mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-06 08:16:43 +00:00
remove unused code (#7456)
This commit is contained in:
Ishaan Jaff
GitHub
parent
82a869a9b6
commit
0e39ebefb2
7 changed files with 0 additions and 343 deletions
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@ -1,43 +0,0 @@
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# # set AUTH STRATEGY FOR LLM APIs - Defaults to using Environment Variables
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# AUTH_STRATEGY = "ENV" # ENV or DYNAMIC, ENV always reads from environment variables, DYNAMIC reads request headers to set LLM api keys
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# OPENAI_API_KEY = ""
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# HUGGINGFACE_API_KEY=""
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# TOGETHERAI_API_KEY=""
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# REPLICATE_API_KEY=""
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# ## bedrock / sagemaker
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# AWS_ACCESS_KEY_ID = ""
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# AWS_SECRET_ACCESS_KEY = ""
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# AZURE_API_KEY = ""
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# AZURE_API_BASE = ""
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# AZURE_API_VERSION = ""
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# ANTHROPIC_API_KEY = ""
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# COHERE_API_KEY = ""
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# ## CONFIG FILE ##
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# # CONFIG_FILE_PATH = "" # uncomment to point to config file
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# ## LOGGING ##
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# SET_VERBOSE = "False" # set to 'True' to see detailed input/output logs
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# ### LANGFUSE
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# LANGFUSE_PUBLIC_KEY = ""
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# LANGFUSE_SECRET_KEY = ""
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# # Optional, defaults to https://cloud.langfuse.com
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# LANGFUSE_HOST = "" # optional
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# ## CACHING ##
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# ### REDIS
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# REDIS_HOST = ""
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# REDIS_PORT = ""
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# REDIS_PASSWORD = ""
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@ -1,10 +0,0 @@
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# FROM python:3.10
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# ENV LITELLM_CONFIG_PATH="/litellm.secrets.toml"
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# COPY . /app
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# WORKDIR /app
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# RUN pip install -r requirements.txt
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# EXPOSE $PORT
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# CMD exec uvicorn main:app --host 0.0.0.0 --port $PORT --workers 10
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@ -1,3 +0,0 @@
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# litellm-server [experimental]
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Deprecated. See litellm/proxy
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@ -1,2 +0,0 @@
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# from .main import *
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# from .server_utils import *
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@ -1,193 +0,0 @@
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# import os, traceback
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# from fastapi import FastAPI, Request, HTTPException
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# from fastapi.routing import APIRouter
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# from fastapi.responses import StreamingResponse, FileResponse
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# from fastapi.middleware.cors import CORSMiddleware
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# import json, sys
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# from typing import Optional
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# sys.path.insert(
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# 0, os.path.abspath("../")
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# ) # Adds the parent directory to the system path - for litellm local dev
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# import litellm
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# try:
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# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose
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# except ImportError:
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# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose
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# import dotenv
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# dotenv.load_dotenv() # load env variables
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# app = FastAPI(docs_url="/", title="LiteLLM API")
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# router = APIRouter()
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# origins = ["*"]
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# app.add_middleware(
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# CORSMiddleware,
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# allow_origins=origins,
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# allow_credentials=True,
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# allow_methods=["*"],
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# allow_headers=["*"],
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# )
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# #### GLOBAL VARIABLES ####
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# llm_router: Optional[litellm.Router] = None
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# llm_model_list: Optional[list] = None
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# server_settings: Optional[dict] = None
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# set_callbacks() # sets litellm callbacks for logging if they exist in the environment
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# if "CONFIG_FILE_PATH" in os.environ:
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# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router, config_file_path=os.getenv("CONFIG_FILE_PATH"))
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# else:
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# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router)
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# #### API ENDPOINTS ####
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# @router.get("/v1/models")
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# @router.get("/models") # if project requires model list
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# def model_list():
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# all_models = litellm.utils.get_valid_models()
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# if llm_model_list:
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# all_models += llm_model_list
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# return dict(
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# data=[
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# {
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# "id": model,
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# "object": "model",
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# "created": 1677610602,
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# "owned_by": "openai",
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# }
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# for model in all_models
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# ],
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# object="list",
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# )
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# # for streaming
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# def data_generator(response):
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# for chunk in response:
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# yield f"data: {json.dumps(chunk)}\n\n"
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# @router.post("/v1/completions")
