Python on David Lang

FastAPI + LangChain: Building Production-Ready AI APIs

Fri, 05 Sep 2025 00:00:00 +0000

FastAPI’s async support and automatic OpenAPI docs pair naturally with LangChain for production AI backends.

Project Structure

app/
  main.py
  routers/chat.py
  services/rag.py
  models/schemas.py

Async Endpoint

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ChatRequest(BaseModel):
    message: str

@app.post("/chat")
async def chat(req: ChatRequest):
    result = await rag_chain.ainvoke({"input": req.message})
    return {"answer": result["answer"]}

Production Checklist

Rate limiting, API keys, structured logging, health checks, timeout on LLM calls, background tasks for long ingest jobs.

Introduction to LangChain: Building AI-Powered Apps

Wed, 08 Mar 2023 00:00:00 +0000

LangChain composes LLM calls with prompts, memory, tools, and retrieval. It standardizes patterns that every AI app eventually needs.

Chains and Prompts

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4")
prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer as a senior engineer."),
    ("user", "{question}"),
])
chain = prompt | llm
response = chain.invoke({"question": "What is RAG?"})

Retrieval

Load documents, chunk text, embed with OpenAI or open models, store in a vector DB, and retrieve relevant chunks at query time-foundation for RAG systems.