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 Learn:
Ollama and Local LLM Setup:
- Install 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 with OCR and table extraction
- Build page-level chunks for accurate retrieval
- Extract metadata from filenames and LLMs
- Remove duplicates using SHA-256 hashing
- Store documents in ChromaDB with metadata filters
