock/claude1.2", + "rpm": 1440, + }, + }, + ] + router = Router( + model_list=model_list, + ) + selection_counts = defaultdict(int) + + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + for _ in range(1000): + selected_model = router.get_available_deployment("claude-1") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] += 1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert ( + selection_counts["bedrock/claude1.2"] / total_requests == 1 + ), f"Assertion failed: Selection counts {selection_counts}" + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_weighted_selection_router_no_rpm_set() + + +def test_model_group_aliases(): + try: + litellm.set_verbose = False + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + "tpm": 1, + }, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION"), + "tpm": 99, + }, + }, + { + "model_name": "claude-1", + "litellm_params": { + "model": "bedrock/claude1.2", + "tpm": 1, + }, + }, + ] + router = Router( + model_list=model_list, + model_group_alias={ + "gpt-4": "gpt-3.5-turbo" + }, # gpt-4 requests sent to gpt-3.5-turbo + ) + + # test that gpt-4 requests are sent to gpt-3.5-turbo + for _ in range(20): + selected_model = router.get_available_deployment("gpt-4") + print("\n selected model", selected_model) + selected_model_name = selected_model.get("model_name") + if selected_model_name != "gpt-3.5-turbo": + pytest.fail( + f"Selected model {selected_model_name} is not gpt-3.5-turbo" + ) + + # test that + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + selection_counts = defaultdict(int) + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] += 1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert ( + selection_counts["azure/chatgpt-v-2"] / total_requests > 0.89 + ), f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + + router.reset() + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_model_group_aliases() diff --git a/litellm/tests/test_router_init.py b/litellm/tests/test_router_init.py new file mode 100644 index 0000000000000000000000000000000000000000..9ab68866f0064aca980833e875d5a12bd97eb836 --- /dev/null +++ b/litellm/tests/test_router_init.py @@ -0,0 +1,309 @@ +# this tests if the router is initialized correctly +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router +from concurrent.futures import ThreadPoolExecutor +from collections import defaultdict +from dotenv import load_dotenv + +load_dotenv() + +# every time we load the router we should have 4 clients: +# Async +# Sync +# Async + Stream +# Sync + Stream + + +def test_init_clients(): + litellm.set_verbose = True + try: + print("testing init 4 clients with diff timeouts") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "timeout": 0.01, + "stream_timeout": 0.000_001, + "max_retries": 7, + }, + }, + ] + router = Router(model_list=model_list) + for elem in router.model_list: + model_id = elem["model_info"]["id"] + assert router.cache.get_cache(f"{model_id}_client") is not None + assert router.cache.get_cache(f"{model_id}_async_client") is not None + assert router.cache.get_cache(f"{model_id}_stream_client") is not None + assert router.cache.get_cache(f"{model_id}_stream_async_client") is not None + + # check if timeout for stream/non stream clients is set correctly + async_client = router.cache.get_cache(f"{model_id}_async_client") + stream_async_client = router.cache.get_cache( + f"{model_id}_stream_async_client" + ) + + assert async_client.timeout == 0.01 + assert stream_async_client.timeout == 0.000_001 + print("PASSED !") + + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_init_clients() + + +def test_init_clients_basic(): + litellm.set_verbose = True + try: + print("Test basic client init") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + }, + ] + router = Router(model_list=model_list) + for elem in router.model_list: + model_id = elem["model_info"]["id"] + assert router.cache.get_cache(f"{model_id}_client") is not None + assert router.cache.get_cache(f"{model_id}_async_client") is not None + assert router.cache.get_cache(f"{model_id}_stream_client") is not None + assert router.cache.get_cache(f"{model_id}_stream_async_client") is not None + print("PASSED !") + + # see if we can init clients without timeout or max retries set + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_init_clients_basic() + + +def test_init_clients_basic_azure_cloudflare(): + # init azure + cloudflare + # init OpenAI gpt-3.5 + # init OpenAI text-embedding + # init OpenAI comptaible - Mistral/mistral-medium + # init OpenAI compatible - xinference/bge + litellm.set_verbose = True + try: + print("Test basic client init") + model_list = [ + { + "model_name": "azure-cloudflare", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": "https://gateway.ai.cloudflare.com/v1/0399b10e77ac6668c80404a5ff49eb37/litellm-test/azure-openai/openai-gpt-4-test-v-1", + }, + }, + { + "model_name": "gpt-openai", + "litellm_params": { + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + }, + { + "model_name": "text-embedding-ada-002", + "litellm_params": { + "model": "text-embedding-ada-002", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + }, + { + "model_name": "mistral", + "litellm_params": { + "model": "mistral/mistral-tiny", + "api_key": os.getenv("MISTRAL_API_KEY"), + }, + }, + { + "model_name": "bge-base-en", + "litellm_params": { + "model": "xinference/bge-base-en", + "api_base": "http://127.0.0.1:9997/v1", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + }, + ] + router = Router(model_list=model_list) + for elem in router.model_list: + model_id = elem["model_info"]["id"] + assert router.cache.get_cache(f"{model_id}_client") is not None + assert router.cache.get_cache(f"{model_id}_async_client") is not None + assert router.cache.get_cache(f"{model_id}_stream_client") is not None + assert router.cache.get_cache(f"{model_id}_stream_async_client") is not None + print("PASSED !") + + # see if we can init clients without timeout or max retries set + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + + +# test_init_clients_basic_azure_cloudflare() + + +def test_timeouts_router(): + """ + Test the timeouts of the router with multiple clients. This HASas to raise a timeout error + """ + import openai + + litellm.set_verbose = True + try: + print("testing init 4 clients with diff timeouts") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "timeout": 0.000001, + "stream_timeout": 0.000_001, + }, + }, + ] + router = Router(model_list=model_list) + + print("PASSED !") + + async def test(): + try: + await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "hello, write a 20 pg essay"} + ], + ) + except Exception as e: + raise e + + asyncio.run(test()) + except openai.APITimeoutError as e: + print( + "Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e + ) + print(type(e)) + pass + except Exception as e: + pytest.fail( + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" + ) + + +# test_timeouts_router() + + +def test_stream_timeouts_router(): + """ + Test the stream timeouts router. See if it selected the correct client with stream timeout + """ + import openai + + litellm.set_verbose = True + try: + print("testing init 4 clients with diff timeouts") + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "timeout": 200, # regular calls will not timeout, stream calls will + "stream_timeout": 0.000_001, + }, + }, + ] + router = Router(model_list=model_list) + + print("PASSED !") + selected_client = router._get_client( + deployment=router.model_list[0], + kwargs={ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "hello, write a 20 pg essay"}], + "stream": True, + }, + client_type=None, + ) + print("Select client timeout", selected_client.timeout) + assert selected_client.timeout == 0.000_001 + except openai.APITimeoutError as e: + print( + "Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e + ) + print(type(e)) + pass + except Exception as e: + pytest.fail( + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" + ) + + +# test_stream_timeouts_router() + + +def test_xinference_embedding(): + # [Test Init Xinference] this tests if we init xinference on the router correctly + # [Test Exception Mapping] tests that xinference is an openai comptiable provider + print("Testing init xinference") + print( + "this tests if we create an OpenAI client for Xinference, with the correct API BASE" + ) + + model_list = [ + { + "model_name": "xinference", + "litellm_params": { + "model": "xinference/bge-base-en", + "api_base": "os.environ/XINFERENCE_API_BASE", + }, + } + ] + + router = Router(model_list=model_list) + + print(router.model_list) + print(router.model_list[0]) + + assert ( + router.model_list[0]["litellm_params"]["api_base"] == "http://0.0.0.0:9997" + ) # set in env + + openai_client = router._get_client( + deployment=router.model_list[0], + kwargs={"input": ["hello"], "model": "xinference"}, + ) + + assert openai_client._base_url == "http://0.0.0.0:9997" + assert "xinference" in litellm.openai_compatible_providers + print("passed") + + +# test_xinference_embedding() diff --git a/litellm/tests/test_router_policy_violation.py b/litellm/tests/test_router_policy_violation.py new file mode 100644 index 0000000000000000000000000000000000000000..52f50eb591ca99c4598535a7f49e1e0d06ef7e18 --- /dev/null +++ b/litellm/tests/test_router_policy_violation.py @@ -0,0 +1,137 @@ +#### What this tests #### +# This tests if the router sends back a policy violation, without retries + +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm +from litellm import Router +from litellm.integrations.custom_logger import CustomLogger + + +class MyCustomHandler(CustomLogger): + success: bool = False + failure: bool = False + previous_models: int = 0 + + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + print( + f"previous_models: {kwargs['litellm_params']['metadata']['previous_models']}" + ) + self.previous_models += len( + kwargs["litellm_params"]["metadata"]["previous_models"] + ) # {"previous_models": [{"model": litellm_model_name, "exception_type": AuthenticationError, "exception_string": }]} + print(f"self.previous_models: {self.previous_models}") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print( + f"Post-API Call - response object: {response_obj}; model: {kwargs['model']}" + ) + + def log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + + +kwargs = { + "model": "azure/gpt-3.5-turbo", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "vorrei vedere la cosa più bella ad Ercolano. Qual’è?", + }, + ], +} + + +@pytest.mark.asyncio +async def test_async_fallbacks(): + litellm.set_verbose = False + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + }, + "tpm": 240000, + "rpm": 1800, + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000, + }, + ] + + router = Router( + model_list=model_list, + num_retries=3, + fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], + # context_window_fallbacks=[ + # {"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, + # {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}, + # ], + set_verbose=False, + ) + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + try: + response = await router.acompletion(**kwargs) + pytest.fail( + f"An exception occurred: {e}" + ) # should've raised azure policy error + except litellm.Timeout as e: + pass + except Exception as e: + await asyncio.sleep( + 0.05 + ) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 0 # 0 retries, 0 fallback + router.reset() + finally: + router.reset() diff --git a/litellm/tests/test_rules.py b/litellm/tests/test_rules.py new file mode 100644 index 0000000000000000000000000000000000000000..664b9db08da77cb151c0b0b4152e943ecf2bf35e --- /dev/null +++ b/litellm/tests/test_rules.py @@ -0,0 +1,86 @@ +#### What this tests #### +# This tests setting rules before / after making llm api calls +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import completion, acompletion + + +def my_pre_call_rule(input: str): + print(f"input: {input}") + print(f"INSIDE MY PRE CALL RULE, len(input) - {len(input)}") + if len(input) > 10: + return False + return True + + +def my_post_call_rule(input: str): + input = input.lower() + print(f"input: {input}") + print(f"INSIDE MY POST CALL RULE, len(input) - {len(input)}") + if "sorry" in input: + return False + return True + + +## Test 1: Pre-call rule +def test_pre_call_rule(): + try: + litellm.pre_call_rules = [my_pre_call_rule] + ### completion + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "say something inappropriate"}], + ) + pytest.fail(f"Completion call should have been failed. ") + except: + pass + + ### async completion + async def test_async_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await acompletion(model="gpt-3.5-turbo", messages=messages) + pytest.fail(f"acompletion call should have been failed. ") + except Exception as e: + pass + + asyncio.run(test_async_response()) + litellm.pre_call_rules = [] + + +# test_pre_call_rule() +## Test 2: Post-call rule +# commenting out of ci/cd since llm's have variable output which was causing our pipeline to fail erratically. +# def test_post_call_rule(): +# try: +# litellm.pre_call_rules = [] +# litellm.post_call_rules = [my_post_call_rule] +# ### completion +# response = completion(model="gpt-3.5-turbo", +# messages=[{"role": "user", "content": "say sorry"}], +# fallbacks=["deepinfra/Gryphe/MythoMax-L2-13b"]) +# pytest.fail(f"Completion call should have been failed. ") +# except: +# pass +# print(f"MAKING ACOMPLETION CALL") +# # litellm.set_verbose = True +# ### async completion +# async def test_async_response(): +# messages=[{"role": "user", "content": "say sorry"}] +# try: +# response = await acompletion(model="gpt-3.5-turbo", messages=messages) +# pytest.fail(f"acompletion call should have been failed.") +# except Exception as e: +# pass +# asyncio.run(test_async_response()) +# litellm.pre_call_rules = [] +# litellm.post_call_rules = [] + +# test_post_call_rule() diff --git a/litellm/tests/test_s3_logs.py b/litellm/tests/test_s3_logs.py new file mode 100644 index 0000000000000000000000000000000000000000..a5347fb084dbbf4a80675efc3b2560ae6e55d3b0 --- /dev/null +++ b/litellm/tests/test_s3_logs.py @@ -0,0 +1,118 @@ +import sys +import os +import io, asyncio + +# import logging +# logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath("../..")) + +from litellm import completion +import litellm + +litellm.num_retries = 3 + +import time, random +import pytest + + +def test_s3_logging(): + # all s3 requests need to be in one test function + # since we are modifying stdout, and pytests runs tests in parallel + # on circle ci - we only test litellm.acompletion() + try: + # redirect stdout to log_file + litellm.cache = litellm.Cache( + type="s3", s3_bucket_name="cache-bucket-litellm", s3_region_name="us-west-2" + ) + + litellm.success_callback = ["s3"] + litellm.s3_callback_params = { + "s3_bucket_name": "litellm-logs", + "s3_aws_secret_access_key": "os.environ/AWS_SECRET_ACCESS_KEY", + "s3_aws_access_key_id": "os.environ/AWS_ACCESS_KEY_ID", + } + litellm.set_verbose = True + + print("Testing async s3 logging") + + expected_keys = [] + + import time + + curr_time = str(time.time()) + + async def _test(): + return await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": f"This is a test {curr_time}"}], + max_tokens=10, + temperature=0.7, + user="ishaan-2", + ) + + response = asyncio.run(_test()) + print(f"response: {response}") + expected_keys.append(response.id) + + async def _test(): + return await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": f"This is a test {curr_time}"}], + max_tokens=10, + temperature=0.7, + user="ishaan-2", + ) + + response = asyncio.run(_test()) + expected_keys.append(response.id) + print(f"response: {response}") + + import boto3 + + s3 = boto3.client("s3") + bucket_name = "litellm-logs" + # List objects in the bucket + response = s3.list_objects(Bucket=bucket_name) + + # Sort the objects based on the LastModified timestamp + objects = sorted( + response["Contents"], key=lambda x: x["LastModified"], reverse=True + ) + # Get the keys of the most recent objects + most_recent_keys = [obj["Key"] for obj in objects] + print(most_recent_keys) + # for each key, get the part before "-" as the key. Do it safely + cleaned_keys = [] + for key in most_recent_keys: + split_key = key.split("-time=") + cleaned_keys.append(split_key[0]) + print("\n most recent keys", most_recent_keys) + print("\n cleaned keys", cleaned_keys) + print("\n Expected keys: ", expected_keys) + matches = 0 + for key in expected_keys: + assert key in cleaned_keys + + if key in cleaned_keys: + matches += 1 + # remove the match key + cleaned_keys.remove(key) + # this asserts we log, the first request + the 2nd cached request + print("we had two matches ! passed ", matches) + assert matches == 2 + try: + # cleanup s3 bucket in test + for key in most_recent_keys: + s3.delete_object(Bucket=bucket_name, Key=key) + except: + # don't let cleanup fail a test + pass + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + finally: + # post, close log file and verify + # Reset stdout to the original value + print("Passed! Testing async s3 logging") + + +test_s3_logging() diff --git a/litellm/tests/test_stream_chunk_builder.py b/litellm/tests/test_stream_chunk_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..001ae07e093563d91e2d3b5d0abc64e9ac383a2a --- /dev/null +++ b/litellm/tests/test_stream_chunk_builder.py @@ -0,0 +1,149 @@ +import sys, os, time +import traceback, asyncio +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from litellm import completion, stream_chunk_builder +import litellm +import os, dotenv +from openai import OpenAI +import pytest + +dotenv.load_dotenv() + +user_message = "What is the current weather in Boston?" +messages = [{"content": user_message, "role": "user"}] + +function_schema = { + "name": "get_weather", + "description": "gets the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + }, + "required": ["location"], + }, +} + + +tools_schema = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +# def test_stream_chunk_builder_tools(): +# try: +# litellm.set_verbose = False +# response = client.chat.completions.create( +# model="gpt-3.5-turbo", +# messages=messages, +# tools=tools_schema, +# # stream=True, +# # complete_response=True # runs stream_chunk_builder under-the-hood +# ) + +# print(f"response: {response}") +# print(f"response usage: {response.usage}") +# except Exception as e: +# pytest.fail(f"An exception occurred - {str(e)}") + +# test_stream_chunk_builder_tools() + + +def test_stream_chunk_builder_litellm_function_call(): + try: + litellm.set_verbose = False + response = litellm.completion( + model="gpt-3.5-turbo", + messages=messages, + functions=[function_schema], + # stream=True, + # complete_response=True # runs stream_chunk_builder under-the-hood + ) + + print(f"response: {response}") + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_stream_chunk_builder_litellm_function_call() + + +def test_stream_chunk_builder_litellm_tool_call(): + try: + litellm.set_verbose = True + response = litellm.completion( + model="gpt-3.5-turbo", + messages=messages, + tools=tools_schema, + stream=True, + complete_response=True, + ) + + print(f"complete response: {response}") + print(f"complete response usage: {response.usage}") + assert response.usage.completion_tokens > 0 + assert response.usage.prompt_tokens > 0 + assert ( + response.usage.total_tokens + == response.usage.completion_tokens + response.usage.prompt_tokens + ) + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_stream_chunk_builder_litellm_tool_call() + + +def test_stream_chunk_builder_litellm_tool_call_regular_message(): + try: + messages = [{"role": "user", "content": "Hey, how's it going?"}] + # litellm.set_verbose = True + response = litellm.completion( + model="gpt-3.5-turbo", + messages=messages, + tools=tools_schema, + stream=True, + complete_response=True, + ) + + print(f"complete response: {response}") + print(f"complete response usage: {response.usage}") + assert response.usage.completion_tokens > 0 + assert response.usage.prompt_tokens > 0 + assert ( + response.usage.total_tokens + == response.usage.completion_tokens + response.usage.prompt_tokens + ) + + # check provider is in hidden params + print("hidden params", response._hidden_params) + assert response._hidden_params["custom_llm_provider"] == "openai" + + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_stream_chunk_builder_litellm_tool_call_regular_message() diff --git a/litellm/tests/test_streaming.py b/litellm/tests/test_streaming.py new file mode 100644 index 0000000000000000000000000000000000000000..81d0815d691838868f8895b9a8fcd2c501ef6b3f --- /dev/null +++ b/litellm/tests/test_streaming.py @@ -0,0 +1,1527 @@ +#### What this tests #### +# This tests streaming for the completion endpoint + +import sys, os, asyncio +import traceback +import time, pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from dotenv import load_dotenv + +load_dotenv() +import litellm +from litellm import ( + completion, + acompletion, + AuthenticationError, + BadRequestError, + RateLimitError, + ModelResponse, +) + +litellm.logging = False +litellm.set_verbose = True +litellm.num_retries = 3 +litellm.cache = None + +score = 0 + + +def logger_fn(model_call_object: dict): + print(f"model call details: {model_call_object}") + + +user_message = "Hello, how are you?" +messages = [{"content": user_message, "role": "user"}] + + +first_openai_chunk_example = { + "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp", + "object": "chat.completion.chunk", + "created": 1694881253, + "model": "gpt-4-0613", + "choices": [ + { + "index": 0, + "delta": {"role": "assistant", "content": ""}, + "finish_reason": None, # it's null + } + ], +} + + +def validate_first_format(chunk): + # write a test to make sure chunk follows the same format as first_openai_chunk_example + assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk["id"], str), "'id' should be a string." + assert isinstance(chunk["object"], str), "'object' should be a string." + assert isinstance(chunk["created"], int), "'created' should be an integer." + assert isinstance(chunk["model"], str), "'model' should be a string." + assert isinstance(chunk["choices"], list), "'choices' should be a list." + + for choice in chunk["choices"]: + assert isinstance(choice["index"], int), "'index' should be an integer." + assert isinstance(choice["delta"]["role"], str), "'role' should be a string." + assert "messages" not in choice + # openai v1.0.0 returns content as None + assert (choice["finish_reason"] is None) or isinstance( + choice["finish_reason"], str + ), "'finish_reason' should be None or a string." + + +second_openai_chunk_example = { + "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp", + "object": "chat.completion.chunk", + "created": 1694881253, + "model": "gpt-4-0613", + "choices": [ + {"index": 0, "delta": {"content": "Hello"}, "finish_reason": None} # it's null + ], +} + + +def validate_second_format(chunk): + assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk["id"], str), "'id' should be a string." + assert isinstance(chunk["object"], str), "'object' should be a string." + assert isinstance(chunk["created"], int), "'created' should be an integer." + assert isinstance(chunk["model"], str), "'model' should be a string." + assert isinstance(chunk["choices"], list), "'choices' should be a list." + + for choice in chunk["choices"]: + assert isinstance(choice["index"], int), "'index' should be an integer." + # openai v1.0.0 returns content as None + assert (choice["finish_reason"] is None) or isinstance( + choice["finish_reason"], str + ), "'finish_reason' should be None or a string." + + +last_openai_chunk_example = { + "id": "chatcmpl-7zSKLBVXnX9dwgRuDYVqVVDsgh2yp", + "object": "chat.completion.chunk", + "created": 1694881253, + "model": "gpt-4-0613", + "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}], +} + + +def validate_last_format(chunk): + assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk["id"], str), "'id' should be a string." + assert isinstance(chunk["object"], str), "'object' should be a string." + assert isinstance(chunk["created"], int), "'created' should be an integer." + assert isinstance(chunk["model"], str), "'model' should be a string." + assert isinstance(chunk["choices"], list), "'choices' should be a list." + + for choice in chunk["choices"]: + assert isinstance(choice["index"], int), "'index' should be an integer." + assert isinstance( + choice["finish_reason"], str + ), "'finish_reason' should be a string." + + +def streaming_format_tests(idx, chunk): + extracted_chunk = "" + finished = False + print(f"chunk: {chunk}") + if idx == 0: # ensure role assistant is set + validate_first_format(chunk=chunk) + role = chunk["choices"][0]["delta"]["role"] + assert role == "assistant" + elif idx == 1: # second chunk + validate_second_format(chunk=chunk) + if idx != 0: # ensure no role + if "role" in chunk["choices"][0]["delta"]: + pass # openai v1.0.0+ passes role = None + if chunk["choices"][0][ + "finish_reason" + ]: # ensure finish reason is only in last chunk + validate_last_format(chunk=chunk) + finished = True + if ( + "content" in chunk["choices"][0]["delta"] + and chunk["choices"][0]["delta"]["content"] is not None + ): + extracted_chunk = chunk["choices"][0]["delta"]["content"] + print(f"extracted chunk: {extracted_chunk}") + return extracted_chunk, finished + + +tools_schema = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +# def test_completion_cohere_stream(): +# # this is a flaky test due to the cohere API endpoint being unstable +# try: +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="command-nightly", messages=messages, stream=True, max_tokens=50, +# ) +# complete_response = "" +# # Add any assertions here to check the response +# has_finish_reason = False +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("Finish reason not in final chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_cohere_stream() + + +def test_completion_cohere_stream_bad_key(): + try: + litellm.cache = None + api_key = "bad-key" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="command-nightly", + messages=messages, + stream=True, + max_tokens=50, + api_key=api_key, + ) + complete_response = "" + # Add any assertions here to check the response + has_finish_reason = False + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + if finished: + break + complete_response += chunk + if has_finish_reason is False: + raise Exception("Finish reason not in final chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except AuthenticationError as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_cohere_stream_bad_key() + + +def test_completion_azure_stream(): + try: + litellm.set_verbose = False + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="azure/chatgpt-v-2", messages=messages, stream=True, max_tokens=50 + ) + complete_response = "" + # Add any assertions here to check the response + for idx, init_chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, init_chunk) + complete_response += chunk + custom_llm_provider = init_chunk._hidden_params["custom_llm_provider"] + print(f"custom_llm_provider: {custom_llm_provider}") + assert custom_llm_provider == "azure" + if finished: + assert isinstance(init_chunk.choices[0], litellm.utils.StreamingChoices) + break + if complete_response.strip() == "": + raise Exception("Empty response received") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +test_completion_azure_stream() + + +def test_completion_azure_function_calling_stream(): + try: + litellm.set_verbose = False + user_message = "What is the current weather in Boston?" + messages = [{"content": user_message, "role": "user"}] + response = completion( + model="azure/chatgpt-functioncalling", + messages=messages, + stream=True, + tools=tools_schema, + ) + # Add any assertions here to check the response + for chunk in response: + print(chunk) + if chunk["choices"][0]["finish_reason"] == "stop": + break + print(chunk["choices"][0]["finish_reason"]) + print(chunk["choices"][0]["delta"]["content"]) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_azure_function_calling_stream() + + +def test_completion_ollama_hosted_stream(): + try: + litellm.set_verbose = True + response = completion( + model="ollama/phi", + messages=messages, + max_tokens=10, + num_retries=3, + timeout=20, + api_base="https://test-ollama-endpoint.onrender.com", + stream=True, + ) + # Add any assertions here to check the response + complete_response = "" + # Add any assertions here to check the response + for idx, init_chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, init_chunk) + complete_response += chunk + if finished: + assert isinstance(init_chunk.choices[0], litellm.utils.StreamingChoices) + break + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_ollama_hosted_stream() + + +def test_completion_claude_stream(): + try: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="claude-instant-1", messages=messages, stream=True, max_tokens=50 + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_claude_stream() + + +def test_completion_palm_stream(): + try: + litellm.set_verbose = False + print("Streaming palm response") + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + print("testing palm streaming") + response = completion(model="palm/chat-bison", messages=messages, stream=True) + + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + print(chunk) + # print(chunk.choices[0].delta) + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_palm_stream() + + +def test_completion_mistral_api_stream(): + try: + litellm.set_verbose = True + print("Testing streaming mistral api response") + response = completion( + model="mistral/mistral-medium", + messages=[ + { + "role": "user", + "content": "Hey, how's it going?", + } + ], + max_tokens=10, + stream=True, + ) + complete_response = "" + for idx, chunk in enumerate(response): + print(chunk) + # print(chunk.choices[0].delta) + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_mistral_api_stream() + + +def test_completion_deep_infra_stream(): + # deep infra currently includes role in the 2nd chunk + # waiting for them to make a fix on this + try: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + print("testing deep infra streaming") + response = completion( + model="deepinfra/meta-llama/Llama-2-70b-chat-hf", + messages=messages, + stream=True, + max_tokens=80, + ) + + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_deep_infra_stream() + + +@pytest.mark.skip() +def test_completion_nlp_cloud_stream(): + try: + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + print("testing nlp cloud streaming") + response = completion( + model="nlp_cloud/finetuned-llama-2-70b", + messages=messages, + stream=True, + max_tokens=20, + ) + + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + complete_response += chunk + if finished: + break + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + print(f"Error occurred: {e}") + pytest.fail(f"Error occurred: {e}") + + +# test_completion_nlp_cloud_stream() + + +def test_completion_claude_stream_bad_key(): + try: + litellm.cache = None + litellm.set_verbose = True + api_key = "bad-key" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="claude-instant-1", + messages=messages, + stream=True, + max_tokens=50, + api_key=api_key, + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"1234completion_response: {complete_response}") + raise Exception("Auth error not raised") + except AuthenticationError as e: + print("Auth Error raised") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_claude_stream_bad_key() +# test_completion_replicate_stream() + +# def test_completion_vertexai_stream(): +# try: +# import os +# os.environ["VERTEXAI_PROJECT"] = "pathrise-convert-1606954137718" +# os.environ["VERTEXAI_LOCATION"] = "us-central1" +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="vertex_ai/chat-bison", messages=messages, stream=True, max_tokens=50 +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_vertexai_stream() + + +# def test_completion_vertexai_stream_bad_key(): +# try: +# import os +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="vertex_ai/chat-bison", messages=messages, stream=True, max_tokens=50 +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_vertexai_stream_bad_key() + +# def test_completion_replicate_stream(): +# TEMP Commented out - replicate throwing an auth error +# try: +# litellm.set_verbose = True +# messages = [ +# {"role": "system", "content": "You are a helpful assistant."}, +# { +# "role": "user", +# "content": "how does a court case get to the Supreme Court?", +# }, +# ] +# response = completion( +# model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", messages=messages, stream=True, max_tokens=50 +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# print(f"completion_response: {complete_response}") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + + +def test_completion_replicate_stream_bad_key(): + try: + api_key = "bad-key" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": "how does a court case get to the Supreme Court?", + }, + ] + response = completion( + model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=messages, + stream=True, + max_tokens=50, + api_key=api_key, + ) + complete_response = "" + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except AuthenticationError as e: + # this is an auth error with a bad key + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_replicate_stream_bad_key() + + +def test_completion_bedrock_claude_stream(): + try: + litellm.set_verbose = False + response = completion( + model="bedrock/anthropic.claude-instant-v1", + messages=[ + { + "role": "user", + "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?", + } + ], + temperature=1, + max_tokens=20, + stream=True, + ) + print(response) + complete_response = "" + has_finish_reason = False + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + # print + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + complete_response += chunk + if finished: + break + if has_finish_reason is False: + raise Exception("finish reason not set for last chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_claude_stream() + + +def test_completion_bedrock_ai21_stream(): + try: + litellm.set_verbose = False + response = completion( + model="bedrock/ai21.j2-mid-v1", + messages=[ + { + "role": "user", + "content": "Be as verbose as possible and give as many details as possible, how does a court case get to the Supreme Court?", + } + ], + temperature=1, + max_tokens=20, + stream=True, + ) + print(response) + complete_response = "" + has_finish_reason = False + # Add any assertions here to check the response + for idx, chunk in enumerate(response): + # print + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + complete_response += chunk + if finished: + break + if has_finish_reason is False: + raise Exception("finish reason not set for last chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except RateLimitError: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_bedrock_ai21_stream() + + +def test_sagemaker_weird_response(): + """ + When the stream ends, flush any remaining holding chunks. + """ + try: + chunk = """[INST] Hey, how's it going? [/INST] + + I'm doing well, thanks for asking! How about you? Is there anything you'd like to chat about or ask? I'm here to help with any questions you might have.""" + + logging_obj = litellm.Logging( + model="berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=messages, + stream=True, + litellm_call_id="1234", + function_id="function_id", + call_type="acompletion", + start_time=time.time(), + ) + response = litellm.CustomStreamWrapper( + completion_stream=chunk, + model="berri-benchmarking-Llama-2-70b-chat-hf-4", + custom_llm_provider="sagemaker", + logging_obj=logging_obj, + ) + complete_response = "" + for chunk in response: + complete_response += chunk["choices"][0]["delta"]["content"] + assert len(complete_response) > 0 + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_sagemaker_weird_response() + + +@pytest.mark.asyncio +async def test_sagemaker_streaming_async(): + try: + messages = [{"role": "user", "content": "Hey, how's it going?"}] + litellm.set_verbose = True + response = await litellm.acompletion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=messages, + max_tokens=100, + temperature=0.7, + stream=True, + ) + + # Add any assertions here to check the response + complete_response = "" + async for chunk in response: + complete_response += chunk.choices[0].delta.content or "" + print(f"complete_response: {complete_response}") + assert len(complete_response) > 0 + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# def test_completion_sagemaker_stream(): +# try: +# response = completion( +# model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", +# messages=messages, +# temperature=0.2, +# max_tokens=80, +# stream=True, +# ) +# complete_response = "" +# has_finish_reason = False +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finish_reason = finished +# if finished: +# break +# complete_response += chunk +# if has_finish_reason is False: +# raise Exception("finish reason not set for last chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_sagemaker_stream() + + +# def test_maritalk_streaming(): +# messages = [{"role": "user", "content": "Hey"}] +# try: +# response = completion("maritalk", messages=messages, stream=True) +# complete_response = "" +# start_time = time.time() +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# complete_response += chunk +# if finished: +# break +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except: +# pytest.fail(f"error occurred: {traceback.format_exc()}") +# test_maritalk_streaming() +# test on openai completion call + + +# # test on ai21 completion call +def ai21_completion_call(): + try: + messages = [ + { + "role": "system", + "content": "You are an all-knowing oracle", + }, + {"role": "user", "content": "What is the meaning of the Universe?"