jphme / fastapi-async-langchain

🚢 Ship production-ready LangChain projects with FastAPI

fastapi-async-langchain

Ship production-ready LangChain projects with FastAPI.

🚀 Features

• supports token streaming over HTTP and Websocket
• supports multiple langchain Chain types
• simple gradio chatbot UI for fast prototyping
• follows FastAPI responses naming convention

❓ Why?

There are great low-code/no-code solutions in the open source to deploy your Langchain projects. However, most of them are opinionated in terms of cloud or deployment code. This project aims to provide FastAPI users with a cloud-agnostic and deployment-agnostic solution which can be easily integrated into existing backend infrastructures.

💾 Installation

The library is available on PyPI and can be installed via pip.

pip install fastapi-async-langchain

🔥 Deploy in under 20 lines of code

from dotenv import load_dotenv
from fastapi import FastAPI
from langchain import ConversationChain
from langchain.chat_models import ChatOpenAI
from pydantic import BaseModel
from fastapi_async_langchain.responses import StreamingResponse

load_dotenv()
app = FastAPI()

class Request(BaseModel):
    query: str

@app.post("/chat")
async def chat(request: Request) -> StreamingResponse:
    chain = ConversationChain(llm=ChatOpenAI(temperature=0, streaming=True), verbose=True)
    return StreamingResponse.from_chain(chain, request.query, media_type="text/event-stream")

See examples/ for list of available demo examples.

Create a .env file using .env.sample and add your OpenAI API key to it before running the examples.

🤝 Contributing

Contributions are more than welcome! If you have an idea for a new feature or want to help improve fastapi-async-langchain, please create an issue or submit a pull request on GitHub.

See CONTRIBUTING.md for more information.

⚖️ License

The library is released under the MIT License.