367 lines
15 KiB
Python
367 lines
15 KiB
Python
import hashlib
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import time
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from pathlib import Path
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from typing import Any
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import faiss
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import numpy as np
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import orjson
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from src.common.config_helpers import resolve_embedding_dimension
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from src.common.database.sqlalchemy_database_api import db_query, db_save
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from src.common.database.sqlalchemy_models import CacheEntries
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from src.common.logger import get_logger
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from src.common.vector_db import vector_db_service
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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logger = get_logger("cache_manager")
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class CacheManager:
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"""
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一个支持分层和语义缓存的通用工具缓存管理器。
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采用单例模式，确保在整个应用中只有一个缓存实例。
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L1缓存: 内存字典 (KV) + FAISS (Vector)。
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L2缓存: 数据库 (KV) + ChromaDB (Vector)。
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"""
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_instance = None
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def __new__(cls, *args, **kwargs):
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if not cls._instance:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self, default_ttl: int = 3600):
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"""
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初始化缓存管理器。
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"""
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if not hasattr(self, "_initialized"):
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self.default_ttl = default_ttl
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self.semantic_cache_collection_name = "semantic_cache"
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# L1 缓存 (内存)
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self.l1_kv_cache: dict[str, dict[str, Any]] = {}
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embedding_dim = resolve_embedding_dimension(global_config.lpmm_knowledge.embedding_dimension)
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if not embedding_dim:
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embedding_dim = global_config.lpmm_knowledge.embedding_dimension
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self.embedding_dimension = embedding_dim
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self.l1_vector_index = faiss.IndexFlatIP(embedding_dim)
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self.l1_vector_id_to_key: dict[int, str] = {}
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# L2 向量缓存 (使用新的服务)
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vector_db_service.get_or_create_collection(self.semantic_cache_collection_name)
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# 嵌入模型
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self.embedding_model = LLMRequest(model_config.model_task_config.embedding)
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self._initialized = True
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logger.info("缓存管理器已初始化: L1 (内存+FAISS), L2 (数据库+ChromaDB)")
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@staticmethod
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def _validate_embedding(embedding_result: Any) -> np.ndarray | None:
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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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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 = (
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getattr(CacheManager, "embedding_dimension", None)
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or global_config.lpmm_knowledge.embedding_dimension
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)
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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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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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@staticmethod
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def _generate_key(tool_name: str, function_args: dict[str, Any], tool_file_path: 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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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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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 = orjson.dumps(function_args, option=orjson.OPT_SORT_KEYS).decode("utf-8")
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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}::{file_hash}"
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async def get(
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self,
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tool_name: str,
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function_args: dict[str, Any],
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tool_file_path: str | Path,
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semantic_query: str | None = None,
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) -> Any | None:
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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_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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if key in self.l1_kv_cache:
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entry = self.l1_kv_cache[key]
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if time.time() < entry["expires_at"]:
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logger.info(f"命中L1键值缓存: {key}")
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return entry["data"]
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else:
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del self.l1_kv_cache[key]
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# 步骤 2: L1/L2 语义和L2精确缓存查询
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query_embedding = None
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if semantic_query and self.embedding_model:
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embedding_result = await self.embedding_model.get_embedding(semantic_query)
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if embedding_result:
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# embedding_result是一个元组(embedding_vector, model_name)，取第一个元素
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embedding_vector = embedding_result[0] if isinstance(embedding_result, tuple) else embedding_result
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validated_embedding = self._validate_embedding(embedding_vector)
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if validated_embedding is not None:
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query_embedding = np.array([validated_embedding], dtype="float32")
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# 步骤 2a: L1 语义缓存 (FAISS)
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if query_embedding is not None and self.l1_vector_index.ntotal > 0:
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faiss.normalize_L2(query_embedding)
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distances, indices = self.l1_vector_index.search(query_embedding, 1) # type: ignore
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if indices.size > 0 and distances[0][0] > 0.75: # IP 越大越相似
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hit_index = indices[0][0]
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l1_hit_key = self.l1_vector_id_to_key.get(hit_index)
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if l1_hit_key and l1_hit_key in self.l1_kv_cache:
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logger.info(f"命中L1语义缓存: {l1_hit_key}")
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return self.l1_kv_cache[l1_hit_key]["data"]
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# 步骤 2b: L2 精确缓存 (数据库)
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cache_results_obj = await db_query(
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model_class=CacheEntries, query_type="get", filters={"cache_key": key}, single_result=True
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)
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if cache_results_obj:
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# 使用 getattr 安全访问属性，避免 Pylance 类型检查错误
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expires_at = getattr(cache_results_obj, "expires_at", 0)
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if time.time() < expires_at:
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logger.info(f"命中L2键值缓存: {key}")
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cache_value = getattr(cache_results_obj, "cache_value", "{}")
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data = orjson.loads(cache_value)
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# 更新访问统计
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await db_query(
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model_class=CacheEntries,
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query_type="update",
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filters={"cache_key": key},
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data={
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"last_accessed": time.time(),
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"access_count": getattr(cache_results_obj, "access_count", 0) + 1,
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},
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)
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# 回填 L1
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self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
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return data
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else:
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# 删除过期的缓存条目
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await db_query(model_class=CacheEntries, query_type="delete", filters={"cache_key": key})
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# 步骤 2c: L2 语义缓存 (VectorDB Service)