}, + ] + response = completion( + model="j2-ultra", messages=messages, stream=True, max_tokens=500 + ) + print(f"response: {response}") + has_finished = False + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finished = finished + complete_response += chunk + if finished: + break + if has_finished is False: + raise Exception("finished reason missing from final chunk") + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except: + pytest.fail(f"error occurred: {traceback.format_exc()}") + + +# ai21_completion_call() + + +def ai21_completion_call_bad_key(): + try: + api_key = "bad-key" + response = completion( + model="j2-ultra", messages=messages, stream=True, api_key=api_key + ) + print(f"response: {response}") + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except: + pytest.fail(f"error occurred: {traceback.format_exc()}") + + +# ai21_completion_call_bad_key() + + +@pytest.mark.asyncio +async def test_hf_completion_tgi_stream(): + try: + response = await acompletion( + model="huggingface/HuggingFaceH4/zephyr-7b-beta", + messages=[{"content": "Hello, how are you?", "role": "user"}], + stream=True, + ) + # Add any assertions here to check the response + print(f"response: {response}") + complete_response = "" + start_time = time.time() + idx = 0 + async for chunk in response: + chunk, finished = streaming_format_tests(idx, chunk) + complete_response += chunk + if finished: + break + idx += 1 + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# hf_test_completion_tgi_stream() + +# def test_completion_aleph_alpha(): +# try: +# response = completion( +# model="luminous-base", messages=messages, stream=True +# ) +# # Add any assertions here to check the response +# has_finished = False +# complete_response = "" +# start_time = time.time() +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finished = finished +# complete_response += chunk +# if finished: +# break +# if has_finished is False: +# raise Exception("finished reason missing from final chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# # test_completion_aleph_alpha() + +# def test_completion_aleph_alpha_bad_key(): +# try: +# api_key = "bad-key" +# response = completion( +# model="luminous-base", messages=messages, stream=True, api_key=api_key +# ) +# # Add any assertions here to check the response +# has_finished = False +# complete_response = "" +# start_time = time.time() +# for idx, chunk in enumerate(response): +# chunk, finished = streaming_format_tests(idx, chunk) +# has_finished = finished +# complete_response += chunk +# if finished: +# break +# if has_finished is False: +# raise Exception("finished reason missing from final chunk") +# if complete_response.strip() == "": +# raise Exception("Empty response received") +# except InvalidRequestError as e: +# pass +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") + +# test_completion_aleph_alpha_bad_key() + + +# test on openai completion call +def test_openai_chat_completion_call(): + try: + litellm.set_verbose = False + print(f"making openai chat completion call") + response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + print(f"outside chunk: {chunk}") + if finished: + break + complete_response += chunk + # print(f'complete_chunk: {complete_response}') + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +# test_openai_chat_completion_call() + + +def test_openai_chat_completion_complete_response_call(): + try: + complete_response = completion( + model="gpt-3.5-turbo", + messages=messages, + stream=True, + complete_response=True, + ) + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +# test_openai_chat_completion_complete_response_call() + + +def test_openai_text_completion_call(): + try: + litellm.set_verbose = True + response = completion( + model="gpt-3.5-turbo-instruct", messages=messages, stream=True + ) + complete_response = "" + start_time = time.time() + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + print(f"chunk: {chunk}") + complete_response += chunk + if finished: + break + # print(f'complete_chunk: {complete_response}') + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +# test_openai_text_completion_call() + + +# # test on together ai completion call - starcoder +def test_together_ai_completion_call_mistral(): + try: + litellm.set_verbose = False + start_time = time.time() + response = completion( + model="together_ai/mistralai/Mistral-7B-Instruct-v0.2", + messages=messages, + logger_fn=logger_fn, + stream=True, + ) + complete_response = "" + print(f"returned response object: {response}") + has_finish_reason = False + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + if finished: + break + complete_response += chunk + if has_finish_reason is False: + raise Exception("Finish reason not set for last chunk") + if complete_response == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +def test_together_ai_completion_call_starcoder_bad_key(): + try: + api_key = "bad-key" + start_time = time.time() + response = completion( + model="together_ai/bigcode/starcoder", + messages=messages, + stream=True, + api_key=api_key, + ) + complete_response = "" + has_finish_reason = False + for idx, chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, chunk) + has_finish_reason = finished + if finished: + break + complete_response += chunk + if has_finish_reason is False: + raise Exception("Finish reason not set for last chunk") + if complete_response == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except BadRequestError as e: + pass + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +# test_together_ai_completion_call_starcoder_bad_key() +#### Test Function calling + streaming #### + + +def test_completion_openai_with_functions(): + function1 = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + ] + try: + litellm.set_verbose = False + response = completion( + model="gpt-3.5-turbo-1106", + messages=[{"role": "user", "content": "what's the weather in SF"}], + functions=function1, + stream=True, + ) + # Add any assertions here to check the response + print(response) + for chunk in response: + print(chunk) + if chunk["choices"][0]["finish_reason"] == "stop": + break + print(chunk["choices"][0]["finish_reason"]) + print(chunk["choices"][0]["delta"]["content"]) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai_with_functions() +#### Test Async streaming #### + + +# # test on ai21 completion call +async def ai21_async_completion_call(): + try: + response = completion( + model="j2-ultra", messages=messages, stream=True, logger_fn=logger_fn + ) + print(f"response: {response}") + complete_response = "" + start_time = time.time() + # Change for loop to async for loop + idx = 0 + async for chunk in response: + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + idx += 1 + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +# asyncio.run(ai21_async_completion_call()) + + +async def completion_call(): + try: + response = completion( + model="gpt-3.5-turbo", + messages=messages, + stream=True, + logger_fn=logger_fn, + max_tokens=10, + ) + print(f"response: {response}") + complete_response = "" + start_time = time.time() + # Change for loop to async for loop + idx = 0 + async for chunk in response: + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + idx += 1 + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"complete response: {complete_response}") + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + +# asyncio.run(completion_call()) + +#### Test Function Calling + Streaming #### + +final_openai_function_call_example = { + "id": "chatcmpl-7zVNA4sXUftpIg6W8WlntCyeBj2JY", + "object": "chat.completion", + "created": 1694892960, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": None, + "function_call": { + "name": "get_current_weather", + "arguments": '{\n "location": "Boston, MA"\n}', + }, + }, + "finish_reason": "function_call", + } + ], + "usage": {"prompt_tokens": 82, "completion_tokens": 18, "total_tokens": 100}, +} + +function_calling_output_structure = { + "id": str, + "object": str, + "created": int, + "model": str, + "choices": [ + { + "index": int, + "message": { + "role": str, + "content": (type(None), str), + "function_call": {"name": str, "arguments": str}, + }, + "finish_reason": str, + } + ], + "usage": {"prompt_tokens": int, "completion_tokens": int, "total_tokens": int}, +} + + +def validate_final_structure(item, structure=function_calling_output_structure): + if isinstance(item, list): + if not all(validate_final_structure(i, structure[0]) for i in item): + return Exception( + "Function calling final output doesn't match expected output format" + ) + elif isinstance(item, dict): + if not all( + k in item and validate_final_structure(item[k], v) + for k, v in structure.items() + ): + return Exception( + "Function calling final output doesn't match expected output format" + ) + else: + if not isinstance(item, structure): + return Exception( + "Function calling final output doesn't match expected output format" + ) + return True + + +first_openai_function_call_example = { + "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0", + "object": "chat.completion.chunk", + "created": 1694893248, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "delta": { + "role": "assistant", + "content": None, + "function_call": {"name": "get_current_weather", "arguments": ""}, + }, + "finish_reason": None, + } + ], +} + + +def validate_first_function_call_chunk_structure(item): + if not isinstance(item, dict): + raise Exception("Incorrect format") + + required_keys = {"id", "object", "created", "model", "choices"} + for key in required_keys: + if key not in item: + raise Exception("Incorrect format") + + if not isinstance(item["choices"], list) or not item["choices"]: + raise Exception("Incorrect format") + + required_keys_in_choices_array = {"index", "delta", "finish_reason"} + for choice in item["choices"]: + if not isinstance(choice, dict): + raise Exception("Incorrect format") + for key in required_keys_in_choices_array: + if key not in choice: + raise Exception("Incorrect format") + + if not isinstance(choice["delta"], dict): + raise Exception("Incorrect format") + + required_keys_in_delta = {"role", "content", "function_call"} + for key in required_keys_in_delta: + if key not in choice["delta"]: + raise Exception("Incorrect format") + + if not isinstance(choice["delta"]["function_call"], dict): + raise Exception("Incorrect format") + + required_keys_in_function_call = {"name", "arguments"} + for key in required_keys_in_function_call: + if key not in choice["delta"]["function_call"]: + raise Exception("Incorrect format") + + return True + + +second_function_call_chunk_format = { + "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0", + "object": "chat.completion.chunk", + "created": 1694893248, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "delta": {"function_call": {"arguments": "{\n"}}, + "finish_reason": None, + } + ], +} + + +def validate_second_function_call_chunk_structure(data): + if not isinstance(data, dict): + raise Exception("Incorrect format") + + required_keys = {"id", "object", "created", "model", "choices"} + for key in required_keys: + if key not in data: + raise Exception("Incorrect format") + + if not isinstance(data["choices"], list) or not data["choices"]: + raise Exception("Incorrect format") + + required_keys_in_choices_array = {"index", "delta", "finish_reason"} + for choice in data["choices"]: + if not isinstance(choice, dict): + raise Exception("Incorrect format") + for key in required_keys_in_choices_array: + if key not in choice: + raise Exception("Incorrect format") + + if ( + "function_call" not in choice["delta"] + or "arguments" not in choice["delta"]["function_call"] + ): + raise Exception("Incorrect format") + + return True + + +final_function_call_chunk_example = { + "id": "chatcmpl-7zVRoE5HjHYsCMaVSNgOjzdhbS3P0", + "object": "chat.completion.chunk", + "created": 1694893248, + "model": "gpt-3.5-turbo-0613", + "choices": [{"index": 0, "delta": {}, "finish_reason": "function_call"}], +} + + +def validate_final_function_call_chunk_structure(data): + if not isinstance(data, dict): + raise Exception("Incorrect format") + + required_keys = {"id", "object", "created", "model", "choices"} + for key in required_keys: + if key not in data: + raise Exception("Incorrect format") + + if not isinstance(data["choices"], list) or not data["choices"]: + raise Exception("Incorrect format") + + required_keys_in_choices_array = {"index", "delta", "finish_reason"} + for choice in data["choices"]: + if not isinstance(choice, dict): + raise Exception("Incorrect format") + for key in required_keys_in_choices_array: + if key not in choice: + raise Exception("Incorrect format") + + return True + + +def streaming_and_function_calling_format_tests(idx, chunk): + extracted_chunk = "" + finished = False + print(f"idx: {idx}") + print(f"chunk: {chunk}") + decision = False + if idx == 0: # ensure role assistant is set + decision = validate_first_function_call_chunk_structure(chunk) + role = chunk["choices"][0]["delta"]["role"] + assert role == "assistant" + elif idx != 0: # second chunk + try: + decision = validate_second_function_call_chunk_structure(data=chunk) + except: # check if it's the last chunk (returns an empty delta {} ) + decision = validate_final_function_call_chunk_structure(data=chunk) + finished = True + if "content" in chunk["choices"][0]["delta"]: + extracted_chunk = chunk["choices"][0]["delta"]["content"] + if decision == False: + raise Exception("incorrect format") + return extracted_chunk, finished + + +# def test_openai_streaming_and_function_calling(): +# function1 = [ +# { +# "name": "get_current_weather", +# "description": "Get the current weather in a given location", +# "parameters": { +# "type": "object", +# "properties": { +# "location": { +# "type": "string", +# "description": "The city and state, e.g. San Francisco, CA", +# }, +# "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, +# }, +# "required": ["location"], +# }, +# } +# ] +# messages=[{"role": "user", "content": "What is the weather like in Boston?"}] +# try: +# response = completion( +# model="gpt-3.5-turbo", functions=function1, messages=messages, stream=True, +# ) +# # Add any assertions here to check the response +# for idx, chunk in enumerate(response): +# streaming_and_function_calling_format_tests(idx=idx, chunk=chunk) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# raise e + +# test_openai_streaming_and_function_calling() + + +def test_success_callback_streaming(): + def success_callback(kwargs, completion_response, start_time, end_time): + print( + { + "success": True, + "input": kwargs, + "output": completion_response, + "start_time": start_time, + "end_time": end_time, + } + ) + + litellm.success_callback = [success_callback] + + messages = [{"role": "user", "content": "hello"}] + print("TESTING LITELLM COMPLETION CALL") + response = litellm.completion( + model="j2-light", + messages=messages, + stream=True, + max_tokens=5, + ) + print(response) + + for chunk in response: + print(chunk["choices"][0]) + + +# test_success_callback_streaming() diff --git a/litellm/tests/test_supabase_integration.py b/litellm/tests/test_supabase_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..e92a1de7a72e7ca776803f5d190c9ab2eeb85306 --- /dev/null +++ b/litellm/tests/test_supabase_integration.py @@ -0,0 +1,79 @@ +#### What this tests #### +# This tests if logging to the supabase integration actually works +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import embedding, completion + +litellm.input_callback = ["supabase"] +litellm.success_callback = ["supabase"] +litellm.failure_callback = ["supabase"] + + +litellm.set_verbose = False + + +def test_supabase_logging(): + try: + response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello tell me hi"}], + user="ishaanRegular", + max_tokens=10, + ) + print(response) + except Exception as e: + print(e) + + +# test_supabase_logging() + + +def test_acompletion_sync(): + import asyncio + import time + + async def completion_call(): + try: + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "write a poem"}], + max_tokens=10, + stream=True, + user="ishaanStreamingUser", + timeout=5, + ) + complete_response = "" + start_time = time.time() + async for chunk in response: + chunk_time = time.time() + # print(chunk) + complete_response += chunk["choices"][0]["delta"].get("content", "") + # print(complete_response) + # print(f"time since initial request: {chunk_time - start_time:.5f}") + + if chunk["choices"][0].get("finish_reason", None) != None: + print("🤗🤗🤗 DONE") + return + + except litellm.Timeout as e: + pass + except: + print(f"error occurred: {traceback.format_exc()}") + pass + + asyncio.run(completion_call()) + + +# test_acompletion_sync() + + +# reset callbacks +litellm.input_callback = [] +litellm.success_callback = [] +litellm.failure_callback = [] diff --git a/litellm/tests/test_text_completion.py b/litellm/tests/test_text_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..abf76acde2b8360f228a8d1b576094c85d89fc63 --- /dev/null +++ b/litellm/tests/test_text_completion.py @@ -0,0 +1,2961 @@ +import sys, os, asyncio +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os, io + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm import ( + embedding, + completion, + text_completion, + completion_cost, + atext_completion, +) +from litellm import RateLimitError + +litellm.num_retries = 3 + + +token_prompt = [ + [ + 32, + 2043, + 32, + 329, + 4585, + 262, + 1644, + 14, + 34, + 3705, + 319, + 616, + 47551, + 30, + 930, + 19219, + 284, + 1949, + 284, + 787, + 428, + 355, + 1790, + 355, + 1744, + 981, + 1390, + 3307, + 2622, + 13, + 220, + 198, + 198, + 40, + 423, + 587, + 351, + 616, + 41668, + 32682, + 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22367, + 11, + 317, + 2043, + 32, + 30, + 4222, + 1037, + 502, + 13, + 383, + 14934, + 318, + 6600, + 502, + 6776, + 13, + 220, + 198, + 24361, + 25, + 1148, + 428, + 2642, + 30, + 198, + 33706, + 25, + 3763, + ], +] + + +def test_completion_openai_prompt(): + try: + print("\n text 003 test\n") + response = text_completion( + model="gpt-3.5-turbo-instruct", prompt="What's the weather in SF?" + ) + print(response) + response_str = response["choices"][0]["text"] + # print(response.choices[0]) + # print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai_prompt() + + +def test_completion_openai_engine_and_model(): + try: + print("\n text 003 test\n") + litellm.set_verbose = True + response = text_completion( + model="gpt-3.5-turbo-instruct", + engine="anything", + prompt="What's the weather in SF?", + max_tokens=5, + ) + print(response) + response_str = response["choices"][0]["text"] + # print(response.choices[0]) + # print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai_engine_and_model() + + +def test_completion_openai_engine(): + try: + print("\n text 003 test\n") + litellm.set_verbose = True + response = text_completion( + engine="gpt-3.5-turbo-instruct", + prompt="What's the weather in SF?", + max_tokens=5, + ) + print(response) + response_str = response["choices"][0]["text"] + # print(response.choices[0]) + # print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_openai_engine() + + +def test_completion_chatgpt_prompt(): + try: + print("\n gpt3.5 test\n") + response = text_completion( + model="gpt-3.5-turbo", prompt="What's the weather in SF?" + ) + print(response) + response_str = response["choices"][0]["text"] + print("\n", response.choices) + print("\n", response.choices[0]) + # print(response.choices[0].text) + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_chatgpt_prompt() + + +def test_text_completion_basic(): + try: + print("\n test 003 with logprobs \n") + litellm.set_verbose = False + response = text_completion( + model="gpt-3.5-turbo-instruct", + prompt="good morning", + max_tokens=10, + logprobs=10, + ) + print(response) + print(response.choices) + print(response.choices[0]) + # print(response.choices[0].text) + response_str = response["choices"][0]["text"] + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_text_completion_basic() + + +def test_completion_text_003_prompt_array(): + try: + litellm.set_verbose = False + response = text_completion( + model="gpt-3.5-turbo-instruct", + prompt=token_prompt, # token prompt is a 2d list + ) + print("\n\n response") + + print(response) + # response_str = response["choices"][0]["text"] + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_completion_text_003_prompt_array() + + +# not including this in our ci cd pipeline, since we don't want to fail tests due to an unstable replit +# def test_text_completion_with_proxy(): +# try: +# litellm.set_verbose=True +# response = text_completion( +# model="facebook/opt-125m", +# prompt='Write a tagline for a traditional bavarian tavern', +# api_base="https://openai-proxy.berriai.repl.co/v1", +# custom_llm_provider="openai", +# temperature=0, +# max_tokens=10, +# ) +# print("\n\n response") + +# print(response) +# except Exception as e: +# pytest.fail(f"Error occurred: {e}") +# test_text_completion_with_proxy() + + +##### hugging face tests +def test_completion_hf_prompt_array(): + try: + litellm.set_verbose = True + print("\n testing hf mistral\n") + response = text_completion( + model="huggingface/mistralai/Mistral-7B-v0.1", + prompt=token_prompt, # token prompt is a 2d list, + max_tokens=0, + temperature=0.0, + # echo=True, # hugging face inference api is currently raising errors for this, looks like they have a regression on their side + ) + print("\n\n response") + + print(response) + print(response.choices) + assert len(response.choices) == 2 + # response_str = response["choices"][0]["text"] + except Exception as e: + if "is currently loading" in str(e): + pass + pytest.fail(f"Error occurred: {e}") + + +# test_completion_hf_prompt_array() + + +def test_text_completion_stream(): + try: + response = text_completion( + model="huggingface/mistralai/Mistral-7B-v0.1", + prompt="good morning", + stream=True, + max_tokens=10, + ) + for chunk in response: + print(f"chunk: {chunk}") + except Exception as e: + pytest.fail(f"GOT exception for HF In streaming{e}") + + +# test_text_completion_stream() + +# async def test_text_completion_async_stream(): +# try: +# response = await atext_completion( +# model="text-completion-openai/gpt-3.5-turbo-instruct", +# prompt="good morning", +# stream=True, +# max_tokens=10, +# ) +# async for chunk in response: +# print(f"chunk: {chunk}") +# except Exception as e: +# pytest.fail(f"GOT exception for HF In streaming{e}") + +# asyncio.run(test_text_completion_async_stream()) + + +def test_async_text_completion(): + litellm.set_verbose = True + print("test_async_text_completion") + + async def test_get_response(): + try: + response = await litellm.atext_completion( + model="gpt-3.5-turbo-instruct", + prompt="good morning", + stream=False, + max_tokens=10, + ) + print(f"response: {response}") + except litellm.Timeout as e: + print(e) + except Exception as e: + print(e) + + asyncio.run(test_get_response()) + + +# test_async_text_completion() + + +def test_async_text_completion_stream(): + # tests atext_completion + streaming - assert only one finish reason sent + litellm.set_verbose = False + print("test_async_text_completion with stream") + + async def test_get_response(): + try: + response = await litellm.atext_completion( + model="gpt-3.5-turbo-instruct", + prompt="good morning", + stream=True, + ) + print(f"response: {response}") + + num_finish_reason = 0 + async for chunk in response: + print(chunk) + if chunk["choices"][0].get("finish_reason") is not None: + num_finish_reason += 1 + print("finish_reason", chunk["choices"][0].get("finish_reason")) + + assert ( + num_finish_reason == 1 + ), f"expected only one finish reason. Got {num_finish_reason}" + except Exception as e: + pytest.fail(f"GOT exception for gpt-3.5 instruct In streaming{e}") + + asyncio.run(test_get_response()) + + +test_async_text_completion_stream() + + +@pytest.mark.asyncio +async def test_async_text_completion_chat_model_stream(): + try: + response = await litellm.atext_completion( + model="gpt-3.5-turbo", + prompt="good morning", + stream=True, + max_tokens=10, + ) + + num_finish_reason = 0 + chunks = [] + async for chunk in response: + print(chunk) + chunks.append(chunk) + if chunk["choices"][0].get("finish_reason") is not None: + num_finish_reason += 1 + + assert ( + num_finish_reason == 1 + ), f"expected only one finish reason. Got {num_finish_reason}" + response_obj = litellm.stream_chunk_builder(chunks=chunks) + cost = litellm.completion_cost(completion_response=response_obj) + assert cost > 0 + except Exception as e: + pytest.fail(f"GOT exception for gpt-3.5 In streaming{e}") + + +# asyncio.run(test_async_text_completion_chat_model_stream()) diff --git a/litellm/tests/test_timeout.py b/litellm/tests/test_timeout.py new file mode 100644 index 0000000000000000000000000000000000000000..84aa7537f09208044150622e8df145396abc0234 --- /dev/null +++ b/litellm/tests/test_timeout.py @@ -0,0 +1,183 @@ +#### What this tests #### +# This tests the timeout decorator + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +import litellm +import openai +import pytest, uuid + + +def test_timeout(): + # this Will Raise a timeout + litellm.set_verbose = False + try: + response = litellm.completion( + model="gpt-3.5-turbo", + timeout=0.01, + messages=[{"role": "user", "content": "hello, write a 20 pg essay"}], + ) + except openai.APITimeoutError as e: + print( + "Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e + ) + print(type(e)) + pass + except Exception as e: + pytest.fail( + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" + ) + + +# test_timeout() + + +def test_hanging_request_azure(): + litellm.set_verbose = True + try: + router = litellm.Router( + model_list=[ + { + "model_name": "azure-gpt", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_base": os.environ["AZURE_API_BASE"], + "api_key": os.environ["AZURE_API_KEY"], + }, + }, + { + "model_name": "openai-gpt", + "litellm_params": {"model": "gpt-3.5-turbo"}, + }, + ] + ) + + encoded = litellm.utils.encode(model="gpt-3.5-turbo", text="blue")[0] + response = router.completion( + model="azure-gpt", + messages=[{"role": "user", "content": f"what color is red {uuid.uuid4()}"}], + logit_bias={encoded: 100}, + timeout=0.01, + ) + print(response) + + if response.choices[0].message.content is not None: + pytest.fail("Got a response, expected a timeout") + except openai.APITimeoutError as e: + print( + "Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e + ) + print(type(e)) + pass + except Exception as e: + pytest.fail( + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" + ) + + +# test_hanging_request_azure() + + +def test_hanging_request_openai(): + litellm.set_verbose = True + try: + router = litellm.Router( + model_list=[ + { + "model_name": "azure-gpt", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_base": os.environ["AZURE_API_BASE"], + "api_key": os.environ["AZURE_API_KEY"], + }, + }, + { + "model_name": "openai-gpt", + "litellm_params": {"model": "gpt-3.5-turbo"}, + }, + ] + ) + + encoded = litellm.utils.encode(model="gpt-3.5-turbo", text="blue")[0] + response = router.completion( + model="openai-gpt", + messages=[{"role": "user", "content": "what color is red"}], + logit_bias={encoded: 100}, + timeout=0.01, + ) + print(response) + + if response.choices[0].message.content is not None: + pytest.fail("Got a response, expected a timeout") + except openai.APITimeoutError as e: + print( + "Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e + ) + print(type(e)) + pass + except Exception as e: + pytest.fail( + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" + ) + + +# test_hanging_request_openai() + +# test_timeout() + + +def test_timeout_streaming(): + # this Will Raise a timeout + litellm.set_verbose = False + try: + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "hello, write a 20 pg essay"}], + timeout=0.0001, + stream=True, + ) + for chunk in response: + print(chunk) + except openai.APITimeoutError as e: + print( + "Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e + ) + print(type(e)) + pass + except Exception as e: + pytest.fail( + f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}" + ) + + +# test_timeout_streaming() + + +def test_timeout_ollama(): + # this Will Raise a timeout + import litellm + + litellm.set_verbose = True + try: + litellm.request_timeout = 0.1 + litellm.set_verbose = True + response = litellm.completion( + model="ollama/phi", + messages=[{"role": "user", "content": "hello, what llm are u"}], + max_tokens=1, + api_base="https://test-ollama-endpoint.onrender.com", + ) + # Add any assertions here to check the response + litellm.request_timeout = None + print(response) + except openai.APITimeoutError as e: + print("got a timeout error! Passed ! ") + pass + + +# test_timeout_ollama() diff --git a/litellm/tests/test_together_ai.py b/litellm/tests/test_together_ai.py new file mode 100644 index 0000000000000000000000000000000000000000..d4d5f968a54892b09747d486eeb30a41f6244ce0 --- /dev/null +++ b/litellm/tests/test_together_ai.py @@ -0,0 +1,64 @@ +# import sys, os +# import traceback + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path +# import time +# import litellm +# import openai +# import pytest + +# ### Together AI +# import together +# together.api_key = "" + +# sample_message = [ +# {"role": "user", "content": "Who are you"}, +# {"role": "assistant", "content": "I am your helpful assistant."}, +# {"role": "user", "content": "Tell me a joke"}, +# ] + + +# def format_prompt_togetherai(messages, prompt_format, stop_words): +# start_token, end_token = prompt_format.split('{prompt}') +# prompt = '' +# for message in messages: +# role = message['role'] +# message_content = message['content'] +# if role == 'system': +# prompt += f"{start_token}\n<>\n{message_content}\n<>\n" +# elif role == 'user': +# prompt += f"{start_token}{message_content}{end_token}" +# else: +# prompt += f'{message_content}{stop_words[0]}' +# return prompt + + +# model = 'togethercomputer/CodeLlama-13b-Instruct' +# stop_words = list(together.Models.info(model)['config']['stop']) +# prompt_format = str(together.Models.info(model)['config']['prompt_format']) +# formatted_prompt = format_prompt_togetherai( +# messages=sample_message, prompt_format=prompt_format, stop_words=stop_words) +# for token in together.Complete.create_streaming(prompt=formatted_prompt, +# model=model, stop=stop_words, max_tokens=512): +# print(token, end="") + + +# ### litellm + +# import os +# from litellm import completion + +# os.environ["TOGETHERAI_API_KEY"] = "" + +# sample_message = [ +# {"role": "user", "content": "Who are you"}, +# {"role": "assistant", "content": "I am your helpful assistant."}, +# {"role": "user", "content": "Tell me a joke"}, +# ] + +# res = completion(model="together_ai/togethercomputer/CodeLlama-13b-Instruct", +# messages=sample_message, stream=False, max_tokens=1000) + +# print(list(res)) diff --git a/litellm/tests/test_token_counter.py b/litellm/tests/test_token_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..bf05232d9c9cb639ff07c8373ad2a496218a613f --- /dev/null +++ b/litellm/tests/test_token_counter.py @@ -0,0 +1,141 @@ +#### What this tests #### +# This tests litellm.token_counter() function + +import sys, os +import traceback +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +from litellm import token_counter, encode, decode + + +def test_token_counter_normal_plus_function_calling(): + try: + messages = [ + {"role": "system", "content": "System prompt"}, + {"role": "user", "content": "content1"}, + {"role": "assistant", "content": "content2"}, + {"role": "user", "content": "conten3"}, + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call_E0lOb1h6qtmflUyok4L06TgY", + "function": { + "arguments": '{"query":"search query","domain":"google.ca","gl":"ca","hl":"en"}', + "name": "SearchInternet", + }, + "type": "function", + } + ], + }, + { + "tool_call_id": "call_E0lOb1h6qtmflUyok4L06TgY", + "role": "tool", + "name": "SearchInternet", + "content": "tool content", + }, + ] + tokens = token_counter(model="gpt-3.5-turbo", messages=messages) + print(f"tokens: {tokens}") + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_token_counter_normal_plus_function_calling() + + +def test_tokenizers(): + try: + ### test the openai, claude, cohere and llama2 tokenizers. + ### The tokenizer value should be different for all + sample_text = "Hellö World, this is my input string!" + + # openai tokenizer + openai_tokens = token_counter(model="gpt-3.5-turbo", text=sample_text) + + # claude tokenizer + claude_tokens = token_counter(model="claude-instant-1", text=sample_text) + + # cohere tokenizer + cohere_tokens = token_counter(model="command-nightly", text=sample_text) + + # llama2 tokenizer + llama2_tokens = token_counter( + model="meta-llama/Llama-2-7b-chat", text=sample_text + ) + + print( + f"openai tokens: {openai_tokens}; claude tokens: {claude_tokens}; cohere tokens: {cohere_tokens}; llama2 tokens: {llama2_tokens}" + ) + + # assert that all token values are different + assert ( + openai_tokens != cohere_tokens != llama2_tokens + ), "Token values are not different." + + print("test tokenizer: It worked!") + except Exception as e: + pytest.fail(f"An exception occured: {e}") + + +# test_tokenizers() + + +def test_encoding_and_decoding(): + try: + sample_text = "Hellö World, this is my input string!" + # openai encoding + decoding + openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text) + openai_text = decode(model="gpt-3.5-turbo", tokens=openai_tokens) + + assert openai_text == sample_text + + # claude encoding + decoding + claude_tokens = encode(model="claude-instant-1", text=sample_text) + claude_text = decode(model="claude-instant-1", tokens=claude_tokens.ids) + + assert claude_text == sample_text + + # cohere encoding + decoding + cohere_tokens = encode(model="command-nightly", text=sample_text) + cohere_text = decode(model="command-nightly", tokens=cohere_tokens.ids) + + assert cohere_text == sample_text + + # llama2 encoding + decoding + llama2_tokens = encode(model="meta-llama/Llama-2-7b-chat", text=sample_text) + llama2_text = decode( + model="meta-llama/Llama-2-7b-chat", tokens=llama2_tokens.ids + ) + + assert llama2_text == sample_text + except Exception as e: + pytest.fail(f"An exception occured: {e}") + + +# test_encoding_and_decoding() + + +def test_gpt_vision_token_counting(): + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What’s in this image?"}, + { + "type": "image_url", + "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", + }, + ], + } + ] + tokens = token_counter(model="gpt-4-vision-preview", messages=messages) + print(f"tokens: {tokens}") + + +# test_gpt_vision_token_counting() diff --git a/litellm/tests/test_tpm_rpm_routing.py b/litellm/tests/test_tpm_rpm_routing.py new file mode 100644 index 0000000000000000000000000000000000000000..588f3fab699604ca4e465eb4d567bbc132eb0922 --- /dev/null +++ b/litellm/tests/test_tpm_rpm_routing.py @@ -0,0 +1,348 @@ +#### What this tests #### +# This tests the router's ability to pick deployment with lowest tpm + +import sys, os, asyncio, time, random +from datetime import datetime +import traceback +from dotenv import load_dotenv + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +from litellm import Router +import litellm +from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler +from litellm.caching import DualCache + +### UNIT TESTS FOR TPM/RPM ROUTING ### + + +def test_tpm_rpm_updated(): + test_cache = DualCache() + model_list = [] + lowest_tpm_logger = LowestTPMLoggingHandler( + router_cache=test_cache, model_list=model_list + ) + model_group = "gpt-3.5-turbo" + deployment_id = "1234" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + end_time = time.time() + lowest_tpm_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + current_minute = datetime.now().strftime("%H-%M") + tpm_count_api_key = f"{model_group}:tpm:{current_minute}" + rpm_count_api_key = f"{model_group}:rpm:{current_minute}" + assert ( + response_obj["usage"]["total_tokens"] + == test_cache.get_cache(key=tpm_count_api_key)[deployment_id] + ) + assert 1 == test_cache.get_cache(key=rpm_count_api_key)[deployment_id] + + +# test_tpm_rpm_updated() + + +def test_get_available_deployments(): + test_cache = DualCache() + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "azure/chatgpt-v-2"}, + "model_info": {"id": "1234"}, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "azure/chatgpt-v-2"}, + "model_info": {"id": "5678"}, + }, + ] + lowest_tpm_logger = LowestTPMLoggingHandler( + router_cache=test_cache, model_list=model_list + ) + model_group = "gpt-3.5-turbo" + ## DEPLOYMENT 1 ## + deployment_id = "1234" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + end_time = time.time() + lowest_tpm_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + ## DEPLOYMENT 2 ## + deployment_id = "5678" + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "gpt-3.5-turbo", + "deployment": "azure/chatgpt-v-2", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 20}} + end_time = time.time() + lowest_tpm_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + + ## CHECK WHAT'S SELECTED ## + print( + lowest_tpm_logger.get_available_deployments( + model_group=model_group, + healthy_deployments=model_list, + input=["Hello world"], + ) + ) + assert ( + lowest_tpm_logger.get_available_deployments( + model_group=model_group, + healthy_deployments=model_list, + input=["Hello world"], + )["model_info"]["id"] + == "5678" + ) + + +# test_get_available_deployments() + + +def test_router_get_available_deployments(): + """ + Test if routers 'get_available_deployments' returns the least busy deployment + """ + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="usage-based-routing", + set_verbose=False, + num_retries=3, + ) # type: ignore + + ## DEPLOYMENT 1 ## + deployment_id = 1 + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "azure-model", + }, + "model_info": {"id": 1}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 50}} + end_time = time.time() + router.lowesttpm_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + ## DEPLOYMENT 2 ## + deployment_id = 2 + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "azure-model", + }, + "model_info": {"id": 2}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 20}} + end_time = time.time() + router.lowesttpm_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + + ## CHECK WHAT'S SELECTED ## + # print(router.lowesttpm_logger.get_available_deployments(model_group="azure-model")) + assert router.get_available_deployment(model="azure-model")["model_info"]["id"] == 2 + + +# test_get_available_deployments() +# test_router_get_available_deployments() + + +def test_router_skip_rate_limited_deployments(): + """ + Test if routers 'get_available_deployments' raises No Models Available error if max tpm would be reached by message + """ + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "tpm": 1440, + }, + "model_info": {"id": 1}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="usage-based-routing", + set_verbose=False, + num_retries=3, + ) # type: ignore + + ## DEPLOYMENT 1 ## + deployment_id = 1 + kwargs = { + "litellm_params": { + "metadata": { + "model_group": "azure-model", + }, + "model_info": {"id": deployment_id}, + } + } + start_time = time.time() + response_obj = {"usage": {"total_tokens": 1439}} + end_time = time.time() + router.lowesttpm_logger.log_success_event( + response_obj=response_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + ) + + ## CHECK WHAT'S SELECTED ## - should skip 2, and pick 1 + # print(router.lowesttpm_logger.get_available_deployments(model_group="azure-model")) + try: + router.get_available_deployment( + model="azure-model", + messages=[{"role": "user", "content": "Hey, how's it going?"}], + ) + pytest.fail(f"Should have raised No Models Available error") + except Exception as e: + pass + + +@pytest.mark.asyncio +async def test_router_completion_streaming(): + messages = [ + {"role": "user", "content": "Hello, can you generate a 500 words poem?"