feat(cache): 增加 LLM 相似性检查功能并优化缓存机制

- 在 embedding 缓存配置中添加 use_llm_check 参数
- 实现 LLM 相似性检查逻辑，作为缓存命中的二次验证- 优化 naive 模式的缓存处理流程
- 调整缓存数据结构，移除不必要的 model 字段

lightrag/utils.py

@@ -15,6 +15,8 @@ import xml.etree.ElementTree as ET
 import numpy as np
 import tiktoken
 
+from lightrag.prompt import PROMPTS
+
 ENCODER = None
 
 logger = logging.getLogger("lightrag")
@@ -314,6 +316,9 @@ async def get_best_cached_response(
     current_embedding,
     similarity_threshold=0.95,
     mode="default",
+    use_llm_check=False,
+    llm_func=None,
+    original_prompt=None,
 ) -> Union[str, None]:
     # Get mode-specific cache
     mode_cache = await hashing_kv.get_by_id(mode)
@@ -348,6 +353,37 @@ async def get_best_cached_response(
             best_cache_id = cache_id
 
     if best_similarity > similarity_threshold:
+        # If LLM check is enabled and all required parameters are provided
+        if use_llm_check and llm_func and original_prompt and best_prompt:
+            compare_prompt = PROMPTS["similarity_check"].format(
+                original_prompt=original_prompt, cached_prompt=best_prompt
+            )
+
+            try:
+                llm_result = await llm_func(compare_prompt)
+                llm_result = llm_result.strip()
+                llm_similarity = float(llm_result)
+
+                # Replace vector similarity with LLM similarity score
+                best_similarity = llm_similarity
+                if best_similarity < similarity_threshold:
+                    log_data = {
+                        "event": "llm_check_cache_rejected",
+                        "original_question": original_prompt[:100] + "..."
+                        if len(original_prompt) > 100
+                        else original_prompt,
+                        "cached_question": best_prompt[:100] + "..."
+                        if len(best_prompt) > 100
+                        else best_prompt,
+                        "similarity_score": round(best_similarity, 4),
+                        "threshold": similarity_threshold,
+                    }
+                    logger.info(json.dumps(log_data, ensure_ascii=False))
+                    return None
+            except Exception as e:  # Catch all possible exceptions
+                logger.warning(f"LLM similarity check failed: {e}")
+                return None  # Return None directly when LLM check fails
+
         prompt_display = (
             best_prompt[:50] + "..." if len(best_prompt) > 50 else best_prompt
         )
@@ -391,21 +427,33 @@ def dequantize_embedding(
     scale = (max_val - min_val) / (2**bits - 1)
     return (quantized * scale + min_val).astype(np.float32)
 
+
 async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
     """Generic cache handling function"""
     if hashing_kv is None:
         return None, None, None, None
 
+    # For naive mode, only use simple cache matching
+    if mode == "naive":
+        mode_cache = await hashing_kv.get_by_id(mode) or {}
+        if args_hash in mode_cache:
+            return mode_cache[args_hash]["return"], None, None, None
+        return None, None, None, None
+
     # Get embedding cache configuration
     embedding_cache_config = hashing_kv.global_config.get(
-        "embedding_cache_config", {"enabled": False, "similarity_threshold": 0.95}
+        "embedding_cache_config",
+        {"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False},
     )
     is_embedding_cache_enabled = embedding_cache_config["enabled"]
+    use_llm_check = embedding_cache_config.get("use_llm_check", False)
 
     quantized = min_val = max_val = None
     if is_embedding_cache_enabled:
         # Use embedding cache
         embedding_model_func = hashing_kv.global_config["embedding_func"]["func"]
+        llm_model_func = hashing_kv.global_config.get("llm_model_func")
+
         current_embedding = await embedding_model_func([prompt])
         quantized, min_val, max_val = quantize_embedding(current_embedding[0])
         best_cached_response = await get_best_cached_response(
@@ -413,6 +461,9 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
             current_embedding[0],
             similarity_threshold=embedding_cache_config["similarity_threshold"],
             mode=mode,
+            use_llm_check=use_llm_check,
+            llm_func=llm_model_func if use_llm_check else None,
+            original_prompt=prompt if use_llm_check else None,
         )
         if best_cached_response is not None:
             return best_cached_response, None, None, None
@@ -429,7 +480,6 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
 class CacheData:
     args_hash: str
     content: str
-    model: str
     prompt: str
     quantized: Optional[np.ndarray] = None
     min_val: Optional[float] = None
@@ -445,7 +495,6 @@ async def save_to_cache(hashing_kv, cache_data: CacheData):
 
     mode_cache[cache_data.args_hash] = {
         "return": cache_data.content,
-        "model": cache_data.model,
         "embedding": cache_data.quantized.tobytes().hex()
         if cache_data.quantized is not None
         else None,