LLama3.3-RAG Application using SambaNova the Lightning-fast inference engine

A high-performance RAG (Retrieval-Augmented Generation) application for document chat, built with state-of-the-art components.

Tech Stack

• SambaNova: Lightning-fast inference engine for Llama 3.3
• Llama Index: RAG orchestration framework
• Qdrant: Vector database for efficient embedding storage
• Streamlit: User interface framework

Prerequisites

• Python 3.11 or later
• Docker (for Qdrant)
• SambaNova API key

Setup Instructions

1. Configure SambaNova

   Get your API key from SambaNova and create a .env file:

   SAMBANOVA_API_KEY=<YOUR_SAMBANOVA_API_KEY>

2. Launch Qdrant VectorDB

   docker run -p 6333:6333 -p 6334:6334 \
   -v $(pwd)/qdrant_storage:/qdrant/storage:z \
   qdrant/qdrant

3. Install Dependencies

   pip install streamlit llama-index-vector-stores-qdrant llama-index-llms-sambanovasystems sseclient-py

4. Launch Application

   streamlit run app.py

Usage

1. Start the application using the command above
2. Upload your documents through the Streamlit interface
3. Begin chatting with your documents using natural language queries

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to your branch
5. Create a Pull Request

License

This project is licensed under the MIT License.

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