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Conversational RAG with PDF Upload and Chat History

This project is a Conversational Retrieval-Augmented Generation (RAG) chatbot built with Streamlit. It allows you to upload PDF files and chat with their content using a powerful LLM (Groq's Gemma2-9b-it) and HuggingFace embeddings. The app maintains chat history for context-aware conversations.

Features

  • PDF Upload: Upload one or more PDF files to use as the knowledge base.
  • Conversational RAG: Ask questions about your PDFs and get concise, context-aware answers.
  • Chat History: Maintains session-based chat history for more natural, contextual conversations.
  • Groq LLM Integration: Uses Groq's Gemma2-9b-it model for high-quality responses.
  • HuggingFace Embeddings: Uses all-MiniLM-L6-v2 for semantic search over your documents.

Demo

image

Getting Started

Prerequisites

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/your-repo-name.git
cd your-repo-name

  2. Install dependencies:

    pip install -r requirements.txt

  3. Set up environment variables:

    • Create a .env file in the project root with your HuggingFace token:

      HF_TOKEN=your_huggingface_token_here

Running the App

streamlit run app.py

Usage

  1. Enter your Groq API key in the input box.
  2. (Optional) Enter a session ID to keep your chat history separate.
  3. Upload one or more PDF files using the file uploader.
  4. Ask questions about the content of your PDFs in the chat box.
  5. The assistant will answer using only the information from your uploaded PDFs, maintaining context from your previous questions.

File Structure

.
├── app.py
├── requirements.txt
├── .env
└── README.md

Requirements

Example requirements.txt:

streamlit
langchain
langchain-community
langchain-chroma
langchain-groq
langchain-huggingface
langchain-text-splitters
python-dotenv

Notes

  • Your data and chat history are stored in memory and are not persisted after the app stops.
  • Make sure your API keys are kept secure and never commit your .env file to public repositories.

License

MIT License

Built with ❤️ using Streamlit and LangChain.