mercury-api-client 0.1.0

Python SDK for Inception Labs Mercury diffusion-LLM API

Mercury Client

A production-ready Python SDK for the Inception Labs Mercury diffusion-LLM API, providing both synchronous and asynchronous interfaces with full type safety.

Features

• 🚀 Synchronous and Asynchronous Support - Use MercuryClient or AsyncMercuryClient based on your needs
• 🔄 Automatic Retry Logic - Built-in exponential backoff with jitter for transient failures
• 🎭 Full Type Safety - Complete type hints and runtime validation with Pydantic
• 🌊 Streaming Support - Real-time streaming for chat completions
• 🛡️ Robust Error Handling - Typed exceptions for different error scenarios
• 🔧 Flexible Configuration - Customize timeouts, retries, and more
• 📝 OpenAI-Compatible Interface - Familiar API design for easy migration

Installation

pip install mercury-client

Or install from source:

git clone https://github.com/hamzaamjad/mercury-client.git
cd mercury-client
pip install -e .

Getting Started

API Key Setup

First, obtain your API key from Inception Labs and set it as an environment variable:

# Using export (Linux/macOS)
export MERCURY_API_KEY="sk_your_api_key_here"

# Using set (Windows)
set MERCURY_API_KEY=sk_your_api_key_here

# Or add to your shell profile (.bashrc, .zshrc, etc.)
echo 'export MERCURY_API_KEY="sk_your_api_key_here"' >> ~/.bashrc

Alternatively, you can pass the API key directly when initializing the client:

from mercury_client import MercuryClient

client = MercuryClient(api_key="sk_your_api_key_here")

Quick Start

Basic Usage

from mercury_client import MercuryClient

# Initialize the client (uses MERCURY_API_KEY env var by default)
client = MercuryClient()

# Create a chat completion
response = client.chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is a diffusion model?"}
    ],
    model="mercury-coder-small"
)

print(response.choices[0].message.content)

Async Usage

import asyncio
from mercury_client import AsyncMercuryClient

async def main():
    async with AsyncMercuryClient() as client:
        response = await client.chat_completion(
            messages=[
                {"role": "user", "content": "Explain quantum computing"}
            ]
        )
        print(response.choices[0].message.content)

asyncio.run(main())

Streaming Chat Completion

# Synchronous streaming
for chunk in client.chat_completion_stream(
    messages=[{"role": "user", "content": "Write a story about AI"}],
    max_tokens=1000
):
    if chunk.choices[0].delta and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

# Async streaming
async for chunk in client.chat_completion_stream(
    messages=[{"role": "user", "content": "Write a poem"}]
):
    if chunk.choices[0].delta and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Fill-in-the-Middle (FIM) Completion

response = client.fim_completion(
    prompt="def fibonacci(",
    suffix="    return a + b",
    max_tokens=100
)

print(response.choices[0].text)

Advanced Usage

Custom Retry Configuration

from mercury_client import MercuryClient, RetryConfig

retry_config = RetryConfig(
    max_retries=5,
    initial_delay=2.0,
    max_delay=30.0,
    exponential_base=2.0,
    jitter=True
)

client = MercuryClient(
    api_key="your-api-key",
    retry_config=retry_config,
    timeout=60.0  # Request timeout in seconds
)

Error Handling

from mercury_client import MercuryClient
from mercury_client.exceptions import (
    AuthenticationError,
    RateLimitError,
    ServerError,
    EngineOverloadedError
)

try:
    response = client.chat_completion(
        messages=[{"role": "user", "content": "Hello"}]
    )
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limit exceeded. Retry after {e.retry_after} seconds")
except EngineOverloadedError:
    print("Service is temporarily overloaded")
except ServerError:
    print("Server error occurred")

Tool/Function Calling

from mercury_client.models import Tool, FunctionDefinition

tools = [
    Tool(
        type="function",
        function=FunctionDefinition(
            name="get_weather",
            description="Get current weather for a location",
            parameters={
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        )
    )
]

response = client.chat_completion(
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    tool_choice="auto"
)

Configuration

Environment Variables

• MERCURY_API_KEY - Your Mercury API key (primary)
• INCEPTION_API_KEY - Alternative environment variable (for backward compatibility)

Client Options

Parameter	Type	Default	Description
api_key	str	None	API key for authentication
base_url	str	https://api.inceptionlabs.ai/v1	Base URL for the API
timeout	float	30.0	Request timeout in seconds
retry_config	RetryConfig	Default config	Retry behavior configuration

API Reference

MercuryClient / AsyncMercuryClient

Methods

• chat_completion() - Create a chat completion
• chat_completion_stream() - Create a streaming chat completion
• fim_completion() - Create a fill-in-the-middle completion (coming soon)
• close() - Close the HTTP client (also supports context manager)

Models

All models are fully typed with Pydantic:

• ChatCompletionRequest / ChatCompletionResponse
• FIMCompletionRequest / FIMCompletionResponse
• Message, Tool, ToolCall, Usage, etc.

Exceptions

• MercuryAPIError - Base exception for all API errors
• AuthenticationError - Invalid or missing API key (401)
• RateLimitError - Rate limit exceeded (429)
• ServerError - Server error (500)
• EngineOverloadedError - Service overloaded (503)

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/hamzaamjad/mercury-client.git
cd mercury-client

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=mercury_client --cov-report=html

# Run specific test file
pytest tests/test_client.py

# Run integration tests (requires MERCURY_API_KEY)
pytest tests/test_integration.py -v -m integration

Code Quality

# Format code
black mercury_client tests

# Sort imports
isort mercury_client tests

# Type checking
mypy mercury_client

# Linting
ruff mercury_client

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure:

• All tests pass
• Code is formatted with Black
• Type hints are added for new code
• Documentation is updated

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions