如何实现LLM的通用function-calling能力？

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原创最新推荐文章于 2026-09-07 13:08:15 发布 1.4k 阅读 AI 写同款· GEO 优化 ›

SenseNova 商汤日日新大模型 Function Call（函数调用）功能讲解和应用示例在 LLM（Large Language Model）语言大模型时代，Function Call（函数调用）是指可靠地连接LLM 与外部工具的能力。让用户能够使用高效的外部工具、与外部API进行交互。GPT-4 和国内 SenseNova 等经过微调的 LLM，能够检测函数是否被调用，随后输出包含调用函数参数的 JSON。通过这一过程被调用的函数能够作为工具添加到您的 AI 应用中，并且您可以在单个请求中定义多个函数。函数调用是一项重要能力。它对于构建 LLM 驱动的聊天机器人或代理至关重要。这些聊天机器阅读详情

众所周知，LLM的函数function-calling能力很强悍，解决了大模型与实际业务系统的交互问题。其本质就是函数调用。

从openai官网摘图：

简而言之：

LLM起到决策的作用，告知业务系统应该调用什么函数，以及入参是什么。
业务系统负责实现对应的函数（比如本地实现，或者调用其他系统提供的服务），并且将函数的响应结果再次抛给LLM。
LLM根据响应结果，组织自然语言，继续与业务系统进行交互。

在这里，有很多小伙伴会有一个误区：误以为函数调用是有LLM本身执行的。其实，LLM仅仅做决策，而实际的调用是由业务系统完成的。

现阶段，function-calling能力的实现有两种主流方式：

LLM本身支持。
利用Prompt模板实现，典型如ReAct模板。

在实际的应用过程中，我们还要解决另一个重要问题：

function-calling触发机制是怎样的？也即：何时要使用function-calling能力，何时不应该使用？

这个问题的处理方式，对于整体流程的运行至关重要。

此时，我们可以使用特定Prompt来解决该问题：

You have access to the following tools:  
{json.dumps(tools)}  
You can select one of the above tools or just response user's content and respond with only a JSON object matching the following schema:  
{{  
  "tool": <name of the selected tool>,  
  "tool_input": <parameters for the selected tool, matching the tool'  
s JSON schema>,  
  "message": <direct response users content>}

该Prompt告知了LLM：如果需要使用function-calling能力，那么就从tools（tools是预定义的functions）中选取一个最匹配的函数；如果不需要，就用自然语言与用户交互，此时与正常的对话流程无异。输出的格式固定为json，方便解析。

由此，我们受到启发：只要LLM基座够强（能够严格遵循Prompt响应诉求），即使LLM本身不支持function-calling，我们也可以自己实现function-calling，脱离对特定LLM的依赖！

拿到function-calling的结果后，若要用自然语言的形式输出结果，还要再调用一次LLM，对结果进行整合。此时可以使用另一个Prompt：

Please generate a natural language description based on the following question and answer.  
Question: [Content of the question]  
Answer: [Content of the answer]  
Generated Description: The result of [key phrase from the question] is [answer].  
If necessary, you can polish the description.Only output the Description, with Chinese language.

该Prompt的作用就是告诉LLM，你要根据我的问题和答案，用自然语言重新描述一遍。这里指定了中文输出，可根据实际需要进行调整。

以下是一个可运行的完整Python脚本：

import requests  
import json  
import random  
  
# 预置函数定义  
tools = [  
    {  
        "name": "get_current_weather",  
        "description": "Get the current weather in a given location",  
        "parameters": {  
            "type": "object",  
            "properties": {  
                "location": {  
                    "type": "string",  
                    "description": "The city e.g. Beijing"  
                },  
                "unit": {  
                    "type": "string",  
                    "enum": [  
                        "celsius"  
                    ]  
                }  
            },  
            "required": [  
                "location"  
            ]  
        }  
    },  
    {  
        "name": "calculator",  
        "description": "计算器",  
        "parameters": {  
            "type": "int",  
            "properties": {  
                "a": {  
                    "type": "int",  
                    "description": "the first number"  
                },  
                "b": {  
                    "type": "int",  
                    "description": "the second number"  
                }  
            },  
            "required": [  
                "a",  
                "b"  
            ]  
        }  
    }  
]  
  
