AI Gateway

from openai import OpenAI

client = OpenAI(
    base_url="https://your-temps-instance.com/ai/v1",
    api_key="your-temps-api-key",
)

response = client.chat.completions.create(
    model="gpt-4o",  # or "claude-sonnet-4-20250514", "gemini-2.5-pro", etc.
    messages=[
        {"role": "user", "content": "Hello!"}
    ],
)

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

import base64

# Read image and encode to base64
with open("screenshot.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

response = client.chat.completions.create(
    model="gpt-4o",  # or claude-sonnet-4-20250514, gemini-2.5-pro
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image? Describe it in detail."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{image_data}"
                    },
                },
            ],
        }
    ],
    max_tokens=1000,
)

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

import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'London'"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Step 1: Send message with tools
response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",  # works with any provider
    messages=[
        {"role": "user", "content": "What's the weather like in London?"}
    ],
    tools=tools,
)

message = response.choices[0].message

# Step 2: Check if the model wants to call a tool
if message.tool_calls:
    tool_call = message.tool_calls[0]
    args = json.loads(tool_call.function.arguments)
    print(f"Model wants to call: {tool_call.function.name}({args})")

    # Step 3: Execute the tool and send the result back
    weather_result = get_weather(args["location"])  # your function

    follow_up = client.chat.completions.create(
        model="claude-sonnet-4-20250514",
        messages=[
            {"role": "user", "content": "What's the weather like in London?"},
            message,  # assistant message with tool_calls
            {
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(weather_result),
            },
        ],
        tools=tools,
    )

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

stream = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Write a haiku about deployment automation."}
    ],
    stream=True,
)

for chunk in stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

print()  # newline at end