SMPTE-Copilot

An open-source AI co-pilot that ingests and indexes text, audio, and video to enable semantic, multimodal search of media archives. The prototype provides modular ingestion, a chat-based retrieval pipeline, transparent citations, and tiered access for public users, members, and staff.

Execution

# Build
docker compose build

# Start Qdrant vector database
docker compose up qdrant

# Ingest all PDFs in data folder
docker compose run --rm ingest python src/cli/ingest.py /app/data/

# Query
docker compose run --rm query python src/cli/query.py "your question"

# Clean up
docker compose down

API Server (OpenAI-Compatible)

The project includes an OpenAI-compatible REST API server that can be integrated with tools like OpenWebUI, or any OpenAI-compatible client.

# Start the API server
docker compose up api

# API will be available at http://localhost:8000
# OpenAI-compatible endpoint: http://localhost:8000/v1/chat/completions

Test the API:

# Using curl
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "smpte-copilot",
    "messages": [{"role": "user", "content": "What is SMPTE ST 2110?"}]
  }'

OpenWebUI Integration

SMPTE-Copilot can be used with OpenWebUI as a chat interface via its OpenAI-compatible API. Once the api and openwebui services are running, access the UI at http://localhost:3000. The backend is automatically configured through OPENAI_API_BASE_URL to use the local RAG API (/v1/chat/completions).

To start OpenWebUI with SMPTE-Copilot:

docker compose up openwebui

For detailed instructions on enabling clickable citations and advanced OpenWebUI features, see the Advanced Usage documentation.

Project Structure

The project is organized into modular components that follow a consistent pattern. Each module implements the Factory pattern to enable easy extension and addition of new components. For detailed information about the architecture, see the Architecture documentation.

SMPTE-Copilot/
├── src/
│   ├── api/               # REST API server
│   ├── chunkers/          # Module for splitting documents into chunks
│   ├── embeddings/        # Module for embedding models
│   ├── llms/              # Module for LLM models
│   ├── loaders/           # Module for loading documents from various sources
│   ├── retrievers/        # Module for document retrieval
│   ├── vector_stores/     # Module for vector storage
│   ├── config/            # Project configuration
│   └── cli/               # Command-line interfaces
├── data/                  # Data and documents to process
├── config.yaml           # Main configuration file
└── docker-compose.yml    # Docker configuration

Documentation

For detailed information about different aspects of the project, see:

• Architecture - Architectural patterns, Factory Pattern, Pipeline Pattern, and module structure
• Configuration - Complete guide to config.yaml, Access Control, and configurable pipelines
• Development Guide - How to add new components and extend the system
• Advanced Usage - Detailed CLI, use cases, and advanced features

Quick Links

• Getting Started: See Execution above for basic setup
• Configuration: See config-example.yaml for a complete configuration example
• Adding Components: See Development Guide for step-by-step instructions
• Pipeline Details: See Architecture for pipeline flow and extensibility