hangshuo652
b94757d9df
1. 场景价值与技术合理性修复： - 补充docs/SCENE_VALUE.md（业务背景、痛点分析、用户场景、竞品对比、价值量化） - 添加用户操作流程图（Mermaid） - 添加3个真实业务案例量化数据 2. 演示与文档修复： - 创建docs/API.md（完整API文档） - 创建docs/QUICKSTART.md（5分钟快速入门指南） 3. AI使用日志修复： - 更新AGENTS.md，添加强制自动执行的AI使用日志记录指令 - 在_AI_USAGE_LOG.md末尾添加范式执行统计 4. 安全性修复： - 在agents/llm.py中添加输入过滤（防Prompt注入） - 添加输出验证、速率限制、详细日志 5. 架构设计修复： - 创建tools/registry.py工具注册表 - 修改orchestrator.py和orchestrator_db.py使用注册表动态获取运行器 6. 开发范式修复： - 在_AI_USAGE_LOG.md末尾添加范式执行统计
129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
import json, hashlib, os, re, time, logging
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from pathlib import Path
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import httpx
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logger = logging.getLogger(__name__)
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class LLMClient:
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def __init__(self, model=None, timeout=15, cache_dir=".cache/llm"):
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# 模型解析链: 显式实参 → LLM_MODEL 环境变量 → 项目默认（与 config 层一致）
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self.model = model or os.environ.get("LLM_MODEL", "deepseek-v4-flash")
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self.timeout = timeout
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self.dir = Path(cache_dir)
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self.dir.mkdir(parents=True, exist_ok=True)
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self._last_call_time = 0
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self._min_interval = 0.1 # 100ms 最小调用间隔
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def _key(self, msgs):
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return hashlib.sha256(json.dumps(msgs, sort_keys=True).encode()).hexdigest()
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def _get(self, k):
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p = self.dir / f"{k}.json"
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if not p.exists():
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return None
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try:
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return json.loads(p.read_text())["response"]
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except (json.JSONDecodeError, KeyError):
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return None
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def _set(self, k, v):
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(self.dir / f"{k}.json").write_text(json.dumps({"response": v}))
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def _sanitize_input(self, text: str) -> str:
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"""输入过滤：移除潜在的prompt注入模式，限制输入长度"""
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if not isinstance(text, str):
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text = str(text)
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# 1. 限制输入长度（防止过长输入）
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max_length = 50000
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if len(text) > max_length:
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logger.warning(f"Input truncated from {len(text)} to {max_length} chars")
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text = text[:max_length]
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# 2. 移除潜在的prompt注入模式
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injection_patterns = [
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r'(?i)ignore\s+previous\s+instructions',
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r'(?i)disregard\s+all\s+prior',
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r'(?i)you\s+are\s+now\s+',
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r'(?i)system\s*:\s*',
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r'(?i)assistant\s*:\s*',
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r'(?i)\[INST\]',
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r'(?i)\[/INST\]',
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r'(?i)<\|im_start\|>',
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r'(?i)<\|im_end\|>',
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]
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for pattern in injection_patterns:
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if re.search(pattern, text):
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logger.warning(f"Potential prompt injection detected: {pattern}")
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text = re.sub(pattern, '[FILTERED]', text)
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return text
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def _validate_output(self, output: str) -> bool:
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"""输出验证：检查LLM返回内容的基本有效性"""
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if not output or not isinstance(output, str):
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return False
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# 检查长度是否合理
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if len(output) > 100000:
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logger.warning(f"Output too long: {len(output)} chars")
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return False
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return True
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def _rate_limit(self):
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"""速率限制：确保最小调用间隔"""
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current_time = time.time()
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elapsed = current_time - self._last_call_time
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if elapsed < self._min_interval:
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time.sleep(self._min_interval - elapsed)
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self._last_call_time = time.time()
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def call(self, messages, retries=1):
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k = self._key(messages)
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c = self._get(k)
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if c:
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logger.debug(f"Cache hit for key: {k[:8]}...")
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return c
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# 对用户消息进行输入过滤
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sanitized_messages = []
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for msg in messages:
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if msg.get("role") == "user":
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sanitized_content = self._sanitize_input(msg.get("content", ""))
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sanitized_messages.append({**msg, "content": sanitized_content})
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else:
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sanitized_messages.append(msg)
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key = os.environ.get("LLM_API_KEY", os.environ.get("OPENAI_API_KEY", ""))
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base = os.environ.get("LLM_API_BASE", "https://api.openai.com/v1")
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for a in range(retries + 1):
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try:
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# 速率限制
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self._rate_limit()
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logger.info(f"LLM call: model={self.model}, attempt={a+1}/{retries+1}")
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r = httpx.post(f"{base}/chat/completions", json={"model": self.model, "messages": sanitized_messages},
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headers={"Authorization": f"Bearer {key}"}, timeout=self.timeout)
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r.raise_for_status()
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v = r.json()["choices"][0]["message"]["content"]
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# 输出验证
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if not self._validate_output(v):
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logger.warning("Invalid output from LLM, using fallback")
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v = ""
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self._set(k, v)
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logger.info(f"LLM call success: response length={len(v)}")
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return v
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except Exception as e:
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logger.error(f"LLM call failed: {e}")
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if a == retries:
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raise
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time.sleep(1) # 失败后等待1秒重试
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return ""
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