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# @router.post("/completions")
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# async def completion(request: Request):
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# data = await request.json()
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# response = litellm.completion(
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# **data
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# )
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# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
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# return StreamingResponse(data_generator(response), media_type='text/event-stream')
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# return response
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# @router.post("/v1/embeddings")
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# @router.post("/embeddings")
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# async def embedding(request: Request):
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# try:
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# data = await request.json()
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# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers
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# if os.getenv("AUTH_STRATEGY", None) == "DYNAMIC" and "authorization" in request.headers: # if users pass LLM api keys as part of header
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# api_key = request.headers.get("authorization")
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# api_key = api_key.replace("Bearer", "").strip() # type: ignore
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# if len(api_key.strip()) > 0:
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# api_key = api_key
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# data["api_key"] = api_key
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# response = litellm.embedding(
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# **data
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# )
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# return response
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# except Exception as e:
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# error_traceback = traceback.format_exc()
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# error_msg = f"{str(e)}\n\n{error_traceback}"
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# return {"error": error_msg}
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# @router.post("/v1/chat/completions")
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# @router.post("/chat/completions")
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# @router.post("/openai/deployments/{model:path}/chat/completions") # azure compatible endpoint
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# async def chat_completion(request: Request, model: Optional[str] = None):
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# global llm_model_list, server_settings
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# try:
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# data = await request.json()
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# server_model = server_settings.get("completion_model", None) if server_settings else None
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# data["model"] = server_model or model or data["model"]
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# ## CHECK KEYS ##
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# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers
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# # env_validation = litellm.validate_environment(model=data["model"])
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# # if (env_validation['keys_in_environment'] is False or os.getenv("AUTH_STRATEGY", None) == "DYNAMIC") and ("authorization" in request.headers or "api-key" in request.headers): # if users pass LLM api keys as part of header
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# # if "authorization" in request.headers:
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# # api_key = request.headers.get("authorization")
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# # elif "api-key" in request.headers:
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# # api_key = request.headers.get("api-key")
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# # print(f"api_key in headers: {api_key}")
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# # if " " in api_key:
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# # api_key = api_key.split(" ")[1]
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# # print(f"api_key split: {api_key}")
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# # if len(api_key) > 0:
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# # api_key = api_key
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# # data["api_key"] = api_key
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# # print(f"api_key in data: {api_key}")
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# ## CHECK CONFIG ##
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# if llm_model_list and data["model"] in [m["model_name"] for m in llm_model_list]:
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# for m in llm_model_list:
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# if data["model"] == m["model_name"]:
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# for key, value in m["litellm_params"].items():
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# data[key] = value
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# break
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# response = litellm.completion(
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# **data
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# )
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# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
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# return StreamingResponse(data_generator(response), media_type='text/event-stream')
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# return response
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# except Exception as e:
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# error_traceback = traceback.format_exc()
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# error_msg = f"{str(e)}\n\n{error_traceback}"
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# # return {"error": error_msg}
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# raise HTTPException(status_code=500, detail=error_msg)
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# @router.post("/router/completions")
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# async def router_completion(request: Request):
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# global llm_router
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# try:
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# data = await request.json()
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# if "model_list" in data:
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# llm_router = litellm.Router(model_list=data.pop("model_list"))
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# if llm_router is None:
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# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body")
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# # openai.ChatCompletion.create replacement
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# response = await llm_router.acompletion(model="gpt-3.5-turbo",
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# messages=[{"role": "user", "content": "Hey, how's it going?"}])
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# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
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# return StreamingResponse(data_generator(response), media_type='text/event-stream')
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# return response
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# except Exception as e:
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# error_traceback = traceback.format_exc()
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# error_msg = f"{str(e)}\n\n{error_traceback}"
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# return {"error": error_msg}
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# @router.post("/router/embedding")
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# async def router_embedding(request: Request):
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# global llm_router
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# try:
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# data = await request.json()
