重构缓存处理逻辑

- 提取通用缓存处理逻辑到新函数 handle_cache 和 save_to_cache
- 使用 CacheData 类统一缓存数据结构
- 优化嵌入式缓存和常规缓存的处理流程
- 添加模式参数以支持不同查询模式的缓存策略
- 重构 get_best_cached_response 函数，提高缓存查询效率

lightrag/utils.py

@@ -310,43 +310,57 @@ def process_combine_contexts(hl, ll):
 
 
 async def get_best_cached_response(
-    hashing_kv, current_embedding, similarity_threshold=0.95
-):
-    """Get the cached response with the highest similarity"""
-    try:
-        # Get all keys
-        all_keys = await hashing_kv.all_keys()
-        max_similarity = 0
-        best_cached_response = None
-
-        # Get cached data one by one
-        for key in all_keys:
-            cache_data = await hashing_kv.get_by_id(key)
-            if cache_data is None or "embedding" not in cache_data:
-                continue
-
-            # Convert cached embedding list to ndarray
-            cached_quantized = np.frombuffer(
-                bytes.fromhex(cache_data["embedding"]), dtype=np.uint8
-            ).reshape(cache_data["embedding_shape"])
-            cached_embedding = dequantize_embedding(
-                cached_quantized,
-                cache_data["embedding_min"],
-                cache_data["embedding_max"],
-            )
-
-            similarity = cosine_similarity(current_embedding, cached_embedding)
-            if similarity > max_similarity:
-                max_similarity = similarity
-                best_cached_response = cache_data["return"]
-
-        if max_similarity > similarity_threshold:
-            return best_cached_response
+    hashing_kv,
+    current_embedding,
+    similarity_threshold=0.95,
+    mode="default",
+) -> Union[str, None]:
+    # Get mode-specific cache
+    mode_cache = await hashing_kv.get_by_id(mode)
+    if not mode_cache:
         return None
 
-    except Exception as e:
-        logger.warning(f"Error in get_best_cached_response: {e}")
-        return None
+    best_similarity = -1
+    best_response = None
+    best_prompt = None
+    best_cache_id = None
+
+    # Only iterate through cache entries for this mode
+    for cache_id, cache_data in mode_cache.items():
+        if cache_data["embedding"] is None:
+            continue
+
+        # Convert cached embedding list to ndarray
+        cached_quantized = np.frombuffer(
+            bytes.fromhex(cache_data["embedding"]), dtype=np.uint8
+        ).reshape(cache_data["embedding_shape"])
+        cached_embedding = dequantize_embedding(
+            cached_quantized,
+            cache_data["embedding_min"],
+            cache_data["embedding_max"],
+        )
+
+        similarity = cosine_similarity(current_embedding, cached_embedding)
+        if similarity > best_similarity:
+            best_similarity = similarity
+            best_response = cache_data["return"]
+            best_prompt = cache_data["original_prompt"]
+            best_cache_id = cache_id
+
+    if best_similarity > similarity_threshold:
+        prompt_display = (
+            best_prompt[:50] + "..." if len(best_prompt) > 50 else best_prompt
+        )
+        log_data = {
+            "event": "cache_hit",
+            "mode": mode,
+            "similarity": round(best_similarity, 4),
+            "cache_id": best_cache_id,
+            "original_prompt": prompt_display,
+        }
+        logger.info(json.dumps(log_data))
+        return best_response
+    return None
 
 
 def cosine_similarity(v1, v2):