pyaibridge 0.2.3

High-performance unified API for all LLM providers

PyAIBridge

High-performance unified API library for all LLM providers with modern Python best practices.

Features

• 🚀 Unified Interface: Single API for multiple LLM providers
• ⚡ High Performance: Async/await, connection pooling, HTTP/2 support
• 🛡️ Robust Error Handling: Comprehensive exception hierarchy
• 🔄 Smart Retries: Exponential backoff with rate limit respect
• 📊 Built-in Metrics: Cost tracking, performance monitoring
• 🌊 Streaming Support: Real-time response streaming
• 🔒 Type Safety: Full type hints and validation with Pydantic
• ✅ Well Tested: Comprehensive test coverage

Supported Providers

• 🤖 OpenAI - GPT-4.1, GPT-4o, GPT-4-turbo, GPT-3.5-turbo, O-series reasoning models
• 🧠 Google - Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash, Gemini 1.5 series
• 🔮 Anthropic - Claude 3 Haiku, Claude 3 Sonnet, Claude 3 Opus, Claude 3.5 Sonnet
• 🚀 xAI - Grok Beta, Grok models
• 🔧 More providers - Cohere, Ollama (coming soon)

Installation

pip install pyaibridge

Quick Start

import asyncio
from pyaibridge import LLMFactory, ChatRequest, Message, MessageRole, ProviderConfig

async def main():
    # Create provider
    config = ProviderConfig(api_key="your-api-key")
    provider = LLMFactory.create_provider("openai", config)
    
    # Create request
    request = ChatRequest(
        messages=[
            Message(role=MessageRole.USER, content="Hello, world!")
        ],
        model="gpt-4.1-mini",
        max_tokens=100,
    )
    
    # Generate response
    async with provider:
        response = await provider.chat(request)
        print(response.content)

asyncio.run(main())

Streaming Example

import asyncio
from pyaibridge import LLMFactory, ChatRequest, Message, MessageRole, ProviderConfig

async def main():
    config = ProviderConfig(api_key="your-api-key")
    provider = LLMFactory.create_provider("openai", config)
    
    request = ChatRequest(
        messages=[Message(role=MessageRole.USER, content="Tell me a story")],
        model="gpt-4.1-mini",
    )
    
    async with provider:
        async for chunk in provider.stream_chat(request):
            if chunk.content:
                print(chunk.content, end="", flush=True)

asyncio.run(main())

Advanced Usage

Error Handling

from pyaibridge import (
    LLMFactory, 
    ProviderConfig,
    AuthenticationError, 
    RateLimitError, 
    ProviderError
)

try:
    config = ProviderConfig(api_key="invalid-key")
    provider = LLMFactory.create_provider("openai", config)
    async with provider:
        response = await provider.chat(request)
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except ProviderError as e:
    print(f"Provider error: {e.message}")

Metrics Collection

from pyaibridge.utils.metrics import metrics

# Metrics are automatically collected
config = ProviderConfig(api_key="your-key")
provider = LLMFactory.create_provider("openai", config)
async with provider:
    response = await provider.chat(request)

# Get metrics summary
summary = metrics.get_summary()
print(f"Total requests: {summary['openai']['request_count']}")
print(f"Total cost: ${summary['openai']['total_cost']:.6f}")

Cost Calculation

# Automatic cost calculation
response = await provider.chat(request)
cost = provider.calculate_cost(response.usage.dict(), response.model)
print(f"Cost: ${cost:.6f}")

Configuration

Provider Configuration

config = ProviderConfig(
    api_key="your-api-key",
    base_url="https://api.openai.com/v1",  # Custom base URL
    max_retries=3,                         # Retry attempts
    timeout=30.0,                          # Request timeout
    rate_limit=60,                         # Requests per minute
)
provider = LLMFactory.create_provider("openai", config)

Request Parameters

request = ChatRequest(
    messages=[...],
    model="gpt-4.1-mini",
    max_tokens=1000,
    temperature=0.7,
    top_p=0.9,
    frequency_penalty=0.0,
    presence_penalty=0.0,
    stop=["\\n", "END"],
    user="user-123",
    timeout=60.0,
)

Real-World Examples

Content Generation Platform

from pyaibridge import LLMFactory, Message, MessageRole, ChatRequest, ProviderConfig

async def generate_summary(posts: list) -> str:
    """Generate AI summary of Reddit discussions."""
    config = ProviderConfig(api_key="your-openai-key")
    provider = LLMFactory.create_provider("openai", config)
    
