aimq 0.1.2

A robust message queue processor for Supabase pgmq with AI-powered document processing capabilities

AIMQ

AIMQ (AI Message Queue) is a robust message queue processor designed specifically for Supabase's pgmq integration. It provides a powerful framework for processing queued tasks with built-in support for AI-powered document processing and OCR capabilities.

Features

• Supabase pgmq Integration: Seamlessly process messages from Supabase's message queue
• Document OCR Processing: Extract text from images using EasyOCR
• Queue-based Processing: Efficient handling of document processing tasks
• AI-powered Analysis: Leverage machine learning for advanced text analysis
• Flexible Architecture: Easy to extend with new processing tools and capabilities

Quick Start (Zero Installation)

The fastest way to get started with AIMQ is using uvx, which requires no installation:

# Initialize a new AIMQ project
uvx aimq init my-project
cd my-project

# Configure your .env file with Supabase credentials
cp .env.example .env
# Edit .env with your SUPABASE_URL and SUPABASE_KEY

# Edit tasks.py to define your task queues
# (A template is already created for you)

# Start the worker
uvx aimq start

That's it! No pip install, no virtual environments, no dependency conflicts.

Installation Options

Option 1: Using uvx (Recommended for Quick Start)

Run AIMQ directly without installing:

# Run any command with uvx
uvx aimq init
uvx aimq start tasks.py
uvx aimq send my-queue '{"message": "hello"}'

Option 2: Install as a Tool (Recommended for Regular Use)

Install AIMQ as a persistent tool using uv:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install aimq as a tool
uv tool install aimq

# Now you can use aimq directly
aimq init my-project
aimq start

Option 3: Traditional pip Install

pip install aimq
aimq start

Option 4: Development Setup

For contributing to AIMQ or building from source:

# Clone the repository
git clone https://github.com/bldxio/aimq.git
cd aimq

# Install dependencies from lockfile (production)
uv sync

# For development (includes test/dev tools)
uv sync --group dev

# Run from source
uv run aimq start

Key uv Commands for Development

# Add a new dependency
uv add requests

# Add to dev dependency group
uv add --group dev pytest

# Remove a dependency
uv remove requests

# Update dependencies
uv lock --upgrade

# Run commands in the uv environment
uv run python -m aimq.worker
uv run pytest

Configuration

Environment Variables

AIMQ uses environment variables for configuration. Create a .env file in your project root:

# Supabase Configuration (Required)
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_KEY=your-anon-key

# Worker Configuration
WORKER_NAME=aimq-worker
WORKER_PATH=./tasks.py
WORKER_LOG_LEVEL=info
WORKER_IDLE_WAIT=10.0

# AI Provider API Keys (Optional)
OPENAI_API_KEY=sk-...
MISTRAL_API_KEY=...

# LangChain Tracing (Optional - for debugging)
LANGCHAIN_TRACING_V2=false
LANGCHAIN_API_KEY=...

When you run aimq init, an .env.example file is created with all available options documented.

Supabase Setup

Configure your Supabase project with pgmq:

1. Go to your Supabase project dashboard
2. Navigate to Database → Extensions
3. Enable the pgmq extension
4. Run the AIMQ migration (created by aimq init --supabase)

For more details, see the Supabase pgmq documentation.

Usage

Defining Tasks

Create a tasks.py file that defines your task queues and processors:

from typing import Any, Dict
from aimq.worker import Worker

# Create a worker instance
worker = Worker()

@worker.task(queue="document-processing", timeout=300)
def process_document(data: Dict[str, Any]) -> Dict[str, Any]:
    """Process a document using AI tools."""
    document_url = data.get("document_url")

    # Use built-in AIMQ tools for OCR, PDF extraction, etc.
    # Your processing logic here...

    return {"status": "processed", "text": extracted_text}

@worker.task(queue="image-analysis")
def analyze_image(data: Dict[str, Any]) -> Dict[str, Any]:
    """Analyze an image using AI models."""
    image_url = data.get("image_url")

    # Your analysis logic here...

    return {"analysis": results}

Starting the Worker

# Start with default tasks.py
aimq start

# Start with a specific tasks file
aimq start my_tasks.py

# Start with debug logging
aimq start --debug

# Using uvx (no installation)
uvx aimq start

Sending Messages to Queues

# Send a message to a queue
aimq send document-processing '{"document_url": "https://example.com/doc.pdf"}'

# Enable/disable queues
aimq enable document-processing
aimq disable document-processing

Or programmatically from Python:

from supabase import create_client
import os

supabase = create_client(
    os.getenv("SUPABASE_URL"),
    os.getenv("SUPABASE_KEY")
)

# Send a message to the queue
supabase.rpc("pgmq_send", {
    "queue_name": "document-processing",
    "msg": {"document_url": "https://example.com/doc.pdf"}
}).execute()

Docker Deployment

AIMQ provides two Docker deployment options: local development setup and using the published image.

Option 1: Local Development (Recommended for Getting Started)

Generate Docker files in your project:

# Initialize with Docker files
aimq init --docker

# This creates:
# - Dockerfile (optimized for your project)
# - docker-compose.yml (with volume mounts)
# - .dockerignore

# Start the worker
docker-compose up -d

# View logs
docker-compose logs -f

# Stop
docker-compose down

Option 2: Published Image (Recommended for Production)

Use the pre-built AIMQ image from the registry:

With local tasks.py (development):

docker run --rm \
  -v $(pwd)/tasks.py:/app/tasks.py:ro \
  -v $(pwd)/.env:/app/.env:ro \
  aimq:latest

With git repository (production):

# Load tasks from a git repository
docker run --rm \
  -e AIMQ_TASKS=git:mycompany/aimq-tasks@production \
  -e SUPABASE_URL=https://xxx.supabase.co \
  -e SUPABASE_KEY=your-key \
  aimq:latest

Docker Compose with git repository:

version: '3.8'

services:
  aimq-worker:
    image: aimq:latest
    environment:
      - AIMQ_TASKS=git:mycompany/aimq-tasks@production
      - SUPABASE_URL=${SUPABASE_URL}
      - SUPABASE_KEY=${SUPABASE_KEY}
      - WORKER_NAME=aimq-worker
    restart: unless-stopped

Git URL Patterns

AIMQ supports npm-style git URLs for loading tasks from repositories:

• git:user/repo - Default branch from GitHub
• git:user/repo@branch - Specific branch or tag
• git:user/repo#path/to/tasks - Subdirectory in monorepo
• git:gitlab.com/user/repo@v1.0.0 - Full URL with version

Production Deployment Tips

• Scaling: Run multiple worker containers for parallel processing
• Git Repos: Store tasks in version-controlled repositories for GitOps workflows
• Secrets: Use Docker secrets or environment variable injection
• Monitoring: Add health checks and logging aggregation
• Resource Limits: Set memory/CPU limits based on AI model requirements
• Authentication: For private git repos, mount SSH keys or use HTTPS tokens

For detailed Docker deployment patterns and troubleshooting, see docker/README.md.

Development

This project uses just as a task runner. Install it with:

# macOS
brew install just

# Other platforms: https://github.com/casey/just#installation

Common Tasks

# Setup development environment
just install

# Run tests
just test
just test-cov          # With coverage

# Code quality
just lint              # Check code style
just format            # Format code
just type-check        # Type checking
just ci                # Run all checks (lint + type + test)

# Docker
just dev               # Start dev environment
just dev-build         # Build and start
just logs              # View logs

# Documentation
just docs-serve        # Serve docs locally

# See all available tasks
just --list

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run quality checks: just ci
5. Submit a pull request

License

MIT License - see LICENSE for details.