Local RAG Workflows with LangGraph and LangServe

Developing private, cost-effective AI applications requires a solid grasp of local model deployment and modern retrieval pipelines. This written course provides a step-by-step foundation in building Retrieval-Augmented Generation (RAG) systems using multiple local models, stateful agent graphs, and secure serving frameworks. You will begin by clarifying key terminology, embedding mechanisms, and local model fundamentals before moving into practical code walkthroughs and workflow structures. What you'll learn: - Understand core concepts of local LLM orchestration, vector databases, and RAG architectures - Configure and manage multiple local language models for specialized tasks - Design stateful, multi-step agent workflows and branching logic with LangGraph - Implement document indexing, chunking, and semantic retrieval pipelines - Deploy API endpoints and configure authentication mechanisms using LangServe - Practice debugging and structuring modular Python code for AI applications The course progresses logically from core definitions and local environment configuration to building complex graph workflows and exposing authenticated APIs. Designed for developers and beginners eager to work with self-hosted AI; introductory programming familiarity is helpful, but no previous AI background is required. Start reading to master local, private LLM application development.