Building Local RAG Systems with LangGraph and LangServe

Modern AI applications require reliable context retrieval, flexible model orchestration, and secure deployment pipelines. This comprehensive text-based course guides you through constructing state-of-the-art retrieval-augmented generation (RAG) systems using LangGraph for agentic control and local language models for privacy and cost efficiency. You will develop a grounded understanding of vector search, document indexing, and multi-agent coordination, progressing all the way to deploying production-ready endpoints with authentication. What you'll learn: - Understand the core terminology and architecture behind retrieval-augmented generation and vector databases - Run and manage multiple local language models efficiently on your own machine - Construct complex agent workflows, loops, and stateful pipelines using LangGraph - Implement semantic search and context retrieval with modern embedding techniques - Configure secure API endpoints and token-based authentication using LangServe - Apply robust error handling, evaluation metrics, and guardrails to conversational systems The course begins with fundamental concepts of embeddings and language model orchestration before moving into structured, step-by-step code walkthroughs and architectural patterns. Designed for developers and AI enthusiasts looking for clear, foundational instruction without assuming prior experience with agent frameworks. Begin reading today to build private, secure, and intelligent language model applications.