langchain-deploy-integration

# LangChain Deploy Integration ## Overview Deploy LangChain chains and agents as APIs using LangServe (Python) or custom Express/Fastify servers (Node.js). Covers containerization, cloud deployment, health checks, and production observability. ## Option A: LangServe API (Python) ```python # serve.py from fastapi import FastAPI from langserve import add_routes from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser app = FastAPI(title="LangChain API", version="1.0.0") # Define chains summarize_chain = ( ChatPromptTemplate.from_template("Summarize in 3 sentences: {text}") | ChatOpenAI(model="gpt-4o-mini", temperature=0) | StrOutputParser() ) qa_chain = ( ChatPromptTemplate.from_messages([ ("system", "Answer based on the given context only."), ("human", "Context: {context}\n\nQuestion: {question}"), ]) | ChatOpenAI(model="gpt-4o-mini") | StrOutputParser() ) # Auto-generates /invoke, /batch, /stream, /input_schema, /output_schema add_routes(app, summarize_chain, path="/summarize") add_routes(app, qa_chain, path="/qa") @app.get("/health") async def health(): return {"status": "healthy"} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) ``` ## Option B: Express API (Node.js/TypeScript) ```typescript // server.ts import express from "express"; import { ChatOpenAI } from "@langchain/open...