Agentic RAG System with LangGraph, LanceDB, and Ollama

This project is an Agentic Retrieval-Augmented Generation (RAG) pipeline built using LangGraph, LangChain, LanceDB, and Ollama. It uses PDF documents as knowledge base, retrieves relevant context using vector search, and answers questions using an LLM agent with tool use and grading capabilities.

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Features

• PDF ingestion with metadata extraction
• Document chunking & vector embedding via all-MiniLM-L6-v2
• Retrieval using LanceDB
• Agent decision making using LangGraph (tool vs end)
• Grading of document relevance before final generation
• Ollama's LLaMA3.1 used for all language tasks
• Frontend powered by Gradio
• Redis-backed LLM caching

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Setup Instructions

1. Clone the repo

git clone https://github.com/lehen20/agentic-rag.git
cd agentic-rag

2. Create a virtual environment and install dependencies

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

OR 

conda activate agent python==3.10

pip install -r requirements.txt

3. Download Ollama model (ensure Ollama is running)

ollama run llama3

4. Add your PDFs Place all relevant PDF documents inside the /docs folder.

5. Run the app

python main.py