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feat(cache): 用文件修改时间替换源码哈希生成缓存键

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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。
This commit is contained in:
minecraft1024a
2025-08-18 20:07:59 +08:00
parent e1d4c30363
commit fb02896359
3 changed files with 36 additions and 19 deletions
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35
src/common/cache_manager.py
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@@ -2,10 +2,12 @@ import time
import json
import hashlib
import inspect
import os
from pathlib import Path
import numpy as np
import faiss
import chromadb
from typing import Any, Dict, Optional
from typing import Any, Dict, Optional, Union
from src.common.logger import get_logger
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
@@ -90,26 +92,33 @@ class CacheManager:
logger.error(f"验证嵌入向量时发生错误: {e}")
return None
def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any) -> str:
"""生成确定性的缓存键，包含代码哈希以实现自动失效。"""
def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path]) -> str:
"""生成确定性的缓存键，包含文件修改时间以实现自动失效。"""
try:
source_code = inspect.getsource(tool_class)
code_hash = hashlib.md5(source_code.encode()).hexdigest()
except (TypeError, OSError) as e:
code_hash = "unknown"
logger.warning(f"无法获取 {tool_class.__name__} 的源代码，代码哈希将为 'unknown'。错误: {e}")
tool_file_path = Path(tool_file_path)
if tool_file_path.exists():
file_name = tool_file_path.name
file_mtime = tool_file_path.stat().st_mtime
file_hash = hashlib.md5(f"{file_name}:{file_mtime}".encode()).hexdigest()
else:
file_hash = "unknown"
logger.warning(f"工具文件不存在: {tool_file_path}")
except (OSError, TypeError) as e:
file_hash = "unknown"
logger.warning(f"无法获取文件信息: {tool_file_path}，错误: {e}")
try:
sorted_args = json.dumps(function_args, sort_keys=True)
except TypeError:
sorted_args = repr(sorted(function_args.items()))
return f"{tool_name}::{sorted_args}::{code_hash}"
return f"{tool_name}::{sorted_args}::{file_hash}"
async def get(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any, semantic_query: Optional[str] = None) -> Optional[Any]:
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]:
"""
从缓存获取结果，查询顺序: L1-KV -> L1-Vector -> L2-KV -> L2-Vector。
"""
# 步骤 1: L1 精确缓存查询
key = self._generate_key(tool_name, function_args, tool_class)
key = self._generate_key(tool_name, function_args, tool_file_path)
logger.debug(f"生成的缓存键: {key}")
if semantic_query:
logger.debug(f"使用的语义查询: '{semantic_query}'")
@@ -220,14 +229,14 @@ class CacheManager:
logger.debug(f"缓存未命中: {key}")
return None
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):
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):
"""将结果存入所有缓存层。"""
if ttl is None:
ttl = self.default_ttl
if ttl <= 0:
return
key = self._generate_key(tool_name, function_args, tool_class)
key = self._generate_key(tool_name, function_args, tool_file_path)
expires_at = time.time() + ttl
# 写入 L1