Free Download Agentic AI - Private Agentic RAG with LangGraph and Ollama
Published 11/2025
Created by Laxmi Kant | KGP Talkie
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 17 Lectures ( 1h 51m ) | Size: 771 MB
LangGraph v1, Ollama, Agentic RAG, Private RAG, Corrective RAG, CRAG, Reflexion, Self-RAG, Adaptive RAG, MySQL Agent
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
Requirements
Basic Python knowledge is helpful, but all steps are explained clearly for beginners.
Description
Private Agentic RAG with LangGraph and Ollama is an advanced, project-based course that teaches you how to build private, production-ready Retrieval-Augmented Generation (RAG) systems using LangGraph, LangChain, Ollama, ChromaDB, Docling, and Python.This course is designed for developers who want strong control over their data, full privacy, and complete end-to-end workflows using local LLMs.You will learn how to build modern RAG systems, implement advanced retrieval pipelines, add agent workflows, use LangGraph state machines, integrate SQL agents, and run everything on your own machine using Ollama. All projects run 100 percent locally, with no external API cost and no data leaving your system.The entire course is practical. Every concept is explained with step-by-step notebooks, complete Python code, and real examples using SEC financial filings from Amazon, Google, Apple, and Microsoft.What You Will LearnOllama and Local LLM SetupInstall and configure Ollama for private LLM deploymentUse models like Qwen3, GPT-OSS, Llama 3.2, and nomic-embedCreate custom LLMs with ModelfilesUse Ollama CLI and REST API for text, chat, and embeddingsLangGraph FundamentalsBuild state machines using TypedDictCreate nodes, reducers, and conditional edgesBuild multi-step workflows with START/END logicVisualize execution with diagramsUnderstand message accumulation and state mergingComplete RAG Systems (from scratch)Ingest PDFs using Docling with OCR and table extractionBuild page-level chunks for accurate retrievalExtract metadata from filenames and LLMsRemove duplicates using SHA-256 hashingStore documents in ChromaDB with metadata filtersTwo-Stage Retrieval PipelineBuild metadata filters from natural languageGenerate financial keywords using structured LLM outputsUse ChromaDB with MMR searchImplement BM25Plus re-ranking for better accuracyExtract headings and sections for improved rankingAgentic RAG using LangGraphBuild tool-calling agents using the ReAct patternImplement document retrieval tools using LangChainBuild agents that call tools multiple timesAdd table-based answers with citationsSupport multi-turn conversations with memoryCorrective RAG (CRAG)Grade retrieved documents using a Pydantic schemaDetect irrelevant results and rewrite queriesAdd web search fallback using DuckDuckGoPrevent infinite loops with controlled retriesGenerate final answers with correct citationsMySQL SQL AgentBuild a natural-language SQL agent with LangGraphRetrieve schema, generate SQL, validate, run, and fix errorsHandle multi-table joins and complex metricsAutomatically correct broken SQL queriesSupport explanations and safe database accessFinancial Document Analysis ProjectWork with real SEC filings: 10-K, 10-Q, 8-KBuild a complete RAG system that answers questions like:"What was Amazon's revenue in 2023?""Compare Google and Apple's cash flow for 2024""Show segment revenue with citations and tables"Use ChromaDB + BM25 for accurate retrievalProduce clean, formatted answers with tables and reasoningWho This Course Is ForDevelopers and engineers who want to build advanced RAG systemsML practitioners who want full privacy using local LLMsAI engineers working on LangGraph, LangChain, or agent systemsBackend developers who want to build real GenAI applicationsAnyone interested in private, production-grade LLM workflowsThis is an advanced-level course. Good LangGraph or Langchain knowledge is required.Why This Course Is DifferentThe entire course runs locally using OllamaZero API cost and complete data privacyCovers modern RAG techniques: PageRAG, CRAG, Reflexion ideasReal datasets from top tech companiesCovers LangGraph deeply with real production workflowsIncludes SQL agents, financial RAG systems, and multi-step agentsStep-by-step, practical, and code-heavyBy the End of This Course You Will Be Able ToBuild private, production-ready RAG systemsDeploy and fine-tune local LLMs with OllamaBuild graph-based agents using LangGraph v1Create advanced retrieval pipelines using MMR and BM25PlusAnalyze financial documents with precise citationsBuild SQL agents for natural language database queriesHandle query rewriting, grading, and web fallbackBuild complete agentic RAG applications end-to-end
Who this course is for
For developers and AI learners who want to build private Agentic RAG systems with LangGraph v1 and Ollama.
For anyone who wants practical skills in LangGraph v1, Ollama, and building real AI agents.
For beginners and professionals who want to create private, secure, and advanced RAG workflows.
For developers looking to master Agentic RAG, LangGraph v1 workflows, and local LLMs.
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