Agentic AI - Private Agentic RAG with LangGraph and Ollama

This course provides a comprehensive, hands-on deep dive into building fully private, local Agentic Retrieval-Augmented Generation (RAG) pipelines. Led by KGP Talkie (Laxmi Kant), learners explore how to combine the power of LangGraph's stateful orchestration with Ollama's local LLM execution. By keeping everything local, developers and organizations can process sensitive data, such as financial reports, without relying on third-party APIs. Throughout the curriculum, students master advanced techniques like Corrective RAG (CRAG), Self-RAG, and Adaptive RAG, ensuring high-accuracy outputs through iterative reasoning loops, query rewriting, and self-correction. The course details practical document ingestion using modern tools like Docling for PDF processing, ChromaDB for high-performance vector storage, and BM25 re-ranking for superior retrieval precision. Whether you are an AI engineer, a software developer, or an enterprise architect looking to deploy secure, private AI agents, this training offers the ultimate roadmap. By mastering conditional routing, multi-step agentic workflows, and local embeddings, you will be equipped to build production-grade, sovereign AI systems that respect data privacy while delivering state-of-the-art cognitive capabilities.