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if query_embedding is not None:
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try:
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results = vector_db_service.query(
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collection_name=self.semantic_cache_collection_name,
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query_embeddings=query_embedding.tolist(),
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n_results=1,
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)
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if results and results.get("ids") and results["ids"][0]:
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distance = (
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results["distances"][0][0] if results.get("distances") and results["distances"][0] else "N/A"
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)
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logger.debug(f"L2语义搜索找到最相似的结果: id={results['ids'][0]}, 距离={distance}")
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if distance != "N/A" and distance < 0.75:
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l2_hit_key = results["ids"][0][0] if isinstance(results["ids"][0], list) else results["ids"][0]
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logger.info(f"命中L2语义缓存: key='{l2_hit_key}', 距离={distance:.4f}")
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# 从数据库获取缓存数据
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semantic_cache_results_obj = await db_query(
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model_class=CacheEntries,
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query_type="get",
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filters={"cache_key": l2_hit_key},
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single_result=True,
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)
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if semantic_cache_results_obj:
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expires_at = getattr(semantic_cache_results_obj, "expires_at", 0)
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if time.time() < expires_at:
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cache_value = getattr(semantic_cache_results_obj, "cache_value", "{}")
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data = orjson.loads(cache_value)
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logger.debug(f"L2语义缓存返回的数据: {data}")
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# 回填 L1
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self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
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if query_embedding is not None:
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try:
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new_id = self.l1_vector_index.ntotal
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faiss.normalize_L2(query_embedding)
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self.l1_vector_index.add(x=query_embedding) # type: ignore
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self.l1_vector_id_to_key[new_id] = key
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except Exception as e:
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logger.error(f"回填L1向量索引时发生错误: {e}")
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return data
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except Exception as e:
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logger.warning(f"VectorDB Service 查询失败: {e}")
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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(
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self,
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tool_name: str,
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function_args: dict[str, Any],
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tool_file_path: str | Path,
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data: Any,
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ttl: int | None = None,
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semantic_query: str | None = None,
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):
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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_file_path)
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expires_at = time.time() + ttl
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# 写入 L1
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self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
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# 写入 L2 (数据库)
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cache_data = {
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"cache_key": key,
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"cache_value": orjson.dumps(data).decode("utf-8"),
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"expires_at": expires_at,
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"tool_name": tool_name,
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"created_at": time.time(),
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"last_accessed": time.time(),
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"access_count": 1,
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}
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await db_save(model_class=CacheEntries, data=cache_data, key_field="cache_key", key_value=key)
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# 写入语义缓存
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if semantic_query and self.embedding_model:
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try:
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embedding_result = await self.embedding_model.get_embedding(semantic_query)
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if embedding_result:
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embedding_vector = embedding_result[0] if isinstance(embedding_result, tuple) else embedding_result
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validated_embedding = self._validate_embedding(embedding_vector)
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if validated_embedding is not None:
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embedding = np.array([validated_embedding], dtype="float32")
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# 写入 L1 Vector
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new_id = self.l1_vector_index.ntotal
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faiss.normalize_L2(embedding)
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self.l1_vector_index.add(x=embedding) # type: ignore
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self.l1_vector_id_to_key[new_id] = key
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# 写入 L2 Vector (使用新的服务)
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vector_db_service.add(
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collection_name=self.semantic_cache_collection_name,
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embeddings=embedding.tolist(),
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ids=[key],
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)
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except Exception as e:
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logger.warning(f"语义缓存写入失败: {e}")
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logger.info(f"已缓存条目: {key}, TTL: {ttl}s")
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def clear_l1(self):
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"""清空L1缓存。"""
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self.l1_kv_cache.clear()
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self.l1_vector_index.reset()
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self.l1_vector_id_to_key.clear()
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logger.info("L1 (内存+FAISS) 缓存已清空。")
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async def clear_l2(self):
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"""清空L2缓存。"""
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# 清空数据库缓存
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await db_query(
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model_class=CacheEntries,
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query_type="delete",
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filters={}, # 删除所有记录
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)
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# 清空 VectorDB
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try:
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vector_db_service.delete_collection(name=self.semantic_cache_collection_name)
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vector_db_service.get_or_create_collection(name=self.semantic_cache_collection_name)
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except Exception as e:
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logger.warning(f"清空 VectorDB 集合失败: {e}")
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logger.info("L2 (数据库 & VectorDB) 缓存已清空。")
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async def clear_all(self):
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"""清空所有缓存。"""
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self.clear_l1()
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await self.clear_l2()
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logger.info("所有缓存层级已清空。")
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async def clean_expired(self):
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"""清理过期的缓存条目"""
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current_time = time.time()
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# 清理L1过期条目
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expired_keys = []
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for key, entry in self.l1_kv_cache.items():
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if current_time >= entry["expires_at"]:
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expired_keys.append(key)
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for key in expired_keys:
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del self.l1_kv_cache[key]
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# 清理L2过期条目
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await db_query(model_class=CacheEntries, query_type="delete", filters={"expires_at": {"$lt": current_time}})
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if expired_keys:
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logger.info(f"清理了 {len(expired_keys)} 个过期的L1缓存条目")
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# 全局实例
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tool_cache = CacheManager()
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