} + ] + model = "azure-model" + model_list = [ + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-turbo", + "api_key": "os.environ/AZURE_FRANCE_API_KEY", + "api_base": "https://openai-france-1234.openai.azure.com", + "rpm": 1440, + }, + "model_info": {"id": 1}, + }, + { + "model_name": "azure-model", + "litellm_params": { + "model": "azure/gpt-35-turbo", + "api_key": "os.environ/AZURE_EUROPE_API_KEY", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com", + "rpm": 6, + }, + "model_info": {"id": 2}, + }, + ] + router = Router( + model_list=model_list, + routing_strategy="usage-based-routing", + set_verbose=False, + ) # type: ignore + + ### Make 3 calls, test if 3rd call goes to lowest tpm deployment + + ## CALL 1+2 + tasks = [] + response = None + final_response = None + for _ in range(2): + tasks.append(router.acompletion(model=model, messages=messages)) + response = await asyncio.gather(*tasks) + + if response is not None: + ## CALL 3 + await asyncio.sleep(1) # let the token update happen + current_minute = datetime.now().strftime("%H-%M") + picked_deployment = router.lowesttpm_logger.get_available_deployments( + model_group=model, + healthy_deployments=router.healthy_deployments, + messages=messages, + ) + final_response = await router.acompletion(model=model, messages=messages) + print(f"min deployment id: {picked_deployment}") + tpm_key = f"{model}:tpm:{current_minute}" + rpm_key = f"{model}:rpm:{current_minute}" + + tpm_dict = router.cache.get_cache(key=tpm_key) + print(f"tpm_dict: {tpm_dict}") + rpm_dict = router.cache.get_cache(key=rpm_key) + print(f"rpm_dict: {rpm_dict}") + print(f"model id: {final_response._hidden_params['model_id']}") + assert ( + final_response._hidden_params["model_id"] + == picked_deployment["model_info"]["id"] + ) + + +# asyncio.run(test_router_completion_streaming()) diff --git a/litellm/tests/test_traceloop.py b/litellm/tests/test_traceloop.py new file mode 100644 index 0000000000000000000000000000000000000000..405a8a35744fdc81453c72ea3f051d2df1069624 --- /dev/null +++ b/litellm/tests/test_traceloop.py @@ -0,0 +1,49 @@ +# Commented out for now - since traceloop break ci/cd +# import sys +# import os +# import io, asyncio + +# sys.path.insert(0, os.path.abspath('../..')) + +# from litellm import completion +# import litellm +# litellm.num_retries = 3 +# litellm.success_callback = [""] +# import time +# import pytest +# from traceloop.sdk import Traceloop +# Traceloop.init(app_name="test-litellm", disable_batch=True) + + +# def test_traceloop_logging(): +# try: +# litellm.set_verbose = True +# response = litellm.completion( +# model="gpt-3.5-turbo", +# messages=[{"role": "user", "content":"This is a test"}], +# max_tokens=1000, +# temperature=0.7, +# timeout=5, +# ) +# print(f"response: {response}") +# except Exception as e: +# pytest.fail(f"An exception occurred - {e}") +# # test_traceloop_logging() + + +# # def test_traceloop_logging_async(): +# # try: +# # litellm.set_verbose = True +# # async def test_acompletion(): +# # return await litellm.acompletion( +# # model="gpt-3.5-turbo", +# # messages=[{"role": "user", "content":"This is a test"}], +# # max_tokens=1000, +# # temperature=0.7, +# # timeout=5, +# # ) +# # response = asyncio.run(test_acompletion()) +# # print(f"response: {response}") +# # except Exception as e: +# # pytest.fail(f"An exception occurred - {e}") +# # test_traceloop_logging_async() diff --git a/litellm/tests/test_utils.py b/litellm/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a9e1c75fe12eaa9515da081a95bd5e7445161f14 --- /dev/null +++ b/litellm/tests/test_utils.py @@ -0,0 +1,319 @@ +import sys, os +from dotenv import load_dotenv +import copy + +load_dotenv() +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm +from litellm.utils import ( + trim_messages, + get_token_count, + get_valid_models, + check_valid_key, + validate_environment, + function_to_dict, + token_counter, +) + +# Assuming your trim_messages, shorten_message_to_fit_limit, and get_token_count functions are all in a module named 'message_utils' + + +# Test 1: Check trimming of normal message +def test_basic_trimming(): + messages = [ + { + "role": "user", + "content": "This is a long message that definitely exceeds the token limit.", + } + ] + trimmed_messages = trim_messages(messages, model="claude-2", max_tokens=8) + print("trimmed messages") + print(trimmed_messages) + # print(get_token_count(messages=trimmed_messages, model="claude-2")) + assert (get_token_count(messages=trimmed_messages, model="claude-2")) <= 8 + + +# test_basic_trimming() + + +def test_basic_trimming_no_max_tokens_specified(): + messages = [ + { + "role": "user", + "content": "This is a long message that is definitely under the token limit.", + } + ] + trimmed_messages = trim_messages(messages, model="gpt-4") + print("trimmed messages for gpt-4") + print(trimmed_messages) + # print(get_token_count(messages=trimmed_messages, model="claude-2")) + assert ( + get_token_count(messages=trimmed_messages, model="gpt-4") + ) <= litellm.model_cost["gpt-4"]["max_tokens"] + + +# test_basic_trimming_no_max_tokens_specified() + + +def test_multiple_messages_trimming(): + messages = [ + { + "role": "user", + "content": "This is a long message that will exceed the token limit.", + }, + { + "role": "user", + "content": "This is another long message that will also exceed the limit.", + }, + ] + trimmed_messages = trim_messages( + messages=messages, model="gpt-3.5-turbo", max_tokens=20 + ) + # print(get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo")) + assert (get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo")) <= 20 + + +# test_multiple_messages_trimming() + + +def test_multiple_messages_no_trimming(): + messages = [ + { + "role": "user", + "content": "This is a long message that will exceed the token limit.", + }, + { + "role": "user", + "content": "This is another long message that will also exceed the limit.", + }, + ] + trimmed_messages = trim_messages( + messages=messages, model="gpt-3.5-turbo", max_tokens=100 + ) + print("Trimmed messages") + print(trimmed_messages) + assert messages == trimmed_messages + + +# test_multiple_messages_no_trimming() + + +def test_large_trimming_multiple_messages(): + messages = [ + {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}, + {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}, + {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}, + {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}, + {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}, + ] + trimmed_messages = trim_messages(messages, max_tokens=20, model="gpt-4-0613") + print("trimmed messages") + print(trimmed_messages) + assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 20 + + +# test_large_trimming() + + +def test_large_trimming_single_message(): + messages = [ + {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."} + ] + trimmed_messages = trim_messages(messages, max_tokens=5, model="gpt-4-0613") + assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 5 + assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) > 0 + + +def test_trimming_with_system_message_within_max_tokens(): + # This message is 33 tokens long + messages = [ + {"role": "system", "content": "This is a short system message"}, + { + "role": "user", + "content": "This is a medium normal message, let's say litellm is awesome.", + }, + ] + trimmed_messages = trim_messages( + messages, max_tokens=30, model="gpt-4-0613" + ) # The system message should fit within the token limit + assert len(trimmed_messages) == 2 + assert trimmed_messages[0]["content"] == "This is a short system message" + + +def test_trimming_with_system_message_exceeding_max_tokens(): + # This message is 33 tokens long. The system message is 13 tokens long. + messages = [ + {"role": "system", "content": "This is a short system message"}, + { + "role": "user", + "content": "This is a medium normal message, let's say litellm is awesome.", + }, + ] + trimmed_messages = trim_messages(messages, max_tokens=12, model="gpt-4-0613") + assert len(trimmed_messages) == 1 + + +def test_trimming_should_not_change_original_messages(): + messages = [ + {"role": "system", "content": "This is a short system message"}, + { + "role": "user", + "content": "This is a medium normal message, let's say litellm is awesome.", + }, + ] + messages_copy = copy.deepcopy(messages) + trimmed_messages = trim_messages(messages, max_tokens=12, model="gpt-4-0613") + assert messages == messages_copy + + +def test_get_valid_models(): + old_environ = os.environ + os.environ = {"OPENAI_API_KEY": "temp"} # mock set only openai key in environ + + valid_models = get_valid_models() + print(valid_models) + + # list of openai supported llms on litellm + expected_models = ( + litellm.open_ai_chat_completion_models + litellm.open_ai_text_completion_models + ) + + assert valid_models == expected_models + + # reset replicate env key + os.environ = old_environ + + +# test_get_valid_models() + + +def test_bad_key(): + key = "bad-key" + response = check_valid_key(model="gpt-3.5-turbo", api_key=key) + print(response, key) + assert response == False + + +def test_good_key(): + key = os.environ["OPENAI_API_KEY"] + response = check_valid_key(model="gpt-3.5-turbo", api_key=key) + assert response == True + + +# test validate environment + + +def test_validate_environment_empty_model(): + api_key = validate_environment() + if api_key is None: + raise Exception() + + +# test_validate_environment_empty_model() + + +def test_function_to_dict(): + print("testing function to dict for get current weather") + + def get_current_weather(location: str, unit: str): + """Get the current weather in a given location + + Parameters + ---------- + location : str + The city and state, e.g. San Francisco, CA + unit : {'celsius', 'fahrenheit'} + Temperature unit + + Returns + ------- + str + a sentence indicating the weather + """ + if location == "Boston, MA": + return "The weather is 12F" + + function_json = litellm.utils.function_to_dict(get_current_weather) + print(function_json) + + expected_output = { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": { + "type": "string", + "description": "Temperature unit", + "enum": "['fahrenheit', 'celsius']", + }, + }, + "required": ["location", "unit"], + }, + } + print(expected_output) + + assert function_json["name"] == expected_output["name"] + assert function_json["description"] == expected_output["description"] + assert function_json["parameters"]["type"] == expected_output["parameters"]["type"] + assert ( + function_json["parameters"]["properties"]["location"] + == expected_output["parameters"]["properties"]["location"] + ) + + # the enum can change it can be - which is why we don't assert on unit + # {'type': 'string', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"} + # {'type': 'string', 'description': 'Temperature unit', 'enum': "['celsius', 'fahrenheit']"} + + assert ( + function_json["parameters"]["required"] + == expected_output["parameters"]["required"] + ) + + print("passed") + + +# test_function_to_dict() + + +def test_token_counter(): + try: + messages = [{"role": "user", "content": "hi how are you what time is it"}] + tokens = token_counter(model="gpt-3.5-turbo", messages=messages) + print("gpt-35-turbo") + print(tokens) + assert tokens > 0 + + tokens = token_counter(model="claude-2", messages=messages) + print("claude-2") + print(tokens) + assert tokens > 0 + + tokens = token_counter(model="palm/chat-bison", messages=messages) + print("palm/chat-bison") + print(tokens) + assert tokens > 0 + + tokens = token_counter(model="ollama/llama2", messages=messages) + print("ollama/llama2") + print(tokens) + assert tokens > 0 + + tokens = token_counter(model="anthropic.claude-instant-v1", messages=messages) + print("anthropic.claude-instant-v1") + print(tokens) + assert tokens > 0 + except Exception as e: + pytest.fail(f"Error occurred: {e}") + + +# test_token_counter() diff --git a/litellm/tests/test_validate_environment.py b/litellm/tests/test_validate_environment.py new file mode 100644 index 0000000000000000000000000000000000000000..dce61b3abbb23f22bab3472f39f9dc95c8f5e53a --- /dev/null +++ b/litellm/tests/test_validate_environment.py @@ -0,0 +1,13 @@ +#### What this tests #### +# This tests the validate environment function + +import sys, os +import traceback + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import time +import litellm + +print(litellm.validate_environment("openai/gpt-3.5-turbo")) diff --git a/litellm/tests/test_wandb.py b/litellm/tests/test_wandb.py new file mode 100644 index 0000000000000000000000000000000000000000..d31310fa6f69125e2026de642dc6c14656048845 --- /dev/null +++ b/litellm/tests/test_wandb.py @@ -0,0 +1,72 @@ +import sys +import os +import io, asyncio + +# import logging +# logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath("../..")) + +from litellm import completion +import litellm + +litellm.num_retries = 3 +litellm.success_callback = ["wandb"] +import time +import pytest + + +def test_wandb_logging_async(): + try: + litellm.set_verbose = False + + async def _test_langfuse(): + from litellm import Router + + model_list = [ + { # list of model deployments + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + } + ] + + router = Router(model_list=model_list) + + # openai.ChatCompletion.create replacement + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "this is a test with litellm router ?"} + ], + ) + print(response) + + response = asyncio.run(_test_langfuse()) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pass + + +test_wandb_logging_async() + + +def test_wandb_logging(): + try: + response = completion( + model="claude-instant-1.2", + messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}], + max_tokens=10, + temperature=0.2, + ) + print(response) + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + + +# test_wandb_logging() diff --git a/litellm/tests/user_cost.json b/litellm/tests/user_cost.json new file mode 100644 index 0000000000000000000000000000000000000000..b3411c777cd5eed30989740cbe08afedbbd70e08 --- /dev/null +++ b/litellm/tests/user_cost.json @@ -0,0 +1,12 @@ +{ + "1234": { + "total_budget": 10, + "current_cost": 7.3e-05, + "model_cost": { + "gpt-3.5-turbo-0613": 7.3e-05 + } + }, + "12345": { + "total_budget": 0 + } +} \ No newline at end of file diff --git a/litellm/tests/vertex_key.json b/litellm/tests/vertex_key.json new file mode 100644 index 0000000000000000000000000000000000000000..bd319ac94abaf47c312c9a21309528401230faca --- /dev/null +++ b/litellm/tests/vertex_key.json @@ -0,0 +1,13 @@ +{ + "type": "service_account", + "project_id": "reliablekeys", + "private_key_id": "", + "private_key": "", + "client_email": "73470430121-compute@developer.gserviceaccount.com", + "client_id": "108560959659377334173", + "auth_uri": "https://accounts.google.com/o/oauth2/auth", + "token_uri": "https://oauth2.googleapis.com/token", + "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", + "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/73470430121-compute%40developer.gserviceaccount.com", + "universe_domain": "googleapis.com" +} \ No newline at end of file diff --git a/litellm/timeout.py b/litellm/timeout.py new file mode 100644 index 0000000000000000000000000000000000000000..b4446edf07f6378e0f8f8f9c0ab69891cb3f407a --- /dev/null +++ b/litellm/timeout.py @@ -0,0 +1,111 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +""" +Module containing "timeout" decorator for sync and async callables. +""" + +import asyncio + +from concurrent import futures +from inspect import iscoroutinefunction +from functools import wraps +from threading import Thread +from litellm.exceptions import Timeout + + +def timeout(timeout_duration: float = 0.0, exception_to_raise=Timeout): + """ + Wraps a function to raise the specified exception if execution time + is greater than the specified timeout. + + Works with both synchronous and asynchronous callables, but with synchronous ones will introduce + some overhead due to the backend use of threads and asyncio. + + :param float timeout_duration: Timeout duration in seconds. If none callable won't time out. + :param OpenAIError exception_to_raise: Exception to raise when the callable times out. + Defaults to TimeoutError. + :return: The decorated function. + :rtype: callable + """ + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + async def async_func(): + return func(*args, **kwargs) + + thread = _LoopWrapper() + thread.start() + future = asyncio.run_coroutine_threadsafe(async_func(), thread.loop) + local_timeout_duration = timeout_duration + if "force_timeout" in kwargs and kwargs["force_timeout"] is not None: + local_timeout_duration = kwargs["force_timeout"] + elif "request_timeout" in kwargs and kwargs["request_timeout"] is not None: + local_timeout_duration = kwargs["request_timeout"] + try: + result = future.result(timeout=local_timeout_duration) + except futures.TimeoutError: + thread.stop_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + raise exception_to_raise( + f"A timeout error occurred. The function call took longer than {local_timeout_duration} second(s).", + model=model, # [TODO]: replace with logic for parsing out llm provider from model name + llm_provider="openai", + ) + thread.stop_loop() + return result + + @wraps(func) + async def async_wrapper(*args, **kwargs): + local_timeout_duration = timeout_duration + if "force_timeout" in kwargs: + local_timeout_duration = kwargs["force_timeout"] + elif "request_timeout" in kwargs and kwargs["request_timeout"] is not None: + local_timeout_duration = kwargs["request_timeout"] + try: + value = await asyncio.wait_for( + func(*args, **kwargs), timeout=timeout_duration + ) + return value + except asyncio.TimeoutError: + model = args[0] if len(args) > 0 else kwargs["model"] + raise exception_to_raise( + f"A timeout error occurred. The function call took longer than {local_timeout_duration} second(s).", + model=model, # [TODO]: replace with logic for parsing out llm provider from model name + llm_provider="openai", + ) + + if iscoroutinefunction(func): + return async_wrapper + return wrapper + + return decorator + + +class _LoopWrapper(Thread): + def __init__(self): + super().__init__(daemon=True) + self.loop = asyncio.new_event_loop() + + def run(self) -> None: + try: + self.loop.run_forever() + self.loop.call_soon_threadsafe(self.loop.close) + except Exception as e: + # Log exception here + pass + finally: + self.loop.close() + asyncio.set_event_loop(None) + + def stop_loop(self): + for task in asyncio.all_tasks(self.loop): + task.cancel() + self.loop.call_soon_threadsafe(self.loop.stop) diff --git a/litellm/types/completion.py b/litellm/types/completion.py new file mode 100644 index 0000000000000000000000000000000000000000..5eac90575601e30d41c3f79fd92989d4f127aa3a --- /dev/null +++ b/litellm/types/completion.py @@ -0,0 +1,36 @@ +from typing import List, Optional, Union + +from pydantic import BaseModel, validator + + +class CompletionRequest(BaseModel): + model: str + messages: List[str] = [] + timeout: Optional[Union[float, int]] = None + temperature: Optional[float] = None + top_p: Optional[float] = None + n: Optional[int] = None + stream: Optional[bool] = None + stop: Optional[dict] = None + max_tokens: Optional[float] = None + presence_penalty: Optional[float] = None + frequency_penalty: Optional[float] = None + logit_bias: Optional[dict] = None + user: Optional[str] = None + response_format: Optional[dict] = None + seed: Optional[int] = None + tools: Optional[List[str]] = None + tool_choice: Optional[str] = None + logprobs: Optional[bool] = None + top_logprobs: Optional[int] = None + deployment_id: Optional[str] = None + functions: Optional[List[str]] = None + function_call: Optional[str] = None + base_url: Optional[str] = None + api_version: Optional[str] = None + api_key: Optional[str] = None + model_list: Optional[List[str]] = None + + class Config: + # allow kwargs + extra = "allow" diff --git a/litellm/types/embedding.py b/litellm/types/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..9db0ef29079f7fdff21e2f3d29d0e4514e8a796c --- /dev/null +++ b/litellm/types/embedding.py @@ -0,0 +1,23 @@ +from typing import List, Optional, Union + +from pydantic import BaseModel, validator + + +class EmbeddingRequest(BaseModel): + model: str + input: List[str] = [] + timeout: int = 600 + api_base: Optional[str] = None + api_version: Optional[str] = None + api_key: Optional[str] = None + api_type: Optional[str] = None + caching: bool = False + user: Optional[str] = None + custom_llm_provider: Optional[Union[str, dict]] = None + litellm_call_id: Optional[str] = None + litellm_logging_obj: Optional[dict] = None + logger_fn: Optional[str] = None + + class Config: + # allow kwargs + extra = "allow" diff --git a/litellm/types/router.py b/litellm/types/router.py new file mode 100644 index 0000000000000000000000000000000000000000..4f0ab444e423a2ef80778401d8e6112aef6f7875 --- /dev/null +++ b/litellm/types/router.py @@ -0,0 +1,41 @@ +from typing import List, Optional, Union, Dict, Tuple, Literal + +from pydantic import BaseModel, validator +from .completion import CompletionRequest +from .embedding import EmbeddingRequest + + +class ModelConfig(BaseModel): + model_name: str + litellm_params: Union[CompletionRequest, EmbeddingRequest] + tpm: int + rpm: int + + +class RouterConfig(BaseModel): + model_list: List[ModelConfig] + + redis_url: Optional[str] = None + redis_host: Optional[str] = None + redis_port: Optional[int] = None + redis_password: Optional[str] = None + + cache_responses: Optional[bool] = False + cache_kwargs: Optional[Dict] = {} + caching_groups: Optional[List[Tuple[str, List[str]]]] = None + client_ttl: Optional[int] = 3600 + num_retries: Optional[int] = 0 + timeout: Optional[float] = None + default_litellm_params: Optional[Dict[str, str]] = {} + set_verbose: Optional[bool] = False + fallbacks: Optional[List] = [] + allowed_fails: Optional[int] = None + context_window_fallbacks: Optional[List] = [] + model_group_alias: Optional[Dict[str, List[str]]] = {} + retry_after: Optional[int] = 0 + routing_strategy: Literal[ + "simple-shuffle", + "least-busy", + "usage-based-routing", + "latency-based-routing", + ] = "simple-shuffle" diff --git a/litellm/utils.py b/litellm/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b61bb59c8ce7d97ea78878b07fd77087e1a2c1f1 --- /dev/null +++ b/litellm/utils.py @@ -0,0 +1,8375 @@ +# +-----------------------------------------------+ +# | | +# | Give Feedback / Get Help | +# | https://github.com/BerriAI/litellm/issues/new | +# | | +# +-----------------------------------------------+ +# +# Thank you users! We ❤️ you! - Krrish & Ishaan + +import sys, re, binascii, struct +import litellm +import dotenv, json, traceback, threading, base64, ast +import subprocess, os +import litellm, openai +import itertools +import random, uuid, requests +from functools import wraps +import datetime, time +import tiktoken +import uuid +import aiohttp +import logging +import asyncio, httpx, inspect +from inspect import iscoroutine +import copy +from tokenizers import Tokenizer +from dataclasses import ( + dataclass, + field, +) # for storing API inputs, outputs, and metadata +import pkg_resources + +filename = pkg_resources.resource_filename(__name__, "llms/tokenizers") +os.environ[ + "TIKTOKEN_CACHE_DIR" +] = filename # use local copy of tiktoken b/c of - https://github.com/BerriAI/litellm/issues/1071 +encoding = tiktoken.get_encoding("cl100k_base") +import importlib.metadata +from .integrations.traceloop import TraceloopLogger +from .integrations.helicone import HeliconeLogger +from .integrations.aispend import AISpendLogger +from .integrations.berrispend import BerriSpendLogger +from .integrations.supabase import Supabase +from .integrations.llmonitor import LLMonitorLogger +from .integrations.prompt_layer import PromptLayerLogger +from .integrations.langsmith import LangsmithLogger +from .integrations.weights_biases import WeightsBiasesLogger +from .integrations.custom_logger import CustomLogger +from .integrations.langfuse import LangFuseLogger +from .integrations.dynamodb import DyanmoDBLogger +from .integrations.s3 import S3Logger +from .integrations.litedebugger import LiteDebugger +from .proxy._types import KeyManagementSystem +from openai import OpenAIError as OriginalError +from openai._models import BaseModel as OpenAIObject +from .exceptions import ( + AuthenticationError, + BadRequestError, + NotFoundError, + RateLimitError, + ServiceUnavailableError, + OpenAIError, + ContextWindowExceededError, + ContentPolicyViolationError, + Timeout, + APIConnectionError, + APIError, + BudgetExceededError, + UnprocessableEntityError, +) +from typing import cast, List, Dict, Union, Optional, Literal, Any +from .caching import Cache +from concurrent.futures import ThreadPoolExecutor + +####### ENVIRONMENT VARIABLES #################### +# Adjust to your specific application needs / system capabilities. +MAX_THREADS = 100 + +# Create a ThreadPoolExecutor +executor = ThreadPoolExecutor(max_workers=MAX_THREADS) +dotenv.load_dotenv() # Loading env variables using dotenv +sentry_sdk_instance = None +capture_exception = None +add_breadcrumb = None +posthog = None +slack_app = None +alerts_channel = None +heliconeLogger = None +promptLayerLogger = None +langsmithLogger = None +weightsBiasesLogger = None +customLogger = None +langFuseLogger = None +dynamoLogger = None +s3Logger = None +llmonitorLogger = None +aispendLogger = None +berrispendLogger = None +supabaseClient = None +liteDebuggerClient = None +callback_list: Optional[List[str]] = [] +user_logger_fn = None +additional_details: Optional[Dict[str, str]] = {} +local_cache: Optional[Dict[str, str]] = {} +last_fetched_at = None +last_fetched_at_keys = None +######## Model Response ######################### +# All liteLLM Model responses will be in this format, Follows the OpenAI Format +# https://docs.litellm.ai/docs/completion/output +# { +# 'choices': [ +# { +# 'finish_reason': 'stop', +# 'index': 0, +# 'message': { +# 'role': 'assistant', +# 'content': " I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." +# } +# } +# ], +# 'created': 1691429984.3852863, +# 'model': 'claude-instant-1', +# 'usage': {'prompt_tokens': 18, 'completion_tokens': 23, 'total_tokens': 41} +# } + + +class UnsupportedParamsError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request(method="POST", url=" https://openai.api.com/v1/") + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +def _generate_id(): # private helper function + return "chatcmpl-" + str(uuid.uuid4()) + + +def map_finish_reason( + finish_reason: str, +): # openai supports 5 stop sequences - 'stop', 'length', 'function_call', 'content_filter', 'null' + # anthropic mapping + if finish_reason == "stop_sequence": + return "stop" + # cohere mapping - https://docs.cohere.com/reference/generate + elif finish_reason == "COMPLETE": + return "stop" + elif finish_reason == "MAX_TOKENS": # cohere + vertex ai + return "length" + elif finish_reason == "ERROR_TOXIC": + return "content_filter" + elif ( + finish_reason == "ERROR" + ): # openai currently doesn't support an 'error' finish reason + return "stop" + # huggingface mapping https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate_stream + elif finish_reason == "eos_token" or finish_reason == "stop_sequence": + return "stop" + elif ( + finish_reason == "FINISH_REASON_UNSPECIFIED" or finish_reason == "STOP" + ): # vertex ai - got from running `print(dir(response_obj.candidates[0].finish_reason))`: ['FINISH_REASON_UNSPECIFIED', 'MAX_TOKENS', 'OTHER', 'RECITATION', 'SAFETY', 'STOP',] + return "stop" + elif finish_reason == "SAFETY": # vertex ai + return "content_filter" + return finish_reason + + +class FunctionCall(OpenAIObject): + arguments: str + name: str + + +class Function(OpenAIObject): + arguments: str + name: str + + +class ChatCompletionMessageToolCall(OpenAIObject): + id: str + function: Function + type: str + + +class Message(OpenAIObject): + def __init__( + self, + content="default", + role="assistant", + logprobs=None, + function_call=None, + tool_calls=None, + **params, + ): + super(Message, self).__init__(**params) + self.content = content + self.role = role + if function_call is not None: + self.function_call = FunctionCall(**function_call) + if tool_calls is not None: + self.tool_calls = [] + for tool_call in tool_calls: + self.tool_calls.append(ChatCompletionMessageToolCall(**tool_call)) + if logprobs is not None: + self._logprobs = logprobs + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + +class Delta(OpenAIObject): + def __init__(self, content=None, role=None, **params): + super(Delta, self).__init__(**params) + self.content = content + self.role = role + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class Choices(OpenAIObject): + def __init__( + self, finish_reason=None, index=0, message=None, logprobs=None, **params + ): + super(Choices, self).__init__(**params) + self.finish_reason = ( + map_finish_reason(finish_reason) or "stop" + ) # set finish_reason for all responses + self.index = index + if message is None: + self.message = Message(content=None) + else: + self.message = message + if logprobs is not None: + self.logprobs = logprobs + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class Usage(OpenAIObject): + def __init__( + self, prompt_tokens=None, completion_tokens=None, total_tokens=None, **params + ): + super(Usage, self).__init__(**params) + if prompt_tokens: + self.prompt_tokens = prompt_tokens + if completion_tokens: + self.completion_tokens = completion_tokens + if total_tokens: + self.total_tokens = total_tokens + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class StreamingChoices(OpenAIObject): + def __init__( + self, + finish_reason=None, + index=0, + delta: Optional[Delta] = None, + logprobs=None, + **params, + ): + super(StreamingChoices, self).__init__(**params) + if finish_reason: + self.finish_reason = finish_reason + else: + self.finish_reason = None + self.index = index + if delta: + self.delta = delta + else: + self.delta = Delta() + + if logprobs is not None: + self.logprobs = logprobs + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class ModelResponse(OpenAIObject): + id: str + """A unique identifier for the completion.""" + + choices: List[Union[Choices, StreamingChoices]] + """The list of completion choices the model generated for the input prompt.""" + + created: int + """The Unix timestamp (in seconds) of when the completion was created.""" + + model: Optional[str] = None + """The model used for completion.""" + + object: str + """The object type, which is always "text_completion" """ + + system_fingerprint: Optional[str] = None + """This fingerprint represents the backend configuration that the model runs with. + + Can be used in conjunction with the `seed` request parameter to understand when + backend changes have been made that might impact determinism. + """ + + usage: Optional[Usage] = None + """Usage statistics for the completion request.""" + + _hidden_params: dict = {} + + def __init__( + self, + id=None, + choices=None, + created=None, + model=None, + object=None, + system_fingerprint=None, + usage=None, + stream=False, + response_ms=None, + hidden_params=None, + **params, + ): + if stream: + object = "chat.completion.chunk" + choices = [StreamingChoices()] + else: + if model in litellm.open_ai_embedding_models: + object = "embedding" + else: + object = "chat.completion" + choices = [Choices()] + if id is None: + id = _generate_id() + else: + id = id + if created is None: + created = int(time.time()) + else: + created = created + model = model + if usage: + usage = usage + else: + usage = Usage() + if hidden_params: + self._hidden_params = hidden_params + super().__init__( + id=id, + choices=choices, + created=created, + model=model, + object=object, + system_fingerprint=system_fingerprint, + usage=usage, + **params, + ) + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + +class Embedding(OpenAIObject): + embedding: list = [] + index: int + object: str + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class EmbeddingResponse(OpenAIObject): + model: Optional[str] = None + """The model used for embedding.""" + + data: Optional[List] = None + """The actual embedding value""" + + object: str + """The object type, which is always "embedding" """ + + usage: Optional[Usage] = None + """Usage statistics for the embedding request.""" + + _hidden_params: dict = {} + + def __init__( + self, model=None, usage=None, stream=False, response_ms=None, data=None + ): + object = "list" + if response_ms: + _response_ms = response_ms + else: + _response_ms = None + if data: + data = data + else: + data = None + + if usage: + usage = usage + else: + usage = Usage() + + model = model + super().__init__(model=model, object=object, data=data, usage=usage) + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + +class TextChoices(OpenAIObject): + def __init__(self, finish_reason=None, index=0, text=None, logprobs=None, **params): + super(TextChoices, self).__init__(**params) + if finish_reason: + self.finish_reason = map_finish_reason(finish_reason) + else: + self.finish_reason = None + self.index = index + if text is not None: + self.text = text + else: + self.text = None + if logprobs: + self.logprobs = [] + else: + self.logprobs = logprobs + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + +class TextCompletionResponse(OpenAIObject): + """ + { + "id": response["id"], + "object": "text_completion", + "created": response["created"], + "model": response["model"], + "choices": [ + { + "text": response["choices"][0]["message"]["content"], + "index": response["choices"][0]["index"], + "logprobs": transformed_logprobs, + "finish_reason": response["choices"][0]["finish_reason"] + } + ], + "usage": response["usage"] + } + """ + + def __init__( + self, + id=None, + choices=None, + created=None, + model=None, + usage=None, + stream=False, + response_ms=None, + **params, + ): + super(TextCompletionResponse, self).__init__(**params) + if stream: + self.object = "text_completion.chunk" + self.choices = [TextChoices()] + else: + self.object = "text_completion" + self.choices = [TextChoices()] + if id is None: + self.id = _generate_id() + else: + self.id = id + if created is None: + self.created = int(time.time()) + else: + self.created = created + if response_ms: + self._response_ms = response_ms + else: + self._response_ms = None + self.model = model + if usage: + self.usage = usage + else: + self.usage = Usage() + self._hidden_params = ( + {} + ) # used in case users want to access the original model response + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + +class ImageResponse(OpenAIObject): + created: Optional[int] = None + + data: Optional[list] = None + + usage: Optional[dict] = None + + _hidden_params: dict = {} + + def __init__(self, created=None, data=None, response_ms=None): + if response_ms: + _response_ms = response_ms + else: + _response_ms = None + if data: + data = data + else: + data = None + + if created: + created = created + else: + created = None + + super().__init__(data=data, created=created) + self.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} + + def __contains__(self, key): + # Define custom behavior for the 'in' operator + return hasattr(self, key) + + def get(self, key, default=None): + # Custom .get() method to access attributes with a default value if the attribute doesn't exist + return getattr(self, key, default) + + def __getitem__(self, key): + # Allow dictionary-style access to attributes + return getattr(self, key) + + def __setitem__(self, key, value): + # Allow dictionary-style assignment of attributes + setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + +############################################################ +def print_verbose(print_statement): + try: + if litellm.set_verbose: + print(print_statement) # noqa + except: + pass + + +####### LOGGING ################### +from enum import Enum + + +class CallTypes(Enum): + embedding = "embedding" + aembedding = "aembedding" + completion = "completion" + acompletion = "acompletion" + atext_completion = "atext_completion" + text_completion = "text_completion" + image_generation = "image_generation" + aimage_generation = "aimage_generation" + + +# Logging function -> log the exact model details + what's being sent | Non-BlockingP +class Logging: + global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, langsmithLogger, capture_exception, add_breadcrumb, llmonitorLogger + + def __init__( + self, + model, + messages, + stream, + call_type, + start_time, + litellm_call_id, + function_id, + ): + if call_type not in [item.value for item in CallTypes]: + allowed_values = ", ".join([item.value for item in CallTypes]) + raise ValueError( + f"Invalid call_type {call_type}. Allowed values: {allowed_values}" + ) + if messages is not None and isinstance(messages, str): + messages = [ + {"role": "user", "content": messages} + ] # convert text completion input to the chat completion format + self.model = model + self.messages = messages + self.stream = stream + self.start_time = start_time # log the call start time + self.call_type = call_type + self.litellm_call_id = litellm_call_id + self.function_id = function_id + self.streaming_chunks = [] # for generating complete stream response + self.model_call_details = {} + + def update_environment_variables( + self, model, user, optional_params, litellm_params, **additional_params + ): + self.optional_params = optional_params + self.model = model + self.user = user + self.litellm_params = litellm_params + self.logger_fn = litellm_params["logger_fn"] + print_verbose(f"self.optional_params: {self.optional_params}") + self.model_call_details = { + "model": self.model, + "messages": self.messages, + "optional_params": self.optional_params, + "litellm_params": self.litellm_params, + "start_time": self.start_time, + "stream": self.stream, + "user": user, + "call_type": str(self.call_type), + **self.optional_params, + **additional_params, + } + + def _pre_call(self, input, api_key, model=None, additional_args={}): + """ + Common helper function across the sync + async pre-call function + """ + # print_verbose(f"logging pre call for model: {self.model} with call type: {self.call_type}") + self.model_call_details["input"] = input + self.model_call_details["api_key"] = api_key + self.model_call_details["additional_args"] = additional_args + self.model_call_details["log_event_type"] = "pre_api_call" + if ( + model + ): # if model name was changes pre-call, overwrite the initial model call name with the new one + self.model_call_details["model"] = model + + def pre_call(self, input, api_key, model=None, additional_args={}): + # Log the exact input to the LLM API + litellm.error_logs["PRE_CALL"] = locals() + try: + self._pre_call( + input=input, + api_key=api_key, + model=model, + additional_args=additional_args, + ) + + # User Logging -> if you pass in a custom logging function + headers = additional_args.get("headers", {}) + if headers is None: + headers = {} + data = additional_args.get("complete_input_dict", {}) + api_base = additional_args.get("api_base", "") + masked_headers = { + k: (v[:-20] + "*" * 20) if (isinstance(v, str) and len(v) > 20) else v + for k, v in headers.items() + } + formatted_headers = " ".join( + [f"-H '{k}: {v}'" for k, v in masked_headers.items()] + ) + + print_verbose(f"PRE-API-CALL ADDITIONAL ARGS: {additional_args}") + + curl_command = "\n\nPOST Request Sent from LiteLLM:\n" + curl_command += "curl -X POST \\\n" + curl_command += f"{api_base} \\\n" + curl_command += ( + f"{formatted_headers} \\\n" if formatted_headers.strip() != "" else "" + ) + curl_command += f"-d '{str(data)}'\n" + if additional_args.get("request_str", None) is not None: + # print the sagemaker / bedrock client request + curl_command = "\nRequest Sent from LiteLLM:\n" + curl_command += additional_args.get("request_str", None) + elif api_base == "": + curl_command = self.model_call_details + print_verbose(f"\033[92m{curl_command}\033[0m\n") + if self.logger_fn and callable(self.logger_fn): + try: + self.logger_fn( + self.model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + + if litellm.max_budget and self.stream: + start_time = self.start_time + end_time = ( + self.start_time + ) # no time has passed as the call hasn't been made yet + time_diff = (end_time - start_time).total_seconds() + float_diff = float(time_diff) + litellm._current_cost += litellm.completion_cost( + model=self.model, + prompt="".join(message["content"] for message in self.messages), + completion="", + total_time=float_diff, + ) + + # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made + for callback in litellm.input_callback: + try: + if callback == "supabase": + print_verbose("reaches supabase for logging!") + model = self.model_call_details["model"] + messages = self.model_call_details["input"] + print_verbose(f"supabaseClient: {supabaseClient}") + supabaseClient.input_log_event( + model=model, + messages=messages, + end_user=self.model_call_details.get("user", "default"), + litellm_call_id=self.litellm_params["litellm_call_id"], + print_verbose=print_verbose, + ) + + elif callback == "lite_debugger": + print_verbose( + f"reaches litedebugger for logging! - model_call_details {self.model_call_details}" + ) + model = self.model_call_details["model"] + messages = self.model_call_details["input"] + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + liteDebuggerClient.input_log_event( + model=model, + messages=messages, + end_user=self.model_call_details.get("user", "default"), + litellm_call_id=self.litellm_params["litellm_call_id"], + litellm_params=self.model_call_details["litellm_params"], + optional_params=self.model_call_details["optional_params"], + print_verbose=print_verbose, + call_type=self.call_type, + ) + elif callback == "sentry" and add_breadcrumb: + print_verbose("reaches sentry breadcrumbing") + add_breadcrumb( + category="litellm.llm_call", + message=f"Model Call Details pre-call: {self.model_call_details}", + level="info", + ) + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_pre_api_call( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + ) + elif callable(callback): # custom logger functions + customLogger.log_input_event( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + print_verbose=print_verbose, + callback_func=callback, + ) + except Exception as e: + traceback.print_exc() + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while input logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + + async def async_pre_call( + self, result=None, start_time=None, end_time=None, **kwargs + ): + """ + Â Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. + """ + start_time, end_time, result = self._success_handler_helper_fn( + start_time=start_time, end_time=end_time, result=result + ) + print_verbose(f"Async input callbacks: {litellm._async_input_callback}") + for callback in litellm._async_input_callback: + try: + if isinstance(callback, CustomLogger): # custom logger class + print_verbose(f"Async input callbacks: CustomLogger") + asyncio.create_task( + callback.async_log_input_event( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + ) + ) + if callable(callback): # custom logger functions + print_verbose(f"Async success callbacks: async_log_event") + asyncio.create_task( + customLogger.async_log_input_event( + model=self.model, + messages=self.messages, + kwargs=self.model_call_details, + print_verbose=print_verbose, + callback_func=callback, + ) + ) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + + def post_call( + self, original_response, input=None, api_key=None, additional_args={} + ): + # Log the exact result from the LLM API, for streaming - log the type of response received + litellm.error_logs["POST_CALL"] = locals() + if isinstance(original_response, dict): + original_response = json.dumps(original_response) + try: + self.model_call_details["input"] = input + self.model_call_details["api_key"] = api_key + self.model_call_details["original_response"] = original_response + self.model_call_details["additional_args"] = additional_args + self.model_call_details["log_event_type"] = "post_api_call" + + # User Logging -> if you pass in a custom logging function + print_verbose( + f"RAW RESPONSE:\n{self.model_call_details.get('original_response', self.model_call_details)}\n\n" + ) + print_verbose( + f"Logging Details Post-API Call: logger_fn - {self.logger_fn} | callable(logger_fn) - {callable(self.logger_fn)}" + ) + print_verbose( + f"Logging Details Post-API Call: LiteLLM Params: {self.model_call_details}" + ) + if self.logger_fn and callable(self.logger_fn): + try: + self.logger_fn( + self.model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + + # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made + for callback in litellm.input_callback: + try: + if callback == "lite_debugger": + print_verbose("reaches litedebugger for post-call logging!") + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + liteDebuggerClient.post_call_log_event( + original_response=original_response, + litellm_call_id=self.litellm_params["litellm_call_id"], + print_verbose=print_verbose, + call_type=self.call_type, + stream=self.stream, + ) + elif callback == "sentry" and add_breadcrumb: + print_verbose("reaches sentry breadcrumbing") + add_breadcrumb( + category="litellm.llm_call", + message=f"Model Call Details post-call: {self.model_call_details}", + level="info", + ) + elif isinstance(callback, CustomLogger): # custom logger class + callback.log_post_api_call( + kwargs=self.model_call_details, + response_obj=None, + start_time=self.start_time, + end_time=None, + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + pass + + def _success_handler_helper_fn( + self, result=None, start_time=None, end_time=None, cache_hit=None + ): + try: + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + self.model_call_details["log_event_type"] = "successful_api_call" + self.model_call_details["end_time"] = end_time + self.model_call_details["cache_hit"] = cache_hit + + if litellm.max_budget and self.stream: + time_diff = (end_time - start_time).total_seconds() + float_diff = float(time_diff) + litellm._current_cost += litellm.completion_cost( + model=self.model, + prompt="", + completion=result["content"], + total_time=float_diff, + ) + + return start_time, end_time, result + except Exception as e: + print_verbose(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}") + + def success_handler( + self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs + ): + print_verbose(f"Logging Details LiteLLM-Success Call") + # print(f"original response in success handler: {self.model_call_details['original_response']}") + try: + print_verbose(f"success callbacks: {litellm.success_callback}") + ## BUILD COMPLETE STREAMED RESPONSE + complete_streaming_response = None + if ( + self.stream + and self.model_call_details.get("litellm_params", {}).get( + "acompletion", False + ) + == False + ): # only call stream chunk builder if it's not acompletion() + if ( + result.choices[0].finish_reason is not None + ): # if it's the last chunk + self.streaming_chunks.append(result) + # print_verbose(f"final set of received chunks: {self.streaming_chunks}") + try: + complete_streaming_response = litellm.stream_chunk_builder( + self.streaming_chunks, + messages=self.model_call_details.get("messages", None), + ) + except: + complete_streaming_response = None + else: + self.streaming_chunks.append(result) + + if complete_streaming_response: + self.model_call_details[ + "complete_streaming_response" + ] = complete_streaming_response + + start_time, end_time, result = self._success_handler_helper_fn( + start_time=start_time, + end_time=end_time, + result=result, + cache_hit=cache_hit, + ) + for callback in litellm.success_callback: + try: + if callback == "lite_debugger": + print_verbose("reaches lite_debugger for logging!") + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + print_verbose( + f"liteDebuggerClient details function {self.call_type} and stream set to {self.stream}" + ) + liteDebuggerClient.log_event( + end_user=kwargs.