Chatbot_RAG

A Retrieval-Augmented Generation (RAG) chatbot designed to provide accurate and context-aware responses by retrieving information from a custom knowledge base. This project combines large language models with vector-based retrieval to enable intelligent question-answering.

Features

• Document Ingestion: Upload and process documents (e.g., PDFs, text) to build a searchable knowledge base.
• Context-Aware Responses: Retrieve relevant document chunks and generate answers using an LLM.
• User-Friendly Interface: Interact with the chatbot via a web or command-line interface.
• Embedding Generation: Convert documents into embeddings for efficient retrieval.
• Customizable Settings: Configure the chatbot for specific use cases or data sources.

Technologies

• Python: Core programming language (assumed).
• LangChain: Framework for RAG pipeline and LLM integration (assumed).
• Vector Database: Chroma, Pinecone, or similar for storing embeddings (assumed).
• LLM: Open-source or API-based model (e.g., Llama, OpenAI GPT) (assumed).
• Streamlit (optional): For a web-based interface (assumed).

Folder Structure

Chatbot_RAG/
├── src/                    # Source code for the chatbot
│   ├── main.py            # Main application script
│   ├── ingestion.py       # Document processing and embedding logic
│   └── chatbot.py         # Chatbot core functionality
├── data/                   # Sample documents or knowledge base
├── requirements.txt        # Python dependencies
├── .env.example           # Example environment variables
└── README.md              # This file

Note: The folder structure is assumed. Please provide the actual structure for accuracy.

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/RPPandey02/Chatbot_RAG.git
   cd Chatbot_RAG

2. Set Up a Virtual Environment:

   python -m venv venv
   source