wehub-resource-sync
53 lines
1.5 KiB
Markdown
53 lines
1.5 KiB
Markdown
# LLama3.3-RAG application
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This project build the fastest stack to build a RAG application to **chat with your docs**.
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We use:
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- SambaNova as the inference engine for Llama 3.3.
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- Llama index for orchestrating the RAG app.
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- Qdrant VectorDB for storing the embeddings.
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- Streamlit to build the UI.
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## Installation and setup
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**Setup SambaNova**:
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Get an API key from [SambaNova](https://sambanova.ai/) and set it in the `.env` file as follows:
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```bash
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SAMBANOVA_API_KEY=<YOUR_SAMBANOVA_API_KEY>
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```
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**Setup Qdrant VectorDB**
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```bash
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docker run -p 6333:6333 -p 6334:6334 \
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-v $(pwd)/qdrant_storage:/qdrant/storage:z \
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qdrant/qdrant
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```
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**Install Dependencies**:
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Ensure you have Python 3.11 or later installed.
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```bash
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pip install streamlit llama-index-vector-stores-qdrant llama-index-llms-sambanovasystems sseclient-py
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```
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**Run the app**:
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Run the app by running the following command:
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```bash
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streamlit run app.py
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```
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---
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## 📬 Stay Updated with Our Newsletter!
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**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)
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[](https://join.dailydoseofds.com)
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---
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## Contribution
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Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
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