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/operate.py

@@ -17,6 +17,10 @@ from .utils import (
     split_string_by_multi_markers,
     truncate_list_by_token_size,
     process_combine_contexts,
+    compute_args_hash,
+    handle_cache,
+    save_to_cache,
+    CacheData,
 )
 from .base import (
     BaseGraphStorage,
@@ -452,8 +456,17 @@ async def kg_query(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
     global_config: dict,
+    hashing_kv: BaseKVStorage = None,
 ) -> str:
-    context = None
+    # Handle cache
+    use_model_func = global_config["llm_model_func"]
+    args_hash = compute_args_hash(query_param.mode, query)
+    cached_response, quantized, min_val, max_val = await handle_cache(
+        hashing_kv, args_hash, query, query_param.mode
+    )
+    if cached_response is not None:
+        return cached_response
+
     example_number = global_config["addon_params"].get("example_number", None)
     if example_number and example_number < len(PROMPTS["keywords_extraction_examples"]):
         examples = "\n".join(
@@ -471,12 +484,9 @@ async def kg_query(
         return PROMPTS["fail_response"]
 
     # LLM generate keywords
-    use_model_func = global_config["llm_model_func"]
     kw_prompt_temp = PROMPTS["keywords_extraction"]
     kw_prompt = kw_prompt_temp.format(query=query, examples=examples, language=language)
-    result = await use_model_func(
-        kw_prompt, keyword_extraction=True, mode=query_param.mode
-    )
+    result = await use_model_func(kw_prompt, keyword_extraction=True)
     logger.info("kw_prompt result:")
     print(result)
     try:
@@ -537,7 +547,6 @@ async def kg_query(
         query,
         system_prompt=sys_prompt,
         stream=query_param.stream,
-        mode=query_param.mode,
     )
     if isinstance(response, str) and len(response) > len(sys_prompt):
         response = (
@@ -550,6 +559,20 @@ async def kg_query(
             .strip()
         )
 
+    # Save to cache
+    await save_to_cache(
+        hashing_kv,
+        CacheData(
+            args_hash=args_hash,
+            content=response,
+            prompt=query,
+            quantized=quantized,
+            min_val=min_val,
+            max_val=max_val,
+            mode=query_param.mode,
+        ),
+    )
+
     return response
 
 
@@ -1013,8 +1036,17 @@ async def naive_query(
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
     global_config: dict,
+    hashing_kv: BaseKVStorage = None,
 ):
+    # Handle cache
     use_model_func = global_config["llm_model_func"]
+    args_hash = compute_args_hash(query_param.mode, query)
+    cached_response, quantized, min_val, max_val = await handle_cache(
+        hashing_kv, args_hash, query, query_param.mode
+    )
+    if cached_response is not None:
+        return cached_response
+
     results = await chunks_vdb.query(query, top_k=query_param.top_k)
     if not len(results):
         return PROMPTS["fail_response"]
@@ -1039,7 +1071,6 @@ async def naive_query(
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
-        mode=query_param.mode,
     )
 
     if len(response) > len(sys_prompt):
@@ -1054,4 +1085,18 @@ async def naive_query(
             .strip()
         )
 
+    # Save to cache
+    await save_to_cache(
+        hashing_kv,
+        CacheData(
+            args_hash=args_hash,
+            content=response,
+            prompt=query,
+            quantized=quantized,
+            min_val=min_val,
+            max_val=max_val,
+            mode=query_param.mode,
+        ),
+    )
+
     return response