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296 lines
8.4 KiB
Python
296 lines
8.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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语义缓存模块（FAISS 版）
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功能：
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- 使用 FAISS 向量索引实现 O(1) 查找
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- 语义级缓存：相似查询也能命中
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- 高性能：10万缓存量下查询 < 1ms
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使用方式：
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from core.semantic_cache import SemanticCache
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cache = SemanticCache(dim=768, threshold=0.92)
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# 查找
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result = cache.get(query_embedding)
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# 存储
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cache.set(query_embedding, result)
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"""
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import numpy as np
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import logging
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from typing import Dict, Optional, List, Any
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import threading
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logger = logging.getLogger(__name__)
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# FAISS 可选依赖
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try:
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import faiss
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FAISS_AVAILABLE = True
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except ImportError:
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FAISS_AVAILABLE = False
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logger.warning("FAISS 未安装，语义缓存将使用降级方案")
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class SemanticCache:
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"""
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语义缓存（FAISS 向量索引）
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使用 FAISS 实现高性能向量检索，支持：
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- O(1) 查找复杂度
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- 10万+ 缓存量
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- 亚毫秒级响应
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"""
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def __init__(
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self,
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dim: int = 768,
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threshold: float = 0.92,
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max_size: int = 10000
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):
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"""
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初始化语义缓存
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Args:
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dim: 向量维度
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threshold: 相似度阈值（cosine 相似度）
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max_size: 最大缓存数量
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"""
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self.dim = dim
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self.threshold = threshold
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self.max_size = max_size
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self._lock = threading.RLock()
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self._cache: Dict[int, Any] = {} # id -> result
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self._next_id = 0
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# 统计信息
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self._hits = 0
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self._misses = 0
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if FAISS_AVAILABLE:
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# 使用内积索引（需要归一化向量）
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self._index = faiss.IndexFlatIP(dim)
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self._use_faiss = True
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logger.info(f"语义缓存初始化（FAISS），维度={dim}，阈值={threshold}")
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else:
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# 降级方案：使用 numpy
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self._embeddings: List[np.ndarray] = []
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self._use_faiss = False
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logger.warning("语义缓存降级为 numpy 方案")
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def get(self, query_emb: np.ndarray) -> Optional[Dict]:
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"""
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查找语义缓存
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Args:
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query_emb: 查询向量（已归一化）
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Returns:
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缓存结果，未命中返回 None
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"""
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with self._lock:
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if self._use_faiss:
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return self._get_faiss(query_emb)
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else:
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return self._get_numpy(query_emb)
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def _get_faiss(self, query_emb: np.ndarray) -> Optional[Dict]:
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"""FAISS 查找"""
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if self._index.ntotal == 0:
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self._misses += 1
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return None
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# 归一化并搜索
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query = self._normalize(query_emb).reshape(1, -1).astype('float32')
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D, I = self._index.search(query, k=1)
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if D[0][0] > self.threshold:
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cache_id = int(I[0][0])
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self._hits += 1
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logger.debug(f"语义缓存命中，相似度={D[0][0]:.3f}")
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return self._cache.get(cache_id)
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self._misses += 1
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return None
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def _get_numpy(self, query_emb: np.ndarray) -> Optional[Dict]:
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"""Numpy 降级查找"""
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if not self._embeddings:
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self._misses += 1
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return None
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query = self._normalize(query_emb)
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best_score = 0
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best_id = -1
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for i, emb in enumerate(self._embeddings):
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score = float(np.dot(query, emb))
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if score > best_score:
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best_score = score
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best_id = i
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if best_score > self.threshold:
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self._hits += 1
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logger.debug(f"语义缓存命中（numpy），相似度={best_score:.3f}")
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return self._cache.get(best_id)
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self._misses += 1
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return None
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def set(self, query_emb: np.ndarray, result: Dict) -> None:
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"""
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存储到语义缓存
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Args:
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query_emb: 查询向量
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result: 缓存结果
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"""
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with self._lock:
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if self._use_faiss:
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self._set_faiss(query_emb, result)
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else:
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self._set_numpy(query_emb, result)
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def _set_faiss(self, query_emb: np.ndarray, result: Dict) -> None:
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"""FAISS 存储"""
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# 检查容量
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if self._index.ntotal >= self.max_size:
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# LRU 淘汰：重建索引（简单实现）
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logger.debug("语义缓存已满，执行淘汰")
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self.clear()
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# 归一化并添加
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query = self._normalize(query_emb).reshape(1, -1).astype('float32')
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self._index.add(query)
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self._cache[self._next_id] = result
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self._next_id += 1
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def _set_numpy(self, query_emb: np.ndarray, result: Dict) -> None:
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"""Numpy 存储"""
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if len(self._embeddings) >= self.max_size:
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# 淘汰最早的
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self._embeddings.pop(0)
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# 重建 cache（ID 偏移）
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old_cache = self._cache
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self._cache = {}
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for i, (k, v) in enumerate(old_cache.items()):
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if i > 0:
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self._cache[i - 1] = v
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query = self._normalize(query_emb)
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self._embeddings.append(query)
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self._cache[len(self._embeddings) - 1] = result
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def _normalize(self, emb: np.ndarray) -> np.ndarray:
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"""归一化向量"""
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emb = np.array(emb, dtype='float32')
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norm = np.linalg.norm(emb)
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if norm > 0:
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emb = emb / norm
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return emb
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def clear(self) -> None:
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"""清空缓存"""
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with self._lock:
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if self._use_faiss:
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self._index = faiss.IndexFlatIP(self.dim)
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else:
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self._embeddings.clear()
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self._cache.clear()
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self._next_id = 0
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logger.info("语义缓存已清空")
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def get_stats(self) -> Dict:
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"""获取统计信息"""
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with self._lock:
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total = self._hits + self._misses
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hit_rate = self._hits / total if total > 0 else 0
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return {
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"total_entries": self._index.ntotal if self._use_faiss else len(self._embeddings),
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"max_size": self.max_size,
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"hits": self._hits,
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"misses": self._misses,
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"hit_rate": hit_rate,
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"use_faiss": self._use_faiss
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}
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# ==================== 全局语义缓存实例 ====================
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_semantic_cache: Optional[SemanticCache] = None
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_semantic_cache_lock = threading.Lock()
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def get_semantic_cache(dim: int = 768) -> SemanticCache:
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"""获取全局语义缓存实例"""
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global _semantic_cache
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if _semantic_cache is None:
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with _semantic_cache_lock:
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if _semantic_cache is None:
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try:
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from config import SEMANTIC_CACHE_THRESHOLD
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threshold = SEMANTIC_CACHE_THRESHOLD
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except ImportError:
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threshold = 0.92
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_semantic_cache = SemanticCache(dim=dim, threshold=threshold)
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return _semantic_cache
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# ==================== 测试 ====================
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if __name__ == "__main__":
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import sys
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if sys.platform == 'win32':
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sys.stdout.reconfigure(encoding='utf-8')
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print("=" * 60)
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print("语义缓存测试")
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print("=" * 60)
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cache = SemanticCache(dim=128, threshold=0.9, max_size=100)
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# 生成测试向量
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np.random.seed(42)
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def random_embedding():
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emb = np.random.randn(128)
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return emb / np.linalg.norm(emb)
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# 存储一些向量
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print("\n存储测试向量...")
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base_emb = random_embedding()
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cache.set(base_emb, {"answer": "测试答案1", "confidence": 0.9})
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for i in range(10):
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emb = random_embedding()
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cache.set(emb, {"answer": f"测试答案{i+2}", "confidence": 0.8})
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print(f"缓存统计: {cache.get_stats()}")
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# 测试精确命中
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print("\n测试精确命中...")
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result = cache.get(base_emb)
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print(f"结果: {result}")
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# 测试相似命中
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print("\n测试相似命中...")
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similar_emb = base_emb + np.random.randn(128) * 0.05
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similar_emb = similar_emb / np.linalg.norm(similar_emb)
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result = cache.get(similar_emb)
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print(f"结果: {result}")
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# 测试未命中
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print("\n测试未命中...")
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different_emb = random_embedding()
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result = cache.get(different_emb)
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print(f"结果: {result}")
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print(f"\n最终统计: {cache.get_stats()}")
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