AI助手和AI智能体不是同一物种——Perplexity与哈佛的对比实验揭示了什么

"""
minimal_agent_orchestrator.py
一个极简AI智能体编排器：搜索→分析→交付成品
依赖：pip install openai matplotlib
运行前设置：export OPENAI_API_KEY="sk-..."
"""

import openai
import matplotlib.pyplot as plt
import json, os, re

client = openai.OpenAI()

# ---------- 工具定义 ----------
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "web_search",
            "description": "模拟网页搜索，返回关键词相关的摘要数据",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "搜索关键词"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "save_chart",
            "description": "将数据保存为柱状图PNG文件",
            "parameters": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "labels": {"type": "array", "items": {"type": "string"}},
                    "values": {"type": "array", "items": {"type": "number"}},
                    "filename": {"type": "string"}
                },
                "required": ["title", "labels", "values", "filename"]
            }
        }
    }
]

# ---------- 工具实现 ----------
def web_search(query: str) -> str:
    """模拟搜索——实际生产中替换为真实搜索API"""
    # 这里用硬编码数据演示；生产环境接入 Perplexity API / SerpAPI 等
    mock_data = {
        "毛利率": {"公司A": [32, 34, 31, 36], "公司B": [28, 30, 29, 33], "公司C": [41, 39, 42, 44]},
        "营收增速": {"公司A": [12, 15, 8, 18], "公司B": [20, 22, 19, 25], "公司C": [5, 7, 6, 9]}
    }
    for key, val in mock_data.items():
        if key in query:
            return json.dumps(val)
    return json.dumps(mock_data["毛利率"])

def save_chart(title: str, labels: list, values: list, filename: str) -> str:
    """生成柱状图并保存为PNG"""
    plt.figure(figsize=(8, 5))
    plt.bar(labels, values, color="#4C78A8")
    plt.title(title)
    plt.ylabel("百分比 (%)")
    plt.tight_layout()
    filepath = os.path.join(os.getcwd(), filename)
    plt.savefig(filepath)
    plt.close()
    return f"图表已保存至 {filepath}"

# ---------- 工具路由 ----------
tool_map = {"web_search": web_search, "save_chart": save_chart}

# ---------- 智能体主循环 ----------
def run_agent(user_request: str, max_steps: int = 5) -> str:
    messages = [{"role": "user", "content": user_request}]
    for _ in range(max_steps):
        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=TOOLS,
            tool_choice="auto"
        )
        msg = resp.choices[0].message

        # 如果模型直接给出最终文本回复，闭环结束
        if not msg.tool_calls:
            return msg.content

        # 否则执行工具调用，把结果喂回模型继续规划
        messages.append(msg)
        for tc in msg.tool_calls:
            fn_name = tc.function.name
            fn_args = json.loads(tc.function.arguments)
            result = tool_map[fn_name](**fn_args)
            messages.append({
                "role": "tool",
                "tool_call_id": tc.id,
                "content": result
            })

    return "达到最大步数限制，任务未完全完成。"

# ---------- 运行 ----------
if __name__ == "__main__":
    # 对比：助手模式只会给你文字建议；智能体模式直接交付文件
    request = "对比公司A、B、C最近四个季度的毛利率，生成柱状图保存为 margin_chart.png"
    final_output = run_agent(request)
    print(final_output)