Build an Agentic RAG Pipeline with LangGraph and ChromaDB - Step-by-Step Tutorial

Learn to build a production-ready Agentic RAG (Retrieval-Augmented Generation) pipeline using LangGraph and ChromaDB in this comprehensive 27-minute step-by-step tutorial. Discover the evolution beyond naive RAG systems by implementing intelligent agents that can orchestrate retrieval, reasoning, and generation processes. Master the setup of LangGraph for agent orchestration while utilizing ChromaDB for efficient vector storage and retrieval operations. Build custom tools, nodes, and agent loops that enable sophisticated context retrieval, routing, and reasoning capabilities. Explore the drawbacks of traditional RAG approaches and understand how agentic systems provide superior performance through modular intelligence. Work with real medical device datasets from Kaggle to implement practical examples of vector database operations and agent-based retrieval systems. Gain hands-on experience coding the entire pipeline from scratch, including prompt engineering, state graph construction, and end-to-end system integration. Access provided GitHub notebooks and datasets to follow along with the implementation while learning optimization techniques for retrieval quality and agent behavior. Perfect for developers, ML engineers, and researchers building advanced LLM-powered applications who want to understand the next generation of retrieval-augmented systems.