This project is an Agentic Retrieval-Augmented Generation (RAG) system that processes PDFs using Gemini as the model, LangChain for agent-based orchestration, and ChromaDB for vector storage and Streamlit for deploying an interactive web application. The project is built with GitHub Copilot assistance.

• Extracts relevant information from PDFs.
• Uses LangChain to create an intelligent agent.
• Stores and retrieves document embeddings with ChromaDB.
• Employs Gemini for natural language understanding and response generation.
• Provides an interactive web interface using Streamlit.

• Model: Gemini
• Framework: LangChain
• Database: ChromaDB
• Web Application: Streamlit
• Assistant: GitHub Copilot

• Add PDFs to the data/ folder.
• Run the script to process and query them.
• Customize the agent’s behavior in agentic-rag.py.

This project was inspired and guided by the following resource:

• An Improved Langchain RAG Tutorial (v2) by pixegami: This tutorial provided valuable insights into implementing a Retrieval-Augmented Generation system using LangChain and local LLMs.