RAG QnA & Summarization Chatbot

A Streamlit-based application that performs Retrieval-Augmented Generation (RAG) to answer questions based on uploaded documents. It also provides document summarization capabilities.

Features

• Document Ingestion: Upload text-based documents (PDF, TXT, MD, HTML, DOCX).
• Automatic Summarization: Instantly generates a summary of the uploaded document using MarkItDown and GenAI.
• RAG Chatbot: Ask questions about the uploaded content. The app uses ChromaDB for vector storage and retrieval to provide accurate, context-aware answers.
• Persistent Storage: Vector embeddings are stored locally in chroma_db for persistent retrieval across sessions.

Architecture

• Frontend: Streamlit
• Vector Database: ChromaDB
• LLM Integration: OpenAI (via tiktoken for tokenization and standard API calls)
• Document Processing: MarkItDown for converting various file formats to text.

Prerequisites

• Python 3.8+
• An OpenAI API Key

Installation

1. Clone the repository:

   git clone https://github.com/abd-RAHEEM/RAGx.git
   cd RAGx

2. Create and activate a virtual environment:

   Windows:

   create_venv.bat

   Or manually:

   python -m venv .venv
   .venv\Scripts\activate

   Linux/Mac:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up Environment Variables: Create a .env file in the root directory (copy from a template if available, excluding secrets from git):

   MODEL_API_KEY=your_openai_api_key_here
   CHROMA_COLLECTION_NAME=your_collection_name

Usage

1. Run the application:

   streamlit run main.py

2. Navigate to the App: Open your browser at http://localhost:8501.

3. Ingest Documents:

   • Go to the Ingest page.
   • Upload a file.
   • View the summary.
   • Click "Upload & Ingest to Chroma DB" to index the content.

4. Chat:

   • Switch to the Chatbot page.
   • Ask questions related to the document you just uploaded.

Directory Structure

RAGx/
├── .env                # Environment variables (not contained in repo)
├── .gitignore          # Git ignore file
├── activate.bat        # Helper script to activate venv
├── create_venv.bat     # Helper script to create venv
├── chroma_db/          # Persistent Vector DB storage
├── chroma_services.py  # ChromaDB interaction logic
├── data.txt            # Sample data file
├── genai_services.py   # AI/LLM service logic
├── main.py             # Entry point for Streamlit app
├── pages/              # Streamlit pages
│   ├── chatbot_page.py
│   └── ingest_page.py
└── requirements.txt    # Python dependencies

Security Note

• API Keys: Ensure your .env file is never pushed to public repositories. This project is configured with a .gitignore that excludes .env and the chroma_db directory.