Build Local RAG Agents with LangGraph

Are you ready to create AI agents that can access and understand specific information beyond their training data? This course introduces you to the powerful world of Retrieval Augmented Generation (RAG) and agentic workflows, enabling you to build sophisticated, context-aware applications that run entirely on your local machine. By the end of this course, you will be able to design, implement, and deploy your own local RAG agents, capable of leveraging external knowledge bases to provide accurate and relevant responses. What you'll learn: * Understand the core concepts of Retrieval Augmented Generation (RAG) and its benefits for AI applications. * Learn to design and implement multi-step AI agent workflows using the LangGraph framework. * Configure and interact with local Large Language Models (LLMs) via Ollama, including models like Llama 3. * Integrate vector databases like ChromaDB for efficient knowledge retrieval in RAG systems. * Apply prompt engineering techniques to guide agent behavior and improve response quality. * Practice building a complete local RAG agent from scratch, combining all components. * Explore fundamental evaluation methods for assessing RAG agent performance and reliability. The course begins with foundational RAG concepts, progresses through setting up local LLM environments, dives into LangGraph for agent creation, and concludes with practical implementation and basic evaluation strategies. This course is designed for beginners interested in building intelligent AI agents. No prior experience with LLMs or RAG is required. Start building your own intelligent local AI agents today.