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Let's build a RAG app over audio files with DeepSeek-R1 (running locally):
Before we begin, here's a quick demo of what we're building! We will use: - @AssemblyAI for transcribing audio files. - @qdrant_engine for the vector database. - @llama_index for orchestration. - DeepSeek-R1 as the LLM. Let's dive in!
Here's an overview of our app: • 1) Takes an audio file and transcribes it using @AssemblyAI. • 2-3) Stores it in a Qdrant vector database. • 4-6) Queries the database to get context. • 7-8) Uses DeepSeek-R1 as the LLM to generate a response. Now let's jump into code!
0️⃣ Get the API key To transcribe audio files, get an API key from AssemblyAI and store it in the `.env` file:
1️⃣ Transcription We use AssemblyAI to transcribe audio with speaker labels. To do this: - We set up the transcriber object. - We enable speaker label detection in the config. - We transcribe the audio using AssemblyAI. Check this code👇
2️⃣ Embed transcripts and store them in a vector database: To do this, we: - Load the embedding model and generate embeddings. - Connect to Qdrant and create a collection. - Store the embeddings Look at this implementation👇
3️⃣ Retrieval Now, we query the vector database to retrieve sentences in the transcripts that are similar to the query: - Convert the query into an embedding. - Search the vector database. - Retrieve the top results. Here's the code 👇
4️⃣ Generate response Finally, after retrieving the context: - We construct a prompt. - We use DeepSeek-R1 through @ollama to generate a response. Look at this implementation👇
5️⃣ Streamlit UI To make this accessible, we wrap the entire app in a @Streamlit interface. It’s a simple UI where you can upload and chat with the audio file directly. Here's the demo again👇
That's a wrap! If you enjoyed this tutorial: Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.