Refactor LLM cache handling and entity extraction

- Removed custom LLM function in entity extraction
- Simplified cache handling logic
- Added `force_llm_cache` parameter
- Updated cache handling conditions

lightrag/operate.py

@@ -352,32 +352,6 @@ async def extract_entities(
         input_text: str, history_messages: list[dict[str, str]] = None
     ) -> str:
         if enable_llm_cache_for_entity_extract and llm_response_cache:
-            custom_llm = None
-            if (
-                global_config["embedding_cache_config"]
-                and global_config["embedding_cache_config"]["enabled"]
-            ):
-                new_config = global_config.copy()
-                new_config["embedding_cache_config"] = None
-                new_config["enable_llm_cache"] = True
-
-                # create a llm function with new_config for handle_cache
-                async def custom_llm(
-                    prompt,
-                    system_prompt=None,
-                    history_messages=[],
-                    keyword_extraction=False,
-                    **kwargs,
-                ) -> str:
-                    # 合并 new_config 和其他 kwargs,保证其他参数不被覆盖
-                    merged_config = {**kwargs, **new_config}
-                    return await use_llm_func(
-                        prompt,
-                        system_prompt=system_prompt,
-                        history_messages=history_messages,
-                        keyword_extraction=keyword_extraction,
-                        **merged_config,
-                    )
 
             if history_messages:
                 history = json.dumps(history_messages, ensure_ascii=False)
@@ -392,7 +366,7 @@ async def extract_entities(
                 _prompt,
                 "default",
                 cache_type="extract",
-                llm=custom_llm,
+                force_llm_cache=True,
             )
             if cached_return:
                 logger.debug(f"Found cache for {arg_hash}")

lightrag/utils.py

@@ -484,10 +484,10 @@ def dequantize_embedding(
 
 
 async def handle_cache(
-    hashing_kv, args_hash, prompt, mode="default", cache_type=None, llm=None
+    hashing_kv, args_hash, prompt, mode="default", cache_type=None, force_llm_cache=False
 ):
     """Generic cache handling function"""
-    if hashing_kv is None or not hashing_kv.global_config.get("enable_llm_cache"):
+    if hashing_kv is None or not (force_llm_cache or hashing_kv.global_config.get("enable_llm_cache")):
         return None, None, None, None
 
     if mode != "default":
@@ -513,9 +513,7 @@ async def handle_cache(
                 similarity_threshold=embedding_cache_config["similarity_threshold"],
                 mode=mode,
                 use_llm_check=use_llm_check,
-                llm_func=llm
+                llm_func=llm_model_func if use_llm_check else None,
-                if (use_llm_check and llm is not None)
-                else (llm_model_func if use_llm_check else None),
                 original_prompt=prompt if use_llm_check else None,
                 cache_type=cache_type,
             )