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# if "model_list" in data:
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# llm_router = litellm.Router(model_list=data.pop("model_list"))
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# if llm_router is None:
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# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body")
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# response = await llm_router.aembedding(model="gpt-3.5-turbo", # type: ignore
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# messages=[{"role": "user", "content": "Hey, how's it going?"}])
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# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
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# return StreamingResponse(data_generator(response), media_type='text/event-stream')
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# return response
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# except Exception as e:
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# error_traceback = traceback.format_exc()
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# error_msg = f"{str(e)}\n\n{error_traceback}"
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# return {"error": error_msg}
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# @router.get("/")
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# async def home(request: Request):
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# return "LiteLLM: RUNNING"
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# app.include_router(router)
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@ -1,7 +0,0 @@
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# openai
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# fastapi
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# uvicorn
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# boto3
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# litellm
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# python-dotenv
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# redis
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@ -1,85 +0,0 @@
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# import os, litellm
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# import pkg_resources
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# import dotenv
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# dotenv.load_dotenv() # load env variables
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# def print_verbose(print_statement):
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# pass
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# def get_package_version(package_name):
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# try:
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# package = pkg_resources.get_distribution(package_name)
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# return package.version
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# except pkg_resources.DistributionNotFound:
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# return None
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# # Usage example
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# package_name = "litellm"
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# version = get_package_version(package_name)
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# if version:
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# print_verbose(f"The version of {package_name} is {version}")
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# else:
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# print_verbose(f"{package_name} is not installed")
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# import yaml
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# import dotenv
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# from typing import Optional
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# dotenv.load_dotenv() # load env variables
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# def set_callbacks():
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# ## LOGGING
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# if len(os.getenv("SET_VERBOSE", "")) > 0:
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# if os.getenv("SET_VERBOSE") == "True":
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# litellm.set_verbose = True
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# print_verbose("\033[92mLiteLLM: Switched on verbose logging\033[0m")
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# else:
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# litellm.set_verbose = False
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# ### LANGFUSE
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# if (len(os.getenv("LANGFUSE_PUBLIC_KEY", "")) > 0 and len(os.getenv("LANGFUSE_SECRET_KEY", ""))) > 0 or len(os.getenv("LANGFUSE_HOST", "")) > 0:
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# litellm.success_callback = ["langfuse"]
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# print_verbose("\033[92mLiteLLM: Switched on Langfuse feature\033[0m")
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# ## CACHING
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# ### REDIS
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# # if len(os.getenv("REDIS_HOST", "")) > 0 and len(os.getenv("REDIS_PORT", "")) > 0 and len(os.getenv("REDIS_PASSWORD", "")) > 0:
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# # print(f"redis host: {os.getenv('REDIS_HOST')}; redis port: {os.getenv('REDIS_PORT')}; password: {os.getenv('REDIS_PASSWORD')}")
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# # from litellm.caching.caching import Cache
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# # litellm.cache = Cache(type="redis", host=os.getenv("REDIS_HOST"), port=os.getenv("REDIS_PORT"), password=os.getenv("REDIS_PASSWORD"))
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# # print("\033[92mLiteLLM: Switched on Redis caching\033[0m")
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# def load_router_config(router: Optional[litellm.Router], config_file_path: Optional[str]='/app/config.yaml'):
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# config = {}
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# server_settings = {}
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# try:
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# if os.path.exists(config_file_path): # type: ignore
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# with open(config_file_path, 'r') as file: # type: ignore
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# config = yaml.safe_load(file)
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# else:
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# pass
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# except Exception:
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# pass
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# ## SERVER SETTINGS (e.g. default completion model = 'ollama/mistral')
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# server_settings = config.get("server_settings", None)
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# if server_settings:
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# server_settings = server_settings
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# ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..)
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# litellm_settings = config.get('litellm_settings', None)
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# if litellm_settings:
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# for key, value in litellm_settings.items():
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# setattr(litellm, key, value)
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# ## MODEL LIST
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# model_list = config.get('model_list', None)
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# if model_list:
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# router = litellm.Router(model_list=model_list)
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# ## ENVIRONMENT VARIABLES
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# environment_variables = config.get('environment_variables', None)
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# if environment_variables:
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# for key, value in environment_variables.items():
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# os.environ[key] = value
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# return router, model_list, server_settings
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