LLM Agent：从聊天机器人到智能助手的进化

让LLM调用外部函数

import json
from typing import List, Dict, Any

class FunctionCallingAgent:
    """基于Function Calling的Agent"""
    
    def __init__(self, api_key: str):
        self.client = OpenAI(api_key=api_key)
        self.functions = []
        self.function_map = {}
    
    def register_function(self, func, description: str, parameters: Dict):
        """注册可调用函数"""
        function_spec = {
            "name": func.__name__,
            "description": description,
            "parameters": parameters
        }
        
        self.functions.append(function_spec)
        self.function_map[func.__name__] = func
    
    def run(self, user_input: str):
        """执行用户请求"""
        messages = [
            {"role": "system", "content": "你是一个智能助手"},
            {"role": "user", "content": user_input}
        ]
        
        # 第一次调用：决定是否需要使用函数
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=messages,
            functions=self.functions,
            function_call="auto"
        )
        
        message = response.choices[0].message
        
        # 检查是否需要调用函数
        if message.function_call:
            # 解析函数调用
            function_name = message.function_call.name
            function_args = json.loads(message.function_call.arguments)
            
            # 执行函数
            if function_name in self.function_map:
                function_result = self.function_map[function_name](**function_args)
                
                # 将函数结果添加到对话
                messages.append(message)
                messages.append({
                    "role": "function",
                    "name": function_name,
                    "content": str(function_result)
                })
                
                # 第二次调用：基于函数结果生成最终回复
                final_response = self.client.chat.completions.create(
                    model="gpt-4",
                    messages=messages
                )
                
                return final_response.choices[0].message.content
        
        return message.content

# 定义工具函数
def get_weather(location: str, unit: str = "celsius"):
    """获取天气信息"""
    # 实际应调用天气API
    return f"{location}的天气是晴天，温度是25{unit}"

def calculate(expression: str):
    """计算数学表达式"""
    try:
        result = eval(expression)
        return f"计算结果：{result}"
    except:
        return "计算错误"

def search_database(query: str, table: str):
    """查询数据库"""
    # 实际应执行数据库查询
    return f"从{table}表查询'{query}'的结果：..."

# 创建Agent并注册函数
agent = FunctionCallingAgent(api_key="your-key")

agent.register_function(
    get_weather,
    "获取指定地点的天气信息",
    {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "地点名称"
            },
            "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "温度单位"
            }
        },
        "required": ["location"]
    }
)

agent.register_function(
    calculate,
    "计算数学表达式",
    {
        "type": "object",
        "properties": {
            "expression": {
                "type": "string",
                "description": "数学表达式"
            }
        },
        "required": ["expression"]
    }
)

# 使用Agent
result = agent.run("北京的天气怎么样？")
print(result)  # 自动调用get_weather函数

result = agent.run("计算 123 * 456 + 789")
print(result)  # 自动调用calculate函数