    # Prepare content for summarization
    content = "\\n".join([f"Post: {post.headline}" for post in posts[:10]])
    
    prompt = f"""
    Summarize these discussions in 2-3 sentences:
    {content}
    
    Focus on main sentiment and key themes.
    """
    
    messages = [
        Message(role=MessageRole.SYSTEM, content="You are a financial news summarizer."),
        Message(role=MessageRole.USER, content=prompt)
    ]
    
    request = ChatRequest(
        messages=messages,
        model="gpt-4.1-mini",
        temperature=0.3,
        max_tokens=100
    )
    
    async with provider:
        response = await provider.chat(request)
        return response.content.strip()

Multi-Provider Comparison

async def compare_providers():
    # Setup multiple providers
    openai_config = ProviderConfig(api_key="openai-key")
    google_config = ProviderConfig(api_key="google-key")
    
    openai_provider = LLMFactory.create_provider("openai", openai_config)
    google_provider = LLMFactory.create_provider("google", google_config)
    
    question = "What are the benefits of renewable energy?"
    messages = [Message(role=MessageRole.USER, content=question)]
    
    async with openai_provider, google_provider:
        # OpenAI response
        openai_request = ChatRequest(messages=messages, model="gpt-4.1-mini")
        openai_response = await openai_provider.chat(openai_request)
        
        # Google response
        google_request = ChatRequest(messages=messages, model="gemini-2.5-flash")
        google_response = await google_provider.chat(google_request)
        
        print("OpenAI:", openai_response.content[:100] + "...")
        print("Google:", google_response.content[:100] + "...")

Examples

Check out the examples/ directory for more examples:

• basic_usage.py - Basic chat completion
• streaming_example.py - Streaming responses
• metrics_example.py - Metrics collection
• multi_provider_comparison.py - Comparing multiple providers
• google_usage.py - Google Gemini integration
• openai_latest_models.py - Latest OpenAI models

Development

# Clone repository
git clone https://github.com/sixteen-dev/pyaibridge.git
cd pyaibridge

# Install with development dependencies
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check src/
uv run ruff format src/

# Run type checking
uv run mypy src/

Testing and Deployment

Automated Testing

# Run comprehensive package tests
uv run python scripts/test_package.py

# Test with real API keys (optional)
export OPENAI_API_KEY="sk-..."
export GOOGLE_API_KEY="AIza..."
export CLAUDE_API_KEY="sk-ant-..."
export XAI_API_KEY="xai-..."
uv run python scripts/test_real_api.py

Automated Deployment via GitHub Actions

The repository includes automated CI/CD with GitHub Actions:

• TestPyPI: Auto-deploys on push to develop branch
• PyPI: Auto-deploys on GitHub release creation
• Security: Automated security scanning and code quality checks

Setup:

1. Configure OIDC trusted publishing on PyPI/TestPyPI
2. Create GitHub environments: pypi, test-pypi, api-testing
3. No API tokens needed - uses secure OIDC authentication

Deploy to TestPyPI:

git push origin develop

Deploy to PyPI:

gh release create v0.1.3 --title "Release v0.1.3"

See GITHUB_DEPLOYMENT.md for complete setup guide.

Manual Deployment

# Build package
uv build

# Test installation locally
uv pip install dist/pyaibridge-*.whl

# Deploy using scripts
uv run python scripts/deploy_testpypi.py  # TestPyPI
uv run twine upload dist/*                # PyPI

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add tests for new functionality
4. Ensure all tests pass (uv run pytest)
5. Run linting (uv run ruff check src/)
6. Submit a pull request

License

MIT License - see LICENSE file for details.

Changelog

0.1.1

• Added Google Gemini provider support
• Comprehensive test coverage (48 tests passing)
• Updated to respx for HTTP mocking
• Fixed Pydantic v2 compatibility
• Added extensive documentation with real-world scenarios

0.1.0

• Initial release with OpenAI provider support
• Basic chat completion and streaming
• Error handling and retry logic
• Metrics collection and cost calculation
• Type safety with Pydantic models

Documentation

• Testing Guide: TESTING.md - Testing with real APIs and TestPyPI
• Deployment Guide: GITHUB_DEPLOYMENT.md - GitHub Actions CI/CD
• OIDC Setup: OIDC_SETUP.md - Secure deployment setup
• Full Documentation: DOCUMENTATION.md - Complete API reference

Repository

• GitHub: https://github.com/sixteen-dev/pyaibridge
• PyPI: https://pypi.org/project/pyaibridge/

Support

For questions, issues, or feature requests, please open an issue on GitHub.