gnosari-realtime 0.1.0

Real-time WebSocket pub/sub server with optional AI processing

Gnosari Realtime

A high-performance, real-time WebSocket pub/sub server with optional AI processing capabilities. Built with Python, Starlette, and modern async technologies.

Features

• Real-time WebSocket Communication: High-performance WebSocket server with channel-based pub/sub
• Python SDK: Easy-to-use client library for programmatic access
• Authentication & Authorization: JWT-based authentication with team and user-level permissions
• Message Persistence: PostgreSQL storage with full message history
• Search & Analytics: OpenSearch integration for message search and analytics
• Redis Integration: Pub/sub broadcasting and caching for horizontal scaling
• Rate Limiting: Built-in rate limiting and connection management
• Extensible Architecture: Clean SOLID architecture ready for AI integrations

Quick Start

Installation

# Clone the repository
git clone <repository-url>
cd realtime-server

# Install dependencies
pip install poetry
poetry install

# Set up environment
cp .env.example .env
# Edit .env with your configuration

Start Services

Option 1: Docker Compose (Recommended)

# Start all services including the server
docker compose up -d

# Wait for services to be healthy, then run migrations
docker compose exec gnosari-server poetry run alembic upgrade head

Option 2: Local Development

# Start supporting services only
docker compose up -d postgres redis opensearch-node1

# Run database migrations
poetry run alembic upgrade head

# Start the server locally
poetry run gnosari-realtime-server

The server will start on http://localhost:8000 by default.

Docker Service Health

Check if all services are running:

# Check service status
docker compose ps

# Check service logs
docker compose logs gnosari-server
docker compose logs postgres
docker compose logs redis

# Check health status
curl http://localhost:8000/health

Usage

Python SDK

Basic Usage

import asyncio
from gnosari_realtime import RealtimeClient

async def message_handler(data):
    print(f"Received: {data}")

async def main():
    client = RealtimeClient("ws://localhost:8000/ws/v1")
    
    await client.connect()
    await client.subscribe("public:notifications", message_handler)
    await client.publish("public:notifications", {"message": "Hello World!"})
    
    # Keep listening
    await asyncio.sleep(10)
    await client.disconnect()

asyncio.run(main())

Authenticated Usage

# Create auth token first
import httpx

async with httpx.AsyncClient() as client:
    response = await client.post("http://localhost:8000/api/v1/auth/token", json={
        "user_id": "user123",
        "teams": ["content-team"],
        "permissions": ["publish_public"]
    })
    token = response.json()["token"]

# Use token with WebSocket client
client = RealtimeClient("ws://localhost:8000/ws/v1", auth_token=token)
await client.connect()
await client.subscribe("team:content-team", team_handler)

HTTP API

Publish Messages

# Create authentication token
curl -X POST http://localhost:8000/api/v1/auth/token \
  -H "Content-Type: application/json" \
  -d '{"user_id": "user123", "teams": ["content-team"]}'

# Publish to channel
curl -X POST http://localhost:8000/api/v1/publish/public:alerts \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{"data": {"message": "System alert", "level": "warning"}}'

Search Messages

curl -X GET "http://localhost:8000/api/v1/search?q=alert&channel=public:alerts" \
  -H "Authorization: Bearer <token>"

Get Analytics

curl -X GET "http://localhost:8000/api/v1/analytics?channel=public:alerts" \
  -H "Authorization: Bearer <token>"

Channels

Dynamic Creation: Channels are created automatically when first used - no pre-configuration required.

Access Control Patterns:

• public:* - Public channels (require publish_public permission to publish)
• team:<team_id> - Team-specific channels (require team membership)
• user:<user_id> - User-private channels (only accessible by the specific user)
• admin:* - Admin-only channels (require admin privileges)

Permissions: Dynamic strings - create any permission name. Built-in ones: publish_public, read_analytics.

WebSocket API

Connection

Connect to ws://localhost:8000/ws/v1 with optional authentication:

• Header: Authorization: Bearer <token>
• Query param: ?token=<token>

Message Format

All WebSocket messages follow this schema:

{
  "id": "uuid",
  "type": "command|query|event|response|error",
  "action": "subscribe|unsubscribe|publish|ping|...",
  "version": "v1",
  "payload": {},
  "metadata": {
    "timestamp": "2024-01-15T10:30:00Z",
    "user_id": "user123",
    "session_id": "session456"
  }
}

Subscribe to Channel

{
  "type": "command",
  "action": "subscribe",
  "payload": {
    "channel": "public:notifications",
    "filters": {"user_id": "user123"}
  }
}

Publish Message

{
  "type": "command",
  "action": "publish",
  "payload": {
    "channel": "public:notifications",
    "data": {"message": "Hello World!"}
  }
}

Receive Messages

{
  "type": "event",
  "action": "message",
  "payload": {
    "channel": "public:notifications",
    "data": {"message": "Hello World!"}
  }
}

Configuration

Environment variables (see .env.example):

# Database
DATABASE_URL=postgresql+asyncpg://gnosari:gnosari123@localhost:5432/gnosari_realtime

# Redis
REDIS_URL=redis://localhost:6379/0

# OpenSearch
OPENSEARCH_URL=http://localhost:9200

# JWT Authentication
JWT_SECRET_KEY=your-secret-key
JWT_ALGORITHM=HS256
JWT_EXPIRE_MINUTES=1440

# Server
HOST=0.0.0.0
PORT=8000
DEBUG=true
LOG_LEVEL=INFO

# Rate Limiting
RATE_LIMIT_PER_MINUTE=100
MAX_CONNECTIONS_PER_CLIENT=5

Development

Running Tests

The project includes a comprehensive test suite with unit, integration, and end-to-end tests.