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=self.litellm_call_id, + print_verbose=print_verbose, + call_type=self.call_type, + stream=self.stream, + ) + if callback == "promptlayer": + print_verbose("reaches promptlayer for logging!") + promptLayerLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "supabase": + print_verbose("reaches supabase for logging!") + kwargs = self.model_call_details + + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + return + else: + print_verbose("reaches supabase for streaming logging!") + result = kwargs["complete_streaming_response"] + + model = kwargs["model"] + messages = kwargs["messages"] + optional_params = kwargs.get("optional_params", {}) + litellm_params = kwargs.get("litellm_params", {}) + supabaseClient.log_event( + model=model, + messages=messages, + end_user=optional_params.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=litellm_params.get( + "litellm_call_id", str(uuid.uuid4()) + ), + print_verbose=print_verbose, + ) + if callback == "wandb": + print_verbose("reaches wandb for logging!") + weightsBiasesLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "langsmith": + print_verbose("reaches langsmith for logging!") + if self.stream: + if "complete_streaming_response" not in kwargs: + break + else: + print_verbose("reaches langfuse for streaming logging!") + result = kwargs["complete_streaming_response"] + langsmithLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "llmonitor": + print_verbose("reaches llmonitor for logging!") + model = self.model + + input = self.model_call_details.get( + "messages", self.model_call_details.get("input", None) + ) + + # if contains input, it's 'embedding', otherwise 'llm' + type = ( + "embed" + if self.call_type == CallTypes.embedding.value + else "llm" + ) + + llmonitorLogger.log_event( + type=type, + event="end", + model=model, + input=input, + user_id=self.model_call_details.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + run_id=self.litellm_call_id, + print_verbose=print_verbose, + ) + if callback == "helicone": + print_verbose("reaches helicone for logging!") + model = self.model + messages = kwargs["messages"] + heliconeLogger.log_success( + model=model, + messages=messages, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "langfuse": + global langFuseLogger + print_verbose("reaches langfuse for logging!") + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + break + else: + print_verbose("reaches langfuse for streaming logging!") + result = kwargs["complete_streaming_response"] + if langFuseLogger is None: + langFuseLogger = LangFuseLogger() + langFuseLogger.log_event( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + user_id=kwargs.get("user", None), + print_verbose=print_verbose, + ) + if callback == "cache" and litellm.cache is not None: + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + print_verbose("success_callback: reaches cache for logging!") + kwargs = self.model_call_details + if self.stream: + if "complete_streaming_response" not in kwargs: + print_verbose( + f"success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n" + ) + return + else: + print_verbose( + "success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache" + ) + result = kwargs["complete_streaming_response"] + # only add to cache once we have a complete streaming response + litellm.cache.add_cache(result, **kwargs) + if callback == "traceloop": + deep_copy = {} + for k, v in self.model_call_details.items(): + if k != "original_response": + deep_copy[k] = v + traceloopLogger.log_event( + kwargs=deep_copy, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif ( + isinstance(callback, CustomLogger) + and self.model_call_details.get("litellm_params", {}).get( + "acompletion", False + ) + == False + and self.model_call_details.get("litellm_params", {}).get( + "aembedding", False + ) + == False + ): # custom logger class + print_verbose(f"success callbacks: Running Custom Logger Class") + if self.stream and complete_streaming_response is None: + callback.log_stream_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + else: + if self.stream and complete_streaming_response: + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} + ) + result = self.model_call_details["complete_response"] + callback.log_success_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if callable(callback): # custom logger functions + print_verbose( + f"success callbacks: Running Custom Callback Function" + ) + customLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + pass + + async def async_success_handler( + self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs + ): + """ + Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. + """ + print_verbose(f"Async success callbacks: {litellm._async_success_callback}") + ## BUILD COMPLETE STREAMED RESPONSE + complete_streaming_response = None + if self.stream: + if result.choices[0].finish_reason is not None: # if it's the last chunk + self.streaming_chunks.append(result) + # print_verbose(f"final set of received chunks: {self.streaming_chunks}") + try: + complete_streaming_response = litellm.stream_chunk_builder( + self.streaming_chunks, + messages=self.model_call_details.get("messages", None), + ) + except Exception as e: + print_verbose( + f"Error occurred building stream chunk: {traceback.format_exc()}" + ) + complete_streaming_response = None + else: + self.streaming_chunks.append(result) + if complete_streaming_response: + print_verbose("Async success callbacks: Got a complete streaming response") + self.model_call_details[ + "complete_streaming_response" + ] = complete_streaming_response + start_time, end_time, result = self._success_handler_helper_fn( + start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit + ) + for callback in litellm._async_success_callback: + try: + if callback == "cache" and litellm.cache is not None: + # set_cache once complete streaming response is built + print_verbose("async success_callback: reaches cache for logging!") + kwargs = self.model_call_details + if self.stream: + if "complete_streaming_response" not in kwargs: + print_verbose( + f"async success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n" + ) + return + else: + print_verbose( + "async success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache" + ) + result = kwargs["complete_streaming_response"] + # only add to cache once we have a complete streaming response + litellm.cache.add_cache(result, **kwargs) + if isinstance(callback, CustomLogger): # custom logger class + print_verbose(f"Async success callbacks: CustomLogger") + if self.stream: + if "complete_streaming_response" in self.model_call_details: + await callback.async_log_success_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + ) + else: + await callback.async_log_stream_event( # [TODO]: move this to being an async log stream event function + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + else: + await callback.async_log_success_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if callable(callback): # custom logger functions + print_verbose(f"Async success callbacks: async_log_event") + await customLogger.async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + if callback == "dynamodb": + global dynamoLogger + if dynamoLogger is None: + dynamoLogger = DyanmoDBLogger() + if self.stream: + if "complete_streaming_response" in self.model_call_details: + print_verbose( + "DynamoDB Logger: Got Stream Event - Completed Stream Response" + ) + await dynamoLogger._async_log_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + else: + print_verbose( + "DynamoDB Logger: Got Stream Event - No complete stream response as yet" + ) + else: + await dynamoLogger._async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "s3": + global s3Logger + if s3Logger is None: + s3Logger = S3Logger() + if self.stream: + if "complete_streaming_response" in self.model_call_details: + print_verbose( + "S3Logger Logger: Got Stream Event - Completed Stream Response" + ) + await s3Logger._async_log_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details[ + "complete_streaming_response" + ], + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + else: + print_verbose( + "S3Logger Logger: Got Stream Event - No complete stream response as yet" + ) + else: + await s3Logger._async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "langfuse": + global langFuseLogger + print_verbose("reaches Async langfuse for logging!") + kwargs = {} + for k, v in self.model_call_details.items(): + if ( + k != "original_response" + ): # copy.deepcopy raises errors as this could be a coroutine + kwargs[k] = v + # this only logs streaming once, complete_streaming_response exists i.e when stream ends + if self.stream: + if "complete_streaming_response" not in kwargs: + return + else: + print_verbose( + "reaches Async langfuse for streaming logging!" + ) + result = kwargs["complete_streaming_response"] + if langFuseLogger is None: + langFuseLogger = LangFuseLogger() + await langFuseLogger._async_log_event( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + user_id=kwargs.get("user", None), + print_verbose=print_verbose, + ) + except: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + pass + + def _failure_handler_helper_fn( + self, exception, traceback_exception, start_time=None, end_time=None + ): + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + + # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions + if not hasattr(self, "model_call_details"): + self.model_call_details = {} + + self.model_call_details["log_event_type"] = "failed_api_call" + self.model_call_details["exception"] = exception + self.model_call_details["traceback_exception"] = traceback_exception + self.model_call_details["end_time"] = end_time + self.model_call_details.setdefault("original_response", None) + return start_time, end_time + + def failure_handler( + self, exception, traceback_exception, start_time=None, end_time=None + ): + print_verbose(f"Logging Details LiteLLM-Failure Call") + try: + start_time, end_time = self._failure_handler_helper_fn( + exception=exception, + traceback_exception=traceback_exception, + start_time=start_time, + end_time=end_time, + ) + result = None # result sent to all loggers, init this to None incase it's not created + for callback in litellm.failure_callback: + try: + if callback == "lite_debugger": + print_verbose("reaches lite_debugger for logging!") + print_verbose(f"liteDebuggerClient: {liteDebuggerClient}") + result = { + "model": self.model, + "created": time.time(), + "error": traceback_exception, + "usage": { + "prompt_tokens": prompt_token_calculator( + self.model, messages=self.messages + ), + "completion_tokens": 0, + }, + } + liteDebuggerClient.log_event( + model=self.model, + messages=self.messages, + end_user=self.model_call_details.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=self.litellm_call_id, + print_verbose=print_verbose, + call_type=self.call_type, + stream=self.stream, + ) + elif callback == "llmonitor": + print_verbose("reaches llmonitor for logging error!") + + model = self.model + + input = self.model_call_details["input"] + + type = ( + "embed" + if self.call_type == CallTypes.embedding.value + else "llm" + ) + + llmonitorLogger.log_event( + type=type, + event="error", + user_id=self.model_call_details.get("user", "default"), + model=model, + input=input, + error=traceback_exception, + run_id=self.litellm_call_id, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif callback == "sentry": + print_verbose("sending exception to sentry") + if capture_exception: + capture_exception(exception) + else: + print_verbose( + f"capture exception not initialized: {capture_exception}" + ) + elif callable(callback): # custom logger functions + customLogger.log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + elif ( + isinstance(callback, CustomLogger) + and self.model_call_details.get("litellm_params", {}).get( + "acompletion", False + ) + == False + and self.model_call_details.get("litellm_params", {}).get( + "aembedding", False + ) + == False + ): # custom logger class + callback.log_failure_event( + start_time=start_time, + end_time=end_time, + response_obj=result, + kwargs=self.model_call_details, + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {traceback.format_exc()}" + ) + print_verbose( + f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}" + ) + if capture_exception: # log this error to sentry for debugging + capture_exception(e) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {traceback.format_exc()}" + ) + pass + + async def async_failure_handler( + self, exception, traceback_exception, start_time=None, end_time=None + ): + """ + Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. + """ + start_time, end_time = self._failure_handler_helper_fn( + exception=exception, + traceback_exception=traceback_exception, + start_time=start_time, + end_time=end_time, + ) + result = None # result sent to all loggers, init this to None incase it's not created + for callback in litellm._async_failure_callback: + try: + if isinstance(callback, CustomLogger): # custom logger class + await callback.async_log_failure_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + ) + if callable(callback): # custom logger functions + await customLogger.async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + callback_func=callback, + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" + ) + + +def exception_logging( + additional_args={}, + logger_fn=None, + exception=None, +): + try: + model_call_details = {} + if exception: + model_call_details["exception"] = exception + model_call_details["additional_args"] = additional_args + # User Logging -> if you pass in a custom logging function or want to use sentry breadcrumbs + print_verbose( + f"Logging Details: logger_fn - {logger_fn} | callable(logger_fn) - {callable(logger_fn)}" + ) + if logger_fn and callable(logger_fn): + try: + logger_fn( + model_call_details + ) # Expectation: any logger function passed in by the user should accept a dict object + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + except Exception as e: + print_verbose( + f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}" + ) + pass + + +####### RULES ################### + + +class Rules: + """ + Fail calls based on the input or llm api output + + Example usage: + import litellm + def my_custom_rule(input): # receives the model response + if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer + return False + return True + + litellm.post_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call + + response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user", + "content": "Hey, how's it going?"}], fallbacks=["openrouter/mythomax"]) + """ + + def __init__(self) -> None: + pass + + def pre_call_rules(self, input: str, model: str): + for rule in litellm.pre_call_rules: + if callable(rule): + decision = rule(input) + if decision is False: + raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore + return True + + def post_call_rules(self, input: str, model: str): + for rule in litellm.post_call_rules: + if callable(rule): + decision = rule(input) + if decision is False: + raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore + return True + + +####### CLIENT ################### +# make it easy to log if completion/embedding runs succeeded or failed + see what happened | Non-Blocking +def client(original_function): + global liteDebuggerClient, get_all_keys + rules_obj = Rules() + + def function_setup( + start_time, *args, **kwargs + ): # just run once to check if user wants to send their data anywhere - PostHog/Sentry/Slack/etc. + try: + global callback_list, add_breadcrumb, user_logger_fn, Logging + function_id = kwargs["id"] if "id" in kwargs else None + if litellm.use_client or ( + "use_client" in kwargs and kwargs["use_client"] == True + ): + print_verbose(f"litedebugger initialized") + if "lite_debugger" not in litellm.input_callback: + litellm.input_callback.append("lite_debugger") + if "lite_debugger" not in litellm.success_callback: + litellm.success_callback.append("lite_debugger") + if "lite_debugger" not in litellm.failure_callback: + litellm.failure_callback.append("lite_debugger") + if len(litellm.callbacks) > 0: + for callback in litellm.callbacks: + if callback not in litellm.input_callback: + litellm.input_callback.append(callback) + if callback not in litellm.success_callback: + litellm.success_callback.append(callback) + if callback not in litellm.failure_callback: + litellm.failure_callback.append(callback) + if callback not in litellm._async_success_callback: + litellm._async_success_callback.append(callback) + if callback not in litellm._async_failure_callback: + litellm._async_failure_callback.append(callback) + print_verbose( + f"Initialized litellm callbacks, Async Success Callbacks: {litellm._async_success_callback}" + ) + if ( + len(litellm.input_callback) > 0 + or len(litellm.success_callback) > 0 + or len(litellm.failure_callback) > 0 + ) and len(callback_list) == 0: + callback_list = list( + set( + litellm.input_callback + + litellm.success_callback + + litellm.failure_callback + ) + ) + set_callbacks(callback_list=callback_list, function_id=function_id) + ## ASYNC CALLBACKS + if len(litellm.input_callback) > 0: + removed_async_items = [] + for index, callback in enumerate(litellm.input_callback): + if inspect.iscoroutinefunction(callback): + litellm._async_input_callback.append(callback) + removed_async_items.append(index) + + # Pop the async items from input_callback in reverse order to avoid index issues + for index in reversed(removed_async_items): + litellm.input_callback.pop(index) + + if len(litellm.success_callback) > 0: + removed_async_items = [] + for index, callback in enumerate(litellm.success_callback): + if inspect.iscoroutinefunction(callback): + litellm._async_success_callback.append(callback) + removed_async_items.append(index) + elif callback == "dynamodb": + # dynamo is an async callback, it's used for the proxy and needs to be async + # we only support async dynamo db logging for acompletion/aembedding since that's used on proxy + litellm._async_success_callback.append(callback) + removed_async_items.append(index) + elif callback == "s3": + # s3 is an async callback, it's used for the proxy and needs to be async + # we only support async s3 logging for acompletion/aembedding since that's used on proxy + litellm._async_success_callback.append(callback) + removed_async_items.append(index) + elif callback == "langfuse" and inspect.iscoroutinefunction( + original_function + ): + # use async success callback for langfuse if this is litellm.acompletion(). Streaming logging does not work otherwise + litellm._async_success_callback.append(callback) + removed_async_items.append(index) + + # Pop the async items from success_callback in reverse order to avoid index issues + for index in reversed(removed_async_items): + litellm.success_callback.pop(index) + + if len(litellm.failure_callback) > 0: + removed_async_items = [] + for index, callback in enumerate(litellm.failure_callback): + if inspect.iscoroutinefunction(callback): + litellm._async_failure_callback.append(callback) + removed_async_items.append(index) + + # Pop the async items from failure_callback in reverse order to avoid index issues + for index in reversed(removed_async_items): + litellm.failure_callback.pop(index) + if add_breadcrumb: + add_breadcrumb( + category="litellm.llm_call", + message=f"Positional Args: {args}, Keyword Args: {kwargs}", + level="info", + ) + if "logger_fn" in kwargs: + user_logger_fn = kwargs["logger_fn"] + # CRASH REPORTING TELEMETRY + crash_reporting(*args, **kwargs) + # INIT LOGGER - for user-specified integrations + model = args[0] if len(args) > 0 else kwargs.get("model", None) + call_type = original_function.__name__ + if ( + call_type == CallTypes.completion.value + or call_type == CallTypes.acompletion.value + ): + messages = None + if len(args) > 1: + messages = args[1] + elif kwargs.get("messages", None): + messages = kwargs["messages"] + ### PRE-CALL RULES ### + if ( + isinstance(messages, list) + and len(messages) > 0 + and isinstance(messages[0], dict) + and "content" in messages[0] + ): + rules_obj.pre_call_rules( + input="".join( + m["content"] + for m in messages + if isinstance(m["content"], str) + ), + model=model, + ) + elif ( + call_type == CallTypes.embedding.value + or call_type == CallTypes.aembedding.value + ): + messages = args[1] if len(args) > 1 else kwargs["input"] + elif ( + call_type == CallTypes.image_generation.value + or call_type == CallTypes.aimage_generation.value + ): + messages = args[0] if len(args) > 0 else kwargs["prompt"] + elif ( + call_type == CallTypes.atext_completion.value + or call_type == CallTypes.text_completion.value + ): + messages = args[0] if len(args) > 0 else kwargs["prompt"] + stream = True if "stream" in kwargs and kwargs["stream"] == True else False + logging_obj = Logging( + model=model, + messages=messages, + stream=stream, + litellm_call_id=kwargs["litellm_call_id"], + function_id=function_id, + call_type=call_type, + start_time=start_time, + ) + return logging_obj + except Exception as e: + import logging + + logging.debug( + f"[Non-Blocking] {traceback.format_exc()}; args - {args}; kwargs - {kwargs}" + ) + raise e + + def post_call_processing(original_response, model): + try: + if original_response is None: + pass + else: + call_type = original_function.__name__ + if ( + call_type == CallTypes.completion.value + or call_type == CallTypes.acompletion.value + ): + model_response = original_response["choices"][0]["message"][ + "content" + ] + ### POST-CALL RULES ### + rules_obj.post_call_rules(input=model_response, model=model) + except Exception as e: + raise e + + def crash_reporting(*args, **kwargs): + if litellm.telemetry: + try: + model = args[0] if len(args) > 0 else kwargs["model"] + exception = kwargs["exception"] if "exception" in kwargs else None + custom_llm_provider = ( + kwargs["custom_llm_provider"] + if "custom_llm_provider" in kwargs + else None + ) + safe_crash_reporting( + model=model, + exception=exception, + custom_llm_provider=custom_llm_provider, + ) # log usage-crash details. Do not log any user details. If you want to turn this off, set `litellm.telemetry=False`. + except: + # [Non-Blocking Error] + pass + + @wraps(original_function) + def wrapper(*args, **kwargs): + # Prints Exactly what was passed to litellm function - don't execute any logic here - it should just print + print_args_passed_to_litellm(original_function, args, kwargs) + start_time = datetime.datetime.now() + result = None + logging_obj = kwargs.get("litellm_logging_obj", None) + + # only set litellm_call_id if its not in kwargs + if "litellm_call_id" not in kwargs: + kwargs["litellm_call_id"] = str(uuid.uuid4()) + try: + model = args[0] if len(args) > 0 else kwargs["model"] + except: + model = None + call_type = original_function.__name__ + if ( + call_type != CallTypes.image_generation.value + and call_type != CallTypes.text_completion.value + ): + raise ValueError("model param not passed in.") + + try: + if logging_obj is None: + logging_obj = function_setup(start_time, *args, **kwargs) + kwargs["litellm_logging_obj"] = logging_obj + + # CHECK FOR 'os.environ/' in kwargs + for k, v in kwargs.items(): + if v is not None and isinstance(v, str) and v.startswith("os.environ/"): + kwargs[k] = litellm.get_secret(v) + # [OPTIONAL] CHECK BUDGET + if litellm.max_budget: + if litellm._current_cost > litellm.max_budget: + raise BudgetExceededError( + current_cost=litellm._current_cost, + max_budget=litellm.max_budget, + ) + + # [OPTIONAL] CHECK MAX RETRIES / REQUEST + if litellm.num_retries_per_request is not None: + # check if previous_models passed in as ['litellm_params']['metadata]['previous_models'] + previous_models = kwargs.get("metadata", {}).get( + "previous_models", None + ) + if previous_models is not None: + if litellm.num_retries_per_request <= len(previous_models): + raise Exception(f"Max retries per request hit!") + + # [OPTIONAL] CHECK CACHE + print_verbose( + f"kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}" + ) + # if caching is false or cache["no-cache"]==True, don't run this + if ( + (kwargs.get("caching", None) is None and litellm.cache is not None) + or kwargs.get("caching", False) == True + or ( + kwargs.get("cache", None) is not None + and kwargs.get("cache", {}).get("no-cache", False) != True + ) + ): # allow users to control returning cached responses from the completion function + # checking cache + print_verbose(f"INSIDE CHECKING CACHE") + if ( + litellm.cache is not None + and str(original_function.__name__) + in litellm.cache.supported_call_types + ): + print_verbose(f"Checking Cache") + preset_cache_key = litellm.cache.get_cache_key(*args, **kwargs) + kwargs[ + "preset_cache_key" + ] = preset_cache_key # for streaming calls, we need to pass the preset_cache_key + cached_result = litellm.cache.get_cache(*args, **kwargs) + if cached_result != None: + if "detail" in cached_result: + # implies an error occurred + pass + else: + call_type = original_function.__name__ + print_verbose( + f"Cache Response Object routing: call_type - {call_type}; cached_result instace: {type(cached_result)}" + ) + if call_type == CallTypes.completion.value and isinstance( + cached_result, dict + ): + return convert_to_model_response_object( + response_object=cached_result, + model_response_object=ModelResponse(), + stream=kwargs.get("stream", False), + ) + elif call_type == CallTypes.embedding.value and isinstance( + cached_result, dict + ): + return convert_to_model_response_object( + response_object=cached_result, + response_type="embedding", + ) + else: + return cached_result + # MODEL CALL + result = original_function(*args, **kwargs) + end_time = datetime.datetime.now() + if "stream" in kwargs and kwargs["stream"] == True: + # TODO: Add to cache for streaming + if ( + "complete_response" in kwargs + and kwargs["complete_response"] == True + ): + chunks = [] + for idx, chunk in enumerate(result): + chunks.append(chunk) + return litellm.stream_chunk_builder( + chunks, messages=kwargs.get("messages", None) + ) + else: + return result + elif "acompletion" in kwargs and kwargs["acompletion"] == True: + return result + elif "aembedding" in kwargs and kwargs["aembedding"] == True: + return result + elif "aimg_generation" in kwargs and kwargs["aimg_generation"] == True: + return result + + ### POST-CALL RULES ### + post_call_processing(original_response=result, model=model or None) + + # [OPTIONAL] ADD TO CACHE + if ( + litellm.cache is not None + and str(original_function.__name__) + in litellm.cache.supported_call_types + ): + litellm.cache.add_cache(result, *args, **kwargs) + + # LOG SUCCESS - handle streaming success logging in the _next_ object, remove `handle_success` once it's deprecated + print_verbose(f"Wrapper: Completed Call, calling success_handler") + threading.Thread( + target=logging_obj.success_handler, args=(result, start_time, end_time) + ).start() + # RETURN RESULT + if hasattr(result, "_hidden_params"): + result._hidden_params["model_id"] = kwargs.get("model_info", {}).get( + "id", None + ) + result._response_ms = ( + end_time - start_time + ).total_seconds() * 1000 # return response latency in ms like openai + return result + except Exception as e: + call_type = original_function.__name__ + if call_type == CallTypes.completion.value: + num_retries = ( + kwargs.get("num_retries", None) or litellm.num_retries or None + ) + litellm.num_retries = ( + None # set retries to None to prevent infinite loops + ) + context_window_fallback_dict = kwargs.get( + "context_window_fallback_dict", {} + ) + + if num_retries: + if isinstance(e, openai.APIError) or isinstance(e, openai.Timeout): + kwargs["num_retries"] = num_retries + return litellm.completion_with_retries(*args, **kwargs) + elif ( + isinstance(e, litellm.exceptions.ContextWindowExceededError) + and context_window_fallback_dict + and model in context_window_fallback_dict + ): + if len(args) > 0: + args[0] = context_window_fallback_dict[model] + else: + kwargs["model"] = context_window_fallback_dict[model] + return original_function(*args, **kwargs) + traceback_exception = traceback.format_exc() + crash_reporting(*args, **kwargs, exception=traceback_exception) + end_time = datetime.datetime.now() + # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated + if logging_obj: + logging_obj.failure_handler( + e, traceback_exception, start_time, end_time + ) # DO NOT MAKE THREADED - router retry fallback relies on this! + my_thread = threading.Thread( + target=handle_failure, + args=(e, traceback_exception, start_time, end_time, args, kwargs), + ) # don't interrupt execution of main thread + my_thread.start() + if hasattr(e, "message"): + if ( + liteDebuggerClient and liteDebuggerClient.dashboard_url != None + ): # make it easy to get to the debugger logs if you've initialized it + e.message += f"\n Check the log in your dashboard - {liteDebuggerClient.dashboard_url}" + raise e + + @wraps(original_function) + async def wrapper_async(*args, **kwargs): + print_args_passed_to_litellm(original_function, args, kwargs) + start_time = datetime.datetime.now() + result = None + logging_obj = kwargs.get("litellm_logging_obj", None) + # only set litellm_call_id if its not in kwargs + if "litellm_call_id" not in kwargs: + kwargs["litellm_call_id"] = str(uuid.uuid4()) + try: + model = args[0] if len(args) > 0 else kwargs["model"] + except: + if ( + call_type != CallTypes.aimage_generation.value # model optional + and call_type != CallTypes.atext_completion.value # can also be engine + ): + raise ValueError("model param not passed in.") + + try: + if logging_obj is None: + logging_obj = function_setup(start_time, *args, **kwargs) + kwargs["litellm_logging_obj"] = logging_obj + + # [OPTIONAL] CHECK BUDGET + if litellm.max_budget: + if litellm._current_cost > litellm.max_budget: + raise BudgetExceededError( + current_cost=litellm._current_cost, + max_budget=litellm.max_budget, + ) + + # [OPTIONAL] CHECK CACHE + print_verbose(f"litellm.cache: {litellm.cache}") + print_verbose( + f"kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}" + ) + # if caching is false, don't run this + if ( + (kwargs.get("caching", None) is None and litellm.cache is not None) + or kwargs.get("caching", False) == True + or ( + kwargs.get("cache", None) is not None + and kwargs.get("cache").get("no-cache", False) != True + ) + ): # allow users to control returning cached responses from the completion function + # checking cache + print_verbose(f"INSIDE CHECKING CACHE") + if ( + litellm.cache is not None + and str(original_function.__name__) + in litellm.cache.supported_call_types + ): + print_verbose(f"Checking Cache") + cached_result = litellm.cache.get_cache(*args, **kwargs) + if cached_result != None: + print_verbose(f"Cache Hit!") + call_type = original_function.__name__ + if call_type == CallTypes.acompletion.value and isinstance( + cached_result, dict + ): + if kwargs.get("stream", False) == True: + cached_result = convert_to_streaming_response_async( + response_object=cached_result, + ) + else: + cached_result = convert_to_model_response_object( + response_object=cached_result, + model_response_object=ModelResponse(), + ) + elif call_type == CallTypes.aembedding.value and isinstance( + cached_result, dict + ): + cached_result = convert_to_model_response_object( + response_object=cached_result, + model_response_object=EmbeddingResponse(), + response_type="embedding", + ) + # LOG SUCCESS + cache_hit = True + end_time = datetime.datetime.now() + ( + model, + custom_llm_provider, + dynamic_api_key, + api_base, + ) = litellm.get_llm_provider( + model=model, + custom_llm_provider=kwargs.get("custom_llm_provider", None), + api_base=kwargs.get("api_base", None), + api_key=kwargs.get("api_key", None), + ) + print_verbose( + f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}" + ) + logging_obj.update_environment_variables( + model=model, + user=kwargs.get("user", None), + optional_params={}, + litellm_params={ + "logger_fn": kwargs.get("logger_fn", None), + "acompletion": True, + "metadata": kwargs.get("metadata", {}), + "model_info": kwargs.get("model_info", {}), + "proxy_server_request": kwargs.get( + "proxy_server_request", None + ), + "preset_cache_key": kwargs.get( + "preset_cache_key", None + ), + "stream_response": kwargs.get("stream_response", {}), + }, + input=kwargs.get("messages", ""), + api_key=kwargs.get("api_key", None), + original_response=str(cached_result), + additional_args=None, + stream=kwargs.get("stream", False), + ) + asyncio.create_task( + logging_obj.async_success_handler( + cached_result, start_time, end_time, cache_hit + ) + ) + threading.Thread( + target=logging_obj.success_handler, + args=(cached_result, start_time, end_time, cache_hit), + ).start() + return cached_result + # MODEL CALL + result = await original_function(*args, **kwargs) + end_time = datetime.datetime.now() + if "stream" in kwargs and kwargs["stream"] == True: + if ( + "complete_response" in kwargs + and kwargs["complete_response"] == True + ): + chunks = [] + for idx, chunk in enumerate(result): + chunks.append(chunk) + return litellm.stream_chunk_builder( + chunks, messages=kwargs.get("messages", None) + ) + else: + return result + + ### POST-CALL RULES ### + post_call_processing(original_response=result, model=model) + + # [OPTIONAL] ADD TO CACHE + if ( + litellm.cache is not None + and str(original_function.__name__) + in litellm.cache.supported_call_types + ): + if isinstance(result, litellm.ModelResponse) or isinstance( + result, litellm.EmbeddingResponse + ): + asyncio.create_task( + litellm.cache._async_add_cache(result.json(), *args, **kwargs) + ) + else: + asyncio.create_task( + litellm.cache._async_add_cache(result, *args, **kwargs) + ) + # LOG SUCCESS - handle streaming success logging in the _next_ object + print_verbose( + f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}" + ) + asyncio.create_task( + logging_obj.async_success_handler(result, start_time, end_time) + ) + threading.Thread( + target=logging_obj.success_handler, args=(result, start_time, end_time) + ).start() + # RETURN RESULT + if hasattr(result, "_hidden_params"): + result._hidden_params["model_id"] = kwargs.get("model_info", {}).get( + "id", None + ) + if isinstance(result, ModelResponse): + result._response_ms = ( + end_time - start_time + ).total_seconds() * 1000 # return response latency in ms like openai + return result + except Exception as e: + traceback_exception = traceback.format_exc() + crash_reporting(*args, **kwargs, exception=traceback_exception) + end_time = datetime.datetime.now() + if logging_obj: + try: + logging_obj.failure_handler( + e, traceback_exception, start_time, end_time + ) # DO NOT MAKE THREADED - router retry fallback relies on this! + except Exception as e: + raise e + try: + await logging_obj.async_failure_handler( + e, traceback_exception, start_time, end_time + ) + except Exception as e: + raise e + + call_type = original_function.