llm-api / apis /chat_api.py
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:gem: [Feature] New ChatAPIApp: Enable fastapi for openai format api call
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import uvicorn
from fastapi import FastAPI
from pydantic import BaseModel, Field
from sse_starlette.sse import EventSourceResponse
from utils.logger import logger
from networks.message_streamer import MessageStreamer
from messagers.message_composer import MessageComposer
class ChatAPIApp:
def __init__(self):
self.app = FastAPI(
docs_url="/",
title="HuggingFace LLM API",
swagger_ui_parameters={"defaultModelsExpandDepth": -1},
version="1.0",
)
self.setup_routes()
def get_available_models(self):
self.available_models = [
{
"id": "mixtral-8x7b",
"description": "Mixtral-8x7B: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1",
},
]
return self.available_models
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: float = Field(
default=0.01,
description="(float) Temperature",
)
max_tokens: int = Field(
default=32000,
description="(int) Max tokens",
)
stream: bool = Field(
default=True,
description="(bool) Stream",
)
def chat_completions(self, item: ChatCompletionsPostItem):
streamer = MessageStreamer(model=item.model)
composer = MessageComposer(model=item.model)
composer.merge(messages=item.messages)
return EventSourceResponse(
streamer.chat(
prompt=composer.merged_str,
temperature=item.temperature,
max_new_tokens=item.max_tokens,
stream=item.stream,
yield_output=True,
),
media_type="text/event-stream",
)
def setup_routes(self):
for prefix in ["", "/v1"]:
self.app.get(
prefix + "/models",
summary="Get available models",
)(self.get_available_models)
self.app.post(
prefix + "/chat/completions",
summary="Chat completions in conversation session",
)(self.chat_completions)
app = ChatAPIApp().app
if __name__ == "__main__":
uvicorn.run("__main__:app", host="0.0.0.0", port=23333, reload=True)