函数调用

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from openai import OpenAI
import json

client = OpenAI()

# 1. Define a list of callable tools for the model
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_horoscope",
            "description": "Get today's horoscope for an astrological sign.",
            "parameters": {
                "type": "object",
                "properties": {
                    "sign": {
                        "type": "string",
                        "description": "An astrological sign like Taurus or Aquarius",
                    },
                },
                "required": ["sign"],
                "additionalProperties": False,
            },
            "strict": True,
        },
    },
]

def get_horoscope(sign):
    return f"{sign}: Next Tuesday you will befriend a baby otter."

messages = [
    {"role": "user", "content": "What is my horoscope? I am an Aquarius."}
]

# 2. Prompt the model with tools defined
response = client.chat.completions.create(
    model="gpt-4.1",
    messages=messages,
    tools=tools,
)

messages.append(response.choices[0].message)

for tool_call in response.choices[0].message.tool_calls or []:
    if tool_call.function.name == "get_horoscope":
        # 3. Execute the function logic for get_horoscope
        args = json.loads(tool_call.function.arguments)
        horoscope = get_horoscope(args["sign"])

        # 4. Provide function call results to the model
        messages.append(
            {
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps({"horoscope": horoscope}),
            }
        )

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

# 5. The model should be able to give a response!
print(response.choices[0].message.content)

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from openai import OpenAI
import json

client = OpenAI()

# 1. Define a list of callable tools for the model
tools = [
    {
        "type": "function",
        "name": "get_horoscope",
        "description": "Get today's horoscope for an astrological sign.",
        "parameters": {
            "type": "object",
            "properties": {
                "sign": {
                    "type": "string",
                    "description": "An astrological sign like Taurus or Aquarius",
                },
            },
            "required": ["sign"],
        },
    },
]

def get_horoscope(sign):
    return f"{sign}: Next Tuesday you will befriend a baby otter."

# Create a running input list we will add to over time
input_list = [
    {"role": "user", "content": "What is my horoscope? I am an Aquarius."}
]

# 2. Prompt the model with tools defined
response = client.responses.create(
    model="gpt-5",
    tools=tools,
    input=input_list,
)

# Save function call outputs for subsequent requests
input_list += response.output

for item in response.output:
    if item.type == "function_call":
        if item.name == "get_horoscope":
            # 3. Execute the function logic for get_horoscope
            sign = json.loads(item.arguments)["sign"]
            horoscope = get_horoscope(sign)
            
            # 4. Provide function call results to the model
            input_list.append({
                "type": "function_call_output",
                "call_id": item.call_id,
                "output": horoscope,
            })

print("Final input:")
print(input_list)

response = client.responses.create(
    model="gpt-5",
    instructions="Respond only with a horoscope generated by a tool.",
    tools=tools,
    input=input_list,
)

# 5. The model should be able to give a response!
print("Final output:")
print(response.model_dump_json(indent=2))
print("\n" + response.output_text)

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for tool_call in completion.choices[0].message.tool_calls:
    name = tool_call.function.name
    args = json.loads(tool_call.function.arguments)

    result = call_function(name, args)
    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": str(result)
    })

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for tool_call in response.output:
    if tool_call.type != "function_call":
        continue

    name = tool_call.name
    args = json.loads(tool_call.arguments)

    result = call_function(name, args)
    input_messages.append({
        "type": "function_call_output",
        "call_id": tool_call.call_id,
        "output": str(result)
    })

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

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response = client.responses.create(
    model="gpt-4.1",
    input=input_messages,
    tools=tools,
)

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from openai import OpenAI

client = OpenAI()

tools = [{
    "type": "function",
    "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. Bogotá, Colombia"
                }
            },
            "required": ["location"],
            "additionalProperties": False
        },
        "strict": True
    }
}]

stream = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "What's the weather like in Paris today?"}],
    tools=tools,
    stream=True
)

for chunk in stream:
    delta = chunk.choices[0].delta
    print(delta.tool_calls)

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final_tool_calls = {}

for chunk in stream:
    for tool_call in chunk.choices[0].delta.tool_calls or []:
        index = tool_call.index

        if index not in final_tool_calls:
            final_tool_calls[index] = tool_call

        final_tool_calls[index].function.arguments += tool_call.function.arguments

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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. Bogotá, Colombia"
            }
        },
        "required": [
            "location"
        ],
        "additionalProperties": False
    }
}]

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

for event in stream:
    print(event)

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final_tool_calls = {}

for event in stream:
    if event.type === 'response.output_item.added':
        final_tool_calls[event.output_index] = event.item;
    elif event.type === 'response.function_call_arguments.delta':
        index = event.output_index

        if final_tool_calls[index]:
            final_tool_calls[index].arguments += event.delta