Agentic AI - Private Agentic RAG with LangGraph and Ollama
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Unclaimed ProfileThis 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.
About the creator
KGP Talkie | Laxmi Kant
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Reviews
Laxmi Kant, widely recognized online as the creator of KGP Talkie, is an experienced educator and software engineer specializing in artificial intelligence, machine learning, and modern backend integration. With a passion for demystifying cutting-edge technology, Laxmi focuses heavily on practical, hands-on tutorials that bridge the gap between academic theory and deployment-ready software. His teaching methodology revolves around building real-world projects, which is evident in his extensive curriculum covering LangChain, LangGraph, Ollama, FastAPI, and Model Context Protocol. Laxmi believes that the best way to master artificial intelligence is through direct implementation. He guides students through the step-by-step process of designing private…Show more
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Program Overview
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Price may change · updated within 1–2 weeks
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Learning format
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Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Build private, production-ready Agentic RAG systems using LangGraph v1 and Ollama.
- Create custom LLM workflows with LangGraph state machines, nodes, edges, and conditional routing.
- Implement PageRAG, metadata extraction, PDF processing with Docling, and page-level ingestion.
- Use ChromaDB, embeddings, metadata filtering, and MMR retrieval for high-accuracy search.
- Apply BM25+ re-ranking and advanced retrieval pipelines for financial document analysis.
- Build Agentic RAG: tool calling, reasoning loops, structured outputs, and multi-step workflows.
- Implement Corrective RAG (CRAG) with document grading, query rewriting, and web search fallback.
- Create custom Ollama models, Modelfiles, embeddings, and integrate with LangChain.
- Build Reflexion, Self-RAG and Adaptive RAG along with MySQL Agent
Best For
- AI engineers and developers focused on building data-private, on-premise generative AI applications.
- Professionals handling sensitive financial or proprietary datasets requiring local, non-cloud RAG solutions.
- LangChain users looking to level up their skills by mastering stateful orchestration with LangGraph.
- Architects interested in implementing advanced agentic patterns like Corrective RAG, Self-RAG, and Adaptive RAG.
Not For
- Absolute beginners who have never touched Python or fundamental LLM prompting concepts.
- Developers looking for cloud-based, managed LLM solutions like OpenAI or Anthropic API integrations.
- Learners seeking a high-level conceptual overview rather than deep, code-heavy implementation of local AI workflows.
- Individuals without the hardware resources to run Ollama and local embedding models efficiently.
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