FastAPI integration patterns in LangChain

Introduction

FastAPI integration patterns help you connect your FastAPI web app with other tools or services smoothly. They make your app work better and faster by organizing how parts talk to each other.

You want to add a chatbot powered by LangChain to your FastAPI app.

You need to call an external API from your FastAPI backend.

You want to handle user input and process it with AI models inside FastAPI.

You want to structure your FastAPI app to keep code clean when using multiple services.

Syntax

LangChain

from fastapi import FastAPI

app = FastAPI()

@app.get("/endpoint")
async def read_data():
    # call to external service or LangChain logic
    return {"message": "Hello from FastAPI"}

Use @app.get, @app.post, etc. to create routes.

Use async functions to handle requests efficiently.

Examples

A simple GET endpoint returning a greeting.

LangChain

from fastapi import FastAPI

app = FastAPI()

@app.get("/hello")
async def say_hello():
    return {"greeting": "Hello World"}

Integrates LangChain's OpenAI model to answer questions sent via POST.

LangChain

from fastapi import FastAPI, Request
from langchain.llms import OpenAI

app = FastAPI()
llm = OpenAI()

@app.post("/ask")
async def ask_question(request: Request):
    data = await request.json()
    question = data.get("question")
    answer = await llm.acall(question)
    return {"answer": answer}

Handles chat messages by passing user input to a LangChain conversation chain.

LangChain

from fastapi import FastAPI, Request
from langchain.llms import OpenAI
from langchain.chains import ConversationChain

app = FastAPI()
llm = OpenAI()
conversation = ConversationChain(llm=llm)

@app.post("/chat")
async def chat(request: Request):
    data = await request.json()
    user_message = data.get("message")
    response = await conversation.arun(user_message)
    return {"response": response}

Sample Program

This FastAPI app has a POST endpoint '/generate' that accepts JSON with a 'prompt'. It uses LangChain's OpenAI model to generate text based on the prompt and returns the result.

LangChain

from fastapi import FastAPI, Request
from langchain.llms import OpenAI

app = FastAPI()
llm = OpenAI()

@app.post("/generate")
async def generate_text(request: Request):
    data = await request.json()
    prompt = data.get("prompt", "")
    if not prompt:
        return {"error": "No prompt provided"}
    result = await llm.acall(prompt)
    return {"result": result}

Important Notes

Always validate input data to avoid errors.

Use async functions to keep your API responsive.

Keep integration code modular for easier maintenance.

Summary

FastAPI integration patterns help connect your app with AI models and services.

Use async routes and clear input/output formats for smooth communication.

Modular code and input validation improve app reliability and clarity.