feat(cache): 用文件修改时间替换源码哈希生成缓存键
BREAKING CHANGE: CacheManager 的 _generate_key/get/set 方法签名变更，现在需要传入 tool_file_path 而非 tool_class 实例，所有调用方需跟进适配。
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minecraft1024a
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Windpicker-owo
Windpicker-owo
parent
1ad1d0dddf
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b3d02ff1c3
@@ -2,10 +2,12 @@ import time
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import json
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import hashlib
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import inspect
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import os
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from pathlib import Path
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import numpy as np
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import faiss
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import chromadb
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from typing import Any, Dict, Optional
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from typing import Any, Dict, Optional, Union
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from src.common.logger import get_logger
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config, model_config
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@@ -90,67 +92,33 @@ class CacheManager:
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logger.error(f"验证嵌入向量时发生错误: {e}")
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return None
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def _validate_embedding(self, embedding_result: Any) -> Optional[np.ndarray]:
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"""
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验证和标准化嵌入向量格式
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"""
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def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path]) -> str:
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"""生成确定性的缓存键，包含文件修改时间以实现自动失效。"""
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try:
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if embedding_result is None:
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return None
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# 确保embedding_result是一维数组或列表
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if isinstance(embedding_result, (list, tuple, np.ndarray)):
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# 转换为numpy数组进行处理
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embedding_array = np.array(embedding_result)
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# 如果是多维数组，展平它
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if embedding_array.ndim > 1:
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embedding_array = embedding_array.flatten()
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# 检查维度是否符合预期
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expected_dim = global_config.lpmm_knowledge.embedding_dimension
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if embedding_array.shape[0] != expected_dim:
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logger.warning(f"嵌入向量维度不匹配: 期望 {expected_dim}, 实际 {embedding_array.shape[0]}")
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return None
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# 检查是否包含有效的数值
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if np.isnan(embedding_array).any() or np.isinf(embedding_array).any():
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logger.warning("嵌入向量包含无效的数值 (NaN 或 Inf)")
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return None
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return embedding_array.astype('float32')
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tool_file_path = Path(tool_file_path)
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if tool_file_path.exists():
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file_name = tool_file_path.name
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file_mtime = tool_file_path.stat().st_mtime
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file_hash = hashlib.md5(f"{file_name}:{file_mtime}".encode()).hexdigest()
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else:
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logger.warning(f"嵌入结果格式不支持: {type(embedding_result)}")
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return None
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except Exception as e:
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logger.error(f"验证嵌入向量时发生错误: {e}")
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return None
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def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any) -> str:
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"""生成确定性的缓存键，包含代码哈希以实现自动失效。"""
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try:
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source_code = inspect.getsource(tool_class)
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code_hash = hashlib.md5(source_code.encode()).hexdigest()
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except Exception as e:
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code_hash = "unknown"
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# 获取更清晰的类名
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class_name = getattr(tool_class, '__name__', str(tool_class))
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# 简化错误信息
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error_msg = str(e).replace(str(tool_class), class_name)
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logger.warning(f"无法获取 {class_name} 的源代码，代码哈希将为 'unknown'。原因: {error_msg}")
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file_hash = "unknown"
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logger.warning(f"工具文件不存在: {tool_file_path}")
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except (OSError, TypeError) as e:
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file_hash = "unknown"
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logger.warning(f"无法获取文件信息: {tool_file_path}，错误: {e}")
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try:
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sorted_args = json.dumps(function_args, sort_keys=True)
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except TypeError:
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sorted_args = repr(sorted(function_args.items()))
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return f"{tool_name}::{sorted_args}::{code_hash}"
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return f"{tool_name}::{sorted_args}::{file_hash}"
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async def get(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any, semantic_query: Optional[str] = None) -> Optional[Any]:
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async def get(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path], semantic_query: Optional[str] = None) -> Optional[Any]:
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"""
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从缓存获取结果，查询顺序: L1-KV -> L1-Vector -> L2-KV -> L2-Vector。
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"""
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# 步骤 1: L1 精确缓存查询
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key = self._generate_key(tool_name, function_args, tool_class)
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key = self._generate_key(tool_name, function_args, tool_file_path)
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logger.debug(f"生成的缓存键: {key}")
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if semantic_query:
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logger.debug(f"使用的语义查询: '{semantic_query}'")
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@@ -252,14 +220,14 @@ class CacheManager:
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logger.debug(f"缓存未命中: {key}")
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return None
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async def set(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any, data: Any, ttl: Optional[int] = None, semantic_query: Optional[str] = None):
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async def set(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path], data: Any, ttl: Optional[int] = None, semantic_query: Optional[str] = None):
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"""将结果存入所有缓存层。"""
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if ttl is None:
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ttl = self.default_ttl
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if ttl <= 0:
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return
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key = self._generate_key(tool_name, function_args, tool_class)
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key = self._generate_key(tool_name, function_args, tool_file_path)
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expires_at = time.time() + ttl
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# 写入 L1
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@@ -87,9 +87,13 @@ class WebSurfingTool(BaseTool):
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if not query:
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return {"error": "搜索查询不能为空。"}
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# 获取当前文件路径用于缓存键
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import os
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current_file_path = os.path.abspath(__file__)
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# 检查缓存
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query = function_args.get("query")
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cached_result = await tool_cache.get(self.name, function_args, tool_class=self.__class__, semantic_query=query)
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cached_result = await tool_cache.get(self.name, function_args, current_file_path, semantic_query=query)
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if cached_result:
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logger.info(f"缓存命中: {self.name} -> {function_args}")
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return cached_result
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@@ -111,7 +115,7 @@ class WebSurfingTool(BaseTool):
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# 保存到缓存
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if "error" not in result:
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query = function_args.get("query")
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await tool_cache.set(self.name, function_args, self.__class__, result, semantic_query=query)
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await tool_cache.set(self.name, function_args, current_file_path, result, semantic_query=query)
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return result
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@@ -464,8 +468,12 @@ class URLParserTool(BaseTool):
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"""
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执行URL内容提取和总结。优先使用Exa，失败后尝试本地解析。
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"""
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# 获取当前文件路径用于缓存键
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import os
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current_file_path = os.path.abspath(__file__)
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# 检查缓存
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cached_result = await tool_cache.get(self.name, function_args, tool_class=self.__class__)
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cached_result = await tool_cache.get(self.name, function_args, current_file_path)
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if cached_result:
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logger.info(f"缓存命中: {self.name} -> {function_args}")
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return cached_result
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@@ -1,5 +1,5 @@
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[inner]
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version = "6.3.7"
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version = "6.3.8"
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#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
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#如果你想要修改配置文件，请递增version的值
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@@ -167,8 +167,8 @@ ban_msgs_regex = [
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[anti_prompt_injection] # LLM反注入系统配置
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enabled = true # 是否启用反注入系统
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enabled_rules = false # 是否启用规则检测
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enabled_LLM = true # 是否启用LLM检测
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enabled_rules = true # 是否启用规则检测
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enabled_LLM = false # 是否启用LLM检测
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process_mode = "lenient" # 处理模式：strict(严格模式，直接丢弃), lenient(宽松模式，消息加盾)
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# 白名单配置
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