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概述

函数调用（也称为工具使用）允许 AI 模型：
  • 调用外部 API - 获取实时数据
  • 执行函数 - 执行计算或操作
  • 访问数据库 - 查询和检索信息
  • 与系统交互 - 控制外部服务
  • 链接操作 - 构建复杂的多步骤工作流程
并行函数调用：MegaLLM 支持同时执行多个函数以提高性能。

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工作原理

1

定义工具

使用 JSON Schema 描述指定可用函数
2

AI 决策

模型根据上下文确定何时以及如何使用工具
3

执行函数

您的应用程序执行请求的函数
4

返回结果

将函数结果发送回 AI
5

AI 响应

模型将结果整合到最终响应中

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工具定义格式

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OpenAI 格式

{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "City and state, e.g. San Francisco, CA"
        },
        "unit": {
          "type": "string",
          "enum": ["celsius", "fahrenheit"],
          "description": "Temperature unit"
        }
      },
      "required": ["location"]
    }
  }
}

​
Anthropic 格式

{
  "name": "get_weather",
  "description": "Get the current weather in a given location",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City and state, e.g. San Francisco, CA"
      }
    },
    "required": ["location"]
  }
}

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请求参数

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OpenAI 格式

参数类型描述
toolsarray工具定义数组
tool_choicestring/object控制工具使用：auto、required、none 或特定函数

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工具选择选项

# 让 AI 决定（默认）
tool_choice="auto"

# 强制 AI 调用函数
tool_choice="required"

# 阻止函数调用
tool_choice="none"

# 强制特定函数
tool_choice={
    "type": "function",
    "function": {"name": "get_weather"}
}

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示例

  • Python
  • JavaScript
  • TypeScript
from openai import OpenAI
import json

client = OpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-api-key"
)

# Define your function
def get_weather(location: str, unit: str = "celsius"):
    """Get current weather (simulated)"""
    return {
        "location": location,
        "temperature": 22,
        "unit": unit,
        "condition": "sunny"
    }

# Define tools for AI
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Initial request
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "What's the weather in London?"}
    ],
    tools=tools,
    tool_choice="auto"
)

message = response.choices[0].message

# Check if AI wants to call a function
if message.tool_calls:
    # Execute the function
    for tool_call in message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        if function_name == "get_weather":
            result = get_weather(**function_args)

            # Send result back to AI
            follow_up = client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "user", "content": "What's the weather in London?"},
                    message,
                    {
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": json.dumps(result)
                    }
                ]
            )

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

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并行函数调用

同时执行多个函数： 
# AI can call multiple functions at once
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Compare weather in London, Paris, and Tokyo"}
    ],
    tools=tools
)

# Process multiple tool calls in parallel
if response.choices[0].message.tool_calls:
    import asyncio

    async def execute_tool(tool_call):
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        result = await async_function_registry[function_name](**function_args)

        return {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result)
        }

    # Execute all functions in parallel
    tool_results = await asyncio.gather(
        *[execute_tool(tc) for tc in response.choices[0].message.tool_calls]
    )

​
高级模式

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函数链

构建复杂的工作流程： 
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_products",
            "description": "Search for products",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "check_inventory",
            "description": "Check product inventory",
            "parameters": {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string"}
                },
                "required": ["product_id"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "place_order",
            "description": "Place an order",
            "parameters": {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string"},
                    "quantity": {"type": "integer"}
                },
                "required": ["product_id", "quantity"]
            }
        }
    }
]

# AI will chain functions to complete complex tasks
# Example: "Find blue t-shirts and order 2 if in stock"
# 1. search_products(query="blue t-shirt")
# 2. check_inventory(product_id="...")
# 3. place_order(product_id="...", quantity=2)

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错误处理

def safe_function_call(function_name, arguments):
    try:
        result = function_registry[function_name](**arguments)
        return {"success": True, "result": result}
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "error_type": type(e).__name__
        }

# In tool response
for tool_call in message.tool_calls:
    function_name = tool_call.function.name
    function_args = json.loads(tool_call.function.arguments)

    result = safe_function_call(function_name, function_args)

    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps(result)
    })

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流式传输中的函数

stream = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    stream=True
)

function_call = None

for chunk in stream:
    delta = chunk.choices[0].delta

    if delta.tool_calls:
        if function_call is None:
            function_call = {"id": "", "name": "", "arguments": ""}

        tool_call = delta.tool_calls[0]
        if tool_call.id:
            function_call["id"] = tool_call.id
        if tool_call.function.name:
            function_call["name"] = tool_call.function.name
        if tool_call.function.arguments:
            function_call["arguments"] += tool_call.function.arguments

    if chunk.choices[0].finish_reason == "tool_calls":
        # Execute function
        result = execute_function(function_call)

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实际应用场景

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数据库查询助手

{
  "type": "function",
  "function": {
    "name": "execute_sql",
    "description": "Execute a SQL query",
    "parameters": {
      "type": "object",
      "properties": {
        "query": {"type": "string"},
        "database": {
          "type": "string",
          "enum": ["users", "products", "orders"]
        }
      },
      "required": ["query", "database"]
    }
  }
}

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API 集成代理

{
  "type": "function",
  "function": {
    "name": "call_api",
    "description": "Make an HTTP API call",
    "parameters": {
      "type": "object",
      "properties": {
        "method": {
          "type": "string",
          "enum": ["GET", "POST", "PUT", "DELETE"]
        },
        "url": {"type": "string"},
        "headers": {"type": "object"},
        "body": {"type": "object"}
      },
      "required": ["method", "url"]
    }
  }
}

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文件系统操作

{
  "type": "function",
  "function": {
    "name": "read_file",
    "description": "Read file contents",
    "parameters": {
      "type": "object",
      "properties": {
        "path": {"type": "string"}
      },
      "required": ["path"]
    }
  }
}

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最佳实践

编写清晰、详细的函数描述。AI 使用这些来决定何时以及如何调用函数。
  1. 清晰的描述 - 具体说明每个函数的功能
  2. 验证输入 - 执行前始终验证函数参数
  3. 优雅地处理错误 - 将错误信息返回给 AI
  4. 使用类型提示 - 利用 TypeScript/Python 类型确保安全
  5. 速率限制 - 为外部 API 调用实现速率限制
  6. 安全性 - 验证和清理所有函数输入
  7. 异步执行 - 使用 async/await 以获得更好的性能

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响应格式

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工具调用对象（OpenAI）

{
  "id": "call_abc123",
  "type": "function",
  "function": {
    "name": "get_weather",
    "arguments": "{\"location\": \"London\", \"unit\": \"celsius\"}"
  }
}

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工具响应格式

{
  "role": "tool",
  "tool_call_id": "call_abc123",
  "content": "{\"temperature\": 22, \"condition\": \"sunny\"}"
}

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模型支持

这些模型支持函数调用：
  • OpenAI: gpt-4, gpt-4-turbo, gpt-3.5-turbo
  • Anthropic: claude-3.5-sonnet, claude-opus-4, claude-sonnet-4
  • 其他：查看模型页面获取完整列表

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