fastest-rag-stack

LLama3.3-RAG application

This project build the fastest stack to build a RAG application to chat with your docs. We use:

• SambaNova as the inference engine for Llama 3.3.
• Llama index for orchestrating the RAG app.
• Qdrant VectorDB for storing the embeddings.
• Streamlit to build the UI.

Installation and setup

Setup SambaNova:

Get an API key from SambaNova and set it in the .env file as follows:

SAMBANOVA_API_KEY=<YOUR_SAMBANOVA_API_KEY> 

Setup Qdrant VectorDB

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

Install Dependencies: Ensure you have Python 3.11 or later installed.

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

Run the app:

Run the app by running the following command:

streamlit run app.py

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Contribution

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.