# +-----------------------------------------------+ # | | # | 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 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 ) from .llms import ( anthropic, together_ai, ai21, sagemaker, bedrock, huggingface_restapi, replicate, aleph_alpha, nlp_cloud, baseten, vllm, ollama, cohere, petals, oobabooga, palm, 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 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, model, messages, **kwargs): for k, v in kwargs.items(): self.params[k] = v response = completion(model=model, messages=messages, **self.params) return response @client async def acompletion(*args, **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() 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(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 == "openrouter" or custom_llm_provider == "deepinfra" or custom_llm_provider == "perplexity" or custom_llm_provider == "text-completion-openai" or custom_llm_provider == "huggingface"): # currently implemented aiohttp calls for just azure and openai, soon all. if kwargs.get("stream", False): response = completion(*args, **kwargs) else: # 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 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=args, ) async def _async_streaming(response, model, custom_llm_provider, args): try: async for line in response: yield line except Exception as e: raise exception_type( model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, ) 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 = [], functions: List = [], function_call: str = "", # optional params 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, 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, ) -> ModelResponse: """ 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. 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) return_async = kwargs.get('return_async', False) 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) 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) 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"] litellm_params = ["metadata", "acompletion", "caching", "return_async", "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"] 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) ### 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 model_api_key and "sk-litellm" in model_api_key: api_base = "https://proxy.litellm.ai" custom_llm_provider = "openai" api_key = model_api_key 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, **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( return_async=return_async, 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 ) 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 ) ## 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 == "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 # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there litellm.api_key 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, 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 ## 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 ): # print("calling custom openai provider") 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 ## LOGGING logging.pre_call( input=prompt, api_key=api_key, additional_args={ "openai_organization": litellm.organization, "headers": headers, "api_base": api_base, "api_type": openai.api_type, }, ) ## 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 ) # if "stream" in optional_params and optional_params["stream"] == True: # response = CustomStreamWrapper(model_response, model, custom_llm_provider="text-completion-openai", logging_obj=logging) # return response 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, response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate") return 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 ) model_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(model_response, model, custom_llm_provider="anthropic", logging_obj=logging) return response response = model_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/" ) model_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(model_response, model, custom_llm_provider="nlp_cloud", logging_obj=logging) return response response = model_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 ) 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, } ) data = { "model": model, "messages": messages, **optional_params } ## LOGGING logging.pre_call(input=messages, api_key=openai.api_key, additional_args={"complete_input_dict": data, "headers": headers}) ## COMPLETION CALL ## 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 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: 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 ) if "stream" in optional_params and optional_params["stream"] == True: 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, 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 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 ) model_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( model_response, model, custom_llm_provider="bedrock", logging_obj=logging ) else: response = CustomStreamWrapper( iter(model_response), model, custom_llm_provider="bedrock", logging_obj=logging ) return response ## RESPONSE OBJECT response = model_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) ## LOGGING if kwargs.get('acompletion', False) == True: if optional_params.get("stream", False) == True: # assume all ollama responses are streamed async_generator = ollama.async_get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging) return async_generator generator = ollama.get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging) if optional_params.get("stream", False) == True: # assume all ollama responses are streamed response = CustomStreamWrapper( generator, model, custom_llm_provider="ollama", logging_obj=logging ) return response else: response_string = "" for chunk in generator: response_string+=chunk['content'] ## RESPONSE OBJECT model_response["choices"][0]["finish_reason"] = "stop" model_response["choices"][0]["message"]["content"] = response_string model_response["created"] = int(time.time()) model_response["model"] = "ollama/" + model prompt_tokens = len(encoding.encode(prompt)) # type: ignore completion_tokens = len(encoding.encode(response_string)) model_response["usage"] = Usage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens) response = model_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: List = [], function_call: str = "", # optional params 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, # 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 #################### 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 == "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 == "huggingface"): # 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) 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) aembedding = kwargs.pop("aembedding", None) optional_params = {} for param in kwargs: if param != "metadata": # filter out metadata from optional_params optional_params[param] = kwargs[param] 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) try: response = None logging = litellm_logging_obj logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn}) 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=kwargs, model_response= EmbeddingResponse() ) else: args = locals() raise ValueError(f"No valid embedding model args passed in - {args}") return response except Exception as e: ## LOGGING logging.post_call( input=input, api_key=openai.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 ################ 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, ) #print(response) 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 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 ####### HELPER FUNCTIONS ################ ## Set verbose to true -> ```litellm.set_verbose = True``` def print_verbose(print_statement): if litellm.set_verbose: print(print_statement) # noqa 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(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) 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 # # 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) response["usage"]["total_tokens"] = response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"] return convert_to_model_response_object(response_object=response, model_response_object=litellm.ModelResponse())