1ba5f0f7d6
- Updated .env.example to include API key placeholder and configuration instructions. - Refactored main.py to support streaming responses from the LLM, improving user experience during chat interactions. - Enhanced LLMClient to include methods for streaming chat and collecting responses. - Modified safety review process to pass static analysis warnings to the LLM for better code safety evaluation. - Improved UI components in chat_view.py to handle streaming messages effectively.
141 lines
4.1 KiB
Python
141 lines
4.1 KiB
Python
"""
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LLM 软规则审查器
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使用 LLM 进行代码安全审查
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"""
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import os
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import json
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from typing import Optional, List
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from dataclasses import dataclass
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from dotenv import load_dotenv
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from llm.client import get_client, LLMClientError, ENV_PATH
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from llm.prompts import SAFETY_REVIEW_SYSTEM, SAFETY_REVIEW_USER
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@dataclass
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class LLMReviewResult:
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"""LLM 审查结果"""
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passed: bool
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reason: str
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raw_response: Optional[str] = None
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class LLMReviewer:
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"""
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LLM 安全审查器
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使用大模型对代码进行语义级别的安全审查
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"""
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def __init__(self):
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load_dotenv(ENV_PATH)
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self.model_name = os.getenv("GENERATION_MODEL_NAME")
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def review(
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self,
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user_input: str,
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execution_plan: str,
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code: str,
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warnings: Optional[List[str]] = None
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) -> LLMReviewResult:
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"""
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审查代码安全性
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Args:
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user_input: 用户原始需求
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execution_plan: 执行计划
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code: 待审查的代码
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warnings: 静态检查产生的警告列表
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Returns:
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LLMReviewResult: 审查结果
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"""
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try:
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client = get_client()
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# 构建警告信息
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warning_text = ""
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if warnings and len(warnings) > 0:
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warning_text = "\n\n【静态检查警告】请重点审查以下内容：\n" + "\n".join(f"- {w}" for w in warnings)
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messages = [
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{"role": "system", "content": SAFETY_REVIEW_SYSTEM},
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{"role": "user", "content": SAFETY_REVIEW_USER.format(
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user_input=user_input,
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execution_plan=execution_plan,
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code=code
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) + warning_text}
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]
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response = client.chat(
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messages=messages,
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model=self.model_name,
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temperature=0.1,
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max_tokens=512,
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timeout=120
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)
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return self._parse_response(response)
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except LLMClientError as e:
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# LLM 调用失败，保守起见判定为不通过
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return LLMReviewResult(
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passed=False,
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reason=f"安全审查失败（{str(e)}），出于安全考虑拒绝执行"
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)
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except Exception as e:
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return LLMReviewResult(
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passed=False,
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reason=f"安全审查异常（{str(e)}），出于安全考虑拒绝执行"
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)
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def _parse_response(self, response: str) -> LLMReviewResult:
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"""解析 LLM 响应"""
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try:
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# 提取 JSON
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json_str = self._extract_json(response)
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data = json.loads(json_str)
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passed = data.get("pass", False)
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reason = data.get("reason", "未提供原因")
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# 确保 passed 是布尔值
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if isinstance(passed, str):
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passed = passed.lower() in ('true', 'yes', '1', 'pass')
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return LLMReviewResult(
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passed=bool(passed),
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reason=reason,
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raw_response=response
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)
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except (json.JSONDecodeError, ValueError, TypeError):
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# 解析失败，保守判定
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return LLMReviewResult(
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passed=False,
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reason=f"审查结果解析失败，出于安全考虑拒绝执行",
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raw_response=response
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)
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def _extract_json(self, text: str) -> str:
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"""从文本中提取 JSON"""
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start = text.find('{')
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end = text.rfind('}')
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if start != -1 and end != -1 and end > start:
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return text[start:end + 1]
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return text
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def review_code_safety(
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user_input: str,
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execution_plan: str,
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code: str,
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warnings: Optional[List[str]] = None
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) -> LLMReviewResult:
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"""便捷函数：审查代码安全性"""
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reviewer = LLMReviewer()
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return reviewer.review(user_input, execution_plan, code, warnings)
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