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AI Foundation Services

Function Calling

Function Calling ermöglicht es Modellen, strukturierte JSON-Argumente für von Ihnen definierte Funktionen zu generieren und so die Integration mit externen Tools und APIs zu ermöglichen.

Was Sie lernen werden:

  • Wie Sie Tools/Funktionen definieren, die das Modell aufrufen kann
  • Wie Sie Function Calling mit der Responses API verwenden
  • Wie Sie strukturierte Daten mithilfe von JSON Schema extrahieren

Einfaches Beispiel

Abschnitt betitelt „Einfaches Beispiel“
from openai import OpenAI


client = OpenAI()


tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use.",
                    },
                },
                "required": ["location", "format"],
            },
        },
    },
]


messages = [
    {"role": "system", "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous."},
    {"role": "user", "content": "What's the weather like today in Hamburg?"},
]


response = client.chat.completions.create(
    model="gpt-4.1",
    messages=messages,
    tools=tools,
)


print(response.choices[0].message)

Beispielausgabe:

ChatCompletionMessage(
    content=None,
    role='assistant',
    tool_calls=[
        ChatCompletionMessageToolCall(
            id='call_adnFRLazqswLI1ky6FU2O40u',
            function=Function(
                arguments='{"location":"Hamburg","format":"celsius"}',
                name='get_current_weather'
            ),
            type='function'
        )
    ]
)

Function Calling mit der Responses API

Abschnitt betitelt „Function Calling mit der Responses API“
from openai import OpenAI


client = OpenAI()


tools = [
    {
        "type": "function",
        "name": "get_weather",
        "description": "Get current temperature for a given location.",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City and country e.g. Bogota, Colombia",
                }
            },
            "required": ["location"],
            "additionalProperties": False,
        },
    }
]


response = client.responses.create(
    model="gpt-4.1",
    input=[{"role": "user", "content": "What is the weather like in Paris today?"}],
    tools=tools,
)


print(response.output)

Strukturierte Ausgabe (JSON Schema)

Abschnitt betitelt „Strukturierte Ausgabe (JSON Schema)“

Text in ein strukturiertes Schema parsen:

import json
from openai import OpenAI


client = OpenAI()


description = """
Our Premium Laptop Backpack features padded compartments that fit
laptops up to 15.6 inches. Available in navy blue, black, and gray.
Price: $79.99, on sale for $64.99.
"""


response = client.responses.create(
    model="gpt-4.1",
    input=f"Extract structured product information: {description}",
    text={
        "format": {
            "type": "json_schema",
            "name": "product_details",
            "schema": {
                "type": "object",
                "properties": {
                    "product_name": {"type": "string"},
                    "features": {"type": "array", "items": {"type": "string"}},
                    "colors": {"type": "array", "items": {"type": "string"}},
                    "pricing": {
                        "type": "object",
                        "properties": {
                            "regular_price": {"type": "number"},
                            "sale_price": {"type": "number"},
                            "currency": {"type": "string"},
                        },
                        "required": ["regular_price", "sale_price", "currency"],
                        "additionalProperties": False,
                    },
                },
                "required": ["product_name", "features", "colors", "pricing"],
                "additionalProperties": False,
            },
            "strict": True,
        }
    },
)


print(json.dumps(json.loads(response.output[0].content[0].text), indent=2))

Kompatibilität

Abschnitt betitelt „Kompatibilität“

Nächste Schritte

Abschnitt betitelt „Nächste Schritte“