# 获取天气（随机返回，实际使用可以替换为api调用）  
def get_current_weather(*args):  
    # 定义可能的天气状态  
    weather_conditions = ["sunny", "cloudy", "rainy", "snowy"]  
    # 定义可能的温度范围  
    temperature_min = -10  # 最低温度，摄氏度  
    temperature_max = 35  # 最高温度，摄氏度  
    # 随机选择一个天气状态  
    condition = random.choice(weather_conditions)  
    # 随机生成一个温度  
    temperature = random.randint(temperature_min, temperature_max)  
    # 返回一个描述当前天气的字符串  
    return f"The weather of {args[0].get('location')} is {condition}, and the temperature is {temperature}°C."  
  
def calculator(args):  
    return sum(value for value in args.values() if isinstance(value, int))  
  
# 函数映射集合  
functions = {  
    "get_current_weather": get_current_weather,  
    "calculator": calculator,  
}  
  
# 驱动整体流程的入口prompt  
entrance_prompt = f"""You have access to the following tools:  
{json.dumps(tools)}  
You can select one of the above tools or just response user's content and respond with only a JSON object matching the following schema:  
{{  
  "tool": <name of the selected tool>,  
  "tool_input": <parameters for the selected tool, matching the tool's JSON schema>,  
  "message": <direct response users content>  
}}"""  
  
# 请以自然语言的形式对结果进行描述  
conformity_prompt = f"""  
Please generate a natural language description based on the following question and answer.  
Question: [Content of the question]  
Answer: [Content of the answer]  
Generated Description: The result of [key phrase from the question] is [answer].  
If necessary, you can polish the description.  
Only output the Description, with Chinese language.  
"""  
  
def extract_json(s):  
    stack = 0  
    start = s.find('{')  
    if start == -1:  
        return None  
  
    for i in range(start, len(s)):  
        if s[i] == '{':  
            stack += 1  
        elif s[i] == '}':  
            stack -= 1  
            if stack == 0:  
                return s[start:i + 1]  
    return None  
  
# 结果包装器，type为func表示是函数调用返回的结果，default表示是自然语言结果。对于func返回的结果，会用LLM再次总结  
class ResultWrapper:  
    def __init__(self, type, result):  
        self.type = type  
        self.result = result  
  
# 解析LLM返回的结果，如果有json则去解析json  
def parse_result(res):  
    json_str = extract_json(res["message"]["content"])  
    if json_str is not None:  
        obj = json.loads(json_str)  
        if "tool" in obj:  
            if obj["tool"] in functions:  
                fun = functions[obj["tool"]]  
                return ResultWrapper("func", fun(obj["tool_input"]))  
            else:  
                return ResultWrapper("default", obj["message"])  
        else:  
            return ResultWrapper("default", res["message"]["content"])  
    else:  
        return ResultWrapper("default", res["message"]["content"])  
  
def invokeLLM(messages):  
    url = "${domain}/v1/chat/completions" #需替换域名  
    model = ""  
    payload = {  
        "model": model,  
        "messages": messages,  
    }  
    payload = json.dumps(payload)  
    headers = {  
        'Content-Type': 'application/json'  
    }  
    print("PAYLOAD: ", payload)  
    response = requests.request("POST", url, headers=headers, data=payload)  
    print("RESPONSE: ", response.text)  
    print("=======================================================================")  
    resp = json.loads(response.text)  
    return resp["choices"][0]  
  
