Building Intelligent RAG Applications with LangServe, LangGraph, and Milvus

This blog post discusses how to build intelligent Retrieval Augmented Generation (RAG) applications using LangServe, LangGraph, and Milvus from the LangChain ecosystem. The author guides readers through setting up a FastAPI application, configuring LangServe and LangGraph, and utilizing Milvus for efficient data retrieval. The post also covers building an LLM agent with LangGraph and integrating Milvus for vector storage and retrieval. Key prerequisites include Python 3.9+, Docker, and basic knowledge of FastAPI and Docker.