cleaned code

lightrag/base.py

@@ -132,62 +132,75 @@ class BaseKVStorage(StorageNameSpace):
 class BaseGraphStorage(StorageNameSpace):
     embedding_func: EmbeddingFunc | None = None
     """Check if a node exists in the graph."""
+
     async def has_node(self, node_id: str) -> bool:
         raise NotImplementedError
 
     """Check if an edge exists in the graph."""
+
     async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
         raise NotImplementedError
 
     """Get the degree of a node."""
+
     async def node_degree(self, node_id: str) -> int:
         raise NotImplementedError
 
     """Get the degree of an edge."""
+
     async def edge_degree(self, src_id: str, tgt_id: str) -> int:
         raise NotImplementedError
 
     """Get a node by its id."""
+
     async def get_node(self, node_id: str) -> Union[dict[str, str], None]:
         raise NotImplementedError
 
     """Get an edge by its source and target node ids."""
+
     async def get_edge(
         self, source_node_id: str, target_node_id: str
     ) -> Union[dict[str, str], None]:
         raise NotImplementedError
 
     """Get all edges connected to a node."""
+
     async def get_node_edges(
         self, source_node_id: str
     ) -> Union[list[tuple[str, str]], None]:
         raise NotImplementedError
 
     """Upsert a node into the graph."""
+
     async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
         raise NotImplementedError
 
     """Upsert an edge into the graph."""
+
     async def upsert_edge(
-        self, source_node_id: str,
-        target_node_id: str, 
-        edge_data: dict[str, str]
+        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
     ) -> None:
         raise NotImplementedError
 
     """Delete a node from the graph."""
+
     async def delete_node(self, node_id: str) -> None:
         raise NotImplementedError
 
     """Embed nodes using an algorithm."""
-    async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray[Any, Any], list[str]]:
+
+    async def embed_nodes(
+        self, algorithm: str
+    ) -> tuple[np.ndarray[Any, Any], list[str]]:
         raise NotImplementedError("Node embedding is not used in lightrag.")
 
     """Get all labels in the graph."""
+
     async def get_all_labels(self) -> list[str]:
         raise NotImplementedError
 
     """Get a knowledge graph of a node."""
+
     async def get_knowledge_graph(
         self, node_label: str, max_depth: int = 5
     ) -> KnowledgeGraph:

lightrag/lightrag.py

@@ -982,10 +982,7 @@ class LightRAG:
                 await self._insert_done()
 
     def query(
-        self, 
-        query: str, 
-        param: QueryParam = QueryParam(), 
-        prompt: str | None = None
+        self, query: str, param: QueryParam = QueryParam(), prompt: str | None = None
     ) -> str | Iterator[str]:
         """
         Perform a sync query.
@@ -1085,10 +1082,7 @@ class LightRAG:
         return response
 
     def query_with_separate_keyword_extraction(
-        self, 
-        query: str, 
-        prompt: str, 
-        param: QueryParam = QueryParam()
+        self, query: str, prompt: str, param: QueryParam = QueryParam()
     ):
         """
         1. Extract keywords from the 'query' using new function in operate.py.
@@ -1100,10 +1094,7 @@ class LightRAG:
         )
 
     async def aquery_with_separate_keyword_extraction(
-        self, 
-        query: str, 
-        prompt: str, 
-        param: QueryParam = QueryParam()
+        self, query: str, prompt: str, param: QueryParam = QueryParam()
     ):
         """
         1. Calls extract_keywords_only to get HL/LL keywords from 'query'.
@@ -1127,8 +1118,8 @@ class LightRAG:
             ),
         )
 
-        param.hl_keywords = (hl_keywords,)
-        param.ll_keywords = (ll_keywords,)
+        param.hl_keywords = hl_keywords
+        param.ll_keywords = ll_keywords
 
         # ---------------------
         # STEP 2: Final Query Logic
@@ -1156,7 +1147,7 @@ class LightRAG:
                         self.namespace_prefix, NameSpace.KV_STORE_LLM_RESPONSE_CACHE
                     ),
                     global_config=asdict(self),
-                    embedding_func=self.embedding_funcne,
+                    embedding_func=self.embedding_func,
                 ),
             )
         elif param.mode == "naive":