import sys, os, platform, time, copy, re, asyncio import threading, ast import shutil, random, traceback, requests from datetime import datetime, timedelta 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 uvicorn import fastapi import appdirs import backoff import yaml import rq import orjson except ImportError: import sys subprocess.check_call( [ sys.executable, "-m", "pip", "install", "uvicorn", "fastapi", "appdirs", "backoff", "pyyaml", "rq", "orjson" ] ) import uvicorn import fastapi import appdirs import backoff import yaml import orjson warnings.warn( "Installed runtime dependencies for proxy server. Specify these dependencies explicitly with `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() print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m") print("\033[1;37m" + "#" + " " * box_width + "#\033[0m") print("\033[1;37m" + "# {:^59} #\033[0m".format(message)) print( "\033[1;37m" + "# {:^59} #\033[0m".format("https://github.com/BerriAI/litellm/issues/new") ) print("\033[1;37m" + "#" + " " * box_width + "#\033[0m") print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m") print() print(" Thank you for using LiteLLM! - Krrish & Ishaan") print() print() print() print( "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m" ) print() print() import litellm from litellm.proxy.utils import ( PrismaClient, get_instance_fn ) import pydantic from litellm.proxy.types import * from litellm.caching import DualCache litellm.suppress_debug_info = True from fastapi import FastAPI, Request, HTTPException, status, Depends, BackgroundTasks 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 # from litellm.proxy.queue import start_rq_worker_in_background app = FastAPI(docs_url="/", title="LiteLLM API") router = APIRouter() origins = ["*"] app.add_middleware( CORSMiddleware, allow_origins=origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) def log_input_output(request, response, custom_logger=None): if custom_logger is not None: custom_logger(request, response) global otel_logging if otel_logging != True: return from opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import SimpleSpanProcessor from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.resources import Resource # Initialize OpenTelemetry components otlp_host = os.environ.get("OTEL_ENDPOINT", "localhost:4317") otlp_exporter = OTLPSpanExporter(endpoint=otlp_host, insecure=True) resource = Resource.create({ "service.name": "LiteLLM Proxy", }) trace.set_tracer_provider(TracerProvider(resource=resource)) tracer = trace.get_tracer(__name__) span_processor = SimpleSpanProcessor(otlp_exporter) trace.get_tracer_provider().add_span_processor(span_processor) with tracer.start_as_current_span("litellm-completion") as current_span: input_event_attributes = {f"{key}": str(value) for key, value in dict(request).items() if value is not None} # Log the input event with attributes current_span.add_event( name="LiteLLM: Request Input", attributes=input_event_attributes ) event_headers = {f"{key}": str(value) for key, value in dict(request.headers).items() if value is not None} current_span.add_event( name="LiteLLM: Request Headers", attributes=event_headers ) input_event_attributes.update(event_headers) input_event_attributes.update({f"{key}": str(value) for key, value in dict(response).items()}) current_span.add_event( name="LiteLLM: Request Outpu", attributes=input_event_attributes ) return True from typing import Dict oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") 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_{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 user_api_key_cache = DualCache() user_custom_auth = None ### REDIS QUEUE ### async_result = None celery_app_conn = None celery_fn = None # Redis Queue for handling requests #### HELPER FUNCTIONS #### def print_verbose(print_statement): global user_debug if user_debug: print(print_statement) 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() async def user_api_key_auth(request: Request, api_key: str = Depends(oauth2_scheme)) -> UserAPIKeyAuth: global master_key, prisma_client, llm_model_list, user_custom_auth try: ### USER-DEFINED AUTH FUNCTION ### if user_custom_auth: response = await user_custom_auth(request=request, api_key=api_key) return UserAPIKeyAuth.model_validate(response) if master_key is None: if isinstance(api_key, str): return UserAPIKeyAuth(api_key=api_key.replace("Bearer ", "")) else: return UserAPIKeyAuth() if api_key is None: raise Exception("No api key passed in.") route = request.url.path # 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) or secrets.compare_digest(api_key, "Bearer " + master_key) if is_master_key_valid: return UserAPIKeyAuth(api_key=master_key) if (route == "/key/generate" or route == "/key/delete" or route == "/key/info") and not is_master_key_valid: raise Exception(f"If master key is set, only master key can be used to generate, delete or get info for new keys") if prisma_client: ## check for cache hit (In-Memory Cache) valid_token = user_api_key_cache.get_cache(key=api_key) if valid_token is None and "Bearer " in api_key: ## check db cleaned_api_key = api_key[len("Bearer "):] valid_token = await prisma_client.get_data(token=cleaned_api_key, expires=datetime.utcnow()) user_api_key_cache.set_cache(key=api_key, value=valid_token, ttl=60) elif