# Run all tests
./scripts/test.sh

# Quick test runner for development
./scripts/run_quick_tests.py

# Run specific test categories
poetry run pytest tests/unit/          # Unit tests only
poetry run pytest tests/integration/   # Integration tests only

# Run with coverage
poetry run pytest --cov=src/gnosari_realtime --cov-report=html

# Run specific test file
poetry run pytest tests/unit/test_schemas.py -v

Test Categories

• Unit Tests: Core component testing with mocked dependencies

  • Message schema validation
  • Authentication and authorization logic
  • Channel management and subscription handling
  • Python SDK client functionality

• Integration Tests: Service integration testing

  • WebSocket communication protocols
  • HTTP API endpoint functionality
  • Database and external service integration

• End-to-End Tests: Complete workflow testing

  • Multi-client pub/sub scenarios
  • Authentication flows
  • Error recovery and resilience

Code Quality

# Format code
poetry run black .

# Lint code
poetry run ruff check .

# Type checking
poetry run mypy .

# Run all quality checks
./scripts/test.sh  # Includes quality checks

Database Migrations

# Create new migration
poetry run alembic revision --autogenerate -m "description"

# Apply migrations
poetry run alembic upgrade head

# Rollback migration
poetry run alembic downgrade -1

Architecture

┌─────────────────────┐
│   Transport Layer   │  (Starlette WebSocket)
├─────────────────────┤
│ Application Layer   │  (Connection Management, Message Routing)
├─────────────────────┤
│  Integration Layer  │  (Optional AI Processing Hooks)
├─────────────────────┤
│    Domain Layer     │  (Channel Management, Message Schemas)
├─────────────────────┤
│ Infrastructure Layer│  (Database, Redis, OpenSearch)
└─────────────────────┘

Key Components

• Connection Manager: Handles WebSocket lifecycle and routing
• Channel Manager: Manages channel subscriptions and message routing
• Message Schemas: Pydantic models for type-safe message handling
• Authentication: JWT-based authentication with role-based access control
• Persistence: PostgreSQL for message history and connection tracking
• Search: OpenSearch for full-text search and analytics
• Caching: Redis for pub/sub and high-performance caching

Scaling

Horizontal Scaling

The server supports horizontal scaling through Redis pub/sub:

1. Run multiple server instances
2. All instances connect to the same Redis
3. Messages are automatically distributed across instances
4. WebSocket connections are load-balanced

Performance Tuning

• Adjust PostgreSQL connection pool size
• Configure Redis memory limits
• Set appropriate OpenSearch sharding
• Tune rate limiting parameters

Security

• Authentication: JWT tokens with configurable expiration
• Authorization: Channel-based access control with team/user permissions
• Rate Limiting: Per-client message and connection limits
• Input Validation: Comprehensive message validation and sanitization
• CORS: Configurable CORS policies for web applications

Monitoring

Health Check

curl http://localhost:8000/health

Server Statistics

curl http://localhost:8000/api/v1/stats

OpenSearch Dashboards

Access analytics at http://localhost:5601 when using Docker Compose.

Examples & Demos

Quick Demo

To see the server in action with real-time messaging:

# Start the server first
docker compose up -d

# Run the simple demo (1 publisher, 1 subscriber)
./run_demo.sh simple

# Run the full live demo (3 publishers, 2 subscribers, 60 seconds)
./run_demo.sh live

Demo Features

• Simple Demo (examples/simple_demo.py):

  • 1 publisher sending 5 different message types
  • 1 subscriber receiving and displaying messages
  • Perfect for quick testing and understanding basic functionality

• Live Demo (examples/live_demo.py):

  • 3 publishers with different sending intervals (fast, medium, slow)
  • 2 subscribers listening to multiple channels
  • Real-time statistics and colored output
  • Demonstrates concurrent messaging and channel routing
  • Generates realistic message types: alerts, metrics, notifications, status updates

Other Examples

See the examples/ directory for complete working examples:

• basic_client.py - Simple pub/sub client
• authenticated_client.py - Client with authentication and team channels
• http_publisher.py - Publishing via HTTP API
• simple_demo.py - Quick demonstration script
• live_demo.py - Full-featured real-time demo
• run_demo.sh - Demo runner script with multiple options

Demo Runner Usage

# Available demo options
./run_demo.sh help

# Quick functionality test
./run_demo.sh simple

# Full real-time demonstration  
./run_demo.sh live

# Check server status
./run_demo.sh check

# Run specific examples
./run_demo.sh basic    # Basic client
./run_demo.sh auth     # Authenticated client
./run_demo.sh http     # HTTP publisher

Documentation

Comprehensive documentation is available in the docs/ directory:

• LLM Quick Reference - Concise guide for AI/LLM agents and developers
• API Reference - Complete WebSocket and HTTP API documentation
• System Architecture - Technical architecture and design patterns
• Kubernetes Deployment - Production deployment guide with Helm charts

Contributing

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Run quality checks (black, ruff, mypy, pytest)
5. Submit a pull request

License

[License information here]