Agentic AI - Private Agentic RAG with LangGraph and Ollama

About this course

This course is not for absolute beginners in AI - you should first learn LangChain fundamentals, then LangGraph, and only after that take this course for the best learning experience.

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 deployment

Use models like Qwen3, GPT-OSS, Llama 3.2, and nomic-embed

Create custom LLMs with Modelfiles

Use Ollama CLI and REST API for text, chat, and embeddings

LangGraph Fundamentals

Build state machines using TypedDict

Create nodes, reducers, and conditional edges

Build multi-step workflows with START/END logic

Visualize execution with diagrams

Understand message accumulation and state merging

Complete RAG Systems (from scratch)Ingest PDFs using Docling w

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