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README.md

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-# Departmental RAG System with Role-Based Access Control
-
-A production-ready Streamlit-based RAG (Retrieval-Augmented Generation) application with MongoDB authentication and role-based access control. This application allows users to securely access and query department-specific documents based on their assigned roles.
-
-![Image](https://github.com/user-attachments/assets/f8a91110-a40f-4f7f-8c76-43fd0932e10c)
-
-## ✨ Features
-
-- **Secure Authentication**: MongoDB-based user authentication
-- **Role-Based Access Control (RBAC)**: Granular access to department-specific documents
-- **Efficient Document Retrieval**: ChromaDB vector store with Cohere embeddings
-- **Natural Language Queries**: Powered by Groq's LLM with Cohere embeddings
-- **Modern UI**: Clean, responsive interface with dark/light theme support
-- **Department-Specific Knowledge Bases**: Isolated document repositories for each department
-
-## 🛠️ Prerequisites
-
-- Python 3.8+
-- MongoDB Atlas account (or local MongoDB instance)
-- Cohere API key (for text embeddings)
-- Groq API key (for LLM, optional but recommended)
-
-## 🚀 Quick Start
-
-1. **Clone the repository**
-   ```bash
-   git clone https://github.com/Bharath8080/RBAC_RAG_V4.git
-   cd RBAC_RAG_V4
-   ```
-
-2. **Set up the environment**
-   ```bash
-   # Install uv (if not already installed)
-   pip install uv
-   
-   # Create and activate virtual environment
-   uv venv
-   .venv\Scripts\activate  # On Windows
-   # OR
-   source .venv/bin/activate  # On Unix/macOS
-   
-   # Install dependencies
-   uv pip install -r requirements.txt
-   ```
-
-3. **Configure environment variables**
-   Create a `.env` file in the project root with the following content:
-   ```
-   # MongoDB Configuration
-   MONGO_URI=mongodb+srv://<username>:<password>@<cluster-address>/<database>?retryWrites=true&w=majority
-   DB_NAME=rag_system
-
-   # Cohere API Key (for embeddings)
-   COHERE_API_KEY=your_cohere_api_key_here
-
-   # Optional: Groq API Key (for LLM)
-   GROQ_API_KEY=your_groq_api_key_here
-   ```
-   Replace the placeholder values with your actual credentials.
-
-4. **Set up the vector store**
-   ```bash
-   python ingest.py
-   ```
-   This will process and index all documents in the `resources/data/` directory.
-
-5. **Initialize the database with sample users**
-   ```bash
-   python ingest_db.py
-   ```
-   This will create sample user accounts in your MongoDB database.
-
-6. **Run the application**
-   ```bash
-   streamlit run app.py
-   ```
-
-7. **Access the application**
-   Open your browser and navigate to `http://localhost:8501`
-
-## 👥 Default User Accounts
-
-| Username | Password    | Department  |
-|----------|-------------|-------------|
-| tony     | password123 | engineering |
-| bruce    | securepass  | marketing   |
-| sam      | financepass | finance     |
-| natasha  | hrpass123   | hr          |
-
-## 📁 Project Structure
-
-```
-RBAC_RAG_V4/
-├── .env.example           # Example environment variables
-├── app.py                # Main Streamlit application
-├── database.py           # Database connection and user management
-├── ingest.py             # Document processing and vector store creation
-├── ingest_db.py          # Initialize MongoDB with sample users
-├── requirements.txt      # Python dependencies
-├── resources/
-│   └── data/             # Department-specific documents
-│       ├── engineering/
-│       ├── finance/
-│       ├── hr/
-│       └── marketing/
-└── chroma_db/            # ChromaDB vector store (created after first run)
-```
-
-## 🔧 Configuration
-
-### Environment Variables
-
-- `MONGO_URI`: MongoDB connection string
-- `DB_NAME`: Database name (default: `rag_system`)
-- `COHERE_API_KEY`: Required for text embeddings
-- `GROQ_API_KEY`: Required for LLM responses (falls back to local model if not provided)
-
-### Adding New Users
-
-1. Run the `ingest_db.py` script with new user data
-2. Or add users directly to MongoDB in the `users` collection
-
-### Adding New Documents
-
-1. Place documents in the appropriate department folder under `resources/data/`
-2. Run `python ingest.py` to update the vector store
-
-## 🛡️ Security Notes
-
-- Always store sensitive information in environment variables, never in code
-- Use strong, unique passwords for MongoDB access
-- Regularly update your dependencies for security patches
-- Consider implementing rate limiting in production
-- Enable MongoDB network access restrictions
-
-## 📄 License
-
-This project is part of the Codebasics Resume Project Challenge.
-
-## 🙏 Acknowledgments
-
-- [Streamlit](https://streamlit.io/) for the web framework
-- [ChromaDB](https://www.trychroma.com/) for vector storage
-- [Cohere](https://cohere.com/) for embeddings
-- [Groq](https://groq.com/) for LLM inference
-- [MongoDB](https://www.mongodb.com/) for user management