Build an Audio RAG with AssemblyAI, Qdrant & DeepSeek-R1

This guide demonstrates building an AI-powered chatbot that transforms audio recordings (meetings, podcasts, interviews) into interactive conversations. It leverages AssemblyAI for transcription, Qdrant for efficient data storage, and DeepSeek-R1 via SambaNova Cloud for intelligent responses, creating a Retrieval Augmented Generation (RAG) system. The chatbot answers questions like "What did [Speaker] say?" or "Summarize this segment." A Streamlit web interface allows users to upload audio, view transcripts, and interact with the chatbot in real time.

Key Features and Learning Objectives:

• Precise Audio Transcription: Utilize the AssemblyAI API for accurate transcription with speaker diarization, converting audio conversations into structured text data.
• Efficient Vector Database: Employ Qdrant to store and quickly retrieve embeddings of the transcribed audio content using Hugging Face models.
• Context-Aware Responses: Implement RAG with the DeepSeek-R1 model (via SambaNova Cloud) to generate contextually relevant chatbot responses.
• Interactive Web Interface: Develop a Streamlit web application for users to upload audio files, visualize transcripts, and engage with the chatbot dynamically.
• End-to-End Workflow: Integrate a complete workflow combining audio processing, vector database management, and AI-driven response generation for a scalable audio-based chat application.

This article is part of the Data Science Blogathon.

Table of Contents:

• AssemblyAI Overview
• SambaNova Cloud Explained
• Qdrant: A High-Speed Vector Database
• DeepSeek-R1: A Powerful Language Model
• Building the RAG Model: AssemblyAI & DeepSeek-R1
  • Prerequisites
  • Retrieval Augmented Generation (RAG) Implementation
  • Streamlit Application Development
• Conclusion
• Frequently Asked Questions

AssemblyAI Overview:

AssemblyAI is a powerful tool for extracting actionable insights from audio. Its AI-driven speech-to-text engine provides highly accurate transcriptions, even handling accents and background noise effectively. This makes it ideal for transcribing podcasts, analyzing customer calls, or generating video captions.

SambaNova Cloud:

SambaNova Cloud allows you to run large open-source models like DeepSeek-R1 (671B parameters) significantly faster than traditional methods, eliminating complex infrastructure management. It utilizes Reconfigurable Dataflow Units (RDUs) for superior performance through:

• High In-Memory Storage: Eliminates constant model reloading.
• Optimized Dataflow: Designed for high-throughput tasks.
• Instant Model Switching: Switch between models in microseconds.
• Simplified DeepSeek-R1 Deployment: No complicated setup needed.
• Unified Training/Fine-tuning: All within a single platform.

Qdrant: A High-Speed Vector Database:

Qdrant is a remarkably fast vector database optimized for AI applications. It excels at similarity searches, making it perfect for tasks like recommendation systems, image search, and chatbots. Qdrant quickly finds the closest matches for complex data such as text embeddings or visual features.

DeepSeek-R1: A Powerful Language Model:

DeepSeek-R1 is an advanced language model that combines human-like adaptability with cutting-edge AI. Its strength lies in its ability to understand context, tone, and intent, producing intuitive and precise responses. It's highly effective for various natural language processing tasks, including content creation, translation, code debugging, and report summarization.

Building the RAG Model: AssemblyAI & DeepSeek-R1

This section details the construction of the RAG system.

1. Prerequisites:

Clone the repository: git clone https://github.com/karthikponna/chat_with_audios.git

Create and activate a virtual environment (instructions provided for macOS/Linux and Windows).

Install dependencies: pip install -r requirements.txt

Set up environment variables (AssemblyAI and SambaNova API keys) in a .env file.

2. Retrieval Augmented Generation (RAG) Implementation:

The code (in rag_code.py) is structured using Llama Index and includes functions for:

• Batch Processing and Embedding: Efficiently handles large datasets.
• Qdrant Database Interaction: Sets up and manages the Qdrant vector database.
• Query Embedding and Retrieval: Transforms queries into embeddings and retrieves relevant results from Qdrant.
• RAG Smart Query Assistant: Combines retrieval and the SambaNova Cloud LLM for context-aware answers.
• Audio Transcription with AssemblyAI: Transcribes audio files with speaker diarization.

(Detailed code snippets are omitted for brevity, but the original response provides the full code.)

3. Streamlit Application Development:

The app.py file creates a Streamlit web application with features for:

• Audio File Upload: Users upload audio files (mp3, wav, m4a).
• Transcription Display: Shows the AssemblyAI-generated transcript.
• Chatbot Interaction: Allows users to ask questions about the audio content.
• Session State Management: Maintains chat history and file caching.

(Detailed code snippets are omitted for brevity, but the original response provides the full code.)

Conclusion:

This project successfully integrates AssemblyAI, SambaNova Cloud, Qdrant, and DeepSeek-R1 to create a powerful audio-based chatbot using RAG. The provided code and instructions enable users to build and deploy this application. The GitHub repository offers further exploration and customization opportunities.

GitHub Repo: https://www.php.cn/link/4803eb7efe3ec7031867d3f9fe9f4dc5

Frequently Asked Questions (FAQs):

(The original response contains answers to FAQs about RAG, embedding model customization, prompt template modification, and the use of Qdrant.)

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