51c0d2a1e8
将原有的基于文件的 `ToolCache` 替换为全新的 `CacheManager`，引入了更复杂和高效的分层语义缓存机制。 新系统特性： - **分层缓存**: - L1 缓存: 内存字典 (KV) + FAISS (向量)，用于极速访问。 - L2 缓存: SQLite (KV) + ChromaDB (向量)，用于持久化存储。 - **语义缓存**: 利用嵌入模型 (Embedding) 对查询进行向量化，实现基于语义相似度的缓存命中，显著提高了缓存命中率。 - **自动失效**: 缓存键包含工具源代码的哈希值，当工具代码更新时，相关缓存会自动失效，避免了脏数据问题。 - **异步支持**: 缓存的 `get` 和 `set` 方法现在是异步的，以适应项目中异步化的工具调用流程。 `web_search_tool` 已更新以使用新的 `CacheManager`，在调用缓存时传递 `tool_class` 和 `semantic_query` 以充分利用新功能。 Co-Authored-By: tt-P607 <68868379+tt-P607@users.noreply.github.com>
344 lines
11 KiB
Python
344 lines
11 KiB
Python
import json
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import hashlib
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import re
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from typing import Any, Dict, Optional
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from datetime import datetime, timedelta
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from pathlib import Path
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from difflib import SequenceMatcher
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from src.common.logger import get_logger
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logger = get_logger("cache_manager")
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class ToolCache:
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"""工具缓存管理器，用于缓存工具调用结果，支持近似匹配"""
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def __init__(
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self,
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cache_dir: str = "data/tool_cache",
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max_age_hours: int = 24,
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similarity_threshold: float = 0.65,
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):
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"""
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初始化缓存管理器
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Args:
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cache_dir: 缓存目录路径
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max_age_hours: 缓存最大存活时间（小时）
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similarity_threshold: 近似匹配的相似度阈值 (0-1)
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"""
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self.cache_dir = Path(cache_dir)
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self.max_age = timedelta(hours=max_age_hours)
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self.max_age_seconds = max_age_hours * 3600
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self.similarity_threshold = similarity_threshold
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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@staticmethod
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def _normalize_query(query: str) -> str:
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"""
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标准化查询文本，用于相似度比较
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Args:
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query: 原始查询文本
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Returns:
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标准化后的查询文本
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"""
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if not query:
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return ""
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# 纯 Python 实现
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normalized = query.lower()
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normalized = re.sub(r"[^\w\s]", " ", normalized)
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normalized = " ".join(normalized.split())
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return normalized
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def _calculate_similarity(self, text1: str, text2: str) -> float:
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"""
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计算两个文本的相似度
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Args:
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text1: 文本1
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text2: 文本2
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Returns:
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相似度分数 (0-1)
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"""
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if not text1 or not text2:
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return 0.0
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# 纯 Python 实现
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norm_text1 = self._normalize_query(text1)
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norm_text2 = self._normalize_query(text2)
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if norm_text1 == norm_text2:
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return 1.0
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return SequenceMatcher(None, norm_text1, norm_text2).ratio()
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@staticmethod
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def _generate_cache_key(tool_name: str, function_args: Dict[str, Any]) -> str:
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"""
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生成缓存键
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Args:
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tool_name: 工具名称
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function_args: 函数参数
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Returns:
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缓存键字符串
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"""
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# 将参数排序后序列化，确保相同参数产生相同的键
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sorted_args = json.dumps(function_args, sort_keys=True, ensure_ascii=False)
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# 纯 Python 实现
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cache_string = f"{tool_name}:{sorted_args}"
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return hashlib.md5(cache_string.encode("utf-8")).hexdigest()
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def _get_cache_file_path(self, cache_key: str) -> Path:
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"""获取缓存文件路径"""
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return self.cache_dir / f"{cache_key}.json"
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def _is_cache_expired(self, cached_time: datetime) -> bool:
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"""检查缓存是否过期"""
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return datetime.now() - cached_time > self.max_age
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def _find_similar_cache(
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self, tool_name: str, function_args: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""
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查找相似的缓存条目
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Args:
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tool_name: 工具名称
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function_args: 函数参数
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Returns:
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相似的缓存结果，如果不存在则返回None
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"""
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query = function_args.get("query", "")
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if not query:
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return None
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candidates = []
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cache_data_list = []
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# 遍历所有缓存文件，收集候选项
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for cache_file in self.cache_dir.glob("*.json"):
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try:
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with open(cache_file, "r", encoding="utf-8") as f:
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cache_data = json.load(f)
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# 检查是否是同一个工具
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if cache_data.get("tool_name") != tool_name:
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continue
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# 检查缓存是否过期
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cached_time = datetime.fromisoformat(cache_data["timestamp"])
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if self._is_cache_expired(cached_time):
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continue
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# 检查其他参数是否匹配（除了query）
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cached_args = cache_data.get("function_args", {})
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args_match = True
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for key, value in function_args.items():
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if key != "query" and cached_args.get(key) != value:
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args_match = False
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break
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if not args_match:
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continue
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# 收集候选项
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cached_query = cached_args.get("query", "")
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candidates.append((cached_query, len(cache_data_list)))
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cache_data_list.append(cache_data)
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except Exception as e:
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logger.warning(f"检查缓存文件时出错: {cache_file}, 错误: {e}")
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continue
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if not candidates:
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logger.debug(
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f"未找到相似缓存: {tool_name}, 查询: '{query}'，相似度阈值: {self.similarity_threshold}"
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)
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return None
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# 纯 Python 实现
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best_match = None
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best_similarity = 0.0
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for cached_query, index in candidates:
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similarity = self._calculate_similarity(query, cached_query)
