This project builds a RAG app over audio files. We use:
- AssemblyAI to generate transcripts from audio files.
- LlamaIndex for orchestrating the RAG app.
- Qdrant VectorDB for storing the embeddings.
- Streamlit to build the UI.
A demo is shown below:
Setup AssemblyAI:
Get an API key from AssemblyAI and set it in the .env file as follows:
ASSEMBLYAI_API_KEY=<YOUR_API_KEY> Setup SambaNova:
Get an API key from SambaNova and set it in the .env file as follows:
SAMBANOVA_API_KEY=<YOUR_SAMBANOVA_API_KEY> Note: Instead of SambaNova, you can also use Ollama.
Setup Qdrant VectorDB
docker run -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
qdrant/qdrantInstall Dependencies: Ensure you have Python 3.11 or later installed.
pip install streamlit assemblyai llama-index-vector-stores-qdrant llama-index-llms-sambanovasystems sseclient-pyRun the app:
Run the app by running the following command:
streamlit run app.pyGet 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!
Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
