audiorag 0.2.0

RAG over audio files with provider-agnostic pipeline

AudioRAG

Provider-agnostic RAG pipeline for audio content. Download, transcribe, chunk, embed, and search audio from YouTube and other sources.

Features

• Multi-provider support: OpenAI, Deepgram, AssemblyAI, Groq (STT); OpenAI, Voyage, Cohere (embeddings); OpenAI, Anthropic, Gemini (generation); ChromaDB, Pinecone, Weaviate, Supabase (vector stores)
• Batch indexing: Index multiple URLs, playlists, and local directories in one command
• Source discovery: Automatically expand playlists and recursively scan directories
• Resumable processing: SQLite state tracking with hash-based IDs
• Automatic chunking: Time-based segmentation with configurable duration
• Audio splitting: Handles large files by splitting before transcription
• Structured logging: Context-aware logging with operation timing
• Type-safe: Python 3.12+ with full type annotations

Quick Start

import asyncio
from audiorag import AudioRAGPipeline, AudioRAGConfig

async def main():
    # Configure with your chosen providers
    config = AudioRAGConfig(
        stt_provider="openai",
        stt_model="whisper-1",
        embedding_provider="openai",
        embedding_model="text-embedding-3-small",
        vector_store_provider="chromadb",
        generation_provider="openai",
        generation_model="gpt-4o-mini",
        # API keys can also be set via environment variables
        openai_api_key="sk-...",
    )
    
    # Initialize pipeline
    pipeline = AudioRAGPipeline(config)
    
    # Index audio from YouTube
    await pipeline.index("https://youtube.com/watch?v=...")
    
    # Query the indexed content
    result = await pipeline.query("What are the main points discussed?")
    print(result.answer)
    
    # Access sources with timestamps
    for source in result.sources:
        print(f"{source.video_title} at {source.start_time}s")
        print(f"URL: {source.youtube_timestamp_url}")

asyncio.run(main())

Installation

# Install with uv (recommended)
uv add audiorag

# Or with pip
pip install audiorag

Optional Dependencies

# Audio scraping utilities (yt-dlp, pydub)
uv add audiorag[defaults]  # or: pip install audiorag[defaults]

# All providers and utilities
uv add audiorag[all]  # or: pip install audiorag[all]

# Specific providers only
uv add audiorag[openai,chromadb,scraping,cohere]

Command Line Interface

AudioRAG includes a premium CLI for easy setup, indexing, and querying.

Setup

Configure your providers and API keys interactively:

audiorag setup

This will guide you through selecting providers for STT, embeddings, vector stores, and generation, saving them to a .env file.

Indexing

Index audio from multiple sources in a single command:

# Single YouTube video
audiorag index "https://youtube.com/watch?v=..."

# YouTube playlist (auto-expanded to individual videos)
audiorag index "https://youtube.com/playlist?list=..."

# Local audio files and folders
audiorag index "./podcast.mp3" "./audio_folder/"

# Multiple URLs at once
audiorag index "https://youtube.com/watch?v=video1" "https://youtube.com/watch?v=video2"

# Mixed inputs
audiorag index "./local_audio/" "https://youtube.com/watch?v=..." "./interview.wav"

Note: Always wrap URLs and paths containing spaces in quotes.

Options:

• --force: Re-process and re-index even if the URL has been processed before.

The CLI automatically:

• Expands YouTube playlists/channels into individual video URLs
• Recursively discovers audio files in directories
• Shows progress tracking for batch operations
• Handles errors per source without stopping the entire batch

Querying

Ask questions about your indexed audio content with a sophisticated results layout:

audiorag query "What are the main points discussed in the audio?"

Configuration

AudioRAG uses pydantic-settings with environment variable support. All settings use the AUDIORAG_ prefix.

# Example: Using OpenAI for STT, embeddings, and generation
export AUDIORAG_OPENAI_API_KEY="sk-..."
export AUDIORAG_STT_PROVIDER="openai"
export AUDIORAG_EMBEDDING_PROVIDER="openai"
export AUDIORAG_VECTOR_STORE_PROVIDER="chromadb"
export AUDIORAG_GENERATION_PROVIDER="openai"

# Example: Using different providers
export AUDIORAG_DEEPGRAM_API_KEY="..."
export AUDIORAG_STT_PROVIDER="deepgram"
export AUDIORAG_VOYAGE_API_KEY="..."
export AUDIORAG_EMBEDDING_PROVIDER="voyage"

# Processing settings
export AUDIORAG_CHUNK_DURATION_SECONDS="30"
export AUDIORAG_RETRIEVAL_TOP_K="10"
export AUDIORAG_RERANK_TOP_N="3"

See Configuration Guide for all options.

Documentation

• Quick Start Guide - Get up and running
• Configuration - All configuration options
• Providers - Available providers and setup
• Architecture - Pipeline stages and data flow
• API Reference - Complete API documentation

Development

# Clone and setup
git clone <repository-url>
cd audiorag
uv sync

# Run tests
uv run pytest

# Run checks
uv run ruff check . --fix
uv run ty check

# Install pre-commit hooks
uv run prek install

Pipeline Stages

1. Download: Fetch audio from URL (YouTube supported)
2. Split: Divide large files into processable chunks
3. Transcribe: Convert audio to text using STT provider
4. Chunk: Group transcription into time-based segments
5. Embed: Generate vector embeddings for each chunk
6. Store: Persist embeddings in vector database

License

MIT License