openai funciton calling使用

tool_choice 的默认行为是 tool_choice: "auto" 。这让模型决定是否调用函数，如果调用，则调用哪些函数。

我们提供三种方法来根据您的用例自定义默认行为：

1. 要强制模型始终调用一个或多个函数，可以设置 tool_choice: “required”。然后模型将选择要调用的函数。

2. 要强制模型仅调用一个特定函数，可以设置 tool_choice: {“type”: “function”, “function”: {“name”: “my_function”}}。

3. 要禁用函数调用并强制模型仅生成面向用户的消息，您可以设置 tool_choice: “none”。

并行函数调用是模型同时执行多个函数调用的能力，允许并行解决这些函数调用的效果和结果。如果函数需要很长时间，这尤其有用，并且可以减少 API 的往返次数。例如，模型可能会调用函数来同时获取 3 个不同位置的天气，这将导致在 tool_calls 数组中产生一条包含 3 个函数调用的消息，每个函数调用都有一个 id。要响应这些函数调用，请向对话中添加 3 条新消息，每条消息都包含一个函数调用的结果，并使用 tool_call_id 引用 tool_calls 中的 id。

在此示例中，我们定义了一个函数 get_current_weather 。模型多次调用该函数，并将函数响应发送回模型后，我们让它决定下一步。它回复了一条面向用户的消息，告诉用户旧金山、东京和巴黎的气温。根据查询，它可能会选择再次调用函数。

from openai import OpenAI
import jsonclient = OpenAI()# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):"""Get the current weather in a given location"""if "tokyo" in location.lower():return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})elif "san francisco" in location.lower():return json.dumps({"location": "San Francisco", "temperature": "72", "unit": unit})elif "paris" in location.lower():return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})else:return json.dumps({"location": location, "temperature": "unknown"})def run_conversation():# Step 1: send the conversation and available functions to the modelmessages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]tools = [{"type": "function","function": {"name": "get_current_weather","description": "Get the current weather in a given location","parameters": {"type": "object","properties": {"location": {"type": "string","description": "The city and state, e.g. San Francisco, CA",},"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},},"required": ["location"],},},}]response = client.chat.completions.create(model="gpt-3.5-turbo-0125",messages=messages,tools=tools,tool_choice="auto",  # auto is default, but we'll be explicit)response_message = response.choices[0].messagetool_calls = response_message.tool_calls# Step 2: check if the model wanted to call a functionif tool_calls:# Step 3: call the function# Note: the JSON response may not always be valid; be sure to handle errorsavailable_functions = {"get_current_weather": get_current_weather,}  # only one function in this example, but you can have multiplemessages.append(response_message)  # extend conversation with assistant's reply# Step 4: send the info for each function call and function response to the modelfor tool_call in tool_calls:function_name = tool_call.function.namefunction_to_call = available_functions[function_name]function_args = json.loads(tool_call.function.arguments)function_response = function_to_call(location=function_args.get("location"),unit=function_args.get("unit"),)messages.append({"tool_call_id": tool_call.id,"role": "tool","name": function_name,"content": function_response,})  # extend conversation with function responsesecond_response = client.chat.completions.create(model="gpt-3.5-turbo-0125",messages=messages,)  # get a new response from the model where it can see the function responsereturn second_responseprint(run_conversation())

out

daichang\.vscode\extensions\ms-python.debugpy-2024.6.0-win32-x64\bundled\libs\debugpy\adapter/../..\debugpy\launcher' '4336' '--' 'E:\OneDrive\aidev\openaidev\02FunctionCalling\get_weather.py'ChatCompletion(id='chatcmpl-9LTUxLZ36t55gdseSRMHYX8zfKv8N', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The current weather in San Francisco is 72°F, in Tokyo it is 10°C, and in Paris it is 22°C.', role='assistant', function_call=None, tool_calls=None))], created=1714905307, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint='fp_a450710239', usage=CompletionUsage(completion_tokens=28, prompt_tokens=147, total_tokens=175))