style: fix lint issues (trailing whitespace and formatting)

lightrag/base.py

@@ -229,16 +229,21 @@ class BaseVectorStorage(StorageNameSpace, ABC):
         # Try to get model identifier from the embedding function
         # If it's a wrapped function (doesn't have get_model_identifier),
         # fallback to the original embedding_func from global_config
-        if hasattr(self.embedding_func, 'get_model_identifier'):
+        if hasattr(self.embedding_func, "get_model_identifier"):
             return self.embedding_func.get_model_identifier()
-        elif 'embedding_func' in self.global_config:
-            original_embedding_func = self.global_config['embedding_func']
-            if original_embedding_func is not None and hasattr(original_embedding_func, 'get_model_identifier'):
+        elif "embedding_func" in self.global_config:
+            original_embedding_func = self.global_config["embedding_func"]
+            if original_embedding_func is not None and hasattr(
+                original_embedding_func, "get_model_identifier"
+            ):
                 return original_embedding_func.get_model_identifier()
             else:
                 # Debug: log why we couldn't get model identifier
                 from lightrag.utils import logger
-                logger.debug(f"Could not get model_identifier: embedding_func is {type(original_embedding_func)}, has method={hasattr(original_embedding_func, 'get_model_identifier') if original_embedding_func else False}")
+
+                logger.debug(
+                    f"Could not get model_identifier: embedding_func is {type(original_embedding_func)}, has method={hasattr(original_embedding_func, 'get_model_identifier') if original_embedding_func else False}"
+                )
 
         # Fallback: no model identifier available
         return ""

lightrag/kg/postgres_impl.py

@@ -2316,10 +2316,10 @@ class PGVectorStorage(BaseVectorStorage):
 
             while True:
                 # Fetch a batch of rows
-                select_query = (
-                    f"SELECT * FROM {legacy_table_name} OFFSET $1 LIMIT $2"
+                select_query = f"SELECT * FROM {legacy_table_name} OFFSET $1 LIMIT $2"
+                rows = await db.query(
+                    select_query, [offset, batch_size], multirows=True
                 )
-                rows = await db.query(select_query, [offset, batch_size], multirows=True)
 
                 if not rows:
                     break
@@ -2561,7 +2561,9 @@ class PGVectorStorage(BaseVectorStorage):
         if not ids:
             return
 
-        delete_sql = f"DELETE FROM {self.table_name} WHERE workspace=$1 AND id = ANY($2)"
+        delete_sql = (
+            f"DELETE FROM {self.table_name} WHERE workspace=$1 AND id = ANY($2)"
+        )
 
         try:
             await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids})
@@ -3359,6 +3361,7 @@ class PGDocStatusStorage(DocStatusStorage):
 
 class PostgreSQLMigrationError(Exception):
     """Exception for PostgreSQL table migration errors."""
+
     pass
 
 

lightrag/kg/qdrant_impl.py

@@ -300,7 +300,7 @@ class QdrantVectorDBStorage(BaseVectorStorage):
         # New naming scheme with model isolation
         # Example: "lightrag_vdb_chunks_text_embedding_ada_002_1536d"
         self.final_namespace = f"lightrag_vdb_{self.namespace}_{model_suffix}"
-        
+
         logger.info(
             f"Qdrant collection naming: "
             f"new='{self.final_namespace}', "

lightrag/lightrag.py

@@ -533,7 +533,7 @@ class LightRAG:
         # Fix global_config now
         global_config = asdict(self)
         # Restore original EmbeddingFunc object (asdict converts it to dict)
-        global_config['embedding_func'] = original_embedding_func
+        global_config["embedding_func"] = original_embedding_func
 
         _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")

lightrag/utils.py

@@ -381,7 +381,7 @@ class EmbeddingFunc:
         """
         model_part = self.model_name if self.model_name else "unknown"
         # Clean model name: remove special chars, convert to lower, replace - with _
-        safe_model_name = re.sub(r'[^a-zA-Z0-9_]', '_', model_part.lower())
+        safe_model_name = re.sub(r"[^a-zA-Z0-9_]", "_", model_part.lower())
         return f"{safe_model_name}_{self.embedding_dim}d"
 
     async def __call__(self, *args, **kwargs) -> np.ndarray: