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import argparse
import markdown2
import os
import sys
import uvicorn
from pathlib import Path
from typing import Union
from fastapi import FastAPI, Depends
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
from constants.envs import CONFIG
from messagers.message_composer import MessageComposer
from mocks.stream_chat_mocker import stream_chat_mock
from networks.huggingface_streamer import HuggingfaceStreamer
from networks.openai_streamer import OpenaiStreamer
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()
def get_available_models(self):
return {"object": "list", "data": AVAILABLE_MODELS_DICTS}
def extract_api_key(
credentials: HTTPAuthorizationCredentials = Depends(
HTTPBearer(auto_error=False)
),
):
api_key = None
if credentials:
api_key = credentials.credentials
else:
api_key = os.getenv("HF_TOKEN")
if api_key:
if api_key.startswith("hf_"):
return api_key
else:
logger.warn(f"Invalid HF Token!")
else:
logger.warn("Not provide HF Token!")
return None
class ChatCompletionsPostItem(BaseModel):
model: str = Field(
default="mixtral-8x7b",
description="(str) `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)
):
if item.model == "gpt-3.5-turbo":
streamer = OpenaiStreamer()
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
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.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
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