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/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
-                if (use_llm_check and llm is not None)
-                else (llm_model_func if use_llm_check else None),
+                llm_func=llm_model_func if use_llm_check else None,
                 original_prompt=prompt if use_llm_check else None,
                 cache_type=cache_type,
             )