用 Python 搭建个人 AI 助手：从入门到实战的完整教程

pip install openai python-dotenv requests beautifulsoup4

import os
from dotenv import load_dotenv

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
MODEL = "gpt-4o-mini"  # 便宜又快，适合日常使用
MAX_TOKENS = 2000
TEMPERATURE = 0.7

import subprocess
import requests
from bs4 import BeautifulSoup
import json

def run_code(code: str, language: str = "python") -> str:
    """执行代码并返回结果"""
    try:
        result = subprocess.run(
            ["python3", "-c", code],
            capture_output=True,
            text=True,
            timeout=30
        )
        return result.stdout or result.stderr
    except subprocess.TimeoutExpired:
        return "错误：代码执行超时（超过30秒）"
    except Exception as e:
        return f"错误：{str(e)}"

def web_search(query: str, max_results: int = 5) -> list:
    """简单的网页搜索功能"""
    url = f"https://html.duckduckgo.com/html/?q={requests.utils.quote(query)}"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
    }
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, "html.parser")
    
    results = []
    for a in soup.select("a.result__a")[:max_results]:
        title = a.text
        link = a["href"]
        results.append({"title": title, "link": link})
    return results

def read_file(filepath: str) -> str:
    """读取本地文件内容"""
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            return f.read()
    except FileNotFoundError:
        return f"错误：找不到文件 {filepath}"
    except Exception as e:
        return f"错误：{str(e)}"

from openai import OpenAI
from tools import run_code, web_search, read_file
from config import OPENAI_API_KEY, MODEL, MAX_TOKENS, TEMPERATURE

client = OpenAI(api_key=OPENAI_API_KEY)

# 定义系统提示词，告诉 AI 它能做什么
SYSTEM_PROMPT = """你是一个实用的个人AI助手。你可以使用以下工具帮助用户：

1. 执行代码 - 当用户需要计算、数据处理或脚本运行时
2. 搜索网页 - 当用户需要最新信息时
3. 读取文件 - 当用户需要分析本地文件时

请用简洁的语言回答问题。回答中适当使用 Markdown 格式。"""

# 初始消息列表
messages = [
    {"role": "system", "content": SYSTEM_PROMPT}
]

print("=== 个人AI助手 ===")
print("输入 'quit' 退出")
print("可用命令：")
print("  @code <代码>  - 执行代码")
print("  @search <关键词>  - 搜索网页")
print("  @file <路径>  - 读取文件")
print()

while True:
    user_input = input("你: ").strip()
    
    if not user_input:
        continue
    
    if user_input.lower() == "quit":
        print("再见！")
        break
    
    # 检查特殊命令
    if user_input.startswith("@code"):
        code = user_input[5:].strip()
        result = run_code(code)
        print(f"\n助手: 代码执行结果：\n{result}")
        continue
    
    if user_input.startswith("@search"):
        query = user_input[7:].strip()
        results = web_search(query)
        output = "搜索结果：\n"
        for i, r in enumerate(results, 1):
            output += f"{i}. {r['title']}\n   {r['link']}\n"
        print(f"\n助手: {output}")
        continue
    
    if user_input.startswith("@file"):
        filepath = user_input[5:].strip()
        content = read_file(filepath)
        print(f"\n助手: {content}")
        continue
    
    # 正常对话
    messages.append({"role": "user", "content": user_input})
    
    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        max_tokens=MAX_TOKENS,
        temperature=TEMPERATURE
    )
    
    assistant_reply = response.choices[0].message.content
    messages.append({"role": "assistant", "content": assistant_reply})
    
    print(f"\n助手: {assistant_reply}\n")

def send_email(to: str, subject: str, body: str):
    """发送邮件（需要配置 SMTP）"""
    # 使用 smtplib 实现
    pass

def generate_image(prompt: str):
    """生成图片（调用 DALL-E API）"""
    response = client.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1024"
    )
    return response.data[0].url

import json
from datetime import datetime

MEMORY_FILE = "memory.json"

def save_memory(messages):
    """保存对话历史"""
    with open(MEMORY_FILE, "w", encoding="utf-8") as f:
        json.dump(messages, f, ensure_ascii=False)

def load_memory():
    """加载对话历史"""
    try:
        with open(MEMORY_FILE, "r", encoding="utf-8") as f:
            return json.load(f)
    except FileNotFoundError:
        return [{"role": "system", "content": SYSTEM_PROMPT}]