📄 RAG System for Dynamic PDF Querying

An AI-powered Retrieval-Augmented Generation (RAG) system that allows you to query PDF documents dynamically. It extracts, embeds, and retrieves context from PDFs using ChromaDB and HuggingFaceEmbeddings, and generates intelligent answers with ChatGroq LLMs.

The system is simple to run, lightweight, and demonstrates how LLMs can be combined with vector databases for accurate context-aware Q&A over documents.

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🚀 Features

• 📄 Upload and parse PDF documents
• 🔍 Chunk and embed PDF content using HuggingFaceEmbeddings
• 🤖 Perform semantic retrieval on relevant chunks using ChromaDB
• 💬 Answer queries using Groq LLMs
• 🗂️ Sample temp.pdf included for quick testing
• ⚡ Minimal setup with requirements.txt

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🧰 Tech Stack

• Python
• ChatGroq API
• HuggingFaceEmbeddings
• ChromaDB (Vector Database)
• LangChain
• PyPDF or similar PDF parser

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📦 Installation

1. Clone the Repo

   git clone https://github.com/jasoncobra3/RAG_system_For_Dynamic_PDF_Querying.git
   cd RAG_system_For_Dynamic_PDF_Querying
   

2. Create Virtual Environment

    python -m venv venv
   

3. Activate the Virtual Environment

    # Windows:
    venv\Scripts\activate
    # macOS/Linux:
    venv/bin/activate
   

4. Install Dependencies

   pip install -r requirements.txt
   

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🔐 Setup

1. Create a .env file in root folder with

    GROQ_API_KEY=your_groq_api_key_here

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🚀Run the App

Run the Script in Terminal

  python app.py

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📁 Project Structure

├── app.py
├── requirement.txt
├── temp.pdf
├── chroma_db/
├── .env
└── README.md

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🤝 Contributing

Pull requests are welcome! For changes, please open an issue first to discuss what you’d like to change.