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 Ollamais 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, implementadvanced 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

Two-Stage Retrieval Pipeline

Build metadata filters from natural language

Generate financial keywords using structured LLM outputs

Use ChromaDB with MMR search

Implement BM25Plus re-ranking for better accuracy

Extract headings and sections for improved ranking

Agentic RAG using LangGraph

Build tool-calling agents using the ReAct pattern

Implement document retrieval tools using LangChain

Build agents that call tools multiple times

Add table-based answers with citations

Support multi-turn conversations with memory

Corrective RAG (CRAG)

Grade retrieved documents using a Pydantic schema

Detect irrelevant results and rewrite queries

Add web search fallback using DuckDuckGo

Prevent infinite loops with controlled retries

Generate final answers with correct citations

MySQL SQL Agent

Build a natural-language SQL agent with LangGraph

Retrieve schema, generate SQL, validate, run, and fix errors

Handle multi-table joins and complex metrics

Automatically correct broken SQL queries

Support explanations and safe database access

Financial Document Analysis Project

Work with real SEC filings: 10-K, 10-Q, 8-K

Build 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 retrieval

Produce clean, formatted answers with tables and reasoning

Who This Course Is For

Developers and engineers who want to build advanced RAG systems

ML practitioners who want full privacy using local LLMs

AI engineers working on LangGraph, LangChain, or agent systems

Backend developers who want to build real GenAI applications

Anyone interested in private, production-grade LLM workflows

This is anadvanced-levelcourse. Good LangGraph or Langchain knowledge is required.

Why This Course Is Different

The entire course runs locally using Ollama

Zero API cost and complete data privacy

Covers modern RAG techniques: PageRAG, CRAG, Reflexion ideas

Real datasets from top tech companies

Covers LangGraph deeply with real production workflows

Includes SQL agents, financial RAG systems, and multi-step agents

Step-by-step, practical, and code-heavy

By the End of This Course You Will Be Able To

Build private, production-ready RAG systems

Deploy and fine-tune local LLMs with Ollama

Build graph-based agents using LangGraph v1

Create advanced retrieval pipelines using MMR and BM25Plus

Analyze financial documents with precise citations

Build SQL agents for natural language database queries

Handle query rewriting, grading, and web fallback

Build 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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