  
if __name__ == '__main__':  
    while True:  
        messages = [  
            {  
                "role": "system",  
                "content": entrance_prompt  
            }  
        ]  
        user_input = input('Enter a string: ')  
        messages.append({  
            "role": "user",  
            "content": user_input  
        })  
        result_wrapper = parse_result(invokeLLM(messages))  
        if result_wrapper.type == "func":  
            messages = [  
                {  
                    "role": "user",  
                    "content": f"{conformity_prompt}\n\nThe question:{user_input}\nThe answer:{result_wrapper.result}"  
                }  
            ]  
            print("FINAL RESULT WITH FUNCTION CALL: ", parse_result(invokeLLM(messages)).result)  
        else:            print("FINAL RESULT: ", result_wrapper.result

实验效果：

Enter a string: 你好  
PAYLOAD: {"model": "", "messages": [{"role": "system", "content": "You have access to the following tools:\n[{\"name\": \"get_current_weather\", \"description\": \"Get the current weather in a given location\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city e.g. Beijing\"}, \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\"]}}, \"required\": [\"location\"]}}, {\"name\": \"calculator\", \"description\": \"\\u8ba1\\u7b97\\u5668\", \"parameters\": {\"type\": \"int\", \"properties\": {\"a\": {\"type\": \"int\", \"description\": \"the first number\"}, \"b\": {\"type\": \"int\", \"description\": \"the second number\"}}, \"required\": [\"a\", \"b\"]}}]\nYou can select one of the above tools or just response user's content and respond with only a JSON object matching the following schema:\n{\n \"tool\": <name of the selected tool>,\n \"tool_input\": <parameters for the selected tool, matching the tool's JSON schema>,\n \"message\": <direct response users content>\n}"}, {"role": "user", "content": "\u4f60\u597d"}]}  
RESPONSE: {"model":"","object":"","choices":[{"index":0,"message":{"role":"assistant","content":"```json\n{\"tool\": null, \"tool_input\": null, \"message\": \"你好，有什么可以帮您的吗？\"}\n```","function_call":null},"finish_reason":"stop"}],"queueTime":0.0020923614501953125,"costTime":0.7685532569885254,"usage":{"prompt_token":244,"completion_token":29,"total_tokens":273}}  
=======================================================================  
FINAL RESULT: 你好，有什么可以帮您的吗？  
  
  
Enter a string: 厦门天气如何？  
PAYLOAD: {"model": "", "messages": [{"role": "system", "content": "You have access to the following tools:\n[{\"name\": \"get_current_weather\", \"description\": \"Get the current weather in a given location\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city e.g. Beijing\"}, \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\"]}}, \"required\": [\"location\"]}}, {\"name\": \"calculator\", \"description\": \"\\u8ba1\\u7b97\\u5668\", \"parameters\": {\"type\": \"int\", \"properties\": {\"a\": {\"type\": \"int\", \"description\": \"the first number\"}, \"b\": {\"type\": \"int\", \"description\": \"the second number\"}}, \"required\": [\"a\", \"b\"]}}]\nYou can select one of the above tools or just response user's content and respond with only a JSON object matching the following schema:\n{\n \"tool\": <name of the selected tool>,\n \"tool_input\": <parameters for the selected tool, matching the tool's JSON schema>,\n \"message\": <direct response users content>\n}"}, {"role": "user", "content": "\u53a6\u95e8\u5929\u6c14\u5982\u4f55\uff1f"}]}  
RESPONSE: {"model":"","object":"","choices":[{"index":0,"message":{"role":"assistant","content":"```json\n{\"tool\": \"get_current_weather\", \"tool_input\": {\"location\": \"Xiamen\", \"unit\": \"celsius\"}, \"message\": \"\"}\n```","function_call":null},"finish_reason":"stop"}],"queueTime":0.0021338462829589844,"costTime":0.9370713233947754,"usage":{"prompt_token":247,"completion_token":36,"total_tokens":283}}  
=======================================================================  
PAYLOAD: {"model": "", "messages": [{"role": "user", "content": "\nPlease generate a natural language description based on the following question and answer.\nQuestion: [Content of the question]\nAnswer: [Content of the answer]\nGenerated Description: The result of [key phrase from the question] is [answer].\nIf necessary, you can polish the description.\nOnly output the Description, with Chinese language.\n\n\nThe question:\u53a6\u95e8\u5929\u6c14\u5982\u4f55\uff1f\nThe answer:The weather of Xiamen is cloudy, and the temperature is 35\u00b0C."}]}  
RESPONSE: {"model":"","object":"","choices":[{"index":0,"message":{"role":"assistant","content":"厦门天气情况是：多云，气温35°C。","function_call":null},"finish_reason":"stop"}],"queueTime":0.008246660232543945,"costTime":0.3240656852722168,"usage":{"prompt_token":143,"completion_token":12,"total_tokens":155}}  
=======================================================================  
FINAL RESULT WITH FUNCTION CALL: 厦门天气情况是：多云，气温35°C。  
  
  
Enter a string: 383加上135721等于多少？  
PAYLOAD: {"model": "", "messages": [{"role": "system", "content": "You have access to the following tools:\n[{\"name\": \"get_current_weather\", \"description\": \"Get the current weather in a given location\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city e.g. Beijing\"}, \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\"]}}, \"required\": [\"location\"]}}, {\"name\": \"calculator\", \"description\": \"\\u8ba1\\u7b97\\u5668\", \"parameters\": {\"type\": \"int\", \"properties\": {\"a\": {\"type\": \"int\", \"description\": \"the first number\"}, \"b\": {\"type\": \"int\", \"description\": \"the second number\"}}, \"required\": [\"a\", \"b\"]}}]\nYou can select one of the above tools or just response user's content and respond with only a JSON object matching the following schema:\n{\n \"tool\": <name of the selected tool>,\n \"tool_input\": <parameters for the selected tool, matching the tool's JSON schema>,\n \"message\": <direct response users content>\n}"}, {"role": "user", "content": "383\u52a0\u4e0a135721\u7b49\u4e8e\u591a\u5c11\uff1f"}]}  
RESPONSE: {"model":"","object":"","choices":[{"index":0,"message":{"role":"assistant","content":"```json\n{\"tool\": \"calculator\", \"tool_input\": {\"a\": 383, \"b\": 135721}, \"message\": null}\n```","function_call":null},"finish_reason":"stop"}],"queueTime":0.0021514892578125,"costTime":0.9161381721496582,"usage":{"prompt_token":252,"completion_token":35,"total_tokens":287}}  
=======================================================================  
PAYLOAD: {"model": "", "messages": [{"role": "user", "content": "\nPlease generate a natural language description based on the following question and answer.\nQuestion: [Content of the question]\nAnswer: [Content of the answer]\nGenerated Description: The result of [key phrase from the question] is [answer].\nIf necessary, you can polish the description.\nOnly output the Description, with Chinese language.\n\n\nThe question:383\u52a0\u4e0a135721\u7b49\u4e8e\u591a\u5c11\uff1f\nThe answer:136104"}]}  
RESPONSE: {"model":"","object":"","choices":[{"index":0,"message":{"role":"assistant","content":"383加上135721等于136104。","function_call":null},"finish_reason":"stop"}],"queueTime":0.0064160823822021484,"costTime":0.28981900215148926,"usage":{"prompt_token":134,"completion_token":11,"total_tokens":145}}  
=======================================================================  
FINAL RESULT WITH FUNCTION CALL: 383加上135721等于136104。

在这个例子中，预置了两个函数，分别为天气查询和计算器，实验效果中进行了三轮，其中第一次属于未命中函数调用的闲聊场景，后两次分别命中了天气查询和计算器。

在实际的工作中，可能需要预置非常多函数能力，此时可能需要考虑到LLM的输入token限制，必要时需要进行模块划分，将一次LLM决策转化为多次决策，更通用一点的说法就是意图层级识别。

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