valid_token is not None: print(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 print("\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 return_dict = {"api_key": valid_token.token} if valid_token.user_id: return_dict["user_id"] = valid_token.user_id return UserAPIKeyAuth(**return_dict) else: data = await request.json() 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") return_dict = {"api_key": valid_token.token} if valid_token.user_id: return_dict["user_id"] = valid_token.user_id return UserAPIKeyAuth(**return_dict) else: raise Exception(f"Invalid token") except Exception as e: print(f"An exception occurred - {traceback.format_exc()}") raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="invalid user key", ) def prisma_setup(database_url: Optional[str]): global prisma_client if database_url: try: prisma_client = PrismaClient(database_url=database_url) except Exception as e: print("Error when initializing prisma, Ensure you run pip install prisma", e) def celery_setup(use_queue: bool): global celery_fn, celery_app_conn, async_result if use_queue: from litellm.proxy.queue.celery_worker import start_worker from litellm.proxy.queue.celery_app import celery_app, process_job from celery.result import AsyncResult start_worker(os.getcwd()) celery_fn = process_job async_result = AsyncResult celery_app_conn = celery_app 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 else: raise Exception(f"Missing KVUri or client_id or client_secret or tenant_id from environment") except Exception as e: print("Error when loading keys from Azure Key Vault. Ensure you run `pip install azure-identity azure-keyvault-secrets`") def cost_tracking(): global prisma_client if prisma_client is not None: if isinstance(litellm.success_callback, list): print("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 else: litellm.success_callback = 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 try: # check if it has collected an entire stream response 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"] input_text = kwargs["messages"] output_text = completion_response["choices"][0]["message"]["content"] response_cost = litellm.completion_cost( model = kwargs["model"], messages = input_text, completion=output_text ) print("streaming response_cost", response_cost) # for non streaming responses elif kwargs["stream"] is False: # regular response input_text = kwargs.get("messages", "") if isinstance(input_text, list): input_text = "".join(m["content"] for m in input_text) print(f"received completion response: {completion_response}") response_cost = litellm.completion_cost(completion_response=completion_response, completion=input_text) print("regular response_cost", response_cost) user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key", None) print(f"user_api_key - {user_api_key}; prisma_client - {prisma_client}") if user_api_key and prisma_client: await update_prisma_database(token=user_api_key, response_cost=response_cost) except Exception as e: print(f"error in tracking cost callback - {str(e)}") async def update_prisma_database(token, response_cost): try: print(f"Enters prisma db call, token: {token}") # Fetch the existing cost for the given token existing_spend_obj = await prisma_client.get_data(token=token) print(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 print(f"new cost: {new_spend}") # Update the cost column for the given token await prisma_client.update_data(token=token, data={"spend": new_spend}) except Exception as e: print(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: print(f""" LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve` """) def load_router_config(router: Optional[litellm.Router], config_file_path: str): global master_key, user_config_file_path, otel_logging, user_custom_auth config = {} try: if os.path.exists(config_file_path): user_config_file_path = config_file_path with open(config_file_path, 'r') as file: config = yaml.safe_load(file) else: raise Exception(f"Path to config does not exist, Current working directory: {os.getcwd()}, 'os.path.exists({config_file_path})' returned False") except Exception as e: raise Exception(f"Exception while reading Config: {e}") ## PRINT YAML FOR CONFIRMING IT WORKS printed_yaml = copy.deepcopy(config) printed_yaml.pop("environment_variables", None) if "model_list" in printed_yaml: for model in printed_yaml["model_list"]: model["litellm_params"].pop("api_key", None) print(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 ## GENERAL SERVER SETTINGS (e.g. master key,..) general_settings = config.get("general_settings", {}) if general_settings is None: general_settings = {} if general_settings: ### LOAD FROM AZURE KEY VAULT ### 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) ### CONNECT TO DATABASE ### database_url = general_settings.get("database_url", None) if database_url and database_url.startswith("os.environ/"): database_url = litellm.get_secret(database_url) prisma_setup(database_url=database_url) ## COST TRACKING ## cost_tracking() ### START REDIS QUEUE ### use_queue = general_settings.get("use_queue", False) celery_setup(use_queue=use_queue) ### MASTER KEY ### master_key = general_settings.get("master_key", None) if master_key and