"# KUVE Chatbot with RAG

An intelligent chatbot system powered by LangChain and Groq LLM, designed to provide information about KUVE - an AI-Powered Classifieds Marketplace platform. The chatbot uses Retrieval Augmented Generation (RAG) to deliver accurate, context-aware responses based on KUVE's documentation.

🌟 Features

• RAG-Enhanced Responses: Utilizes FAISS vector store for accurate information retrieval
• Real-time Streaming: Stream responses as they're generated
• Multi-Interface Support:
  • CLI interface for quick interactions
  • Streamlit web interface for rich user experience
• Conversation History: Track and manage chat history
• Source Attribution: View source documents for RAG responses
• Flexible Deployment: Run with or without RAG capabilities

🛠️ Technology Stack

• LangChain: Framework for RAG and chain-of-thought operations
• Groq: Fast LLM inference engine
• FAISS: Vector store for efficient similarity search
• HuggingFace: Sentence transformers for embeddings
• Streamlit: Web interface
• Python: Core programming language

📋 Prerequisites

• Python 3.13+
• Groq API Key

🚀 Getting Started

1. Clone the repository

   git clone https://github.com/Smartking1/Kuve-Task-1.git
   cd KUVE-1

2. Set up virtual environment

   python -m venv kuve
   source kuve/bin/activate  # Linux/Mac
   .\kuve\Scripts\activate   # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment variables

   • Create a .env file in the project root
   • Add your Groq API key:

     GROQ_API_KEY=your_api_key_here
     

5. Prepare the data (for RAG support)

   python scripts/prepare_data.py

💻 Usage

CLI Interface

python scripts/run_chatbot.py

Available commands:

• Type your question to chat
• clear: Clear conversation memory
• history: View conversation history
• quit or exit: Exit the chatbot

Web Interface

streamlit run streamlit_app/app.py

Features:

• Chat interface with streaming responses
• Source document attribution
• Conversation history management
• Settings panel

📁 Project Structure

KUVE-1/
├── config/               # Configuration files
├── data/                # Data directory
│   ├── chat_history/    # Conversation logs
│   ├── processed/       # Processed vector store
│   └── raw/            # Raw text data
├── scripts/             # Utility scripts
│   ├── prepare_data.py  # Data preparation
│   └── run_chatbot.py  # CLI interface
├── src/                 # Source code
│   ├── chatbot.py      # Core chatbot logic
│   ├── data_loader.py  # Data loading utilities
│   └── vector_store.py # Vector store management
├── streamlit_app/      # Web interface
├── requirements.txt    # Dependencies
└── README.md          # Documentation

⚙️ Configuration

Key settings in config.py:

• Model parameters (temperature, max tokens)
• RAG settings (chunk size, overlap)
• Conversation settings (history length)
• File paths and environment variables

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📜 License

This project is proprietary software. All rights reserved.

🔗 Links

• KUVE Website
• Contact Support"