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if similarity > best_similarity and similarity >= self.similarity_threshold:
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best_similarity = similarity
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best_match = cache_data_list[index]
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if best_match is not None:
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cached_query = best_match["function_args"].get("query", "")
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logger.info(
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f"相似缓存命中，相似度: {best_similarity:.2f}, 原查询: '{cached_query}', 当前查询: '{query}'"
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)
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return best_match["result"]
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logger.debug(
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f"未找到相似缓存: {tool_name}, 查询: '{query}'，相似度阈值: {self.similarity_threshold}"
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)
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return None
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def get(
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self, tool_name: str, function_args: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""
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从缓存获取结果，支持精确匹配和近似匹配
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Args:
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tool_name: 工具名称
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function_args: 函数参数
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Returns:
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缓存的结果，如果不存在或已过期则返回None
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"""
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# 首先尝试精确匹配
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cache_key = self._generate_cache_key(tool_name, function_args)
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cache_file = self._get_cache_file_path(cache_key)
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if cache_file.exists():
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try:
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with open(cache_file, "r", encoding="utf-8") as f:
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cache_data = json.load(f)
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# 检查缓存是否过期
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cached_time = datetime.fromisoformat(cache_data["timestamp"])
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if self._is_cache_expired(cached_time):
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logger.debug(f"缓存已过期: {cache_key}")
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cache_file.unlink() # 删除过期缓存
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else:
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logger.debug(f"精确匹配缓存: {tool_name}")
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return cache_data["result"]
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except (json.JSONDecodeError, KeyError, ValueError) as e:
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logger.warning(f"读取缓存文件失败: {cache_file}, 错误: {e}")
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# 删除损坏的缓存文件
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if cache_file.exists():
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cache_file.unlink()
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# 如果精确匹配失败，尝试近似匹配
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return self._find_similar_cache(tool_name, function_args)
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def set(
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self, tool_name: str, function_args: Dict[str, Any], result: Dict[str, Any]
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) -> None:
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"""
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将结果保存到缓存
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Args:
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tool_name: 工具名称
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function_args: 函数参数
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result: 缓存结果
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"""
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cache_key = self._generate_cache_key(tool_name, function_args)
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cache_file = self._get_cache_file_path(cache_key)
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cache_data = {
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"tool_name": tool_name,
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"function_args": function_args,
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"result": result,
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"timestamp": datetime.now().isoformat(),
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}
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try:
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with open(cache_file, "w", encoding="utf-8") as f:
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json.dump(cache_data, f, ensure_ascii=False, indent=2)
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logger.debug(f"缓存已保存: {tool_name} -> {cache_key}")
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except Exception as e:
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logger.error(f"保存缓存失败: {cache_file}, 错误: {e}")
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def clear_expired(self) -> int:
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"""
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清理过期缓存
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Returns:
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删除的文件数量
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"""
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removed_count = 0
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for cache_file in self.cache_dir.glob("*.json"):
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try:
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with open(cache_file, "r", encoding="utf-8") as f:
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cache_data = json.load(f)
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cached_time = datetime.fromisoformat(cache_data["timestamp"])
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if self._is_cache_expired(cached_time):
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cache_file.unlink()
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removed_count += 1
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logger.debug(f"删除过期缓存: {cache_file}")
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except Exception as e:
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logger.warning(f"清理缓存文件时出错: {cache_file}, 错误: {e}")
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# 删除损坏的文件
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try:
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cache_file.unlink()
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removed_count += 1
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except (OSError, json.JSONDecodeError, KeyError, ValueError):
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logger.warning(f"删除损坏的缓存文件失败: {cache_file}, 错误: {e}")
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logger.info(f"清理完成，删除了 {removed_count} 个过期缓存文件")
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return removed_count
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def clear_all(self) -> int:
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"""
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清空所有缓存
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Returns:
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删除的文件数量
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"""
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removed_count = 0
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for cache_file in self.cache_dir.glob("*.json"):
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try:
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cache_file.unlink()
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removed_count += 1
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except Exception as e:
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logger.warning(f"删除缓存文件失败: {cache_file}, 错误: {e}")
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logger.info(f"清空缓存完成，删除了 {removed_count} 个文件")
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return removed_count
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def get_stats(self) -> Dict[str, Any]:
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"""
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获取缓存统计信息
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Returns:
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缓存统计信息字典
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"""
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total_files = 0
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expired_files = 0
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total_size = 0
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for cache_file in self.cache_dir.glob("*.json"):
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try:
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total_files += 1
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total_size += cache_file.stat().st_size
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with open(cache_file, "r", encoding="utf-8") as f:
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cache_data = json.load(f)
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cached_time = datetime.fromisoformat(cache_data["timestamp"])
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if self._is_cache_expired(cached_time):
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expired_files += 1
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except (OSError, json.JSONDecodeError, KeyError, ValueError):
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expired_files += 1 # 损坏的文件也算作过期
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return {
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"total_files": total_files,
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"expired_files": expired_files,
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"total_size_bytes": total_size,
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"cache_dir": str(self.cache_dir),
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"max_age_hours": self.max_age.total_seconds() / 3600,
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"similarity_threshold": self.similarity_threshold,
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}
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tool_cache = ToolCache() |