LangServe deploys LangChain chains and models as REST APIs, turning application workflows into accessible services. It integrates with FastAPI, validates data with pydantic, and supports invoking, batching, and streaming endpoints. section 14.3
LangServe provides practical capabilities for real-time AI applications:
- Infers input and output schemas from LangChain objects. section 14.3
- Handles concurrent requests with Python libraries such as uvloop and asyncio. section 14.3
- Exposes a playground for changing configurable parameters, trying inputs, and viewing streamed responses. section 14.3
- Works with LangChain Templates, including reference architectures backed by MongoDB Atlas Vector Search and OpenAI. section 14.3
A typical workflow uses the LangChain CLI to bootstrap a project, starts the server with `langchain serve`, and tests the resulting route through the playground or direct HTTP requests. section 14.5 section 14.6
langchain serve
Use the supplied `langchain serve` command listing as a quick operational starting point for launching a LangServe project. section 14.5.4
Testing a MongoDB RAG chatbot
After starting the local server on port 8000, open `http://127.0.0.1:8000/rag-mongo/playground/` and ask “What indexes does MongoDB support?”. The playground displays the response and can reveal intermediate retrieval and generation steps, including embedding conversion, Atlas Vector Search retrieval, prompt construction, and output parsing. section 14.5.4
