wehub-resource-sync
67 lines
1.8 KiB
Markdown
67 lines
1.8 KiB
Markdown
# RAG over audio files using AssemblyAI
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This project builds a RAG app over audio files.
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We use:
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- AssemblyAI to generate transcripts from audio files.
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- LlamaIndex 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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A demo is shown below:
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[Video demo](demo.mp4)
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## Installation and setup
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**Setup AssemblyAI**:
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Get an API key from [AssemblyAI](http://bit.ly/4bGBdux) and set it in the `.env` file as follows:
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```bash
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ASSEMBLYAI_API_KEY=<YOUR_API_KEY>
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```
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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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Note: Instead of SambaNova, you can also use Ollama.
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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 assemblyai 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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