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# import os, traceback
# from fastapi import FastAPI, Request, HTTPException
# from fastapi.routing import APIRouter
# from fastapi.responses import StreamingResponse, FileResponse
# from fastapi.middleware.cors import CORSMiddleware
# import json, sys
# from typing import Optional
# sys.path.insert(
# 0, os.path.abspath("../")
# ) # Adds the parent directory to the system path - for litellm local dev
# import litellm
# try:
# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose
# except ImportError:
# from litellm.deprecated_litellm_server.server_utils import set_callbacks, load_router_config, print_verbose
# import dotenv
# dotenv.load_dotenv() # load env variables
# app = FastAPI(docs_url="/", title="LiteLLM API")
# router = APIRouter()
# origins = ["*"]
# app.add_middleware(
# CORSMiddleware,
# allow_origins=origins,
# allow_credentials=True,
# allow_methods=["*"],
# allow_headers=["*"],
# )
# #### GLOBAL VARIABLES ####
# llm_router: Optional[litellm.Router] = None
# llm_model_list: Optional[list] = None
# server_settings: Optional[dict] = None
# set_callbacks() # sets litellm callbacks for logging if they exist in the environment
# if "CONFIG_FILE_PATH" in os.environ:
# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router, config_file_path=os.getenv("CONFIG_FILE_PATH"))
# else:
# llm_router, llm_model_list, server_settings = load_router_config(router=llm_router)
# #### API ENDPOINTS ####
# @router.get("/v1/models")
# @router.get("/models") # if project requires model list
# def model_list():
# all_models = litellm.utils.get_valid_models()
# if llm_model_list:
# all_models += llm_model_list
# return dict(
# data=[
# {
# "id": model,
# "object": "model",
# "created": 1677610602,
# "owned_by": "openai",
# }
# for model in all_models
# ],
# object="list",
# )
# # for streaming
# def data_generator(response):
# for chunk in response:
# yield f"data: {json.dumps(chunk)}\n\n"
# @router.post("/v1/completions")
# @router.post("/completions")
# async def completion(request: Request):
# data = await request.json()
# response = litellm.completion(
# **data
# )
# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
# return StreamingResponse(data_generator(response), media_type='text/event-stream')
# return response
# @router.post("/v1/embeddings")
# @router.post("/embeddings")
# async def embedding(request: Request):
# try:
# data = await request.json()
# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers
# if os.getenv("AUTH_STRATEGY", None) == "DYNAMIC" and "authorization" in request.headers: # if users pass LLM api keys as part of header
# api_key = request.headers.get("authorization")
# api_key = api_key.replace("Bearer", "").strip() # type: ignore
# if len(api_key.strip()) > 0:
# api_key = api_key
# data["api_key"] = api_key
# response = litellm.embedding(
# **data
# )
# return response
# except Exception as e:
# error_traceback = traceback.format_exc()
# error_msg = f"{str(e)}\n\n{error_traceback}"
# return {"error": error_msg}
# @router.post("/v1/chat/completions")
# @router.post("/chat/completions")
# @router.post("/openai/deployments/{model:path}/chat/completions") # azure compatible endpoint
# async def chat_completion(request: Request, model: Optional[str] = None):
# global llm_model_list, server_settings
# try:
# data = await request.json()
# server_model = server_settings.get("completion_model", None) if server_settings else None
# data["model"] = server_model or model or data["model"]
# ## CHECK KEYS ##
# # default to always using the "ENV" variables, only if AUTH_STRATEGY==DYNAMIC then reads headers
# # env_validation = litellm.validate_environment(model=data["model"])
# # if (env_validation['keys_in_environment'] is False or os.getenv("AUTH_STRATEGY", None) == "DYNAMIC") and ("authorization" in request.headers or "api-key" in request.headers): # if users pass LLM api keys as part of header
# # if "authorization" in request.headers:
# # api_key = request.headers.get("authorization")
# # elif "api-key" in request.headers:
# # api_key = request.headers.get("api-key")
# # print(f"api_key in headers: {api_key}")
# # if " " in api_key:
# # api_key = api_key.split(" ")[1]
# # print(f"api_key split: {api_key}")
# # if len(api_key) > 0:
# # api_key = api_key
# # data["api_key"] = api_key
# # print(f"api_key in data: {api_key}")
# ## CHECK CONFIG ##
# if llm_model_list and data["model"] in [m["model_name"] for m in llm_model_list]:
# for m in llm_model_list:
# if data["model"] == m["model_name"]:
# for key, value in m["litellm_params"].items():
# data[key] = value
# break
# response = litellm.completion(
# **data
# )
# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
# return StreamingResponse(data_generator(response), media_type='text/event-stream')
# return response
# except Exception as e:
# error_traceback = traceback.format_exc()
# error_msg = f"{str(e)}\n\n{error_traceback}"
# # return {"error": error_msg}
# raise HTTPException(status_code=500, detail=error_msg)
# @router.post("/router/completions")
# async def router_completion(request: Request):
# global llm_router
# try:
# data = await request.json()
# if "model_list" in data:
# llm_router = litellm.Router(model_list=data.pop("model_list"))
# if llm_router is None:
# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body")
# # openai.ChatCompletion.create replacement
# response = await llm_router.acompletion(model="gpt-3.5-turbo",
# messages=[{"role": "user", "content": "Hey, how's it going?"}])
# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
# return StreamingResponse(data_generator(response), media_type='text/event-stream')
# return response
# except Exception as e:
# error_traceback = traceback.format_exc()
# error_msg = f"{str(e)}\n\n{error_traceback}"
# return {"error": error_msg}
# @router.post("/router/embedding")
# async def router_embedding(request: Request):
# global llm_router
# try:
# data = await request.json()
# if "model_list" in data:
# llm_router = litellm.Router(model_list=data.pop("model_list"))
# if llm_router is None:
# raise Exception("Save model list via config.yaml. Eg.: ` docker build -t myapp --build-arg CONFIG_FILE=myconfig.yaml .` or pass it in as model_list=[..] as part of the request body")
# response = await llm_router.aembedding(model="gpt-3.5-turbo", # type: ignore
# messages=[{"role": "user", "content": "Hey, how's it going?"}])
# if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
# return StreamingResponse(data_generator(response), media_type='text/event-stream')
# return response
# except Exception as e:
# error_traceback = traceback.format_exc()
# error_msg = f"{str(e)}\n\n{error_traceback}"
# return {"error": error_msg}
# @router.get("/")
# async def home(request: Request):
# return "LiteLLM: RUNNING"
# app.include_router(router)
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