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"""
五个基础工具中的「非工具库」部分:web_search / read_webpage / code_interpreter。
设计原则(对应补充案例“最小预定义,最大自我进化”):
- 这里 **不包含任何领域工具**(没有 get_stock_price、没有 get_youtube_transcript ...)。
- Agent 只能靠 web_search 找开源库/APIread_webpage 读文档,
code_interpreter 在子进程沙箱里真实执行代码来验证方案是否可行。
- 所有输出都基于「真实网络结果 / 真实执行结果」,从而抑制大模型的幻觉。
"""
import json
import os
import subprocess
import sys
import tempfile
import time
from pathlib import Path
import requests
from bs4 import BeautifulSoup
# 沙箱内 pip install --target 的目标目录:安装的第三方包会持久化到这里,
# 后续被封装的工具在同一个 PYTHONPATH 下也能直接 import 使用。
PROJECT_DIR = Path(__file__).resolve().parent
SANDBOX_PKG_DIR = PROJECT_DIR / ".sandbox_packages"
# --------------------------------------------------------------------------- #
# 工具 1web_search —— DuckDuckGo(无需 API key
# --------------------------------------------------------------------------- #
def web_search(query: str, num_results: int = 6) -> dict:
"""
使用 DuckDuckGo 进行网页搜索(免费、无需 key)。
实现要点(参考 chapter4/perception-tools 的风格):
- 主用 lite.duckduckgo.com(返回更稳定、不易被限流);
- 备用 html.duckduckgo.com
- 带指数退避重试,DDG 偶发返回 202(限流)时自动重试,避免「网络抖动即失败」。
"""
query = (query or "").strip()
if not query:
return {"success": False, "error": "search query is empty", "results": []}
try: # 模型可能传 null 或非数字字符串,兜底为默认值
num_results = max(1, min(int(num_results or 6), 10))
except (TypeError, ValueError):
num_results = 6
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
"AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15"
)
}
last_err = None
# 两个端点各重试若干次
for endpoint in ("https://lite.duckduckgo.com/lite/", "https://html.duckduckgo.com/html/"):
for attempt in range(3):
try:
resp = requests.post(
endpoint, data={"q": query, "kl": "wt-wt"}, headers=headers, timeout=15
)
if resp.status_code == 202: # DDG 限流信号
raise RuntimeError("rate limited (202)")
resp.raise_for_status()
results = _parse_ddg(endpoint, resp.text, num_results)
if results:
return {"success": True, "query": query, "count": len(results), "results": results}
last_err = "no results parsed"
except Exception as e: # noqa: BLE001
last_err = str(e)
time.sleep(1.5 * (attempt + 1)) # 退避
return {"success": False, "error": f"search failed: {last_err}", "results": []}
def _parse_ddg(endpoint: str, html: str, num_results: int) -> list:
"""解析 DuckDuckGo 的两种页面结构。"""
soup = BeautifulSoup(html, "html.parser")
results = []
if "html.duckduckgo" in endpoint:
for div in soup.find_all("div", class_="result")[:num_results]:
a = div.find("a", class_="result__a")
if not a:
continue
snip = div.find("a", class_="result__snippet")
results.append(
{
"title": a.get_text(strip=True),
"url": a.get("href", ""),
"snippet": snip.get_text(strip=True) if snip else "",
}
)
else: # lite 版:结果是普通 <a href="http...">
for a in soup.find_all("a"):
href = a.get("href", "")
text = a.get_text(strip=True)
if href.startswith("http") and text:
results.append({"title": text, "url": href, "snippet": ""})
if len(results) >= num_results:
break
return results
# --------------------------------------------------------------------------- #
# 工具 2read_webpage —— 抓取网页并抽取正文
# --------------------------------------------------------------------------- #
def read_webpage(url: str, max_chars: int = 6000) -> dict:
"""抓取网页并抽取纯文本正文,供 Agent 阅读 README / API 文档。"""
if not url or not url.startswith(("http://", "https://")):
return {"success": False, "error": "invalid url"}
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
"AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15"
)
}
try:
resp = requests.get(url, headers=headers, timeout=20)
resp.raise_for_status()
except Exception as e: # noqa: BLE001
return {"success": False, "error": f"fetch failed: {e}", "url": url}
soup = BeautifulSoup(resp.text, "html.parser")
for tag in soup(["script", "style", "noscript", "nav", "footer", "header"]):
tag.decompose()
text = "\n".join(line.strip() for line in soup.get_text("\n").splitlines() if line.strip())
truncated = len(text) > max_chars
return {
"success": True,
"url": url,
"title": soup.title.get_text(strip=True) if soup.title else "",
"text": text[:max_chars],
"truncated": truncated,
}
# --------------------------------------------------------------------------- #
# 工具 3code_interpreter —— 子进程沙箱执行 Python
# --------------------------------------------------------------------------- #
def code_interpreter(code: str, pip_install: list | None = None, timeout: int = 60) -> dict:
"""
在 **独立子进程** 中执行 Python 代码(沙箱),用于验证从网上找到的库 / API。
- pip_install: 需要先安装的第三方包列表;安装到临时目录 .sandbox_packages--target),
不污染系统环境,并通过 PYTHONPATH 让子进程可 import。
- timeout: 超时强制终止,避免死循环 / 挂起。
安全边界提醒:这是「演示级」沙箱(仅进程隔离 + 超时),不是安全沙箱。
生产环境请使用容器 / gVisor / 无网络命名空间等强隔离,并审计要安装的包(供应链风险)。
"""
SANDBOX_PKG_DIR.mkdir(exist_ok=True)
logs = []
# 子进程环境:把沙箱包目录加入 PYTHONPATH(系统 site-packages 仍可用)
env = os.environ.copy()
env["PYTHONPATH"] = str(SANDBOX_PKG_DIR) + os.pathsep + env.get("PYTHONPATH", "")
# 1) 按需 pip install --target
if pip_install:
for pkg in pip_install:
try:
r = subprocess.run(
[sys.executable, "-m", "pip", "install", "--quiet",
"--target", str(SANDBOX_PKG_DIR), pkg],
capture_output=True, text=True, timeout=180, env=env,
)
if r.returncode != 0:
logs.append(f"[pip install {pkg}] FAILED: {r.stderr.strip()[-500:]}")
else:
logs.append(f"[pip install {pkg}] ok")
except Exception as e: # noqa: BLE001
logs.append(f"[pip install {pkg}] error: {e}")
# 2) 执行代码
with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False, dir=SANDBOX_PKG_DIR) as f:
f.write(code)
script = f.name
try:
r = subprocess.run(
[sys.executable, script],
capture_output=True, text=True, timeout=timeout, env=env,
)
out = r.stdout[-8000:]
result = {
"success": r.returncode == 0,
"stdout": out,
"stderr": r.stderr[-4000:],
"returncode": r.returncode,
"pip_logs": logs,
}
# 提醒模型:跑通但没有任何 print 输出 = 没有拿到真实数据,不能据此作答或封装工具。
if r.returncode == 0 and not out.strip():
result["note"] = (
"代码执行成功但 stdout 为空——你没有打印出任何真实数据。"
"这不算验证通过:请修改代码,真正调用库并 print 出真实数字。"
)
return result
except subprocess.TimeoutExpired:
return {"success": False, "error": f"timeout after {timeout}s", "pip_logs": logs}
finally:
try:
os.unlink(script)
except OSError:
pass
def run_python_snippet(code: str, timeout: int = 60) -> dict:
"""供 tool_manager 复用:在同一沙箱环境执行一段脚本并返回结果(不做 pip)。"""
return code_interpreter(code, pip_install=None, timeout=timeout)