import argparse import markdown2 import os import sys import uvicorn from pathlib import Path from typing import Union from fastapi import FastAPI, Depends, HTTPException from fastapi.responses import HTMLResponse from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials from pydantic import BaseModel, Field from sse_starlette.sse import EventSourceResponse, ServerSentEvent from tclogger import logger from constants.models import AVAILABLE_MODELS_DICTS, PRO_MODELS from constants.envs import CONFIG, SECRETS from networks.exceptions import HfApiException, INVALID_API_KEY_ERROR from messagers.message_composer import MessageComposer from mocks.stream_chat_mocker import stream_chat_mock from networks.huggingface_streamer import HuggingfaceStreamer from networks.huggingchat_streamer import HuggingchatStreamer from networks.openai_streamer import OpenaiStreamer from sentence_transformers import SentenceTransformer class ChatAPIApp: def __init__(self): self.app = FastAPI( docs_url="/", title=CONFIG["app_name"], swagger_ui_parameters={"defaultModelsExpandDepth": -1}, version=CONFIG["version"], ) self.setup_routes() self.embeddings = { "mxbai-embed-large":SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1"), "nomic-embed-text": SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) } def get_available_models(self): return {"object": "list", "data": AVAILABLE_MODELS_DICTS} def extract_api_key( credentials: HTTPAuthorizationCredentials = Depends(HTTPBearer()), ): api_key = None if credentials: api_key = credentials.credentials env_api_key = SECRETS["HF_LLM_API_KEY"] return api_key def auth_api_key(self, api_key: str): env_api_key = SECRETS["HF_LLM_API_KEY"] # require no api_key if not env_api_key: return None # user provides HF_TOKEN if api_key and api_key.startswith("hf_"): return api_key # user provides correct API_KEY if str(api_key) == str(env_api_key): return None raise INVALID_API_KEY_ERROR class ChatCompletionsPostItem(BaseModel): model: str = Field( default="nous-mixtral-8x7b", description="(str) `nous-mixtral-8x7b`", ) messages: list = Field( default=[{"role": "user", "content": "Hello, who are you?"}], description="(list) Messages", ) temperature: Union[float, None] = Field( default=0.5, description="(float) Temperature", ) top_p: Union[float, None] = Field( default=0.95, description="(float) top p", ) max_tokens: Union[int, None] = Field( default=-1, description="(int) Max tokens", ) use_cache: bool = Field( default=False, description="(bool) Use cache", ) stream: bool = Field( default=True, description="(bool) Stream", ) def chat_completions( self, item: ChatCompletionsPostItem, api_key: str = Depends(extract_api_key) ): try: api_key = self.auth_api_key(api_key) if item.model == "gpt-3.5-turbo": streamer = OpenaiStreamer() stream_response = streamer.chat_response(messages=item.messages) elif item.model in PRO_MODELS: streamer = HuggingchatStreamer(model=item.model) stream_response = streamer.chat_response( messages=item.messages, ) else: streamer = HuggingfaceStreamer(model=item.model) composer = MessageComposer(model=item.model) composer.merge(messages=item.messages) stream_response = streamer.chat_response( prompt=composer.merged_str, temperature=item.temperature, top_p=item.top_p, max_new_tokens=item.max_tokens, api_key=api_key, use_cache=item.use_cache, ) if item.stream: event_source_response = EventSourceResponse( streamer.chat_return_generator(stream_response), media_type="text/event-stream", ping=2000, ping_message_factory=lambda: ServerSentEvent(**{"comment": ""}), ) return event_source_response else: data_response = streamer.chat_return_dict(stream_response) return data_response except HfApiException as e: raise HTTPException(status_code=e.status_code, detail=e.detail) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) class EmbeddingRequest(BaseModel): model: str prompt: str options: Optional[dict] = None def get_embeddings(self, item: EmbeddingRequest, api_key: str = Depends(extract_api_key)): try: model = request.model model_kwargs = request.options embeddings = models[model].encode(request.prompt, convert_to_tensor=True)#, **model_kwargs) return {"embedding": embeddings.tolist()} except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) def get_readme(self): readme_path = Path(__file__).parents[1] / "README.md" with open(readme_path, "r", encoding="utf-8") as rf: readme_str = rf.read() readme_html = markdown2.markdown( readme_str, extras=["table", "fenced-code-blocks", "highlightjs-lang"] ) return readme_html def setup_routes(self): for prefix in ["", "/v1", "/api", "/api/v1"]: if prefix in ["/api/v1"]: include_in_schema = True else: include_in_schema = False self.app.get( prefix + "/models", summary="Get available models", include_in_schema=include_in_schema, )(self.get_available_models) self.app.post( prefix + "/chat/completions", summary="Chat completions in conversation session", include_in_schema=include_in_schema, )(self.chat_completions) self.app.post( "/api/embeddings", summary="Get Embeddings with prompt", include_in_schema=True, )(self.get_embeddings) self.app.get( "/readme", summary="README of HF LLM API", response_class=HTMLResponse, include_in_schema=False, )(self.get_readme) class ArgParser(argparse.ArgumentParser): def __init__(self, *args, **kwargs): super(ArgParser, self).__init__(*args, **kwargs) self.add_argument( "-s", "--host", type=str, default=CONFIG["host"], help=f"Host for {CONFIG['app_name']}", ) self.add_argument( "-p", "--port", type=int, default=CONFIG["port"], help=f"Port for {CONFIG['app_name']}", ) self.add_argument( "-d", "--dev", default=False, action="store_true", help="Run in dev mode", ) self.args = self.parse_args(sys.argv[1:]) app = ChatAPIApp().app if __name__ == "__main__": args = ArgParser().args if args.dev: uvicorn.run("__main__:app", host=args.host, port=args.port, reload=True) else: uvicorn.run("__main__:app", host=args.host, port=args.port, reload=False) # python -m apis.chat_api # [Docker] on product mode # python -m apis.chat_api -d # [Dev] on develop mode