Revert "Refactor embedding functions and add async query limit"

This reverts commit 21481dba8f.

lightrag/kg/nano_vector_db_impl.py

@@ -76,8 +76,6 @@ class NanoVectorDBStorage(BaseVectorStorage):
     cosine_better_than_threshold: float = float(os.getenv("COSINE_THRESHOLD", "0.2"))
 
     def __post_init__(self):
-        # Initialize lock only for file operations
-        self._save_lock = asyncio.Lock()
         # Use global config value if specified, otherwise use default
         config = self.global_config.get("vector_db_storage_cls_kwargs", {})
         self.cosine_better_than_threshold = config.get(
@@ -212,6 +210,4 @@ class NanoVectorDBStorage(BaseVectorStorage):
             logger.error(f"Error deleting relations for {entity_name}: {e}")
 
     async def index_done_callback(self):
-        # Protect file write operation
+        self._client.save()
-        async with self._save_lock:
-            self._client.save()

lightrag/lightrag.py

@@ -154,7 +154,6 @@ class LightRAG:
     embedding_func: EmbeddingFunc = None  # This must be set (we do want to separate llm from the corte, so no more default initialization)
     embedding_batch_num: int = 32
     embedding_func_max_async: int = 16
-    embedding_func_max_async_query: int = 4
 
     # LLM
     llm_model_func: callable = None  # This must be set (we do want to separate llm from the corte, so no more default initialization)
@@ -196,13 +195,10 @@ class LightRAG:
         _print_config = ",\n  ".join([f"{k} = {v}" for k, v in global_config.items()])
         logger.debug(f"LightRAG init with param:\n  {_print_config}\n")
 
-        # Init embedding functions with separate instances for insert and query
+        # Init LLM
-        self.insert_embedding_func = limit_async_func_call(
+        self.embedding_func = limit_async_func_call(self.embedding_func_max_async)(
-            self.embedding_func_max_async
+            self.embedding_func
-        )(self.embedding_func)
+        )
-        self.query_embedding_func = limit_async_func_call(
-            self.embedding_func_max_async_query
-        )(self.embedding_func)
 
         # Initialize all storages
         self.key_string_value_json_storage_cls: Type[BaseKVStorage] = (
@@ -242,15 +238,15 @@ class LightRAG:
         ####
         self.full_docs = self.key_string_value_json_storage_cls(
             namespace="full_docs",
-            embedding_func=self.insert_embedding_func,
+            embedding_func=self.embedding_func,
         )
         self.text_chunks = self.key_string_value_json_storage_cls(
             namespace="text_chunks",
-            embedding_func=self.insert_embedding_func,
+            embedding_func=self.embedding_func,
         )
         self.chunk_entity_relation_graph = self.graph_storage_cls(
             namespace="chunk_entity_relation",
-            embedding_func=self.insert_embedding_func,
+            embedding_func=self.embedding_func,
         )
         ####
         # add embedding func by walter over
@@ -258,17 +254,17 @@ class LightRAG:
 
         self.entities_vdb = self.vector_db_storage_cls(
             namespace="entities",
-            embedding_func=self.query_embedding_func,
+            embedding_func=self.embedding_func,
             meta_fields={"entity_name"},
         )
         self.relationships_vdb = self.vector_db_storage_cls(
             namespace="relationships",
-            embedding_func=self.query_embedding_func,
+            embedding_func=self.embedding_func,
             meta_fields={"src_id", "tgt_id"},
         )
         self.chunks_vdb = self.vector_db_storage_cls(
             namespace="chunks",
-            embedding_func=self.query_embedding_func,
+            embedding_func=self.embedding_func,
         )
 
         if self.llm_response_cache and hasattr(