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RAG Chatbot using NVIDIA NIM

A simple Streamlit-based chatbot application that allows users to interact with their documents using a Retrieval-Augmented Generation (RAG) pipeline. Upload your PDF files, generate embeddings, and chat with the documents to get precise answers to your queries.

Demo

Chatbot Demo

Features

  • Document Upload: Upload one or multiple PDF documents for processing.
  • Document Embeddings: Generate embeddings for the uploaded documents using NVIDIA embeddings.
  • Chat Interface: Ask questions and receive context-aware answers from your documents.
  • Streamlined Workflow: Perform the entire workflow in a simple and clean user interface.

How to Use

Upload Documents (Optional):

  • Use the file uploader in the sidebar to upload your custom PDF documents.
  • If no documents are uploaded, the app will process documents from the default ./data directory.

Generate Document Embeddings:

  • Click the "Generate Document Embeddings" button in the sidebar to process the documents.
  • This step prepares the documents for querying.

Ask Questions:

  • Type your question in the input box and press Enter.
  • The bot will provide a context-aware answer based on your documents.

Repeat:

  • Ask more questions as needed. The bot will respond to each query dynamically.

PRequirements

  • Python 3.9 or higher
  • Streamlit
  • NVIDIA LangChain integration
  • Additional Python dependencies listed in requirements.txt

Installation

  1. Clone the repository:
   git clone https://github.com/SurajBhar/rag_nim.git
   cd rag_nim
  1. Set up a virtual environment:
   conda create -p nim_env python==3.10
   conda activate nim_env
  1. Install Dependencies:
   pip install -r requirements.txt
  1. Set up your NVIDIA API Key:
  • Create a .env file in the root directory.
  • Add your NVIDIA API key to the .env file:
   NVIDIA_API_KEY='your_api_key'
  1. Run the application:
  • There are different versions of the chatbot available in this repo:
  • To run a simple one:
   streamlit run rag_chatbot_1.py
  • To run a more complex (under develeopment phase) chatbot:
   streamlit run complex_rag_chatbot.py

Directory Structure

rag_nim/
├── chatbot.py               # Main application file
├── .env                  # Environment variables (API key)
├── data/                 # Default directory for PDF documents
├── complex_rag_chatbot.py # Other version of the chatbot
├── requirements.txt     # Dependencies
└── README.md            # Project documentation
└── demo.png             # Project demonstration

Contributing

Contributions are welcome! Feel free to fork this repository and submit pull requests for enhancements or bug fixes.