__name__ + if call_type == CallTypes.acompletion.value: + num_retries = ( + kwargs.get("num_retries", None) or litellm.num_retries or None + ) + litellm.num_retries = ( + None # set retries to None to prevent infinite loops + ) + context_window_fallback_dict = kwargs.get( + "context_window_fallback_dict", {} + ) + + if num_retries: + try: + kwargs["num_retries"] = num_retries + kwargs["original_function"] = original_function + if isinstance( + e, openai.RateLimitError + ): # rate limiting specific error + kwargs["retry_strategy"] = "exponential_backoff_retry" + elif isinstance(e, openai.APIError): # generic api error + kwargs["retry_strategy"] = "constant_retry" + return await litellm.acompletion_with_retries(*args, **kwargs) + except: + pass + elif ( + isinstance(e, litellm.exceptions.ContextWindowExceededError) + and context_window_fallback_dict + and model in context_window_fallback_dict + ): + if len(args) > 0: + args[0] = context_window_fallback_dict[model] + else: + kwargs["model"] = context_window_fallback_dict[model] + return await original_function(*args, **kwargs) + raise e + + is_coroutine = inspect.iscoroutinefunction(original_function) + + # Return the appropriate wrapper based on the original function type + if is_coroutine: + return wrapper_async + else: + return wrapper + + +####### USAGE CALCULATOR ################ + + +# Extract the number of billion parameters from the model name +# only used for together_computer LLMs +def get_model_params_and_category(model_name): + import re + + model_name = model_name.lower() + params_match = re.search( + r"(\d+b)", model_name + ) # catch all decimals like 3b, 70b, etc + category = None + if params_match != None: + params_match = params_match.group(1) + params_match = params_match.replace("b", "") + params_billion = float(params_match) + # Determine the category based on the number of parameters + if params_billion <= 3.0: + category = "together-ai-up-to-3b" + elif params_billion <= 7.0: + category = "together-ai-3.1b-7b" + elif params_billion <= 20.0: + category = "together-ai-7.1b-20b" + elif params_billion <= 40.0: + category = "together-ai-20.1b-40b" + elif params_billion <= 70.0: + category = "together-ai-40.1b-70b" + return category + + return None + + +def get_replicate_completion_pricing(completion_response=None, total_time=0.0): + # see https://replicate.com/pricing + a100_40gb_price_per_second_public = 0.001150 + # for all litellm currently supported LLMs, almost all requests go to a100_80gb + a100_80gb_price_per_second_public = ( + 0.001400 # assume all calls sent to A100 80GB for now + ) + if total_time == 0.0: + start_time = completion_response["created"] + end_time = completion_response["ended"] + total_time = end_time - start_time + + return a100_80gb_price_per_second_public * total_time + + +def _select_tokenizer(model: str): + # cohere + import pkg_resources + + if model in litellm.cohere_models: + tokenizer = Tokenizer.from_pretrained("Cohere/command-nightly") + return {"type": "huggingface_tokenizer", "tokenizer": tokenizer} + # anthropic + elif model in litellm.anthropic_models: + # Read the JSON file + filename = pkg_resources.resource_filename( + __name__, "llms/tokenizers/anthropic_tokenizer.json" + ) + with open(filename, "r") as f: + json_data = json.load(f) + # Decode the JSON data from utf-8 + json_data_decoded = json.dumps(json_data, ensure_ascii=False) + # Convert to str + json_str = str(json_data_decoded) + # load tokenizer + tokenizer = Tokenizer.from_str(json_str) + return {"type": "huggingface_tokenizer", "tokenizer": tokenizer} + # llama2 + elif "llama-2" in model.lower(): + tokenizer = Tokenizer.from_pretrained("hf-internal-testing/llama-tokenizer") + return {"type": "huggingface_tokenizer", "tokenizer": tokenizer} + # default - tiktoken + else: + return {"type": "openai_tokenizer", "tokenizer": encoding} + + +def encode(model: str, text: str): + """ + Encodes the given text using the specified model. + + Args: + model (str): The name of the model to use for tokenization. + text (str): The text to be encoded. + + Returns: + enc: The encoded text. + """ + tokenizer_json = _select_tokenizer(model=model) + enc = tokenizer_json["tokenizer"].encode(text) + return enc + + +def decode(model: str, tokens: List[int]): + tokenizer_json = _select_tokenizer(model=model) + dec = tokenizer_json["tokenizer"].decode(tokens) + return dec + + +def openai_token_counter( + messages: Optional[list] = None, + model="gpt-3.5-turbo-0613", + text: Optional[str] = None, + is_tool_call: Optional[bool] = False, + count_response_tokens: Optional[ + bool + ] = False, # Flag passed from litellm.stream_chunk_builder, to indicate counting tokens for LLM Response. We need this because for LLM input we add +3 tokens per message - based on OpenAI's token counter +): + """ + Return the number of tokens used by a list of messages. + + Borrowed from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb. + """ + print_verbose(f"LiteLLM: Utils - Counting tokens for OpenAI model={model}") + try: + encoding = tiktoken.encoding_for_model(model) + except KeyError: + print_verbose("Warning: model not found. Using cl100k_base encoding.") + encoding = tiktoken.get_encoding("cl100k_base") + if model == "gpt-3.5-turbo-0301": + tokens_per_message = ( + 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n + ) + tokens_per_name = -1 # if there's a name, the role is omitted + elif model in litellm.open_ai_chat_completion_models: + tokens_per_message = 3 + tokens_per_name = 1 + elif model in litellm.azure_llms: + tokens_per_message = 3 + tokens_per_name = 1 + else: + raise NotImplementedError( + f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""" + ) + num_tokens = 0 + + if is_tool_call and text is not None: + # if it's a tool call we assembled 'text' in token_counter() + num_tokens = len(encoding.encode(text, disallowed_special=())) + elif messages is not None: + for message in messages: + num_tokens += tokens_per_message + for key, value in message.items(): + if isinstance(value, str): + num_tokens += len(encoding.encode(value, disallowed_special=())) + if key == "name": + num_tokens += tokens_per_name + elif isinstance(value, List): + for c in value: + if c["type"] == "text": + text += c["text"] + elif c["type"] == "image_url": + if isinstance(c["image_url"], dict): + image_url_dict = c["image_url"] + detail = image_url_dict.get("detail", "auto") + url = image_url_dict.get("url") + num_tokens += calculage_img_tokens( + data=url, mode=detail + ) + elif isinstance(c["image_url"], str): + image_url_str = c["image_url"] + num_tokens += calculage_img_tokens( + data=image_url_str, mode="auto" + ) + elif text is not None and count_response_tokens == True: + # This is the case where we need to count tokens for a streamed response. We should NOT add +3 tokens per message in this branch + num_tokens = len(encoding.encode(text, disallowed_special=())) + return num_tokens + num_tokens += 3 # every reply is primed with <|start|>assistant<|message|> + return num_tokens + + +def resize_image_high_res(width, height): + # Maximum dimensions for high res mode + max_short_side = 768 + max_long_side = 2000 + + # Determine the longer and shorter sides + longer_side = max(width, height) + shorter_side = min(width, height) + + # Calculate the aspect ratio + aspect_ratio = longer_side / shorter_side + + # Resize based on the short side being 768px + if width <= height: # Portrait or square + resized_width = max_short_side + resized_height = int(resized_width * aspect_ratio) + # if the long side exceeds the limit after resizing, adjust both sides accordingly + if resized_height > max_long_side: + resized_height = max_long_side + resized_width = int(resized_height / aspect_ratio) + else: # Landscape + resized_height = max_short_side + resized_width = int(resized_height * aspect_ratio) + # if the long side exceeds the limit after resizing, adjust both sides accordingly + if resized_width > max_long_side: + resized_width = max_long_side + resized_height = int(resized_width / aspect_ratio) + + return resized_width, resized_height + + +# Test the function with the given example +def calculate_tiles_needed( + resized_width, resized_height, tile_width=512, tile_height=512 +): + tiles_across = (resized_width + tile_width - 1) // tile_width + tiles_down = (resized_height + tile_height - 1) // tile_height + total_tiles = tiles_across * tiles_down + return total_tiles + + +def get_image_dimensions(data): + img_data = None + + # Check if data is a URL by trying to parse it + try: + response = requests.get(data) + response.raise_for_status() # Check if the request was successful + img_data = response.content + except Exception: + # Data is not a URL, handle as base64 + header, encoded = data.split(",", 1) + img_data = base64.b64decode(encoded) + + # Try to determine dimensions from headers + # This is a very simplistic check, primarily works with PNG and non-progressive JPEG + if img_data[:8] == b"\x89PNG\r\n\x1a\n": + # PNG Image; width and height are 4 bytes each and start at offset 16 + width, height = struct.unpack(">ii", img_data[16:24]) + return width, height + elif img_data[:2] == b"\xff\xd8": + # JPEG Image; for dimensions, SOF0 block (0xC0) gives dimensions at offset 3 for length, and then 5 and 7 for height and width + # This will NOT find dimensions for all JPEGs (e.g., progressive JPEGs) + # Find SOF0 marker (0xFF followed by 0xC0) + sof = re.search(b"\xff\xc0....", img_data) + if sof: + # Parse SOF0 block to find dimensions + height, width = struct.unpack(">HH", sof.group()[5:9]) + return width, height + else: + return None, None + else: + # Unsupported format + return None, None + + +def calculage_img_tokens( + data, + mode: Literal["low", "high", "auto"] = "auto", + base_tokens: int = 85, # openai default - https://openai.com/pricing +): + if mode == "low" or mode == "auto": + return base_tokens + elif mode == "high": + width, height = get_image_dimensions(data=data) + resized_width, resized_height = resize_image_high_res( + width=width, height=height + ) + tiles_needed_high_res = calculate_tiles_needed(resized_width, resized_height) + tile_tokens = (base_tokens * 2) * tiles_needed_high_res + total_tokens = base_tokens + tile_tokens + return total_tokens + + +def token_counter( + model="", + text: Optional[Union[str, List[str]]] = None, + messages: Optional[List] = None, + count_response_tokens: Optional[bool] = False, +): + """ + Count the number of tokens in a given text using a specified model. + + Args: + model (str): The name of the model to use for tokenization. Default is an empty string. + text (str): The raw text string to be passed to the model. Default is None. + messages (Optional[List[Dict[str, str]]]): Alternative to passing in text. A list of dictionaries representing messages with "role" and "content" keys. Default is None. + + Returns: + int: The number of tokens in the text. + """ + # use tiktoken, anthropic, cohere or llama2's tokenizer depending on the model + is_tool_call = False + num_tokens = 0 + if text == None: + if messages is not None: + print_verbose(f"token_counter messages received: {messages}") + text = "" + for message in messages: + if message.get("content", None) is not None: + content = message.get("content") + if isinstance(content, str): + text += message["content"] + elif isinstance(content, List): + for c in content: + if c["type"] == "text": + text += c["text"] + elif c["type"] == "image_url": + if isinstance(c["image_url"], dict): + image_url_dict = c["image_url"] + detail = image_url_dict.get("detail", "auto") + url = image_url_dict.get("url") + num_tokens += calculage_img_tokens( + data=url, mode=detail + ) + elif isinstance(c["image_url"], str): + image_url_str = c["image_url"] + num_tokens += calculage_img_tokens( + data=image_url_str, mode="auto" + ) + if "tool_calls" in message: + is_tool_call = True + for tool_call in message["tool_calls"]: + if "function" in tool_call: + function_arguments = tool_call["function"]["arguments"] + text += function_arguments + else: + raise ValueError("text and messages cannot both be None") + elif isinstance(text, List): + text = "".join(t for t in text if isinstance(t, str)) + + if model is not None: + tokenizer_json = _select_tokenizer(model=model) + if tokenizer_json["type"] == "huggingface_tokenizer": + print_verbose( + f"Token Counter - using hugging face token counter, for model={model}" + ) + enc = tokenizer_json["tokenizer"].encode(text) + num_tokens = len(enc.ids) + elif tokenizer_json["type"] == "openai_tokenizer": + if ( + model in litellm.open_ai_chat_completion_models + or model in litellm.azure_llms + ): + if model in litellm.azure_llms: + # azure llms use gpt-35-turbo instead of gpt-3.5-turbo 🙃 + model = model.replace("-35", "-3.5") + + print_verbose( + f"Token Counter - using OpenAI token counter, for model={model}" + ) + num_tokens = openai_token_counter( + text=text, # type: ignore + model=model, + messages=messages, + is_tool_call=is_tool_call, + count_response_tokens=count_response_tokens, + ) + else: + print_verbose( + f"Token Counter - using generic token counter, for model={model}" + ) + enc = tokenizer_json["tokenizer"].encode(text) + num_tokens = len(enc) + else: + num_tokens = len(encoding.encode(text)) # type: ignore + return num_tokens + + +def cost_per_token( + model="", prompt_tokens=0, completion_tokens=0, custom_llm_provider=None +): + """ + Calculates the cost per token for a given model, prompt tokens, and completion tokens. + + Parameters: + model (str): The name of the model to use. Default is "" + prompt_tokens (int): The number of tokens in the prompt. + completion_tokens (int): The number of tokens in the completion. + + Returns: + tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively. + """ + # given + prompt_tokens_cost_usd_dollar = 0 + completion_tokens_cost_usd_dollar = 0 + model_cost_ref = litellm.model_cost + if custom_llm_provider is not None: + model_with_provider = custom_llm_provider + "/" + model + else: + model_with_provider = model + # see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models + print_verbose(f"Looking up model={model} in model_cost_map") + + if model in model_cost_ref: + prompt_tokens_cost_usd_dollar = ( + model_cost_ref[model]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref[model]["output_cost_per_token"] * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + elif model_with_provider in model_cost_ref: + print_verbose(f"Looking up model={model_with_provider} in model_cost_map") + prompt_tokens_cost_usd_dollar = ( + model_cost_ref[model_with_provider]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref[model_with_provider]["output_cost_per_token"] + * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + elif "ft:gpt-3.5-turbo" in model: + print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM") + # fuzzy match ft:gpt-3.5-turbo:abcd-id-cool-litellm + prompt_tokens_cost_usd_dollar = ( + model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref["ft:gpt-3.5-turbo"]["output_cost_per_token"] + * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + elif model in litellm.azure_llms: + print_verbose(f"Cost Tracking: {model} is an Azure LLM") + model = litellm.azure_llms[model] + prompt_tokens_cost_usd_dollar = ( + model_cost_ref[model]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref[model]["output_cost_per_token"] * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + elif model in litellm.azure_embedding_models: + print_verbose(f"Cost Tracking: {model} is an Azure Embedding Model") + model = litellm.azure_embedding_models[model] + prompt_tokens_cost_usd_dollar = ( + model_cost_ref[model]["input_cost_per_token"] * prompt_tokens + ) + completion_tokens_cost_usd_dollar = ( + model_cost_ref[model]["output_cost_per_token"] * completion_tokens + ) + return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar + else: + # if model is not in model_prices_and_context_window.json. Raise an exception-let users know + error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}\n" + raise litellm.exceptions.NotFoundError( # type: ignore + message=error_str, + model=model, + response=httpx.Response( + status_code=404, + content=error_str, + request=httpx.Request(method="cost_per_token", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + llm_provider="", + ) + + +def completion_cost( + completion_response=None, + model=None, + prompt="", + messages: List = [], + completion="", + total_time=0.0, # used for replicate +): + """ + Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm. + + Parameters: + completion_response (litellm.ModelResponses): [Required] The response received from a LiteLLM completion request. + + [OPTIONAL PARAMS] + model (str): Optional. The name of the language model used in the completion calls + prompt (str): Optional. The input prompt passed to the llm + completion (str): Optional. The output completion text from the llm + total_time (float): Optional. (Only used for Replicate LLMs) The total time used for the request in seconds + + Returns: + float: The cost in USD dollars for the completion based on the provided parameters. + + Note: + - If completion_response is provided, the function extracts token information and the model name from it. + - If completion_response is not provided, the function calculates token counts based on the model and input text. + - The cost is calculated based on the model, prompt tokens, and completion tokens. + - For certain models containing "togethercomputer" in the name, prices are based on the model size. + - For Replicate models, the cost is calculated based on the total time used for the request. + + Exceptions: + - If an error occurs during execution, the function returns 0.0 without blocking the user's execution path. + """ + try: + # Handle Inputs to completion_cost + prompt_tokens = 0 + completion_tokens = 0 + custom_llm_provider = None + if completion_response is not None: + # get input/output tokens from completion_response + prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0) + completion_tokens = completion_response.get("usage", {}).get( + "completion_tokens", 0 + ) + model = ( + model or completion_response["model"] + ) # check if user passed an override for model, if it's none check completion_response['model'] + if completion_response is not None and hasattr( + completion_response, "_hidden_params" + ): + custom_llm_provider = completion_response._hidden_params.get( + "custom_llm_provider", "" + ) + else: + if len(messages) > 0: + prompt_tokens = token_counter(model=model, messages=messages) + elif len(prompt) > 0: + prompt_tokens = token_counter(model=model, text=prompt) + completion_tokens = token_counter(model=model, text=completion) + if model == None: + raise ValueError( + f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}" + ) + + # Calculate cost based on prompt_tokens, completion_tokens + if "togethercomputer" in model or "together_ai" in model: + # together ai prices based on size of llm + # get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json + model = get_model_params_and_category(model) + # replicate llms are calculate based on time for request running + # see https://replicate.com/pricing + elif model in litellm.replicate_models or "replicate" in model: + return get_replicate_completion_pricing(completion_response, total_time) + ( + prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar, + ) = cost_per_token( + model=model, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + custom_llm_provider=custom_llm_provider, + ) + return prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar + except Exception as e: + raise e + + +####### HELPER FUNCTIONS ################ +def register_model(model_cost: Union[str, dict]): + """ + Register new / Override existing models (and their pricing) to specific providers. + Provide EITHER a model cost dictionary or a url to a hosted json blob + Example usage: + model_cost_dict = { + "gpt-4": { + "max_tokens": 8192, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + }, + } + """ + loaded_model_cost = {} + if isinstance(model_cost, dict): + loaded_model_cost = model_cost + elif isinstance(model_cost, str): + loaded_model_cost = litellm.get_model_cost_map(url=model_cost) + + for key, value in loaded_model_cost.items(): + ## override / add new keys to the existing model cost dictionary + if key in litellm.model_cost: + for k, v in loaded_model_cost[key].items(): + litellm.model_cost[key][k] = v + # add new model names to provider lists + if value.get("litellm_provider") == "openai": + if key not in litellm.open_ai_chat_completion_models: + litellm.open_ai_chat_completion_models.append(key) + elif value.get("litellm_provider") == "text-completion-openai": + if key not in litellm.open_ai_text_completion_models: + litellm.open_ai_text_completion_models.append(key) + elif value.get("litellm_provider") == "cohere": + if key not in litellm.cohere_models: + litellm.cohere_models.append(key) + elif value.get("litellm_provider") == "anthropic": + if key not in litellm.anthropic_models: + litellm.anthropic_models.append(key) + elif value.get("litellm_provider") == "openrouter": + split_string = key.split("/", 1) + if key not in litellm.openrouter_models: + litellm.openrouter_models.append(split_string[1]) + elif value.get("litellm_provider") == "vertex_ai-text-models": + if key not in litellm.vertex_text_models: + litellm.vertex_text_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-code-text-models": + if key not in litellm.vertex_code_text_models: + litellm.vertex_code_text_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-chat-models": + if key not in litellm.vertex_chat_models: + litellm.vertex_chat_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-code-chat-models": + if key not in litellm.vertex_code_chat_models: + litellm.vertex_code_chat_models.append(key) + elif value.get("litellm_provider") == "ai21": + if key not in litellm.ai21_models: + litellm.ai21_models.append(key) + elif value.get("litellm_provider") == "nlp_cloud": + if key not in litellm.nlp_cloud_models: + litellm.nlp_cloud_models.append(key) + elif value.get("litellm_provider") == "aleph_alpha": + if key not in litellm.aleph_alpha_models: + litellm.aleph_alpha_models.append(key) + elif value.get("litellm_provider") == "bedrock": + if key not in litellm.bedrock_models: + litellm.bedrock_models.append(key) + return model_cost + + +def get_litellm_params( + api_key=None, + force_timeout=600, + azure=False, + logger_fn=None, + verbose=False, + hugging_face=False, + replicate=False, + together_ai=False, + custom_llm_provider=None, + api_base=None, + litellm_call_id=None, + model_alias_map=None, + completion_call_id=None, + metadata=None, + model_info=None, + proxy_server_request=None, + acompletion=None, + preset_cache_key=None, +): + litellm_params = { + "acompletion": acompletion, + "api_key": api_key, + "force_timeout": force_timeout, + "logger_fn": logger_fn, + "verbose": verbose, + "custom_llm_provider": custom_llm_provider, + "api_base": api_base, + "litellm_call_id": litellm_call_id, + "model_alias_map": model_alias_map, + "completion_call_id": completion_call_id, + "metadata": metadata, + "model_info": model_info, + "proxy_server_request": proxy_server_request, + "preset_cache_key": preset_cache_key, + "stream_response": {}, # litellm_call_id: ModelResponse Dict + } + + return litellm_params + + +def get_optional_params_image_gen( + n: Optional[int] = None, + quality: Optional[str] = None, + response_format: Optional[str] = None, + size: Optional[str] = None, + style: Optional[str] = None, + user: Optional[str] = None, + custom_llm_provider: Optional[str] = None, + **kwargs, +): + # retrieve all parameters passed to the function + passed_params = locals() + custom_llm_provider = passed_params.pop("custom_llm_provider") + special_params = passed_params.pop("kwargs") + for k, v in special_params.items(): + passed_params[k] = v + + default_params = { + "n": None, + "quality": None, + "response_format": None, + "size": None, + "style": None, + "user": None, + } + + non_default_params = { + k: v + for k, v in passed_params.items() + if (k in default_params and v != default_params[k]) + } + ## raise exception if non-default value passed for non-openai/azure embedding calls + if custom_llm_provider != "openai" and custom_llm_provider != "azure": + if len(non_default_params.keys()) > 0: + if litellm.drop_params is True: # drop the unsupported non-default values + keys = list(non_default_params.keys()) + for k in keys: + non_default_params.pop(k, None) + return non_default_params + raise UnsupportedParamsError( + status_code=500, + message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.", + ) + + final_params = {**non_default_params, **kwargs} + return final_params + + +def get_optional_params_embeddings( + # 2 optional params + user=None, + encoding_format=None, + custom_llm_provider="", + **kwargs, +): + # retrieve all parameters passed to the function + passed_params = locals() + custom_llm_provider = passed_params.pop("custom_llm_provider", None) + special_params = passed_params.pop("kwargs") + for k, v in special_params.items(): + passed_params[k] = v + + default_params = {"user": None, "encoding_format": None} + + non_default_params = { + k: v + for k, v in passed_params.items() + if (k in default_params and v != default_params[k]) + } + ## raise exception if non-default value passed for non-openai/azure embedding calls + if ( + custom_llm_provider != "openai" + and custom_llm_provider != "azure" + and custom_llm_provider not in litellm.openai_compatible_providers + ): + if len(non_default_params.keys()) > 0: + if litellm.drop_params is True: # drop the unsupported non-default values + keys = list(non_default_params.keys()) + for k in keys: + non_default_params.pop(k, None) + return non_default_params + raise UnsupportedParamsError( + status_code=500, + message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.", + ) + + final_params = {**non_default_params, **kwargs} + return final_params + + +def get_optional_params( + # use the openai defaults + # https://platform.openai.com/docs/api-reference/chat/create + functions=None, + function_call=None, + temperature=None, + top_p=None, + n=None, + stream=False, + stop=None, + max_tokens=None, + presence_penalty=None, + frequency_penalty=None, + logit_bias=None, + user=None, + model=None, + custom_llm_provider="", + response_format=None, + seed=None, + tools=None, + tool_choice=None, + max_retries=None, + logprobs=None, + top_logprobs=None, + **kwargs, +): + # retrieve all parameters passed to the function + passed_params = locals() + special_params = passed_params.pop("kwargs") + for k, v in special_params.items(): + passed_params[k] = v + default_params = { + "functions": None, + "function_call": None, + "temperature": None, + "top_p": None, + "n": None, + "stream": None, + "stop": None, + "max_tokens": None, + "presence_penalty": None, + "frequency_penalty": None, + "logit_bias": None, + "user": None, + "model": None, + "custom_llm_provider": "", + "response_format": None, + "seed": None, + "tools": None, + "tool_choice": None, + "max_retries": None, + "logprobs": None, + "top_logprobs": None, + } + # filter out those parameters that were passed with non-default values + non_default_params = { + k: v + for k, v in passed_params.items() + if ( + k != "model" + and k != "custom_llm_provider" + and k in default_params + and v != default_params[k] + ) + } + optional_params = {} + ## raise exception if function calling passed in for a provider that doesn't support it + if ( + "functions" in non_default_params + or "function_call" in non_default_params + or "tools" in non_default_params + ): + if ( + custom_llm_provider != "openai" + and custom_llm_provider != "text-completion-openai" + and custom_llm_provider != "azure" + and custom_llm_provider != "vertex_ai" + ): + if custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat": + # ollama actually supports json output + optional_params["format"] = "json" + litellm.add_function_to_prompt = ( + True # so that main.py adds the function call to the prompt + ) + if "tools" in non_default_params: + optional_params[ + "functions_unsupported_model" + ] = non_default_params.pop("tools") + non_default_params.pop( + "tool_choice", None + ) # causes ollama requests to hang + elif "functions" in non_default_params: + optional_params[ + "functions_unsupported_model" + ] = non_default_params.pop("functions") + elif ( + custom_llm_provider == "anyscale" + and model == "mistralai/Mistral-7B-Instruct-v0.1" + ): # anyscale just supports function calling with mistral + pass + elif ( + litellm.add_function_to_prompt + ): # if user opts to add it to prompt instead + optional_params["functions_unsupported_model"] = non_default_params.pop( + "tools", non_default_params.pop("functions") + ) + else: + raise UnsupportedParamsError( + status_code=500, + message=f"Function calling is not supported by {custom_llm_provider}. To add it to the prompt, set `litellm.add_function_to_prompt = True`.", + ) + + def _check_valid_arg(supported_params): + print_verbose( + f"\nLiteLLM completion() model= {model}; provider = {custom_llm_provider}" + ) + print_verbose(f"\nLiteLLM: Params passed to completion() {passed_params}") + print_verbose( + f"\nLiteLLM: Non-Default params passed to completion() {non_default_params}" + ) + unsupported_params = {} + for k in non_default_params.keys(): + if k not in supported_params: + if k == "n" and n == 1: # langchain sends n=1 as a default value + continue # skip this param + if ( + k == "max_retries" + ): # TODO: This is a patch. We support max retries for OpenAI, Azure. For non OpenAI LLMs we need to add support for max retries + continue # skip this param + # Always keeps this in elif code blocks + else: + unsupported_params[k] = non_default_params[k] + if unsupported_params and not litellm.drop_params: + raise UnsupportedParamsError( + status_code=500, + message=f"{custom_llm_provider} does not support parameters: {unsupported_params}. To drop these, set `litellm.drop_params=True`.", + ) + + def _map_and_modify_arg(supported_params: dict, provider: str, model: str): + """ + filter params to fit the required provider format, drop those that don't fit if user sets `litellm.drop_params = True`. + """ + filtered_stop = None + if "stop" in supported_params and litellm.drop_params: + if provider == "bedrock" and "amazon" in model: + filtered_stop = [] + if isinstance(stop, list): + for s in stop: + if re.match(r"^(\|+|User:)$", s): + filtered_stop.append(s) + if filtered_stop is not None: + supported_params["stop"] = filtered_stop + + return supported_params + + ## raise exception if provider doesn't support passed in param + if custom_llm_provider == "anthropic": + ## check if unsupported param passed in + supported_params = ["stream", "stop", "temperature", "top_p", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + # handle anthropic params + if stream: + optional_params["stream"] = stream + if stop is not None: + if type(stop) == str: + stop = [stop] # openai can accept str/list for stop + optional_params["stop_sequences"] = stop + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if max_tokens is not None: + optional_params["max_tokens_to_sample"] = max_tokens + elif custom_llm_provider == "cohere": + ## check if unsupported param passed in + supported_params = [ + "stream", + "temperature", + "max_tokens", + "logit_bias", + "top_p", + "frequency_penalty", + "presence_penalty", + "stop", + "n", + ] + _check_valid_arg(supported_params=supported_params) + # handle cohere params + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if n is not None: + optional_params["num_generations"] = n + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if top_p is not None: + optional_params["p"] = top_p + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "maritalk": + ## check if unsupported param passed in + supported_params = [ + "stream", + "temperature", + "max_tokens", + "top_p", + "presence_penalty", + "stop", + ] + _check_valid_arg(supported_params=supported_params) + # handle cohere params + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if top_p is not None: + optional_params["p"] = top_p + if presence_penalty is not None: + optional_params["repetition_penalty"] = presence_penalty + if stop is not None: + optional_params["stopping_tokens"] = stop + elif custom_llm_provider == "replicate": + ## check if unsupported param passed in + supported_params = [ + "stream", + "temperature", + "max_tokens", + "top_p", + "stop", + "seed", + ] + _check_valid_arg(supported_params=supported_params) + + if stream: + optional_params["stream"] = stream + return optional_params + if max_tokens is not None: + if "vicuna" in model or "flan" in model: + optional_params["max_length"] = max_tokens + elif "meta/codellama-13b" in model: + optional_params["max_tokens"] = max_tokens + else: + optional_params["max_new_tokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "huggingface": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "n"] + _check_valid_arg(supported_params=supported_params) + # temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None + if temperature is not None: + if temperature == 0.0 or temperature == 0: + # hugging face exception raised when temp==0 + # Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive + temperature = 0.01 + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if n is not None: + optional_params["best_of"] = n + optional_params[ + "do_sample" + ] = True # Need to sample if you want best of for hf inference endpoints + if stream is not None: + optional_params["stream"] = stream + if stop is not None: + optional_params["stop"] = stop + if max_tokens is not None: + # HF TGI raises the following exception when max_new_tokens==0 + # Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive + if max_tokens == 0: + max_tokens = 1 + optional_params["max_new_tokens"] = max_tokens + if n is not None: + optional_params["best_of"] = n + if presence_penalty is not None: + optional_params["repetition_penalty"] = presence_penalty + if "echo" in passed_params: + # https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details + # Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False + optional_params["decoder_input_details"] = special_params["echo"] + passed_params.pop( + "echo", None + ) # since we handle translating echo, we should not send it to TGI request + elif custom_llm_provider == "together_ai": + ## check if unsupported param passed in + supported_params = [ + "stream", + "temperature", + "max_tokens", + "top_p", + "stop", + "frequency_penalty", + ] + _check_valid_arg(supported_params=supported_params) + + if stream: + optional_params["stream_tokens"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if frequency_penalty is not None: + optional_params[ + "repetition_penalty" + ] = frequency_penalty # https://docs.together.ai/reference/inference + if stop is not None: + optional_params["stop"] = stop + elif custom_llm_provider == "ai21": + ## check if unsupported param passed in + supported_params = [ + "stream", + "n", + "temperature", + "max_tokens", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + ] + _check_valid_arg(supported_params=supported_params) + + if stream: + optional_params["stream"] = stream + if n is not None: + optional_params["numResults"] = n + if max_tokens is not None: + optional_params["maxTokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["topP"] = top_p + if stop is not None: + optional_params["stopSequences"] = stop + if frequency_penalty is not None: + optional_params["frequencyPenalty"] = {"scale": frequency_penalty} + if presence_penalty is not None: + optional_params["presencePenalty"] = {"scale": presence_penalty} + elif ( + custom_llm_provider == "palm" or custom_llm_provider == "gemini" + ): # https://developers.generativeai.google/tutorials/curl_quickstart + ## check if unsupported param passed in + supported_params = ["temperature", "top_p", "stream", "n", "stop", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if n is not None: + optional_params["candidate_count"] = n + if stop is not None: + if isinstance(stop, str): + optional_params["stop_sequences"] = [stop] + elif isinstance(stop, list): + optional_params["stop_sequences"] = stop + if max_tokens is not None: + optional_params["max_output_tokens"] = max_tokens + elif custom_llm_provider == "vertex_ai": + ## check if unsupported param passed in + supported_params = [ + "temperature", + "top_p", + "max_tokens", + "stream", + "tools", + "tool_choice", + ] + _check_valid_arg(supported_params=supported_params) + + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if max_tokens is not None: + optional_params["max_output_tokens"] = max_tokens + if tools is not None and isinstance(tools, list): + from vertexai.preview import generative_models + + gtools = [] + for tool in tools: + gtool = generative_models.FunctionDeclaration( + name=tool["function"]["name"], + description=tool["function"].get("description", ""), + parameters=tool["function"].get("parameters", {}), + ) + gtool_func_declaration = generative_models.Tool( + function_declarations=[gtool] + ) + gtools.append(gtool_func_declaration) + optional_params["tools"] = gtools + elif custom_llm_provider == "sagemaker": + ## check if unsupported param passed in + supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "n"] + _check_valid_arg(supported_params=supported_params) + # temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None + if temperature is not None: + if temperature == 0.0 or temperature == 0: + # hugging face exception raised when temp==0 + # Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive + temperature = 0.01 + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if n is not None: + optional_params["best_of"] = n + optional_params[ + "do_sample" + ] = True # Need to sample if you want best of for hf inference endpoints + if stream is not None: + optional_params["stream"] = stream + if stop is not None: + optional_params["stop"] = stop + if max_tokens is not None: + # HF TGI raises the following exception when max_new_tokens==0 + # Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive + if max_tokens == 0: + max_tokens = 1 + optional_params["max_new_tokens"] = max_tokens + elif custom_llm_provider == "bedrock": + if "ai21" in model: + supported_params = ["max_tokens", "temperature", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # params "maxTokens":200,"temperature":0,"topP":250,"stop_sequences":[], + # https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra + if max_tokens is not None: + optional_params["maxTokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["topP"] = top_p + if stream: + optional_params["stream"] = stream + elif "anthropic" in model: + supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # anthropic params on bedrock + # \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}" + if max_tokens is not None: + optional_params["max_tokens_to_sample"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stop is not None: + optional_params["stop_sequences"] = stop + if stream: + optional_params["stream"] = stream + elif "amazon" in model: # amazon titan llms + supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large + if max_tokens is not None: + optional_params["maxTokenCount"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if stop is not None: + filtered_stop = _map_and_modify_arg( + {"stop": stop}, provider="bedrock", model=model + ) + optional_params["stopSequences"] = filtered_stop["stop"] + if top_p is not None: + optional_params["topP"] = top_p + if stream: + optional_params["stream"] = stream + elif "meta" in model: # amazon / meta llms + supported_params = ["max_tokens", "temperature", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large + if max_tokens is not None: + optional_params["max_gen_len"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + elif "cohere" in model: # cohere models on bedrock + supported_params = ["stream", "temperature", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + # handle cohere params + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + elif custom_llm_provider == "aleph_alpha": + supported_params = [ + "max_tokens", + "stream", + "top_p", + "temperature", + "presence_penalty", + "frequency_penalty", + "n", + "stop", + ] + _check_valid_arg(supported_params=supported_params) + if max_tokens is not None: + optional_params["maximum_tokens"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if n is not None: + optional_params["n"] = n + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "cloudflare": + # https://developers.cloudflare.com/workers-ai/models/text-generation/#input + supported_params = ["max_tokens", "stream"] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if stream is not None: + optional_params["stream"] = stream + elif custom_llm_provider == "ollama": + supported_params = [ + "max_tokens", + "stream", + "top_p", + "temperature", + "frequency_penalty", + "stop", + ] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["num_predict"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if frequency_penalty is not None: + optional_params["repeat_penalty"] = frequency_penalty + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "ollama_chat": + supported_params = [ + "max_tokens", + "stream", + "top_p", + "temperature", + "frequency_penalty", + "stop", + ] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["num_predict"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if frequency_penalty is not None: + optional_params["repeat_penalty"] = frequency_penalty + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "nlp_cloud": + supported_params = [ + "max_tokens", + "stream", + "temperature", + "top_p", + "presence_penalty", + "frequency_penalty", + "n", + "stop", + ] + _check_valid_arg(supported_params=supported_params) + + if max_tokens is not None: + optional_params["max_length"] = max_tokens + if stream: + optional_params["stream"] = stream + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if n is not None: + optional_params["num_return_sequences"] = n + if stop is not None: + optional_params["stop_sequences"] = stop + elif custom_llm_provider == "petals": + supported_params = ["max_tokens", "temperature", "top_p", "stream"] + _check_valid_arg(supported_params=supported_params) + # max_new_tokens=1,temperature=0.9, top_p=0.6 + if max_tokens is not None: + optional_params["max_new_tokens"] = max_tokens + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + elif custom_llm_provider == "deepinfra": + supported_params = [ + "temperature", + "top_p", + "n", + "stream", + "stop", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "user", + ] + _check_valid_arg(supported_params=supported_params) + if temperature is not None: + if ( + temperature == 0 and model == "mistralai/Mistral-7B-Instruct-v0.1" + ): # this model does no support temperature == 0 + temperature = 0.0001 # close to 0 + optional_params["temperature"] = temperature + if top_p: + optional_params["top_p"] = top_p + if n: + optional_params["n"] = n + if stream: + optional_params["stream"] = stream + if stop: + optional_params["stop"] = stop + if max_tokens: + optional_params["max_tokens"] = max_tokens + if presence_penalty: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias: + optional_params["logit_bias"] = logit_bias + if user: + optional_params["user"] = user + elif custom_llm_provider == "perplexity": + supported_params = [ + "temperature", + "top_p", + "stream", + "max_tokens", + "presence_penalty", + "frequency_penalty", + ] + _check_valid_arg(supported_params=supported_params) + if temperature is not None: + if ( + temperature == 0 and model == "mistral-7b-instruct" + ): # this model does no support temperature == 0 + temperature = 0.0001 # close to 0 + optional_params["temperature"] = temperature + if top_p: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if max_tokens: + optional_params["max_tokens"] = max_tokens + if presence_penalty: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty: + optional_params["frequency_penalty"] = frequency_penalty + elif custom_llm_provider == "anyscale": + supported_params = [ + "temperature", + "top_p", + "stream", + "max_tokens", + "stop", + "frequency_penalty", + "presence_penalty", + ] + if model in [ + "mistralai/Mistral-7B-Instruct-v0.1", + "mistralai/Mixtral-8x7B-Instruct-v0.1", + ]: + supported_params += [ + "functions", + "function_call", + "tools", + "tool_choice", + "response_format", + ] + _check_valid_arg(supported_params=supported_params) + optional_params = non_default_params + if temperature is not None: + if temperature == 0 and model in [ + "mistralai/Mistral-7B-Instruct-v0.1", + "mistralai/Mixtral-8x7B-Instruct-v0.1", + ]: # this model does no support temperature == 0 + temperature = 0.0001 # close to 0 + optional_params["temperature"] = temperature + if top_p: + optional_params["top_p"] = top_p + if stream: + optional_params["stream"] = stream + if max_tokens: + optional_params["max_tokens"] = max_tokens + elif custom_llm_provider == "mistral": + supported_params = ["temperature", "top_p", "stream", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + optional_params = non_default_params + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream is not None: + optional_params["stream"] = stream + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + + # check safe_mode, random_seed: https://docs.mistral.ai/api/#operation/createChatCompletion + safe_mode = passed_params.pop("safe_mode", None) + random_seed = passed_params.pop("random_seed", None) + extra_body = {} + if safe_mode is not None: + extra_body["safe_mode"] = safe_mode + if random_seed is not None: + extra_body["random_seed"] = random_seed + optional_params[ + "extra_body" + ] = extra_body # openai client supports `extra_body` param + elif custom_llm_provider == "openrouter": + supported_params = [ + "functions", + "function_call", + "temperature", + "top_p", + "n", + "stream", + "stop", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "user", + "response_format", + "seed", + "tools", + "tool_choice", + "max_retries", + ] + _check_valid_arg(supported_params=supported_params) + + if functions is not None: + optional_params["functions"] = functions + if function_call is not None: + optional_params["function_call"] = function_call + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if n is not None: + optional_params["n"] = n + if stream is not None: + optional_params["stream"] = stream + if stop is not None: + optional_params["stop"] = stop + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if user is not None: + optional_params["user"] = user + if response_format is not None: + optional_params["response_format"] = response_format + if seed is not None: + optional_params["seed"] = seed + if tools is not None: + optional_params["tools"] = tools + if tool_choice is not None: + optional_params["tool_choice"] = tool_choice + if max_retries is not None: + optional_params["max_retries"] = max_retries + + # OpenRouter-only parameters + extra_body = {} + transforms = passed_params.pop("transforms", None) + models = passed_params.pop("models", None) + route = passed_params.pop("route", None) + if transforms is not None: + extra_body["transforms"] = transforms + if models is not None: + extra_body["models"] = models + if route is not None: + extra_body["route"] = route + optional_params[ + "extra_body" + ] = extra_body # openai client supports `extra_body` param + else: # assume passing in params for openai/azure openai + supported_params = [ + "functions", + "function_call", + "temperature", + "top_p", + "n", + "stream", + "stop", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "user", + "response_format", + "seed", + "tools", + "tool_choice", + "max_retries", + "logprobs", + "top_logprobs", + ] + _check_valid_arg(supported_params=supported_params) + if functions is not None: + optional_params["functions"] = functions + if function_call is not None: + optional_params["function_call"] = function_call + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if n is not None: + optional_params["n"] = n + if stream is not None: + optional_params["stream"] = stream + if stop is not None: + optional_params["stop"] = stop + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + if presence_penalty is not None: + optional_params["presence_penalty"] = presence_penalty + if frequency_penalty is not None: + optional_params["frequency_penalty"] = frequency_penalty + if logit_bias is not None: + optional_params["logit_bias"] = logit_bias + if user is not None: + optional_params["user"] = user + if response_format is not None: + optional_params["response_format"] = response_format + if seed is not None: + optional_params["seed"] = seed + if tools is not None: + optional_params["tools"] = tools + if tool_choice is not None: + optional_params["tool_choice"] = tool_choice + if max_retries is not None: + optional_params["max_retries"] = max_retries + if logprobs is not None: + optional_params["logprobs"] = logprobs + if top_logprobs is not None: + optional_params["top_logprobs"] = top_logprobs + if custom_llm_provider in ["openai", "azure"] + litellm.openai_compatible_providers: + # for openai, azure we should pass the extra/passed params within `extra_body` https://github.com/openai/openai-python/blob/ac33853ba10d13ac149b1fa3ca6dba7d613065c9/src/openai/resources/models.py#L46 + extra_body = passed_params.pop("extra_body", {}) + for k in passed_params.keys(): + if k not in default_params.keys(): + extra_body[k] = passed_params[k] + optional_params["extra_body"] = extra_body + else: + # if user passed in non-default kwargs for specific providers/models, pass them along + for k in passed_params.keys(): + if k not in default_params.keys(): + optional_params[k] = passed_params[k] + return optional_params + + +def get_llm_provider( + model: str, + custom_llm_provider: Optional[str] = None, + api_base: Optional[str] = None, + api_key: Optional[str] = None, +): + try: + dynamic_api_key = None + # check if llm provider provided + + if custom_llm_provider: + return model, custom_llm_provider, dynamic_api_key, api_base + + if api_key and api_key.startswith("os.environ/"): + dynamic_api_key = get_secret(api_key) + # check if llm provider part of model name + if ( + model.split("/", 1)[0] in litellm.provider_list + and model.split("/", 1)[0] not in litellm.model_list + and len(model.split("/")) + > 1 # handle edge case where user passes in `litellm --model mistral` https://github.com/BerriAI/litellm/issues/1351 + ): + custom_llm_provider = model.split("/", 1)[0] + model = model.split("/", 1)[1] + if custom_llm_provider == "perplexity": + # perplexity is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.perplexity.ai + api_base = "https://api.perplexity.ai" + dynamic_api_key = get_secret("PERPLEXITYAI_API_KEY") + elif custom_llm_provider == "anyscale": + # anyscale is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 + api_base = "https://api.endpoints.anyscale.com/v1" + dynamic_api_key = get_secret("ANYSCALE_API_KEY") + elif custom_llm_provider == "deepinfra": + # deepinfra is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 + api_base = "https://api.deepinfra.com/v1/openai" + dynamic_api_key = get_secret("DEEPINFRA_API_KEY") + elif custom_llm_provider == "mistral": + # mistral is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.mistral.ai + api_base = "https://api.mistral.ai/v1" + dynamic_api_key = get_secret("MISTRAL_API_KEY") + elif custom_llm_provider == "voyage": + # voyage is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.voyageai.com/v1 + api_base = "https://api.voyageai.com/v1" + dynamic_api_key = get_secret("VOYAGE_API_KEY") + return model, custom_llm_provider, dynamic_api_key, api_base + elif model.split("/", 1)[0] in litellm.provider_list: + custom_llm_provider = model.split("/", 1)[0] + model = model.split("/", 1)[1] + return model, custom_llm_provider, dynamic_api_key, api_base + # check if api base is a known openai compatible endpoint + if api_base: + for endpoint in litellm.openai_compatible_endpoints: + if endpoint in api_base: + if endpoint == "api.perplexity.ai": + custom_llm_provider = "perplexity" + dynamic_api_key = get_secret("PERPLEXITYAI_API_KEY") + elif endpoint == "api.endpoints.anyscale.com/v1": + custom_llm_provider = "anyscale" + dynamic_api_key = get_secret("ANYSCALE_API_KEY") + elif endpoint == "api.deepinfra.com/v1/openai": + custom_llm_provider = "deepinfra" + dynamic_api_key = get_secret("DEEPINFRA_API_KEY") + elif endpoint == "api.mistral.ai/v1": + custom_llm_provider = "mistral" + dynamic_api_key = get_secret("MISTRAL_API_KEY") + return model, custom_llm_provider, dynamic_api_key, api_base + + # check if model in known model provider list -> for huggingface models, raise exception as they don't have a fixed provider (can be togetherai, anyscale, baseten, runpod, et.) + ## openai - chatcompletion + text completion + if ( + model in litellm.open_ai_chat_completion_models + or "ft:gpt-3.5-turbo" in model + or model in litellm.openai_image_generation_models + ): + custom_llm_provider = "openai" + elif model in litellm.open_ai_text_completion_models: + custom_llm_provider = "text-completion-openai" + ## anthropic + elif model in litellm.anthropic_models: + custom_llm_provider = "anthropic" + ## cohere + elif model in litellm.cohere_models or model in litellm.cohere_embedding_models: + custom_llm_provider = "cohere" + ## replicate + elif model in litellm.replicate_models or (":" in model and len(model) > 64): + model_parts = model.split(":") + if ( + len(model_parts) > 1 and len(model_parts[1]) == 64 + ): ## checks if model name has a 64 digit code - e.g. "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3" + custom_llm_provider = "replicate" + elif model in litellm.replicate_models: + custom_llm_provider = "replicate" + ## openrouter + elif model in litellm.openrouter_models: + custom_llm_provider = "openrouter" + ## openrouter + elif model in litellm.maritalk_models: + custom_llm_provider = "maritalk" + ## vertex - text + chat + language (gemini) models + elif ( + model in litellm.vertex_chat_models + or model in litellm.vertex_code_chat_models + or model in litellm.vertex_text_models + or model in litellm.vertex_code_text_models + or model in litellm.vertex_language_models + ): + custom_llm_provider = "vertex_ai" + ## ai21 + elif model in litellm.ai21_models: + custom_llm_provider = "ai21" + ## aleph_alpha + elif model in litellm.aleph_alpha_models: + custom_llm_provider = "aleph_alpha" + ## baseten + elif model in litellm.baseten_models: + custom_llm_provider = "baseten" + ## nlp_cloud + elif model in litellm.nlp_cloud_models: + custom_llm_provider = "nlp_cloud" + ## petals + elif model in litellm.petals_models: + custom_llm_provider = "petals" + ## bedrock + elif ( + model in litellm.bedrock_models or model in litellm.bedrock_embedding_models + ): + custom_llm_provider = "bedrock" + # openai embeddings + elif model in litellm.open_ai_embedding_models: + custom_llm_provider = "openai" + if custom_llm_provider is None or custom_llm_provider == "": + if litellm.suppress_debug_info == False: + print() # noqa + print( # noqa + "\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m" # noqa + ) # noqa + print() # noqa + error_str = f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. You passed model={model}\n Pass model as E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/starcoder',..)` Learn more: https://docs.litellm.ai/docs/providers" + # maps to openai.NotFoundError, this is raised when openai does not recognize the llm + raise litellm.exceptions.BadRequestError( # type: ignore + message=error_str, + model=model, + response=httpx.Response( + status_code=400, + content=error_str, + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + llm_provider="", + ) + return model, custom_llm_provider, dynamic_api_key, api_base + except Exception as e: + if isinstance(e, litellm.exceptions.BadRequestError): + raise e + else: + raise litellm.exceptions.BadRequestError( # type: ignore + message=f"GetLLMProvider Exception - {str(e)}\n\noriginal model: {model}", + model=model, + response=httpx.Response( + status_code=400, + content=error_str, + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + llm_provider="", + ) + + +def get_api_key(llm_provider: str, dynamic_api_key: Optional[str]): + api_key = dynamic_api_key or litellm.api_key + # openai + if llm_provider == "openai" or llm_provider == "text-completion-openai": + api_key = api_key or litellm.openai_key or get_secret("OPENAI_API_KEY") + # anthropic + elif llm_provider == "anthropic": + api_key = api_key or litellm.anthropic_key or get_secret("ANTHROPIC_API_KEY") + # ai21 + elif llm_provider == "ai21": + api_key = api_key or litellm.ai21_key or get_secret("AI211_API_KEY") + # aleph_alpha + elif llm_provider == "aleph_alpha": + api_key = ( + api_key or litellm.aleph_alpha_key or get_secret("ALEPH_ALPHA_API_KEY") + ) + # baseten + elif llm_provider == "baseten": + api_key = api_key or litellm.baseten_key or get_secret("BASETEN_API_KEY") + # cohere + elif llm_provider == "cohere": + api_key = api_key or litellm.cohere_key or get_secret("COHERE_API_KEY") + # huggingface + elif llm_provider == "huggingface": + api_key = ( + api_key or litellm.huggingface_key or get_secret("HUGGINGFACE_API_KEY") + ) + # nlp_cloud + elif llm_provider == "nlp_cloud": + api_key = api_key or litellm.nlp_cloud_key or get_secret("NLP_CLOUD_API_KEY") + # replicate + elif llm_provider == "replicate": + api_key = api_key or litellm.replicate_key or get_secret("REPLICATE_API_KEY") + # together_ai + elif llm_provider == "together_ai": + api_key = ( + api_key + or litellm.togetherai_api_key + or get_secret("TOGETHERAI_API_KEY") + or get_secret("TOGETHER_AI_TOKEN") + ) + return api_key + + +def get_max_tokens(model: str): + """ + Get the maximum number of tokens allowed for a given model. + + Parameters: + model (str): The name of the model. + + Returns: + int: The maximum number of tokens allowed for the given model. + + Raises: + Exception: If the model is not mapped yet. + + Example: + >>> get_max_tokens("gpt-4") + 8192 + """ + + def _get_max_position_embeddings(model_name): + # Construct the URL for the config.json file + config_url = f"https://huggingface.co/{model_name}/raw/main/config.json" + + try: + # Make the HTTP request to get the raw JSON file + response = requests.get(config_url) + response.raise_for_status() # Raise an exception for bad responses (4xx or 5xx) + + # Parse the JSON response + config_json = response.json() + + # Extract and return the max_position_embeddings + max_position_embeddings = config_json.get("max_position_embeddings") + + if max_position_embeddings is not None: + return max_position_embeddings + else: + return None + except requests.exceptions.RequestException as e: + return None + + try: + if model in litellm.model_cost: + return litellm.model_cost[model]["max_tokens"] + model, custom_llm_provider, _, _ = get_llm_provider(model=model) + if custom_llm_provider == "huggingface": + max_tokens = _get_max_position_embeddings(model_name=model) + return max_tokens + else: + raise Exception() + except: + raise Exception( + "This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json" + ) + + +def get_model_info(model: str): + """ + Get a dict for the maximum tokens (context window), + input_cost_per_token, output_cost_per_token for a given model. + + Parameters: + model (str): The name of the model. + + Returns: + dict: A dictionary containing the following information: + - max_tokens (int): The maximum number of tokens allowed for the given model. + - input_cost_per_token (float): The cost per token for input. + - output_cost_per_token (float): The cost per token for output. + - litellm_provider (str): The provider of the model (e.g., "openai"). + - mode (str): The mode of the model (e.g., "chat" or "completion"). + + Raises: + Exception: If the model is not mapped yet. + + Example: + >>> get_model_info("gpt-4") + { + "max_tokens": 8192, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + } + """ + + def _get_max_position_embeddings(model_name): + # Construct the URL for the config.json file + config_url = f"https://huggingface.co/{model_name}/raw/main/config.json" + + try: + # Make the HTTP request to get the raw JSON file + response = requests.get(config_url) + response.raise_for_status() # Raise an exception for bad responses (4xx or 5xx) + + # Parse the JSON response + config_json = response.json() + + # Extract and return the max_position_embeddings + max_position_embeddings = config_json.get("max_position_embeddings") + + if max_position_embeddings is not None: + return max_position_embeddings + else: + return None + except requests.exceptions.RequestException as e: + return None + + try: + azure_llms = litellm.azure_llms + if model in azure_llms: + model = azure_llms[model] + if model in litellm.model_cost: + return litellm.model_cost[model] + model, custom_llm_provider, _, _ = get_llm_provider(model=model) + if custom_llm_provider == "huggingface": + max_tokens = _get_max_position_embeddings(model_name=model) + return { + "max_tokens": max_tokens, + "input_cost_per_token": 0, + "output_cost_per_token": 0, + "litellm_provider": "huggingface", + "mode": "chat", + } + else: + raise Exception() + except: + raise Exception( + "This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json" + ) + + +def json_schema_type(python_type_name: str): + """Converts standard python types to json schema types + + Parameters + ---------- + python_type_name : str + __name__ of type + + Returns + ------- + str + a standard JSON schema type, "string" if not recognized. + """ + python_to_json_schema_types = { + str.__name__: "string", + int.__name__: "integer", + float.__name__: "number", + bool.__name__: "boolean", + list.__name__: "array", + dict.__name__: "object", + "NoneType": "null", + } + + return python_to_json_schema_types.get(python_type_name, "string") + + +def function_to_dict(input_function): # noqa: C901 + """Using type hints and numpy-styled docstring, + produce a dictionnary usable for OpenAI function calling + + Parameters + ---------- + input_function : function + A function with a numpy-style docstring + + Returns + ------- + dictionnary + A dictionnary to add to the list passed to `functions` parameter of `litellm.completion` + """ + # Get function name and docstring + try: + import inspect + from numpydoc.docscrape import NumpyDocString + from ast import literal_eval + except Exception as e: + raise e + + name = input_function.__name__ + docstring = inspect.getdoc(input_function) + numpydoc = NumpyDocString(docstring) + description = "\n".join([s.strip() for s in numpydoc["Summary"]]) + + # Get function parameters and their types from annotations and docstring + parameters = {} + required_params = [] + param_info = inspect.signature(input_function).parameters + + for param_name, param in param_info.items(): + if hasattr(param, "annotation"): + param_type = json_schema_type(param.annotation.__name__) + else: + param_type = None + param_description = None + param_enum = None + + # Try to extract param description from docstring using numpydoc + for param_data in numpydoc["Parameters"]: + if param_data.name == param_name: + if hasattr(param_data, "type"): + # replace type from docstring rather than annotation + param_type = param_data.type + if "optional" in param_type: + param_type = param_type.split(",")[0] + elif "{" in param_type: + # may represent a set of acceptable values + # translating as enum for function calling + try: + param_enum = str(list(literal_eval(param_type))) + param_type = "string" + except Exception: + pass + param_type = json_schema_type(param_type) + param_description = "\n".join([s.strip() for s in param_data.desc]) + + param_dict = { + "type": param_type, + "description": param_description, + "enum": param_enum, + } + + parameters[param_name] = dict( + [(k, v) for k, v in param_dict.items() if isinstance(v, str)] + ) + + # Check if the parameter has no default value (i.e., it's required) + if param.default == param.empty: + required_params.append(param_name) + + # Create the dictionary + result = { + "name": name, + "description": description, + "parameters": { + "type": "object", + "properties": parameters, + }, + } + + # Add "required" key if there are required parameters + if required_params: + result["parameters"]["required"] = required_params + + return result + + +def load_test_model( + model: str, + custom_llm_provider: str = "", + api_base: str = "", + prompt: str = "", + num_calls: int = 0, + force_timeout: int = 0, +): + test_prompt = "Hey, how's it going" + test_calls = 100 + if prompt: + test_prompt = prompt + if num_calls: + test_calls = num_calls + messages = [[{"role": "user", "content": test_prompt}] for _ in range(test_calls)] + start_time = time.time() + try: + litellm.batch_completion( + model=model, + messages=messages, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + force_timeout=force_timeout, + ) + end_time = time.time() + response_time = end_time - start_time + return { + "total_response_time": response_time, + "calls_made": 100, + "status": "success", + "exception": None, + } + except Exception as e: + end_time = time.time() + response_time = end_time - start_time + return { + "total_response_time": response_time, + "calls_made": 100, + "status": "failed", + "exception": e, + } + + +def validate_environment(model: Optional[str] = None) -> dict: + """ + Checks if the environment variables are valid for the given model. + + Args: + model (Optional[str]): The name of the model. Defaults to None. + + Returns: + dict: A dictionary containing the following keys: + - keys_in_environment (bool): True if all the required keys are present in the environment, False otherwise. + - missing_keys (List[str]): A list of missing keys in the environment. + """ + keys_in_environment = False + missing_keys: List[str] = [] + + if model is None: + return { + "keys_in_environment": keys_in_environment, + "missing_keys": missing_keys, + } + ## EXTRACT LLM PROVIDER - if model name provided + try: + custom_llm_provider = get_llm_provider(model=model) + except: + custom_llm_provider = None + # # check if llm provider part of model name + # if model.split("/",1)[0] in litellm.provider_list: + # custom_llm_provider = model.split("/", 1)[0] + # model = model.split("/", 1)[1] + # custom_llm_provider_passed_in = True + + if custom_llm_provider: + if custom_llm_provider == "openai": + if "OPENAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENAI_API_KEY") + elif custom_llm_provider == "azure": + if ( + "AZURE_API_BASE" in os.environ + and "AZURE_API_VERSION" in os.environ + and "AZURE_API_KEY" in os.environ + ): + keys_in_environment = True + else: + missing_keys.extend( + ["AZURE_API_BASE", "AZURE_API_VERSION", "AZURE_API_KEY"] + ) + elif custom_llm_provider == "anthropic": + if "ANTHROPIC_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ANTHROPIC_API_KEY") + elif custom_llm_provider == "cohere": + if "COHERE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("COHERE_API_KEY") + elif custom_llm_provider == "replicate": + if "REPLICATE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("REPLICATE_API_KEY") + elif custom_llm_provider == "openrouter": + if "OPENROUTER_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENROUTER_API_KEY") + elif custom_llm_provider == "vertex_ai": + if "VERTEXAI_PROJECT" in os.environ and "VERTEXAI_LOCATION" in os.environ: + keys_in_environment = True + else: + missing_keys.extend(["VERTEXAI_PROJECT", "VERTEXAI_PROJECT"]) + elif custom_llm_provider == "huggingface": + if "HUGGINGFACE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("HUGGINGFACE_API_KEY") + elif custom_llm_provider == "ai21": + if "AI21_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("AI21_API_KEY") + elif custom_llm_provider == "together_ai": + if "TOGETHERAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("TOGETHERAI_API_KEY") + elif custom_llm_provider == "aleph_alpha": + if "ALEPH_ALPHA_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ALEPH_ALPHA_API_KEY") + elif custom_llm_provider == "baseten": + if "BASETEN_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("BASETEN_API_KEY") + elif custom_llm_provider == "nlp_cloud": + if "NLP_CLOUD_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("NLP_CLOUD_API_KEY") + elif custom_llm_provider == "bedrock": + if ( + "AWS_ACCESS_KEY_ID" in os.environ + and "AWS_SECRET_ACCESS_KEY" in os.environ + ): + keys_in_environment = True + else: + missing_keys.append("AWS_ACCESS_KEY_ID") + missing_keys.append("AWS_SECRET_ACCESS_KEY") + else: + ## openai - chatcompletion + text completion + if ( + model in litellm.open_ai_chat_completion_models + or litellm.open_ai_text_completion_models + ): + if "OPENAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENAI_API_KEY") + ## anthropic + elif model in litellm.anthropic_models: + if "ANTHROPIC_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ANTHROPIC_API_KEY") + ## cohere + elif model in litellm.cohere_models: + if "COHERE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("COHERE_API_KEY") + ## replicate + elif model in litellm.replicate_models: + if "REPLICATE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("REPLICATE_API_KEY") + ## openrouter + elif model in litellm.openrouter_models: + if "OPENROUTER_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("OPENROUTER_API_KEY") + ## vertex - text + chat models + elif model in litellm.vertex_chat_models or model in litellm.vertex_text_models: + if "VERTEXAI_PROJECT" in os.environ and "VERTEXAI_LOCATION" in os.environ: + keys_in_environment = True + else: + missing_keys.extend(["VERTEXAI_PROJECT", "VERTEXAI_PROJECT"]) + ## huggingface + elif model in litellm.huggingface_models: + if "HUGGINGFACE_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("HUGGINGFACE_API_KEY") + ## ai21 + elif model in litellm.ai21_models: + if "AI21_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("AI21_API_KEY") + ## together_ai + elif model in litellm.together_ai_models: + if "TOGETHERAI_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("TOGETHERAI_API_KEY") + ## aleph_alpha + elif model in litellm.aleph_alpha_models: + if "ALEPH_ALPHA_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("ALEPH_ALPHA_API_KEY") + ## baseten + elif model in litellm.baseten_models: + if "BASETEN_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("BASETEN_API_KEY") + ## nlp_cloud + elif model in litellm.nlp_cloud_models: + if "NLP_CLOUD_API_KEY" in os.environ: + keys_in_environment = True + else: + missing_keys.append("NLP_CLOUD_API_KEY") + return {"keys_in_environment": keys_in_environment, "missing_keys": missing_keys} + + +def set_callbacks(callback_list, function_id=None): + global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger, dynamoLogger, s3Logger + try: + for callback in callback_list: + print_verbose(f"callback: {callback}") + if callback == "sentry": + try: + import sentry_sdk + except ImportError: + print_verbose("Package 'sentry_sdk' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "sentry_sdk"] + ) + import sentry_sdk + sentry_sdk_instance = sentry_sdk + sentry_trace_rate = ( + os.environ.get("SENTRY_API_TRACE_RATE") + if "SENTRY_API_TRACE_RATE" in os.environ + else "1.0" + ) + sentry_sdk_instance.init( + dsn=os.environ.get("SENTRY_DSN"), + traces_sample_rate=float(sentry_trace_rate), + ) + capture_exception = sentry_sdk_instance.capture_exception + add_breadcrumb = sentry_sdk_instance.add_breadcrumb + elif callback == "posthog": + try: + from posthog import Posthog + except ImportError: + print_verbose("Package 'posthog' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "posthog"] + ) + from posthog import Posthog + posthog = Posthog( + project_api_key=os.environ.get("POSTHOG_API_KEY"), + host=os.environ.get("POSTHOG_API_URL"), + ) + elif callback == "slack": + try: + from slack_bolt import App + except ImportError: + print_verbose("Package 'slack_bolt' is missing. Installing it...") + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "slack_bolt"] + ) + from slack_bolt import App + slack_app = App( + token=os.environ.get("SLACK_API_TOKEN"), + signing_secret=os.environ.get("SLACK_API_SECRET"), + ) + alerts_channel = os.environ["SLACK_API_CHANNEL"] + print_verbose(f"Initialized Slack App: {slack_app}") + elif callback == "traceloop": + traceloopLogger = TraceloopLogger() + elif callback == "helicone": + heliconeLogger = HeliconeLogger() + elif callback == "llmonitor": + llmonitorLogger = LLMonitorLogger() + elif callback == "promptlayer": + promptLayerLogger = PromptLayerLogger() + elif callback == "langfuse": + langFuseLogger = LangFuseLogger() + elif callback == "dynamodb": + dynamoLogger = DyanmoDBLogger() + elif callback == "s3": + s3Logger = S3Logger() + elif callback == "wandb": + weightsBiasesLogger = WeightsBiasesLogger() + elif callback == "langsmith": + langsmithLogger = LangsmithLogger() + elif callback == "aispend": + aispendLogger = AISpendLogger() + elif callback == "berrispend": + berrispendLogger = BerriSpendLogger() + elif callback == "supabase": + print_verbose(f"instantiating supabase") + supabaseClient = Supabase() + elif callback == "lite_debugger": + print_verbose(f"instantiating lite_debugger") + if function_id: + liteDebuggerClient = LiteDebugger(email=function_id) + elif litellm.token: + liteDebuggerClient = LiteDebugger(email=litellm.token) + elif litellm.email: + liteDebuggerClient = LiteDebugger(email=litellm.email) + else: + liteDebuggerClient = LiteDebugger(email=str(uuid.uuid4())) + elif callable(callback): + customLogger = CustomLogger() + except Exception as e: + raise e + + +# NOTE: DEPRECATING this in favor of using failure_handler() in Logging: +def handle_failure(exception, traceback_exception, start_time, end_time, args, kwargs): + global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger + try: + # print_verbose(f"handle_failure args: {args}") + # print_verbose(f"handle_failure kwargs: {kwargs}") + + success_handler = additional_details.pop("success_handler", None) + failure_handler = additional_details.pop("failure_handler", None) + + additional_details["Event_Name"] = additional_details.pop( + "failed_event_name", "litellm.failed_query" + ) + print_verbose(f"self.failure_callback: {litellm.failure_callback}") + for callback in litellm.failure_callback: + try: + if callback == "slack": + slack_msg = "" + if len(kwargs) > 0: + for key in kwargs: + slack_msg += f"{key}: {kwargs[key]}\n" + if len(args) > 0: + for i, arg in enumerate(args): + slack_msg += f"LiteLLM_Args_{str(i)}: {arg}" + for detail in additional_details: + slack_msg += f"{detail}: {additional_details[detail]}\n" + slack_msg += f"Traceback: {traceback_exception}" + slack_app.client.chat_postMessage( + channel=alerts_channel, text=slack_msg + ) + elif callback == "sentry": + capture_exception(exception) + elif callback == "posthog": + print_verbose( + f"inside posthog, additional_details: {len(additional_details.keys())}" + ) + ph_obj = {} + if len(kwargs) > 0: + ph_obj = kwargs + if len(args) > 0: + for i, arg in enumerate(args): + ph_obj["litellm_args_" + str(i)] = arg + for detail in additional_details: + ph_obj[detail] = additional_details[detail] + event_name = additional_details["Event_Name"] + print_verbose(f"ph_obj: {ph_obj}") + print_verbose(f"PostHog Event Name: {event_name}") + if "user_id" in additional_details: + posthog.capture( + additional_details["user_id"], event_name, ph_obj + ) + else: # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python + unique_id = str(uuid.uuid4()) + posthog.capture(unique_id, event_name) + print_verbose(f"successfully logged to PostHog!") + elif callback == "berrispend": + print_verbose("reaches berrispend for logging!") + model = args[0] if len(args) > 0 else kwargs["model"] + messages = args[1] if len(args) > 1 else kwargs["messages"] + result = { + "model": model, + "created": time.time(), + "error": traceback_exception, + "usage": { + "prompt_tokens": prompt_token_calculator( + model, messages=messages + ), + "completion_tokens": 0, + }, + } + berrispendLogger.log_event( + model=model, + messages=messages, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif callback == "aispend": + print_verbose("reaches aispend for logging!") + model = args[0] if len(args) > 0 else kwargs["model"] + messages = args[1] if len(args) > 1 else kwargs["messages"] + result = { + "model": model, + "created": time.time(), + "usage": { + "prompt_tokens": prompt_token_calculator( + model, messages=messages + ), + "completion_tokens": 0, + }, + } + aispendLogger.log_event( + model=model, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + elif callback == "supabase": + print_verbose("reaches supabase for logging!") + print_verbose(f"supabaseClient: {supabaseClient}") + model = args[0] if len(args) > 0 else kwargs["model"] + messages = args[1] if len(args) > 1 else kwargs["messages"] + result = { + "model": model, + "created": time.time(), + "error": traceback_exception, + "usage": { + "prompt_tokens": prompt_token_calculator( + model, messages=messages + ), + "completion_tokens": 0, + }, + } + supabaseClient.log_event( + model=model, + messages=messages, + end_user=kwargs.get("user", "default"), + response_obj=result, + start_time=start_time, + end_time=end_time, + litellm_call_id=kwargs["litellm_call_id"], + print_verbose=print_verbose, + ) + except: + print_verbose( + f"Error Occurred while logging failure: {traceback.format_exc()}" + ) + pass + + if failure_handler and callable(failure_handler): + call_details = { + "exception": exception, + "additional_details": additional_details, + } + failure_handler(call_details) + pass + except Exception as e: + # LOGGING + exception_logging(logger_fn=user_logger_fn, exception=e) + pass + + +async def convert_to_streaming_response_async(response_object: Optional[dict] = None): + """ + Asynchronously converts a response object to a streaming response. + + Args: + response_object (Optional[dict]): The response object to be converted. Defaults to None. + + Raises: + Exception: If the response object is None. + + Yields: + ModelResponse: The converted streaming response object. + + Returns: + None + """ + if response_object is None: + raise Exception("Error in response object format") + + model_response_object = ModelResponse(stream=True) + + if model_response_object is None: + raise Exception("Error in response creating model response object") + + choice_list = [] + + for idx, choice in enumerate(response_object["choices"]): + delta = Delta( + content=choice["message"].get("content", None), + role=choice["message"]["role"], + function_call=choice["message"].get("function_call", None), + tool_calls=choice["message"].get("tool_calls", None), + ) + finish_reason = choice.get("finish_reason", None) + + if finish_reason is None: + finish_reason = choice.get("finish_details") + + logprobs = choice.get("logprobs", None) + + choice = StreamingChoices( + finish_reason=finish_reason, index=idx, delta=delta, logprobs=logprobs + ) + choice_list.append(choice) + + model_response_object.choices = choice_list + + if "usage" in response_object and response_object["usage"] is not None: + model_response_object.usage = Usage( + completion_tokens=response_object["usage"].get("completion_tokens", 0), + prompt_tokens=response_object["usage"].get("prompt_tokens", 0), + total_tokens=response_object["usage"].get("total_tokens", 0), + ) + + if "id" in response_object: + model_response_object.id = response_object["id"] + + if "created" in response_object: + model_response_object.created = response_object["created"] + + if "system_fingerprint" in response_object: + model_response_object.system_fingerprint = response_object["system_fingerprint"] + + if "model" in response_object: + model_response_object.model = response_object["model"] + + yield model_response_object + await asyncio.sleep(0) + + +def convert_to_streaming_response(response_object: Optional[dict] = None): + # used for yielding Cache hits when stream == True + if response_object is None: + raise Exception("Error in response object format") + + model_response_object = ModelResponse(stream=True) + choice_list = [] + for idx, choice in enumerate(response_object["choices"]): + delta = Delta( + content=choice["message"].get("content", None), + role=choice["message"]["role"], + function_call=choice["message"].get("function_call", None), + tool_calls=choice["message"].get("tool_calls", None), + ) + finish_reason = choice.get("finish_reason", None) + if finish_reason == None: + # gpt-4 vision can return 'finish_reason' or 'finish_details' + finish_reason = choice.get("finish_details") + logprobs = choice.get("logprobs", None) + choice = StreamingChoices( + finish_reason=finish_reason, index=idx, delta=delta, logprobs=logprobs + ) + + choice_list.append(choice) + model_response_object.choices = choice_list + + if "usage" in response_object and response_object["usage"] is not None: + model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0) # type: ignore + model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0) # type: ignore + model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0) # type: ignore + + if "id" in response_object: + model_response_object.id = response_object["id"] + + if "created" in response_object: + model_response_object.created = response_object["created"] + + if "system_fingerprint" in response_object: + model_response_object.system_fingerprint = response_object["system_fingerprint"] + + if "model" in response_object: + model_response_object.model = response_object["model"] + yield model_response_object + + +def convert_to_model_response_object( + response_object: Optional[dict] = None, + model_response_object: Optional[ + Union[ModelResponse, EmbeddingResponse, ImageResponse] + ] = None, + response_type: Literal[ + "completion", "embedding", "image_generation" + ] = "completion", + stream=False, +): + try: + if response_type == "completion" and ( + model_response_object is None + or isinstance(model_response_object, ModelResponse) + ): + if response_object is None or model_response_object is None: + raise Exception("Error in response object format") + if stream == True: + # for returning cached responses, we need to yield a generator + return convert_to_streaming_response(response_object=response_object) + choice_list = [] + for idx, choice in enumerate(response_object["choices"]): + message = Message( + content=choice["message"].get("content", None), + role=choice["message"]["role"], + function_call=choice["message"].get("function_call", None), + tool_calls=choice["message"].get("tool_calls", None), + ) + finish_reason = choice.get("finish_reason", None) + if finish_reason == None: + # gpt-4 vision can return 'finish_reason' or 'finish_details' + finish_reason = choice.get("finish_details") + logprobs = choice.get("logprobs", None) + choice = Choices( + finish_reason=finish_reason, + index=idx, + message=message, + logprobs=logprobs, + ) + choice_list.append(choice) + model_response_object.choices = choice_list + + if "usage" in response_object and response_object["usage"] is not None: + model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0) # type: ignore + model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0) # type: ignore + model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0) # type: ignore + + if "created" in response_object: + model_response_object.created = response_object["created"] + + if "id" in response_object: + model_response_object.id = response_object["id"] + + if "system_fingerprint" in response_object: + model_response_object.system_fingerprint = response_object[ + "system_fingerprint" + ] + + if "model" in response_object: + model_response_object.model = response_object["model"] + return model_response_object + elif response_type == "embedding" and ( + model_response_object is None + or isinstance(model_response_object, EmbeddingResponse) + ): + if response_object is None: + raise Exception("Error in response object format") + + if model_response_object is None: + model_response_object = EmbeddingResponse() + + if "model" in response_object: + model_response_object.model = response_object["model"] + + if "object" in response_object: + model_response_object.object = response_object["object"] + + model_response_object.data = response_object["data"] + + if "usage" in response_object and response_object["usage"] is not None: + model_response_object.usage.completion_tokens = response_object["usage"].get("completion_tokens", 0) # type: ignore + model_response_object.usage.prompt_tokens = response_object["usage"].get("prompt_tokens", 0) # type: ignore + model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0) # type: ignore + + return model_response_object + elif response_type == "image_generation" and ( + model_response_object is None + or isinstance(model_response_object, ImageResponse) + ): + if response_object is None: + raise Exception("Error in response object format") + + if model_response_object is None: + model_response_object = ImageResponse() + + if "created" in response_object: + model_response_object.created = response_object["created"] + + if "data" in response_object: + model_response_object.data = response_object["data"] + + return model_response_object + except Exception as e: + raise Exception(f"Invalid response object {e}") + + +# NOTE: DEPRECATING this in favor of using success_handler() in Logging: +def handle_success(args, kwargs, result, start_time, end_time): + global heliconeLogger, aispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger + try: + model = args[0] if len(args) > 0 else kwargs["model"] + input = ( + args[1] + if len(args) > 1 + else kwargs.get("messages", kwargs.get("input", None)) + ) + success_handler = additional_details.pop("success_handler", None) + failure_handler = additional_details.pop("failure_handler", None) + additional_details["Event_Name"] = additional_details.pop( + "successful_event_name", "litellm.succes_query" + ) + for callback in litellm.success_callback: + try: + if callback == "posthog": + ph_obj = {} + for detail in additional_details: + ph_obj[detail] = additional_details[detail] + event_name = additional_details["Event_Name"] + if "user_id" in additional_details: + posthog.capture( + additional_details["user_id"], event_name, ph_obj + ) + else: # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python + unique_id = str(uuid.uuid4()) + posthog.capture(unique_id, event_name, ph_obj) + pass + elif callback == "slack": + slack_msg = "" + for detail in additional_details: + slack_msg += f"{detail}: {additional_details[detail]}\n" + slack_app.client.chat_postMessage( + channel=alerts_channel, text=slack_msg + ) + elif callback == "aispend": + print_verbose("reaches aispend for logging!") + model = args[0] if len(args) > 0 else kwargs["model"] + aispendLogger.log_event( + model=model, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + except Exception as e: + # LOGGING + exception_logging(logger_fn=user_logger_fn, exception=e) + print_verbose( + f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}" + ) + pass + + if success_handler and callable(success_handler): + success_handler(args, kwargs) + pass + except Exception as e: + # LOGGING + exception_logging(logger_fn=user_logger_fn, exception=e) + print_verbose( + f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}" + ) + pass + + +def acreate(*args, **kwargs): ## Thin client to handle the acreate langchain call + return litellm.acompletion(*args, **kwargs) + + +def prompt_token_calculator(model, messages): + # use tiktoken or anthropic's tokenizer depending on the model + text = " ".join(message["content"] for message in messages) + num_tokens = 0 + if "claude" in model: + try: + import anthropic + except: + Exception("Anthropic import failed please run `pip install anthropic`") + from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT + + anthropic = Anthropic() + num_tokens = anthropic.count_tokens(text) + else: + num_tokens = len(encoding.encode(text)) + return num_tokens + + +def valid_model(model): + try: + # for a given model name, check if the user has the right permissions to access the model + if ( + model in litellm.open_ai_chat_completion_models + or model in litellm.open_ai_text_completion_models + ): + openai.Model.retrieve(model) + else: + messages = [{"role": "user", "content": "Hello World"}] + litellm.completion(model=model, messages=messages) + except: + raise BadRequestError(message="", model=model, llm_provider="") + + +def check_valid_key(model: str, api_key: str): + """ + Checks if a given API key is valid for a specific model by making a litellm.completion call with max_tokens=10 + + Args: + model (str): The name of the model to check the API key against. + api_key (str): The API key to be checked. + + Returns: + bool: True if the API key is valid for the model, False otherwise. + """ + messages = [{"role": "user", "content": "Hey, how's it going?"}] + try: + litellm.completion( + model=model, messages=messages, api_key=api_key, max_tokens=10 + ) + return True + except AuthenticationError as e: + return False + except Exception as e: + return False + + +def _should_retry(status_code: int): + """ + Reimplementation of openai's should retry logic, since that one can't be imported. + https://github.com/openai/openai-python/blob/af67cfab4210d8e497c05390ce14f39105c77519/src/openai/_base_client.py#L639 + """ + # If the server explicitly says whether or not to retry, obey. + # Retry on request timeouts. + if status_code == 408: + return True + + # Retry on lock timeouts. + if status_code == 409: + return True + + # Retry on rate limits. + if status_code == 429: + return True + + # Retry internal errors. + if status_code >= 500: + return True + + return False + + +def _calculate_retry_after( + remaining_retries: int, + max_retries: int, + response_headers: Optional[httpx.Headers] = None, + min_timeout: int = 0, +): + """ + Reimplementation of openai's calculate retry after, since that one can't be imported. + https://github.com/openai/openai-python/blob/af67cfab4210d8e497c05390ce14f39105c77519/src/openai/_base_client.py#L631 + """ + try: + import email # openai import + + # About the Retry-After header: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After + # + # ". See https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After#syntax for + # details. + if response_headers is not None: + retry_header = response_headers.get("retry-after") + try: + retry_after = int(retry_header) + except Exception: + retry_date_tuple = email.utils.parsedate_tz(retry_header) + if retry_date_tuple is None: + retry_after = -1 + else: + retry_date = email.utils.mktime_tz(retry_date_tuple) + retry_after = int(retry_date - time.time()) + else: + retry_after = -1 + + except Exception: + retry_after = -1 + + # If the API asks us to wait a certain amount of time (and it's a reasonable amount), just do what it says. + if 0 < retry_after <= 60: + return retry_after + + initial_retry_delay = 0.5 + max_retry_delay = 8.0 + nb_retries = max_retries - remaining_retries + + # Apply exponential backoff, but not more than the max. + sleep_seconds = min(initial_retry_delay * pow(2.0, nb_retries), max_retry_delay) + + # Apply some jitter, plus-or-minus half a second. + jitter = 1 - 0.25 * random.random() + timeout = sleep_seconds * jitter + return timeout if timeout >= min_timeout else min_timeout + + +# integration helper function +def modify_integration(integration_name, integration_params): + global supabaseClient + if integration_name == "supabase": + if "table_name" in integration_params: + Supabase.supabase_table_name = integration_params["table_name"] + + +# custom prompt helper function +def register_prompt_template( + model: str, + roles: dict, + initial_prompt_value: str = "", + final_prompt_value: str = "", +): + """ + Register a prompt template to follow your custom format for a given model + + Args: + model (str): The name of the model. + roles (dict): A dictionary mapping roles to their respective prompt values. + initial_prompt_value (str, optional): The initial prompt value. Defaults to "". + final_prompt_value (str, optional): The final prompt value. Defaults to "". + + Returns: + dict: The updated custom prompt dictionary. + Example usage: + ``` + import litellm + litellm.register_prompt_template( + model="llama-2", + initial_prompt_value="You are a good assistant" # [OPTIONAL] + roles={ + "system": { + "pre_message": "[INST] <>\n", # [OPTIONAL] + "post_message": "\n<>\n [/INST]\n" # [OPTIONAL] + }, + "user": { + "pre_message": "[INST] ", # [OPTIONAL] + "post_message": " [/INST]" # [OPTIONAL] + }, + "assistant": { + "pre_message": "\n" # [OPTIONAL] + "post_message": "\n" # [OPTIONAL] + } + } + final_prompt_value="Now answer as best you can:" # [OPTIONAL] + ) + ``` + """ + model = get_llm_provider(model=model)[0] + litellm.custom_prompt_dict[model] = { + "roles": roles, + "initial_prompt_value": initial_prompt_value, + "final_prompt_value": final_prompt_value, + } + return litellm.custom_prompt_dict + + +####### DEPRECATED ################ + + +def get_all_keys(llm_provider=None): + try: + global last_fetched_at_keys + # if user is using hosted product -> instantiate their env with their hosted api keys - refresh every 5 minutes + print_verbose(f"Reaches get all keys, llm_provider: {llm_provider}") + user_email = ( + os.getenv("LITELLM_EMAIL") + or litellm.email + or litellm.token + or os.getenv("LITELLM_TOKEN") + ) + if user_email: + time_delta = 0 + if last_fetched_at_keys != None: + current_time = time.time() + time_delta = current_time - last_fetched_at_keys + if ( + time_delta > 300 or last_fetched_at_keys == None or llm_provider + ): # if the llm provider is passed in , assume this happening due to an AuthError for that provider + # make the api call + last_fetched_at = time.time() + print_verbose(f"last_fetched_at: {last_fetched_at}") + response = requests.post( + url="http://api.litellm.ai/get_all_keys", + headers={"content-type": "application/json"}, + data=json.dumps({"user_email": user_email}), + ) + print_verbose(f"get model key response: {response.text}") + data = response.json() + # update model list + for key, value in data[ + "model_keys" + ].items(): # follows the LITELLM API KEY format - _API_KEY - e.g. HUGGINGFACE_API_KEY + os.environ[key] = value + # set model alias map + for model_alias, value in data["model_alias_map"].items(): + litellm.model_alias_map[model_alias] = value + return "it worked!" + return None + return None + except: + print_verbose( + f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}" + ) + pass + + +def get_model_list(): + global last_fetched_at, print_verbose + try: + # if user is using hosted product -> get their updated model list + user_email = ( + os.getenv("LITELLM_EMAIL") + or litellm.email + or litellm.token + or os.getenv("LITELLM_TOKEN") + ) + if user_email: + # make the api call + last_fetched_at = time.time() + print_verbose(f"last_fetched_at: {last_fetched_at}") + response = requests.post( + url="http://api.litellm.ai/get_model_list", + headers={"content-type": "application/json"}, + data=json.dumps({"user_email": user_email}), + ) + print_verbose(f"get_model_list response: {response.text}") + data = response.json() + # update model list + model_list = data["model_list"] + # # check if all model providers are in environment + # model_providers = data["model_providers"] + # missing_llm_provider = None + # for item in model_providers: + # if f"{item.upper()}_API_KEY" not in os.environ: + # missing_llm_provider = item + # break + # # update environment - if required + # threading.Thread(target=get_all_keys, args=(missing_llm_provider)).start() + return model_list + return [] # return empty list by default + except: + print_verbose( + f"[Non-Blocking Error] get_model_list error - {traceback.format_exc()}" + ) + + +####### EXCEPTION MAPPING ################ +def exception_type( + model, + original_exception, + custom_llm_provider, + completion_kwargs={}, +): + global user_logger_fn, liteDebuggerClient + exception_mapping_worked = False + if litellm.suppress_debug_info is False: + print() # noqa + print( # noqa + "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" # noqa + ) # noqa + print( # noqa + "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'." # noqa + ) # noqa + print() # noqa + try: + if model: + error_str = str(original_exception) + if isinstance(original_exception, BaseException): + exception_type = type(original_exception).__name__ + else: + exception_type = "" + + if "Request Timeout Error" in error_str or "Request timed out" in error_str: + exception_mapping_worked = True + raise Timeout( + message=f"APITimeoutError - Request timed out", + model=model, + llm_provider=custom_llm_provider, + ) + + if ( + custom_llm_provider == "openai" + or custom_llm_provider == "text-completion-openai" + or custom_llm_provider == "custom_openai" + or custom_llm_provider in litellm.openai_compatible_providers + ): + if ( + "This model's maximum context length is" in error_str + or "Request too large" in error_str + ): + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response, + ) + elif ( + "invalid_request_error" in error_str + and "model_not_found" in error_str + ): + exception_mapping_worked = True + raise NotFoundError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response, + ) + elif ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ): + exception_mapping_worked = True + raise ContentPolicyViolationError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response, + ) + elif ( + "invalid_request_error" in error_str + and "Incorrect API key provided" not in error_str + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response, + ) + elif hasattr(original_exception, "status_code"): + exception_mapping_worked = True + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 404: + exception_mapping_worked = True + raise NotFoundError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response, + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + ) + elif original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response, + ) + elif original_exception.status_code == 503: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response, + ) + elif original_exception.status_code == 504: # gateway timeout error + exception_mapping_worked = True + raise Timeout( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + request=original_exception.request, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + __cause__=original_exception.__cause__, + llm_provider=custom_llm_provider, + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "anthropic": # one of the anthropics + if hasattr(original_exception, "message"): + if ( + "prompt is too long" in original_exception.message + or "prompt: length" in original_exception.message + ): + exception_mapping_worked = True + raise ContextWindowExceededError( + message=original_exception.message, + model=model, + llm_provider="anthropic", + response=original_exception.response, + ) + if "Invalid API Key" in original_exception.message: + exception_mapping_worked = True + raise AuthenticationError( + message=original_exception.message, + model=model, + llm_provider="anthropic", + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + print_verbose(f"status_code: {original_exception.status_code}") + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + response=original_exception.response, + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"AnthropicException - {original_exception.message}", + model=model, + llm_provider="anthropic", + response=original_exception.response, + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"AnthropicException - {original_exception.message}", + model=model, + llm_provider="anthropic", + request=original_exception.request, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + response=original_exception.response, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"AnthropicException - {original_exception.message}", + llm_provider="anthropic", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "replicate": + if "Incorrect authentication token" in error_str: + exception_mapping_worked = True + raise AuthenticationError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=original_exception.response, + ) + elif "input is too long" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=original_exception.response, + ) + elif exception_type == "ModelError": + exception_mapping_worked = True + raise BadRequestError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=original_exception.response, + ) + elif "Request was throttled" in error_str: + exception_mapping_worked = True + raise RateLimitError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=original_exception.response, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=original_exception.response, + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 422 + or original_exception.status_code == 413 + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=original_exception.response, + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + request=original_exception.request, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=original_exception.response, + ) + exception_mapping_worked = True + raise APIError( + status_code=500, + message=f"ReplicateException - {str(original_exception)}", + llm_provider="replicate", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "bedrock": + if ( + "too many tokens" in error_str + or "expected maxLength:" in error_str + or "Input is too long" in error_str + or "Too many input tokens" in error_str + ): + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"BedrockException: Context Window Error - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response, + ) + if "Malformed input request" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response, + ) + if ( + "Unable to locate credentials" in error_str + or "The security token included in the request is invalid" + in error_str + ): + exception_mapping_worked = True + raise AuthenticationError( + message=f"BedrockException Invalid Authentication - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response, + ) + if ( + "throttlingException" in error_str + or "ThrottlingException" in error_str + ): + exception_mapping_worked = True + raise RateLimitError( + message=f"BedrockException: Rate Limit Error - {error_str}", + model=model, + llm_provider="bedrock", + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 404: + exception_mapping_worked = True + raise NotFoundError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=original_exception.response, + ) + elif custom_llm_provider == "sagemaker": + if "Unable to locate credentials" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=original_exception.response, + ) + elif ( + "Input validation error: `best_of` must be > 0 and <= 2" + in error_str + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", + model=model, + llm_provider="sagemaker", + response=original_exception.response, + ) + elif custom_llm_provider == "vertex_ai": + if ( + "Vertex AI API has not been used in project" in error_str + or "Unable to find your project" in error_str + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response, + ) + elif "403" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response, + ) + elif "The response was blocked." in error_str: + exception_mapping_worked = True + raise UnprocessableEntityError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response, + ) + if original_exception.status_code == 500: + exception_mapping_worked = True + raise APIError( + message=f"VertexAIException - {error_str}", + status_code=500, + model=model, + llm_provider="vertex_ai", + request=original_exception.request, + ) + elif custom_llm_provider == "palm": + if "503 Getting metadata" in error_str: + # auth errors look like this + # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate. + exception_mapping_worked = True + raise BadRequestError( + message=f"PalmException - Invalid api key", + model=model, + llm_provider="palm", + response=original_exception.response, + ) + if "400 Request payload size exceeds" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"PalmException - {error_str}", + model=model, + llm_provider="palm", + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"PalmException - {error_str}", + model=model, + llm_provider="palm", + response=original_exception.response, + ) + # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes + elif custom_llm_provider == "cloudflare": + if "Authentication error" in error_str: + exception_mapping_worked = True + raise AuthenticationError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=original_exception.response, + ) + if "must have required property" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=original_exception.response, + ) + elif custom_llm_provider == "cohere": # Cohere + if ( + "invalid api token" in error_str + or "No API key provided." in error_str + ): + exception_mapping_worked = True + raise AuthenticationError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response, + ) + elif "too many tokens" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"CohereException - {original_exception.message}", + model=model, + llm_provider="cohere", + response=original_exception.response, + ) + elif hasattr(original_exception, "status_code"): + if ( + original_exception.status_code == 400 + or original_exception.status_code == 498 + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response, + ) + elif ( + "CohereConnectionError" in exception_type + ): # cohere seems to fire these errors when we load test it (1k+ messages / min) + exception_mapping_worked = True + raise RateLimitError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response, + ) + elif "invalid type:" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response, + ) + elif "Unexpected server error" in error_str: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=original_exception.response, + ) + else: + if hasattr(original_exception, "status_code"): + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + request=original_exception.request, + ) + raise original_exception + elif custom_llm_provider == "huggingface": + if "length limit exceeded" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=error_str, + model=model, + llm_provider="huggingface", + response=original_exception.response, + ) + elif "A valid user token is required" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=error_str, + llm_provider="huggingface", + model=model, + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + response=original_exception.response, + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + request=original_exception.request, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=original_exception.response, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "ai21": + if hasattr(original_exception, "message"): + if "Prompt has too many tokens" in original_exception.message: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=original_exception.response, + ) + if "Bad or missing API token." in original_exception.message: + exception_mapping_worked = True + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + request=original_exception.request, + ) + if original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=original_exception.response, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=original_exception.response, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "nlp_cloud": + if "detail" in error_str: + if "Input text length should not exceed" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=original_exception.response, + ) + elif "value is not a valid" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=original_exception.response, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=500, + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + request=original_exception.request, + ) + if hasattr( + original_exception, "status_code" + ): # https://docs.nlpcloud.com/?shell#errors + if ( + original_exception.status_code == 400 + or original_exception.status_code == 406 + or original_exception.status_code == 413 + or original_exception.status_code == 422 + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=original_exception.response, + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + exception_mapping_worked = True + raise AuthenticationError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=original_exception.response, + ) + elif ( + original_exception.status_code == 522 + or original_exception.status_code == 524 + ): + exception_mapping_worked = True + raise Timeout( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + request=original_exception.request, + ) + elif ( + original_exception.status_code == 429 + or original_exception.status_code == 402 + ): + exception_mapping_worked = True + raise RateLimitError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=original_exception.response, + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 503 + ): + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=original_exception.request, + ) + elif ( + original_exception.status_code == 504 + or original_exception.status_code == 520 + ): + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + response=original_exception.response, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "together_ai": + import json + + try: + error_response = json.loads(error_str) + except: + error_response = {"error": error_str} + if ( + "error" in error_response + and "`inputs` tokens + `max_new_tokens` must be <=" + in error_response["error"] + ): + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response, + ) + elif ( + "error" in error_response + and "invalid private key" in error_response["error"] + ): + exception_mapping_worked = True + raise AuthenticationError( + message=f"TogetherAIException - {error_response['error']}", + llm_provider="together_ai", + model=model, + response=original_exception.response, + ) + elif ( + "error" in error_response + and "INVALID_ARGUMENT" in error_response["error"] + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response, + ) + + elif ( + "error" in error_response + and "API key doesn't match expected format." + in error_response["error"] + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response, + ) + elif ( + "error_type" in error_response + and error_response["error_type"] == "validation" + ): + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + model=model, + llm_provider="together_ai", + request=original_exception.request, + ) + elif original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=original_exception.response, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 524: + exception_mapping_worked = True + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "aleph_alpha": + if ( + "This is longer than the model's maximum context length" + in error_str + ): + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response, + ) + elif "InvalidToken" in error_str or "No token provided" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response, + ) + elif hasattr(original_exception, "status_code"): + print_verbose(f"status code: {original_exception.status_code}") + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + ) + elif original_exception.status_code == 400: + exception_mapping_worked = True + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 500: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=original_exception.response, + ) + raise original_exception + raise original_exception + elif ( + custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat" + ): + if isinstance(original_exception, dict): + error_str = original_exception.get("error", "") + else: + error_str = str(original_exception) + if "no such file or directory" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", + model=model, + llm_provider="ollama", + response=original_exception.response, + ) + elif "Failed to establish a new connection" in error_str: + exception_mapping_worked = True + raise ServiceUnavailableError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=original_exception.response, + ) + elif "Invalid response object from API" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=original_exception.response, + ) + elif "Read timed out" in error_str: + exception_mapping_worked = True + raise Timeout( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + ) + elif custom_llm_provider == "vllm": + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 0: + exception_mapping_worked = True + raise APIConnectionError( + message=f"VLLMException - {original_exception.message}", + llm_provider="vllm", + model=model, + request=original_exception.request, + ) + elif custom_llm_provider == "azure": + if "This model's maximum context length is" in error_str: + exception_mapping_worked = True + raise ContextWindowExceededError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response, + ) + elif "DeploymentNotFound" in error_str: + exception_mapping_worked = True + raise NotFoundError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response, + ) + elif ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ): + exception_mapping_worked = True + raise ContentPolicyViolationError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response, + ) + elif "invalid_request_error" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response, + ) + elif hasattr(original_exception, "status_code"): + exception_mapping_worked = True + if original_exception.status_code == 401: + exception_mapping_worked = True + raise AuthenticationError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response, + ) + elif original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"AzureException - {original_exception.message}", + model=model, + llm_provider="azure", + request=original_exception.request, + ) + if original_exception.status_code == 422: + exception_mapping_worked = True + raise BadRequestError( + message=f"AzureException - {original_exception.message}", + model=model, + llm_provider="azure", + response=original_exception.response, + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"AzureException - {original_exception.message}", + model=model, + llm_provider="azure", + response=original_exception.response, + ) + else: + exception_mapping_worked = True + raise APIError( + status_code=original_exception.status_code, + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + request=original_exception.request, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + __cause__=original_exception.__cause__, + llm_provider="azure", + model=model, + request=original_exception.request, + ) + if ( + "BadRequestError.__init__() missing 1 required positional argument: 'param'" + in str(original_exception) + ): # deal with edge-case invalid request error bug in openai-python sdk + exception_mapping_worked = True + raise BadRequestError( + message=f"OpenAIException: This can happen due to missing AZURE_API_VERSION: {str(original_exception)}", + model=model, + llm_provider=custom_llm_provider, + response=original_exception.response, + ) + else: # ensure generic errors always return APIConnectionError= + exception_mapping_worked = True + if hasattr(original_exception, "request"): + raise APIConnectionError( + message=f"{str(original_exception)}", + llm_provider=custom_llm_provider, + model=model, + request=original_exception.request, + ) + else: + raise APIConnectionError( + message=f"{str(original_exception)}", + llm_provider=custom_llm_provider, + model=model, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), # stub the request + ) + except Exception as e: + # LOGGING + exception_logging( + logger_fn=user_logger_fn, + additional_args={ + "exception_mapping_worked": exception_mapping_worked, + "original_exception": original_exception, + }, + exception=e, + ) + ## AUTH ERROR + if isinstance(e, AuthenticationError) and ( + litellm.email or "LITELLM_EMAIL" in os.environ + ): + threading.Thread(target=get_all_keys, args=(e.llm_provider,)).start() + # don't let an error with mapping interrupt the user from receiving an error from the llm api calls + if exception_mapping_worked: + raise e + else: + raise original_exception + + +####### CRASH REPORTING ################ +def safe_crash_reporting(model=None, exception=None, custom_llm_provider=None): + data = { + "model": model, + "exception": str(exception), + "custom_llm_provider": custom_llm_provider, + } + executor.submit(litellm_telemetry, data) + # threading.Thread(target=litellm_telemetry, args=(data,), daemon=True).start() + + +def get_or_generate_uuid(): + temp_dir = os.path.join(os.path.abspath(os.sep), "tmp") + uuid_file = os.path.join(temp_dir, "litellm_uuid.txt") + try: + # Try to open the file and load the UUID + with open(uuid_file, "r") as file: + uuid_value = file.read() + if uuid_value: + uuid_value = uuid_value.strip() + else: + raise FileNotFoundError + + except FileNotFoundError: + # Generate a new UUID if the file doesn't exist or is empty + try: + new_uuid = uuid.uuid4() + uuid_value = str(new_uuid) + with open(uuid_file, "w") as file: + file.write(uuid_value) + except: # if writing to tmp/litellm_uuid.txt then retry writing to litellm_uuid.txt + try: + new_uuid = uuid.uuid4() + uuid_value = str(new_uuid) + with open("litellm_uuid.txt", "w") as file: + file.write(uuid_value) + except: # if this 3rd attempt fails just pass + # Good first issue for someone to improve this function :) + return + except: + # [Non-Blocking Error] + return + return uuid_value + + +def litellm_telemetry(data): + # Load or generate the UUID + uuid_value = "" + try: + uuid_value = get_or_generate_uuid() + except: + uuid_value = str(uuid.uuid4()) + try: + # Prepare the data to send to litellm logging api + try: + pkg_version = importlib.metadata.version("litellm") + except: + pkg_version = None + if "model" not in data: + data["model"] = None + payload = {"uuid": uuid_value, "data": data, "version:": pkg_version} + # Make the POST request to litellm logging api + response = requests.post( + "https://litellm-logging.onrender.com/logging", + headers={"Content-Type": "application/json"}, + json=payload, + ) + response.raise_for_status() # Raise an exception for HTTP errors + except: + # [Non-Blocking Error] + return + + +######### Secret Manager ############################ +# checks if user has passed in a secret manager client +# if passed in then checks the secret there +def _is_base64(s): + try: + return base64.b64encode(base64.b64decode(s)).decode() == s + except binascii.Error: + return False + + +def get_secret( + secret_name: str, + default_value: Optional[Union[str, bool]] = None, +): + key_management_system = litellm._key_management_system + if secret_name.startswith("os.environ/"): + secret_name = secret_name.replace("os.environ/", "") + try: + if litellm.secret_manager_client is not None: + try: + client = litellm.secret_manager_client + key_manager = "local" + if key_management_system is not None: + key_manager = key_management_system.value + if ( + key_manager == KeyManagementSystem.AZURE_KEY_VAULT + or type(client).__module__ + "." + type(client).__name__ + == "azure.keyvault.secrets._client.SecretClient" + ): # support Azure Secret Client - from azure.keyvault.secrets import SecretClient + secret = client.get_secret(secret_name).value + elif ( + key_manager == KeyManagementSystem.GOOGLE_KMS + or client.__class__.