master_key.startswith("os.environ/"): master_key = litellm.get_secret(master_key) #### OpenTelemetry Logging (OTEL) ######## otel_logging = general_settings.get("otel", False) if otel_logging == True: print("\nOpenTelemetry Logging Activated") ### CUSTOM API KEY AUTH ### custom_auth = general_settings.get("custom_auth", None) if custom_auth: user_custom_auth = get_instance_fn(value=custom_auth, config_file_path=config_file_path) ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..) litellm_settings = config.get('litellm_settings', None) 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") from litellm.caching import Cache cache_type = value["type"] cache_host = litellm.get_secret("REDIS_HOST") cache_port = litellm.get_secret("REDIS_PORT") cache_password = litellm.get_secret("REDIS_PASSWORD") # Assuming cache_type, cache_host, cache_port, and cache_password are strings print(f"{blue_color_code}Cache Type:{reset_color_code} {cache_type}") print(f"{blue_color_code}Cache Host:{reset_color_code} {cache_host}") print(f"{blue_color_code}Cache Port:{reset_color_code} {cache_port}") print(f"{blue_color_code}Cache Password:{reset_color_code} {cache_password}") print() litellm.cache = Cache( type=cache_type, host=cache_host, port=cache_port, password=cache_password ) elif key == "callbacks": litellm.callbacks = [get_instance_fn(value=value)] else: setattr(litellm, key, value) ## MODEL LIST model_list = config.get('model_list', None) if model_list: router = litellm.Router(model_list=model_list, num_retries=3) print(f"\033[32mLiteLLM: Proxy initialized with Config, Set models:\033[0m") for model in model_list: print(f"\033[32m {model.get('model_name', '')}\033[0m") litellm_model_name = model["litellm_params"]["model"] if "ollama" in litellm_model_name: run_ollama_serve() return router, model_list, general_settings async def generate_key_helper_fn(duration_str: Optional[str], models: list, aliases: dict, config: dict, spend: float, token: Optional[str]=None, user_id: Optional[str]=None): global prisma_client if prisma_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_str is None: # allow tokens that never expire expires = None else: duration = _duration_in_seconds(duration=duration_str) expires = datetime.utcnow() + timedelta(seconds=duration) aliases_json = json.dumps(aliases) config_json = json.dumps(config) 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) verification_token_data = { "token": token, "expires": expires, "models": models, "aliases": aliases_json, "config": config_json, "spend": spend, "user_id": user_id } new_verification_token = await prisma_client.insert_data(data=verification_token_data) except Exception as e: traceback.print_exc() raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR) return {"token": token, "expires": new_verification_token.expires, "user_id": user_id} 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) def initialize( model, alias, api_base, api_version, debug, temperature, max_tokens, request_timeout, max_budget, telemetry, drop_params, add_function_to_prompt, headers, save, config, use_queue ): global user_model, user_api_base, user_debug, user_max_tokens, user_request_timeout, user_temperature, user_telemetry, user_headers, experimental, llm_model_list, llm_router, general_settings generate_feedback_box() user_model = model user_debug = debug dynamic_config = {"general": {}, user_model: {}} if config: llm_router, llm_model_list, general_settings = load_router_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 debug==True: # litellm-specific param litellm.set_verbose = True if use_queue: celery_setup(use_queue=use_queue) if experimental: pass user_telemetry = telemetry usage_telemetry(feature="local_proxy_server") curl_command = """ curl --location 'http://0.0.0.0:8000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data ' { "model": "gpt-3.5-turbo", "messages": [ { "role": "user", "content": "what llm are you" } ] }' \n """ print() print(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(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") # for streaming def data_generator(response): print_verbose("inside generator") for chunk in response: print_verbose(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): print_verbose("inside generator") async for chunk in response: print_verbose(f"returned chunk: {chunk}") try: yield f"data: {json.dumps(chunk.dict())}\n\n" except: yield f"data: {json.dumps(chunk)}\n\n" def litellm_completion(*args, **kwargs): global user_temperature, user_request_timeout, user_max_tokens, user_api_base call_type = kwargs.pop("call_type") # override with user settings, these are params passed via cli if user_temperature: kwargs["temperature"] = user_temperature if user_request_timeout: kwargs["request_timeout"] = user_request_timeout if user_max_tokens: kwargs["max_tokens"] = user_max_tokens if user_api_base: kwargs["api_base"] = user_api_base ## ROUTE TO CORRECT ENDPOINT ## router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] try: if llm_router is not None and kwargs["model"] in router_model_names: # model in router model list if call_type == "chat_completion": response = llm_router.completion(*args, **kwargs) elif call_type == "text_completion": response = llm_router.text_completion(*args, **kwargs) else: if call_type == "chat_completion": response = litellm.completion(*args, **kwargs) elif call_type == "text_completion": response = litellm.text_completion(*args, **kwargs) except Exception as e: raise e if 'stream' in kwargs and kwargs['stream'] == True: # use generate_responses to stream responses return StreamingResponse(data_generator(response), media_type='text/event-stream') return response @router.on_event("startup") async def startup_event(): global prisma_client, master_key import json worker_config = json.loads(os.getenv("WORKER_CONFIG")) print(f"worker_config: {worker_config}") initialize(**worker_config) print(f"prisma client - {prisma_client}") if prisma_client: await prisma_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_str=None, models=[], aliases={}, config={}, spend=0, token=master_key) @router.on_event("shutdown") async def shutdown_event(): global prisma_client if prisma_client: print("Disconnecting from Prisma") await prisma_client.disconnect() #### API ENDPOINTS #### @router.get("/v1/models", dependencies=[Depends(user_api_key_auth)]) @router.get("/models", dependencies=[Depends(user_api_key_auth)]) # 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] print_verbose(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)]) @router.post("/completions", dependencies=[Depends(user_api_key_auth)]) @router.post("/engines/{model:path}/completions", dependencies=[Depends(user_api_key_auth)]) async def completion(request: Request, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth)): try: body = await request.body() body_str = body.decode() try: data = ast.literal_eval(body_str) except: data = json.loads(body_str) data["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 data["call_type"] = "text_completion" if "metadata" in data: data["metadata"]["user_api_key"] = user_api_key_dict.api_key else: data["metadata"] = {"user_api_key": user_api_key_dict.api_key} return litellm_completion( **data ) except Exception as e: print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`") 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, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), background_tasks: BackgroundTasks = BackgroundTasks()): global general_settings, user_debug try: data = {} data = await request.json() # type: ignore print_verbose(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: # if users are using user_api_key_auth, set `user` in `data` data["user"] = user_api_key_dict.user_id if "metadata" in data: data["metadata"]["user_api_key"] = user_api_key_dict.api_key data["metadata"]["headers"] = request.headers else: data["metadata"] = {"user_api_key": user_api_key_dict.api_key} data["metadata"]["headers"] = request.headers 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 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) else: response = await litellm.acompletion(**data) if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses return StreamingResponse(async_data_generator(response), media_type='text/event-stream') background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL return response except Exception as e: print(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: print("Results from router") print("\nRouter stats") print("\nTotal Calls made") for key, value in llm_router.total_calls.items(): print(f"{key}: {value}") print("\nSuccess Calls made") for key, value in llm_router.success_calls.items(): print(f"{key}: {value}") print("\nFail Calls made") for key, value in llm_router.fail_calls.items(): print(f"{key}: {value}") if user_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/embeddings", dependencies=[Depends(user_api_key_auth)], response_class=ORJSONResponse) @router.post("/embeddings", dependencies=[Depends(user_api_key_auth)], response_class=ORJSONResponse) async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), background_tasks: BackgroundTasks = BackgroundTasks()): try: # Use orjson to parse JSON data, orjson speeds up requests significantly body = await request.body() data = orjson.loads(body) 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 else: data["metadata"] = {"user_api_key": user_api_key_dict.api_key} ## ROUTE TO CORRECT ENDPOINT ## 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.aembedding(**data) else: response = await litellm.aembedding(**data) background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL return response except Exception as e: traceback.print_exc() raise e except Exception as e: pass #### 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): """ 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 Returns: - key: The generated api key - expires: Datetime object for when key expires. """ # data = await request.json() duration_str = data.duration # Default to 1 hour if duration is not provided models = data.models # Default to an empty list (meaning allow token to call all models) aliases = data.aliases # Default to an empty dict (no alias mappings, on top of anything in the config.yaml model_list) config = data.config spend = data.spend user_id = data.user_id if isinstance(models, list): response = await generate_key_helper_fn(duration_str=duration_str, models=models, aliases=aliases, config=config, spend=spend, user_id=user_id) return