# 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](https://sambanova.ai/) and set it in the `.env` file as follows:

```bash
SAMBANOVA_API_KEY=<YOUR_SAMBANOVA_API_KEY> 
```

**Setup Qdrant VectorDB**
   ```bash
   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.
   ```bash
   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:

   ```bash
   streamlit run app.py
   ```

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## Contribution

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