__name__ == "KeyManagementServiceClient" + ): + encrypted_secret: Any = os.getenv(secret_name) + if encrypted_secret is None: + raise ValueError( + f"Google KMS requires the encrypted secret to be in the environment!" + ) + b64_flag = _is_base64(encrypted_secret) + if b64_flag == True: # if passed in as encoded b64 string + encrypted_secret = base64.b64decode(encrypted_secret) + if not isinstance(encrypted_secret, bytes): + # If it's not, assume it's a string and encode it to bytes + ciphertext = eval( + encrypted_secret.encode() + ) # assuming encrypted_secret is something like - b'\n$\x00D\xac\xb4/t)07\xe5\xf6..' + else: + ciphertext = encrypted_secret + + response = client.decrypt( + request={ + "name": litellm._google_kms_resource_name, + "ciphertext": ciphertext, + } + ) + secret = response.plaintext.decode( + "utf-8" + ) # assumes the original value was encoded with utf-8 + else: # assume the default is infisicial client + secret = client.get_secret(secret_name).secret_value + except Exception as e: # check if it's in os.environ + secret = os.getenv(secret_name) + try: + secret_value_as_bool = ast.literal_eval(secret) + if isinstance(secret_value_as_bool, bool): + return secret_value_as_bool + else: + return secret + except: + return secret + else: + secret = os.environ.get(secret_name) + try: + secret_value_as_bool = ast.literal_eval(secret) + if isinstance(secret_value_as_bool, bool): + return secret_value_as_bool + else: + return secret + except: + return secret + except Exception as e: + if default_value is not None: + return default_value + else: + raise e + + +######## Streaming Class ############################ +# wraps the completion stream to return the correct format for the model +# replicate/anthropic/cohere +class CustomStreamWrapper: + def __init__( + self, completion_stream, model, custom_llm_provider=None, logging_obj=None + ): + self.model = model + self.custom_llm_provider = custom_llm_provider + self.logging_obj = logging_obj + self.completion_stream = completion_stream + self.sent_first_chunk = False + self.sent_last_chunk = False + self.special_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "", ""] + self.holding_chunk = "" + self.complete_response = "" + _model_info = ( + self.logging_obj.model_call_details.get("litellm_params", {}).get( + "model_info", {} + ) + or {} + ) + self._hidden_params = { + "model_id": (_model_info.get("id", None)) + } # returned as x-litellm-model-id response header in proxy + + def __iter__(self): + return self + + def __aiter__(self): + return self + + def process_chunk(self, chunk: str): + """ + NLP Cloud streaming returns the entire response, for each chunk. Process this, to only return the delta. + """ + try: + chunk = chunk.strip() + self.complete_response = self.complete_response.strip() + + if chunk.startswith(self.complete_response): + # Remove last_sent_chunk only if it appears at the start of the new chunk + chunk = chunk[len(self.complete_response) :] + + self.complete_response += chunk + return chunk + except Exception as e: + raise e + + def check_special_tokens(self, chunk: str, finish_reason: Optional[str]): + hold = False + if finish_reason: + for token in self.special_tokens: + if token in chunk: + chunk = chunk.replace(token, "") + return hold, chunk + + if self.sent_first_chunk is True: + return hold, chunk + + curr_chunk = self.holding_chunk + chunk + curr_chunk = curr_chunk.strip() + + for token in self.special_tokens: + if len(curr_chunk) < len(token) and curr_chunk in token: + hold = True + elif len(curr_chunk) >= len(token): + if token in curr_chunk: + self.holding_chunk = curr_chunk.replace(token, "") + hold = True + else: + pass + + if hold is False: # reset + self.holding_chunk = "" + return hold, curr_chunk + + def handle_anthropic_chunk(self, chunk): + str_line = chunk.decode("utf-8") # Convert bytes to string + text = "" + is_finished = False + finish_reason = None + if str_line.startswith("data:"): + data_json = json.loads(str_line[5:]) + text = data_json.get("completion", "") + if data_json.get("stop_reason", None): + is_finished = True + finish_reason = data_json["stop_reason"] + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif "error" in str_line: + raise ValueError(f"Unable to parse response. Original response: {str_line}") + else: + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + + def handle_together_ai_chunk(self, chunk): + chunk = chunk.decode("utf-8") + text = "" + is_finished = False + finish_reason = None + if "text" in chunk: + text_index = chunk.find('"text":"') # this checks if text: exists + text_start = text_index + len('"text":"') + text_end = chunk.find('"}', text_start) + if text_index != -1 and text_end != -1: + extracted_text = chunk[text_start:text_end] + text = extracted_text + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif "[DONE]" in chunk: + return {"text": text, "is_finished": True, "finish_reason": "stop"} + elif "error" in chunk: + raise litellm.together_ai.TogetherAIError( + status_code=422, message=f"{str(chunk)}" + ) + else: + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + + def handle_huggingface_chunk(self, chunk): + try: + if type(chunk) != str: + chunk = chunk.decode( + "utf-8" + ) # DO NOT REMOVE this: This is required for HF inference API + Streaming + text = "" + is_finished = False + finish_reason = "" + print_verbose(f"chunk: {chunk}") + if chunk.startswith("data:"): + data_json = json.loads(chunk[5:]) + print_verbose(f"data json: {data_json}") + if "token" in data_json and "text" in data_json["token"]: + text = data_json["token"]["text"] + if data_json.get("details", False) and data_json["details"].get( + "finish_reason", False + ): + is_finished = True + finish_reason = data_json["details"]["finish_reason"] + elif data_json.get( + "generated_text", False + ): # if full generated text exists, then stream is complete + text = "" # don't return the final bos token + is_finished = True + finish_reason = "stop" + + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif "error" in chunk: + raise ValueError(chunk) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except Exception as e: + traceback.print_exc() + # raise(e) + + def handle_ai21_chunk(self, chunk): # fake streaming + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = data_json["completions"][0]["data"]["text"] + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_maritalk_chunk(self, chunk): # fake streaming + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = data_json["answer"] + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_nlp_cloud_chunk(self, chunk): + text = "" + is_finished = False + finish_reason = "" + try: + if "dolphin" in self.model: + chunk = self.process_chunk(chunk=chunk) + else: + data_json = json.loads(chunk) + chunk = data_json["generated_text"] + text = chunk + if "[DONE]" in text: + text = text.replace("[DONE]", "") + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except Exception as e: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_aleph_alpha_chunk(self, chunk): + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = data_json["completions"][0]["completion"] + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_cohere_chunk(self, chunk): + chunk = chunk.decode("utf-8") + data_json = json.loads(chunk) + try: + text = "" + is_finished = False + finish_reason = "" + if "text" in data_json: + text = data_json["text"] + elif "is_finished" in data_json: + is_finished = data_json["is_finished"] + finish_reason = data_json["finish_reason"] + else: + raise Exception(data_json) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_azure_chunk(self, chunk): + is_finished = False + finish_reason = "" + text = "" + print_verbose(f"chunk: {chunk}") + if "data: [DONE]" in chunk: + text = "" + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif chunk.startswith("data:"): + data_json = json.loads(chunk[5:]) # chunk.startswith("data:"): + try: + if len(data_json["choices"]) > 0: + text = data_json["choices"][0]["delta"].get("content", "") + if data_json["choices"][0].get("finish_reason", None): + is_finished = True + finish_reason = data_json["choices"][0]["finish_reason"] + print_verbose( + f"text: {text}; is_finished: {is_finished}; finish_reason: {finish_reason}" + ) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except: + raise ValueError( + f"Unable to parse response. Original response: {chunk}" + ) + elif "error" in chunk: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + else: + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + + def handle_replicate_chunk(self, chunk): + try: + text = "" + is_finished = False + finish_reason = "" + if "output" in chunk: + text = chunk["output"] + if "status" in chunk: + if chunk["status"] == "succeeded": + is_finished = True + finish_reason = "stop" + elif chunk.get("error", None): + raise Exception(chunk["error"]) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + except: + raise ValueError(f"Unable to parse response. Original response: {chunk}") + + def handle_openai_chat_completion_chunk(self, chunk): + try: + print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + str_line = chunk + text = "" + is_finished = False + finish_reason = None + logprobs = None + original_chunk = None # this is used for function/tool calling + if len(str_line.choices) > 0: + if str_line.choices[0].delta.content is not None: + text = str_line.choices[0].delta.content + else: # function/tool calling chunk - when content is None. in this case we just return the original chunk from openai + original_chunk = str_line + if str_line.choices[0].finish_reason: + is_finished = True + finish_reason = str_line.choices[0].finish_reason + + # checking for logprobs + if ( + hasattr(str_line.choices[0], "logprobs") + and str_line.choices[0].logprobs is not None + ): + logprobs = str_line.choices[0].logprobs + else: + logprobs = None + + if ( + hasattr(str_line.choices[0], "content_filter_result") + and str_line.choices[0].content_filter_result is not None + ): + error_message = json.dumps( + str_line.choices[0].content_filter_result + ) + raise litellm.AzureOpenAIError( + status_code=400, message=error_message + ) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + "logprobs": logprobs, + "original_chunk": str_line, + } + except Exception as e: + traceback.print_exc() + raise e + + def handle_openai_text_completion_chunk(self, chunk): + try: + print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + str_line = chunk + text = "" + is_finished = False + finish_reason = None + if "data: [DONE]" in str_line or self.sent_last_chunk == True: + raise StopIteration + elif str_line.startswith("data:"): + data_json = json.loads(str_line[5:]) + print_verbose(f"delta content: {data_json}") + text = data_json["choices"][0].get("text", "") + if data_json["choices"][0].get("finish_reason", None): + is_finished = True + finish_reason = data_json["choices"][0]["finish_reason"] + self.sent_last_chunk = True + print_verbose( + f"text: {text}; is_finished: {is_finished}; finish_reason: {finish_reason}" + ) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif "error" in str_line: + raise ValueError( + f"Unable to parse response. Original response: {str_line}" + ) + else: + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + + except Exception as e: + raise e + + def handle_baseten_chunk(self, chunk): + try: + chunk = chunk.decode("utf-8") + if len(chunk) > 0: + if chunk.startswith("data:"): + data_json = json.loads(chunk[5:]) + if "token" in data_json and "text" in data_json["token"]: + return data_json["token"]["text"] + else: + return "" + data_json = json.loads(chunk) + if "model_output" in data_json: + if ( + isinstance(data_json["model_output"], dict) + and "data" in data_json["model_output"] + and isinstance(data_json["model_output"]["data"], list) + ): + return data_json["model_output"]["data"][0] + elif isinstance(data_json["model_output"], str): + return data_json["model_output"] + elif "completion" in data_json and isinstance( + data_json["completion"], str + ): + return data_json["completion"] + else: + raise ValueError( + f"Unable to parse response. Original response: {chunk}" + ) + else: + return "" + else: + return "" + except: + traceback.print_exc() + return "" + + def handle_cloudlfare_stream(self, chunk): + try: + print_verbose(f"\nRaw OpenAI Chunk\n{chunk}\n") + chunk = chunk.decode("utf-8") + str_line = chunk + text = "" + is_finished = False + finish_reason = None + + if "[DONE]" in chunk: + return {"text": text, "is_finished": True, "finish_reason": "stop"} + elif str_line.startswith("data:"): + data_json = json.loads(str_line[5:]) + print_verbose(f"delta content: {data_json}") + text = data_json["response"] + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + else: + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + + except Exception as e: + raise e + + def handle_ollama_stream(self, chunk): + try: + if isinstance(chunk, dict): + json_chunk = chunk + else: + json_chunk = json.loads(chunk) + if "error" in json_chunk: + raise Exception(f"Ollama Error - {json_chunk}") + + text = "" + is_finished = False + finish_reason = None + if json_chunk["done"] == True: + text = "" + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif json_chunk["response"]: + print_verbose(f"delta content: {json_chunk}") + text = json_chunk["response"] + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + else: + raise Exception(f"Ollama Error - {json_chunk}") + except Exception as e: + raise e + + def handle_ollama_chat_stream(self, chunk): + # for ollama_chat/ provider + try: + if isinstance(chunk, dict): + json_chunk = chunk + else: + json_chunk = json.loads(chunk) + if "error" in json_chunk: + raise Exception(f"Ollama Error - {json_chunk}") + + text = "" + is_finished = False + finish_reason = None + if json_chunk["done"] == True: + text = "" + is_finished = True + finish_reason = "stop" + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + elif "message" in json_chunk: + print_verbose(f"delta content: {json_chunk}") + text = json_chunk["message"]["content"] + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + else: + raise Exception(f"Ollama Error - {json_chunk}") + except Exception as e: + raise e + + def handle_bedrock_stream(self, chunk): + if hasattr(chunk, "get"): + chunk = chunk.get("chunk") + chunk_data = json.loads(chunk.get("bytes").decode()) + else: + chunk_data = json.loads(chunk.decode()) + if chunk_data: + text = "" + is_finished = False + finish_reason = "" + if "outputText" in chunk_data: + text = chunk_data["outputText"] + # ai21 mapping + if "ai21" in self.model: # fake ai21 streaming + text = chunk_data.get("completions")[0].get("data").get("text") + is_finished = True + finish_reason = "stop" + # anthropic mapping + elif "completion" in chunk_data: + text = chunk_data["completion"] # bedrock.anthropic + stop_reason = chunk_data.get("stop_reason", None) + if stop_reason != None: + is_finished = True + finish_reason = stop_reason + ######## bedrock.cohere mappings ############### + # meta mapping + elif "generation" in chunk_data: + text = chunk_data["generation"] # bedrock.meta + # cohere mapping + elif "text" in chunk_data: + text = chunk_data["text"] # bedrock.cohere + # cohere mapping for finish reason + elif "finish_reason" in chunk_data: + finish_reason = chunk_data["finish_reason"] + is_finished = True + elif chunk_data.get("completionReason", None): + is_finished = True + finish_reason = chunk_data["completionReason"] + elif chunk.get("error", None): + raise Exception(chunk["error"]) + return { + "text": text, + "is_finished": is_finished, + "finish_reason": finish_reason, + } + return "" + + def chunk_creator(self, chunk): + model_response = ModelResponse(stream=True, model=self.model) + model_response._hidden_params["custom_llm_provider"] = self.custom_llm_provider + model_response.choices = [StreamingChoices()] + model_response.choices[0].finish_reason = None + response_obj = {} + try: + # return this for all models + completion_obj = {"content": ""} + if self.custom_llm_provider and self.custom_llm_provider == "anthropic": + response_obj = self.handle_anthropic_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.model == "replicate" or self.custom_llm_provider == "replicate": + response_obj = self.handle_replicate_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider and self.custom_llm_provider == "together_ai": + response_obj = self.handle_together_ai_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider and self.custom_llm_provider == "huggingface": + response_obj = self.handle_huggingface_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif ( + self.custom_llm_provider and self.custom_llm_provider == "baseten" + ): # baseten doesn't provide streaming + completion_obj["content"] = self.handle_baseten_chunk(chunk) + elif ( + self.custom_llm_provider and self.custom_llm_provider == "ai21" + ): # ai21 doesn't provide streaming + response_obj = self.handle_ai21_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": + response_obj = self.handle_maritalk_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider and self.custom_llm_provider == "vllm": + completion_obj["content"] = chunk[0].outputs[0].text + elif ( + self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" + ): # aleph alpha doesn't provide streaming + response_obj = self.handle_aleph_alpha_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider == "nlp_cloud": + try: + response_obj = self.handle_nlp_cloud_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + except Exception as e: + if self.sent_last_chunk: + raise e + else: + if self.sent_first_chunk is False: + raise Exception("An unknown error occurred with the stream") + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + elif self.custom_llm_provider and self.custom_llm_provider == "vertex_ai": + try: + # print(chunk) + if hasattr(chunk, "text"): + # vertexAI chunks return + # MultiCandidateTextGenerationResponse(text=' ```python\n# This Python code says "Hi" 100 times.\n\n# Create', _prediction_response=Prediction(predictions=[{'candidates': [{'content': ' ```python\n# This Python code says "Hi" 100 times.\n\n# Create', 'author': '1'}], 'citationMetadata': [{'citations': None}], 'safetyAttributes': [{'blocked': False, 'scores': None, 'categories': None}]}], deployed_model_id='', model_version_id=None, model_resource_name=None, explanations=None), is_blocked=False, safety_attributes={}, candidates=[ ```python + # This Python code says "Hi" 100 times. + # Create]) + completion_obj["content"] = chunk.text + else: + completion_obj["content"] = str(chunk) + except StopIteration as e: + if self.sent_last_chunk: + raise e + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + elif self.custom_llm_provider == "cohere": + response_obj = self.handle_cohere_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider == "bedrock": + if self.sent_last_chunk: + raise StopIteration + response_obj = self.handle_bedrock_stream(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + self.sent_last_chunk = True + elif self.custom_llm_provider == "sagemaker": + print_verbose(f"ENTERS SAGEMAKER STREAMING") + if len(self.completion_stream) == 0: + if self.sent_last_chunk: + raise StopIteration + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + new_chunk = self.completion_stream + print_verbose(f"sagemaker chunk: {new_chunk}") + completion_obj["content"] = new_chunk + self.completion_stream = self.completion_stream[ + len(self.completion_stream) : + ] + elif self.custom_llm_provider == "petals": + if len(self.completion_stream) == 0: + if self.sent_last_chunk: + raise StopIteration + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + chunk_size = 30 + new_chunk = self.completion_stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = self.completion_stream[chunk_size:] + time.sleep(0.05) + elif self.custom_llm_provider == "palm": + # fake streaming + response_obj = {} + if len(self.completion_stream) == 0: + if self.sent_last_chunk: + raise StopIteration + else: + model_response.choices[0].finish_reason = "stop" + self.sent_last_chunk = True + chunk_size = 30 + new_chunk = self.completion_stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = self.completion_stream[chunk_size:] + time.sleep(0.05) + elif self.custom_llm_provider == "ollama": + response_obj = self.handle_ollama_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider == "ollama_chat": + response_obj = self.handle_ollama_chat_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider == "cloudflare": + response_obj = self.handle_cloudlfare_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + elif self.custom_llm_provider == "text-completion-openai": + response_obj = self.handle_openai_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + else: # openai / azure chat model + if self.custom_llm_provider == "azure": + if hasattr(chunk, "model"): + # for azure, we need to pass the model from the orignal chunk + self.model = chunk.model + response_obj = self.handle_openai_chat_completion_chunk(chunk) + if response_obj == None: + return + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj[ + "finish_reason" + ] + if response_obj["logprobs"] is not None: + model_response.choices[0].logprobs = response_obj["logprobs"] + + model_response.model = self.model + print_verbose( + f"model_response: {model_response}; completion_obj: {completion_obj}" + ) + print_verbose( + f"model_response finish reason 3: {model_response.choices[0].finish_reason}" + ) + if ( + len(completion_obj["content"]) > 0 + ): # cannot set content of an OpenAI Object to be an empty string + hold, model_response_str = self.check_special_tokens( + chunk=completion_obj["content"], + finish_reason=model_response.choices[0].finish_reason, + ) # filter out bos/eos tokens from openai-compatible hf endpoints + print_verbose( + f"hold - {hold}, model_response_str - {model_response_str}" + ) + if hold is False: + ## check if openai/azure chunk + original_chunk = response_obj.get("original_chunk", None) + if original_chunk: + model_response.id = original_chunk.id + if len(original_chunk.choices) > 0: + try: + delta = dict(original_chunk.choices[0].delta) + model_response.choices[0].delta = Delta(**delta) + except Exception as e: + model_response.choices[0].delta = Delta() + else: + return + model_response.system_fingerprint = ( + original_chunk.system_fingerprint + ) + if self.sent_first_chunk == False: + model_response.choices[0].delta["role"] = "assistant" + self.sent_first_chunk = True + else: + ## else + completion_obj["content"] = model_response_str + if self.sent_first_chunk == False: + completion_obj["role"] = "assistant" + self.sent_first_chunk = True + model_response.choices[0].delta = Delta(**completion_obj) + print_verbose(f"model_response: {model_response}") + return model_response + else: + return + elif model_response.choices[0].finish_reason: + # flush any remaining holding chunk + if len(self.holding_chunk) > 0: + if model_response.choices[0].delta.content is None: + model_response.choices[0].delta.content = self.holding_chunk + else: + model_response.choices[0].delta.content = ( + self.holding_chunk + model_response.choices[0].delta.content + ) + self.holding_chunk = "" + model_response.choices[0].finish_reason = map_finish_reason( + model_response.choices[0].finish_reason + ) # ensure consistent output to openai + return model_response + elif ( + response_obj is not None + and response_obj.get("original_chunk", None) is not None + ): # function / tool calling branch - only set for openai/azure compatible endpoints + # enter this branch when no content has been passed in response + original_chunk = response_obj.get("original_chunk", None) + model_response.id = original_chunk.id + if len(original_chunk.choices) > 0: + if ( + original_chunk.choices[0].delta.function_call is not None + or original_chunk.choices[0].delta.tool_calls is not None + ): + try: + delta = dict(original_chunk.choices[0].delta) + model_response.choices[0].delta = Delta(**delta) + except Exception as e: + model_response.choices[0].delta = Delta() + else: + return + else: + return + model_response.system_fingerprint = original_chunk.system_fingerprint + if self.sent_first_chunk == False: + model_response.choices[0].delta["role"] = "assistant" + self.sent_first_chunk = True + return model_response + else: + return + except StopIteration: + raise StopIteration + except Exception as e: + traceback_exception = traceback.format_exc() + e.message = str(e) + raise exception_type( + model=self.model, + custom_llm_provider=self.custom_llm_provider, + original_exception=e, + ) + + ## needs to handle the empty string case (even starting chunk can be an empty string) + def __next__(self): + try: + while True: + if isinstance(self.completion_stream, str) or isinstance( + self.completion_stream, bytes + ): + chunk = self.completion_stream + else: + chunk = next(self.completion_stream) + if chunk is not None and chunk != b"": + print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}") + response = self.chunk_creator(chunk=chunk) + print_verbose(f"PROCESSED CHUNK POST CHUNK CREATOR: {response}") + if response is None: + continue + ## LOGGING + threading.Thread( + target=self.logging_obj.success_handler, args=(response,) + ).start() # log response + # RETURN RESULT + return response + except StopIteration: + raise # Re-raise StopIteration + except Exception as e: + traceback_exception = traceback.format_exc() + # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated + threading.Thread( + target=self.logging_obj.failure_handler, args=(e, traceback_exception) + ).start() + raise e + + async def __anext__(self): + try: + if ( + self.custom_llm_provider == "openai" + or self.custom_llm_provider == "azure" + or self.custom_llm_provider == "custom_openai" + or self.custom_llm_provider == "text-completion-openai" + or self.custom_llm_provider == "huggingface" + or self.custom_llm_provider == "ollama" + or self.custom_llm_provider == "ollama_chat" + or self.custom_llm_provider == "vertex_ai" + ): + print_verbose(f"INSIDE ASYNC STREAMING!!!") + print_verbose( + f"value of async completion stream: {self.completion_stream}" + ) + async for chunk in self.completion_stream: + print_verbose(f"value of async chunk: {chunk}") + if chunk == "None" or chunk is None: + raise Exception + + # chunk_creator() does logging/stream chunk building. We need to let it know its being called in_async_func, so we don't double add chunks. + # __anext__ also calls async_success_handler, which does logging + print_verbose(f"PROCESSED ASYNC CHUNK PRE CHUNK CREATOR: {chunk}") + processed_chunk = self.chunk_creator(chunk=chunk) + print_verbose( + f"PROCESSED ASYNC CHUNK POST CHUNK CREATOR: {processed_chunk}" + ) + if processed_chunk is None: + continue + ## LOGGING + threading.Thread( + target=self.logging_obj.success_handler, args=(processed_chunk,) + ).start() # log response + asyncio.create_task( + self.logging_obj.async_success_handler( + processed_chunk, + ) + ) + return processed_chunk + raise StopAsyncIteration + else: # temporary patch for non-aiohttp async calls + # example - boto3 bedrock llms + processed_chunk = next(self) + asyncio.create_task( + self.logging_obj.async_success_handler( + processed_chunk, + ) + ) + return processed_chunk + except StopAsyncIteration: + raise + except StopIteration: + raise StopAsyncIteration # Re-raise StopIteration + except Exception as e: + traceback_exception = traceback.format_exc() + # Handle any exceptions that might occur during streaming + asyncio.create_task( + self.logging_obj.async_failure_handler(e, traceback_exception) + ) + raise e + + +class TextCompletionStreamWrapper: + def __init__(self, completion_stream, model): + self.completion_stream = completion_stream + self.model = model + + def __iter__(self): + return self + + def __aiter__(self): + return self + + def convert_to_text_completion_object(self, chunk: ModelResponse): + try: + response = TextCompletionResponse() + response["id"] = chunk.get("id", None) + response["object"] = "text_completion" + response["created"] = chunk.get("created", None) + response["model"] = chunk.get("model", None) + text_choices = TextChoices() + if isinstance( + chunk, Choices + ): # chunk should always be of type StreamingChoices + raise Exception + text_choices["text"] = chunk["choices"][0]["delta"]["content"] + text_choices["index"] = chunk["choices"][0]["index"] + text_choices["finish_reason"] = chunk["choices"][0]["finish_reason"] + response["choices"] = [text_choices] + return response + except Exception as e: + raise Exception( + f"Error occurred converting to text completion object - chunk: {chunk}; Error: {str(e)}" + ) + + def __next__(self): + # model_response = ModelResponse(stream=True, model=self.model) + response = TextCompletionResponse() + try: + for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + processed_chunk = self.convert_to_text_completion_object(chunk=chunk) + return processed_chunk + raise StopIteration + except StopIteration: + raise StopIteration + except Exception as e: + print(f"got exception {e}") # noqa + + async def __anext__(self): + try: + async for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + processed_chunk = self.convert_to_text_completion_object(chunk=chunk) + return processed_chunk + raise StopIteration + except StopIteration: + raise StopAsyncIteration + + +def mock_completion_streaming_obj(model_response, mock_response, model): + for i in range(0, len(mock_response), 3): + completion_obj = {"role": "assistant", "content": mock_response[i : i + 3]} + model_response.choices[0].delta = completion_obj + yield model_response + + +########## Reading Config File ############################ +def read_config_args(config_path) -> dict: + try: + import os + + current_path = os.getcwd() + with open(config_path, "r") as config_file: + config = json.load(config_file) + + # read keys/ values from config file and return them + return config + except Exception as e: + raise e + + +########## experimental completion variants ############################ + + +def completion_with_fallbacks(**kwargs): + nested_kwargs = kwargs.pop("kwargs", {}) + response = None + rate_limited_models = set() + model_expiration_times = {} + start_time = time.time() + original_model = kwargs["model"] + fallbacks = [kwargs["model"]] + nested_kwargs.get("fallbacks", []) + if "fallbacks" in nested_kwargs: + del nested_kwargs["fallbacks"] # remove fallbacks so it's not recursive + litellm_call_id = str(uuid.uuid4()) + + # max time to process a request with fallbacks: default 45s + while response == None and time.time() - start_time < 45: + for model in fallbacks: + # loop thru all models + try: + # check if it's dict or new model string + if isinstance( + model, dict + ): # completion(model="gpt-4", fallbacks=[{"api_key": "", "api_base": ""}, {"api_key": "", "api_base": ""}]) + kwargs["api_key"] = model.get("api_key", None) + kwargs["api_base"] = model.get("api_base", None) + model = model.get("model", original_model) + elif ( + model in rate_limited_models + ): # check if model is currently cooling down + if ( + model_expiration_times.get(model) + and time.time() >= model_expiration_times[model] + ): + rate_limited_models.remove( + model + ) # check if it's been 60s of cool down and remove model + else: + continue # skip model + + # delete model from kwargs if it exists + if kwargs.get("model"): + del kwargs["model"] + + print_verbose(f"trying to make completion call with model: {model}") + kwargs["litellm_call_id"] = litellm_call_id + kwargs = { + **kwargs, + **nested_kwargs, + } # combine the openai + litellm params at the same level + response = litellm.completion(**kwargs, model=model) + print_verbose(f"response: {response}") + if response != None: + return response + + except Exception as e: + print_verbose(e) + rate_limited_models.add(model) + model_expiration_times[model] = ( + time.time() + 60 + ) # cool down this selected model + pass + return response + + +def process_system_message(system_message, max_tokens, model): + system_message_event = {"role": "system", "content": system_message} + system_message_tokens = get_token_count([system_message_event], model) + + if system_message_tokens > max_tokens: + print_verbose( + "`tokentrimmer`: Warning, system message exceeds token limit. Trimming..." + ) + # shorten system message to fit within max_tokens + new_system_message = shorten_message_to_fit_limit( + system_message_event, max_tokens, model + ) + system_message_tokens = get_token_count([new_system_message], model) + + return system_message_event, max_tokens - system_message_tokens + + +def process_messages(messages, max_tokens, model): + # Process messages from older to more recent + messages = messages[::-1] + final_messages = [] + + for message in messages: + used_tokens = get_token_count(final_messages, model) + available_tokens = max_tokens - used_tokens + if available_tokens <= 3: + break + final_messages = attempt_message_addition( + final_messages=final_messages, + message=message, + available_tokens=available_tokens, + max_tokens=max_tokens, + model=model, + ) + + return final_messages + + +def attempt_message_addition( + final_messages, message, available_tokens, max_tokens, model +): + temp_messages = [message] + final_messages + temp_message_tokens = get_token_count(messages=temp_messages, model=model) + + if temp_message_tokens <= max_tokens: + return temp_messages + + # if temp_message_tokens > max_tokens, try shortening temp_messages + elif "function_call" not in message: + # fit updated_message to be within temp_message_tokens - max_tokens (aka the amount temp_message_tokens is greate than max_tokens) + updated_message = shorten_message_to_fit_limit(message, available_tokens, model) + if can_add_message(updated_message, final_messages, max_tokens, model): + return [updated_message] + final_messages + + return final_messages + + +def can_add_message(message, messages, max_tokens, model): + if get_token_count(messages + [message], model) <= max_tokens: + return True + return False + + +def get_token_count(messages, model): + return token_counter(model=model, messages=messages) + + +def shorten_message_to_fit_limit(message, tokens_needed, model): + """ + Shorten a message to fit within a token limit by removing characters from the middle. + """ + + # For OpenAI models, even blank messages cost 7 token, + # and if the buffer is less than 3, the while loop will never end, + # hence the value 10. + if "gpt" in model and tokens_needed <= 10: + return message + + content = message["content"] + + while True: + total_tokens = get_token_count([message], model) + + if total_tokens <= tokens_needed: + break + + ratio = (tokens_needed) / total_tokens + + new_length = int(len(content) * ratio) - 1 + new_length = max(0, new_length) + + half_length = new_length // 2 + left_half = content[:half_length] + right_half = content[-half_length:] + + trimmed_content = left_half + ".." + right_half + message["content"] = trimmed_content + content = trimmed_content + + return message + + +# LiteLLM token trimmer +# this code is borrowed from https://github.com/KillianLucas/tokentrim/blob/main/tokentrim/tokentrim.py +# Credits for this code go to Killian Lucas +def trim_messages( + messages, + model: Optional[str] = None, + trim_ratio: float = 0.75, + return_response_tokens: bool = False, + max_tokens=None, +): + """ + Trim a list of messages to fit within a model's token limit. + + Args: + messages: Input messages to be trimmed. Each message is a dictionary with 'role' and 'content'. + model: The LiteLLM model being used (determines the token limit). + trim_ratio: Target ratio of tokens to use after trimming. Default is 0.75, meaning it will trim messages so they use about 75% of the model's token limit. + return_response_tokens: If True, also return the number of tokens left available for the response after trimming. + max_tokens: Instead of specifying a model or trim_ratio, you can specify this directly. + + Returns: + Trimmed messages and optionally the number of tokens available for response. + """ + # Initialize max_tokens + # if users pass in max tokens, trim to this amount + messages = copy.deepcopy(messages) + try: + print_verbose(f"trimming messages") + if max_tokens == None: + # Check if model is valid + if model in litellm.model_cost: + max_tokens_for_model = litellm.model_cost[model]["max_tokens"] + max_tokens = int(max_tokens_for_model * trim_ratio) + else: + # if user did not specify max tokens + # or passed an llm litellm does not know + # do nothing, just return messages + return + + system_message = "" + for message in messages: + if message["role"] == "system": + system_message += "\n" if system_message else "" + system_message += message["content"] + + current_tokens = token_counter(model=model, messages=messages) + print_verbose(f"Current tokens: {current_tokens}, max tokens: {max_tokens}") + + # Do nothing if current tokens under messages + if current_tokens < max_tokens: + return messages + + #### Trimming messages if current_tokens > max_tokens + print_verbose( + f"Need to trim input messages: {messages}, current_tokens{current_tokens}, max_tokens: {max_tokens}" + ) + if system_message: + system_message_event, max_tokens = process_system_message( + system_message=system_message, max_tokens=max_tokens, model=model + ) + + if max_tokens == 0: # the system messages are too long + return [system_message_event] + + # Since all system messages are combined and trimmed to fit the max_tokens, + # we remove all system messages from the messages list + messages = [message for message in messages if message["role"] != "system"] + + final_messages = process_messages( + messages=messages, max_tokens=max_tokens, model=model + ) + + # Add system message to the beginning of the final messages + if system_message: + final_messages = [system_message_event] + final_messages + + if ( + return_response_tokens + ): # if user wants token count with new trimmed messages + response_tokens = max_tokens - get_token_count(final_messages, model) + return final_messages, response_tokens + return final_messages + except Exception as e: # [NON-Blocking, if error occurs just return final_messages + print_verbose(f"Got exception while token trimming{e}") + return messages + + +def get_valid_models(): + """ + Returns a list of valid LLMs based on the set environment variables + + Args: + None + + Returns: + A list of valid LLMs + """ + try: + # get keys set in .env + environ_keys = os.environ.keys() + valid_providers = [] + # for all valid providers, make a list of supported llms + valid_models = [] + + for provider in litellm.provider_list: + # edge case litellm has together_ai as a provider, it should be togetherai + provider = provider.replace("_", "") + + # litellm standardizes expected provider keys to + # PROVIDER_API_KEY. Example: OPENAI_API_KEY, COHERE_API_KEY + expected_provider_key = f"{provider.upper()}_API_KEY" + if expected_provider_key in environ_keys: + # key is set + valid_providers.append(provider) + + for provider in valid_providers: + if provider == "azure": + valid_models.append("Azure-LLM") + else: + models_for_provider = litellm.models_by_provider.get(provider, []) + valid_models.extend(models_for_provider) + return valid_models + except: + return [] # NON-Blocking + + +# used for litellm.text_completion() to transform HF logprobs to OpenAI.Completion() format +def transform_logprobs(hf_response): + # Initialize an empty list for the transformed logprobs + transformed_logprobs = [] + + # For each Hugging Face response, transform the logprobs + for response in hf_response: + # Extract the relevant information from the response + response_details = response["details"] + top_tokens = response_details.get("top_tokens", {}) + + # Initialize an empty list for the token information + token_info = { + "tokens": [], + "token_logprobs": [], + "text_offset": [], + "top_logprobs": [], + } + + for i, token in enumerate(response_details["prefill"]): + # Extract the text of the token + token_text = token["text"] + + # Extract the logprob of the token + token_logprob = token["logprob"] + + # Add the token information to the 'token_info' list + token_info["tokens"].append(token_text) + token_info["token_logprobs"].append(token_logprob) + + # stub this to work with llm eval harness + top_alt_tokens = {"": -1, "": -2, "": -3} + token_info["top_logprobs"].append(top_alt_tokens) + + # For each element in the 'tokens' list, extract the relevant information + for i, token in enumerate(response_details["tokens"]): + # Extract the text of the token + token_text = token["text"] + + # Extract the logprob of the token + token_logprob = token["logprob"] + + top_alt_tokens = {} + temp_top_logprobs = [] + if top_tokens != {}: + temp_top_logprobs = top_tokens[i] + + # top_alt_tokens should look like this: { "alternative_1": -1, "alternative_2": -2, "alternative_3": -3 } + for elem in temp_top_logprobs: + text = elem["text"] + logprob = elem["logprob"] + top_alt_tokens[text] = logprob + + # Add the token information to the 'token_info' list + token_info["tokens"].append(token_text) + token_info["token_logprobs"].append(token_logprob) + token_info["top_logprobs"].append(top_alt_tokens) + + # Add the text offset of the token + # This is computed as the sum of the lengths of all previous tokens + token_info["text_offset"].append( + sum(len(t["text"]) for t in response_details["tokens"][:i]) + ) + + # Add the 'token_info' list to the 'transformed_logprobs' list + transformed_logprobs = token_info + + return transformed_logprobs + + +def print_args_passed_to_litellm(original_function, args, kwargs): + try: + # we've already printed this for acompletion, don't print for completion + if ( + "acompletion" in kwargs + and kwargs["acompletion"] == True + and original_function.__name__ == "completion" + ): + return + elif ( + "aembedding" in kwargs + and kwargs["aembedding"] == True + and original_function.__name__ == "embedding" + ): + return + elif ( + "aimg_generation" in kwargs + and kwargs["aimg_generation"] == True + and original_function.__name__ == "img_generation" + ): + return + + args_str = ", ".join(map(repr, args)) + kwargs_str = ", ".join(f"{key}={repr(value)}" for key, value in kwargs.items()) + print_verbose("\n") # new line before + print_verbose("\033[92mRequest to litellm:\033[0m") + if args and kwargs: + print_verbose( + f"\033[92mlitellm.{original_function.__name__}({args_str}, {kwargs_str})\033[0m" + ) + elif args: + print_verbose( + f"\033[92mlitellm.{original_function.__name__}({args_str})\033[0m" + ) + elif kwargs: + print_verbose( + f"\033[92mlitellm.{original_function.__name__}({kwargs_str})\033[0m" + ) + else: + print_verbose(f"\033[92mlitellm.{original_function.__name__}()\033[0m") + print_verbose("\n") # new line after + except: + # This should always be non blocking + pass