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Agentic AI - Private Agentic RAG with LangGraph and Ollama
Published 11/2025
Duration: 13h 26m | .MP4 1280x720 30fps(r) | AAC, 44100Hz, 2ch | 771.62 MB
Genre: eLearning | Language: English
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.
More Info
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