RAG Chatbot

A Retrieval-Augmented Generation (RAG) chatbot that combines document retrieval with language generation to provide accurate, context-aware responses.

Features

• Document Processing: Upload and process PDF documents
• Vector Database: Uses ChromaDB for efficient document storage and retrieval
• Intelligent Retrieval: Finds relevant document chunks based on user queries
• Response Generation: Generates contextual responses using retrieved information
• Interactive Interface: Easy-to-use chat interface

Technologies Used

• Python: Core programming language
• LangChain: Framework for building LLM applications
• ChromaDB: Vector database for document storage
• PDF Processing: Document parsing and text extraction
• Vector Embeddings: For semantic search and retrieval

Installation

1. Clone the repository:

git clone https://github.com/aayushyatiwari/RAGfinal.git
cd RAGfinal

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys and configurations

Usage

1. Start the application:

python transcipt_1_agent.py

2. Upload documents: Add PDF file location of transcipt_1

3. Ask questions: Query the chatbot about the uploaded documents

4. Get responses: Receive accurate, context-aware answers based on your documents

Project Structure

RAG-chatbot/
├── generation.py           # Response generation logic
├── chroma_langchain_db/   # ChromaDB vector database
├── transcipt_1.pdf       # Sample document
├── transcipt_1_agent.py  # Agent processing script
├── BFS_Share_Price.csv   # Sample data file
├── requirements.txt      # Python dependencies
├── README.md            # Project documentation
└── .env                 # Environment variables (not tracked)

Configuration

Edit the .env file to configure:

• API keys for language models
• Database connection settings
• Model parameters
• Other application settings

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

• Author: Aayush Yatiwari
• GitHub: @aayushyatiwari
• Repository: RAG-chatbot

Acknowledgments

• LangChain community for the excellent framework
• ChromaDB for the vector database solution
• OpenAI for language model capabilities