docker-model-runner 0.1.2

Python Client for Docker Model Runner

Docker Model Runner Client

A Python client for interacting with Docker Model Runner, providing a standard API interface for chat completions, embeddings, and more.

(Note :- In Code We Have Used gemma3 model please use tool callling supported model else it might possible you face issue or errors for more information please check dockerhub AI Models.)

Installation

Install via pip:

pip install docker-model-runner

Quick Start

Synchronous Client

from docker_model_runner import Client

client = Client(base_url="http://localhost:12434/engines/v1")  # API key optional

# Chat completion
response = client.chat.completions.create(
    model="your-model",
    messages=[{"role": "user", "content": "Hello, world!"}]
)

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

Asynchronous Client

import asyncio
from docker_model_runner import AsyncClient

async def main():
    async with AsyncClient(base_url="http://localhost:12434/engines/v1") as client:  # API key optional
        response = await client.chat.completions.create(
            model="your-model",
            messages=[{"role": "user", "content": "Hello, world!"}]
        )
        print(response['choices'][0]['message']['content'])

asyncio.run(main())

Tool Calls

from docker_model_runner import Client

client = Client()  # Uses default base_url, API key optional

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather",
            "parameters": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
                "required": ["location"]
            }
        }
    }
]

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

# Handle tool calls
message = response['choices'][0]['message']
if message.get("tool_calls"):
    for tool_call in message["tool_calls"]:
        print(f"Tool: {tool_call['function']['name']}")

Features

• Synchronous and asynchronous clients
• Chat completions with streaming support
• Embeddings
• Tool calls with local handling
• Compatible with standard API format

API Reference

Client

• Client(base_url, api_key): Initialize sync client (api_key optional)
• client.chat.completions.create(model, messages, **kwargs): Create chat completion
• client.chat.completions.stream(model, messages, **kwargs): Stream chat completion
• client.embeddings.create(model, input, **kwargs): Create embeddings
• client.models.list(): List available models

AsyncClient

• AsyncClient(base_url, api_key): Initialize async client (api_key optional)
• Similar methods as Client, but async

Tool Choice

• "auto": Let model decide (default)
• "none": Don't use tools
• "always": Force tool usage

License

MIT License - see LICENSE file for details