GenerateKeyResponse(key=response["token"], expires=response["expires"], user_id=response["user_id"]) else: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail={"error": "models param must be a list"}, ) @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)}, ) #### 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 try: # Load existing config if os.path.exists(f"{user_config_file_path}"): with open(f"{user_config_file_path}", "r") as config_file: config = yaml.safe_load(config_file) else: config = {"model_list": []} # Add the new model to the config config['model_list'].append({ 'model_name': model_params.model_name, 'litellm_params': model_params.litellm_params, 'model_info': model_params.model_info }) # Save the updated config with open(f"{user_config_file_path}", "w") as config_file: yaml.dump(config, config_file, default_flow_style=False) # update Router llm_router, llm_model_list, general_settings = load_router_config(router=llm_router, config_file_path=config) return {"message": "Model added successfully"} except Exception as e: 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 @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)]) async def model_info(request: Request): global llm_model_list, general_settings, user_config_file_path # Load existing config with open(f"{user_config_file_path}", "r") as config_file: config = yaml.safe_load(config_file) all_models = config['model_list'] for model in all_models: # get the model cost map info ## make an api call data = copy.deepcopy(model["litellm_params"]) data["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] data["max_tokens"] = 10 print(f"data going to litellm acompletion: {data}") response = await litellm.acompletion(**data) response_model = response["model"] print(f"response model: {response_model}; response - {response}") litellm_model_info = litellm.get_model_info(response_model) model_info = model.get("model_info", {}) 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) # all_models = list(set([m["model_name"] for m in llm_model_list])) print_verbose(f"all_models: {all_models}") return dict( data=[ { "id": model, "object": "model", "created": 1677610602, "owned_by": "openai", } for model in all_models ], object="list", ) #### EXPERIMENTAL QUEUING #### @router.post("/queue/request", dependencies=[Depends(user_api_key_auth)]) async def async_queue_request(request: Request): global celery_fn, llm_model_list if celery_fn is not None: body = await request.body() body_str = body.decode() try: data = ast.literal_eval(body_str) except: data = json.loads(body_str) data["model"] = ( general_settings.get("completion_model", None) # server default or user_model # model name passed via cli args or data["model"] # default passed in http request ) data["llm_model_list"] = llm_model_list print(f"data: {data}") job = celery_fn.apply_async(kwargs=data) return {"id": job.id, "url": f"/queue/response/{job.id}", "eta": 5, "status": "queued"} else: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail={"error": "Queue not initialized"}, ) @router.get("/queue/response/{task_id}", dependencies=[Depends(user_api_key_auth)]) async def async_queue_response(request: Request, task_id: str): global celery_app_conn, async_result try: if celery_app_conn is not None and async_result is not None: job = async_result(task_id, app=celery_app_conn) if job.ready(): return {"status": "finished", "result": job.result} else: return {'status': 'queued'} else: raise Exception() except Exception as e: return {"status": "finished", "result": str(e)} @router.get("/ollama_logs", dependencies=[Depends(user_api_key_auth)]) async def retrieve_server_log(request: Request): filepath = os.path.expanduser("~/.ollama/logs/server.log") return FileResponse(filepath) #### BASIC ENDPOINTS #### @router.get("/test") async def test_endpoint(request: Request): return {"route": request.url.path} @router.get("/health", description="Check the health of all the endpoints in config.yaml", tags=["health"]) async def health_endpoint(request: Request, model: Optional[str] = fastapi.Query(None, description="Specify the model name (optional)")): global llm_model_list healthy_endpoints = [] unhealthy_endpoints = [] if llm_model_list: for model_name in llm_model_list: try: if model is None or model == model_name["litellm_params"]["model"]: # if model specified, just call that one. litellm_params = model_name["litellm_params"] model_name = litellm.utils.remove_model_id(litellm_params["model"]) # removes, ids set by litellm.router if model_name not in litellm.all_embedding_models: # filter out embedding models litellm_params["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] litellm_params["model"] = model_name litellm.completion(**litellm_params) cleaned_params = {} for key in litellm_params: if key != "api_key" and key != "messages": cleaned_params[key] = litellm_params[key] healthy_endpoints.append(cleaned_params) except Exception as e: print("Got Exception", e) cleaned_params = {} for key in litellm_params: if key != "api_key" and key != "messages": cleaned_params[key] = litellm_params[key] unhealthy_endpoints.append(cleaned_params) pass return { "healthy_endpoints": healthy_endpoints, "unhealthy_endpoints": unhealthy_endpoints } @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} app.include_router(router)