chore: import upstream snapshot with attribution

2026-07-13 12:37:47 +08:00
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+# 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
+   ```
+
+---
+
+## 📬 Stay Updated with Our Newsletter!
+**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
+
+[![Daily Dose of Data Science Newsletter](https://github.com/patchy631/ai-engineering/blob/main/resources/join_ddods.png)](https://join.dailydoseofds.com)
+
+---
+
+## Contribution
+
+Contributions are welcome! Please fork the repository and submit a pull request with your improvements.