fix lint

run-llama · Jan 9, 2025 · d08e9d9 · d08e9d9
1 parent ba6d5c6
commit d08e9d9
Showing 1 changed file with 15 additions and 12 deletions.
diff --git a/llama-index-core/llama_index/core/node_parser/text/semantic_splitter.py b/llama-index-core/llama_index/core/node_parser/text/semantic_splitter.py
@@ -136,19 +136,20 @@ def _parse_nodes(
 
         return all_nodes
 
-
     async def _aparse_nodes(
-            self,
-            nodes: Sequence[BaseNode],
-            show_progress: bool = False,
-            **kwargs: Any,
+        self,
+        nodes: Sequence[BaseNode],
+        show_progress: bool = False,
+        **kwargs: Any,
     ) -> List[BaseNode]:
         """Asynchronously parse document into nodes."""
         all_nodes: List[BaseNode] = []
         nodes_with_progress = get_tqdm_iterable(nodes, show_progress, "Parsing nodes")
 
         for node in nodes_with_progress:
-            nodes = await self.abuild_semantic_nodes_from_documents([node], show_progress)
+            nodes = await self.abuild_semantic_nodes_from_documents(
+                [node], show_progress
+            )
             all_nodes.extend(nodes)
 
         return all_nodes
@@ -189,9 +190,9 @@ def build_semantic_nodes_from_documents(
         return all_nodes
 
     async def abuild_semantic_nodes_from_documents(
-            self,
-            documents: Sequence[Document],
-            show_progress: bool = False,
+        self,
+        documents: Sequence[Document],
+        show_progress: bool = False,
     ) -> List[BaseNode]:
         """Asynchronously build window nodes from documents."""
         all_nodes: List[BaseNode] = []
@@ -201,9 +202,11 @@ async def abuild_semantic_nodes_from_documents(
 
             sentences = self._build_sentence_groups(text_splits)
 
-            combined_sentence_embeddings = await self.embed_model.aget_text_embedding_batch(
-                [s["combined_sentence"] for s in sentences],
-                show_progress=show_progress,
+            combined_sentence_embeddings = (
+                await self.embed_model.aget_text_embedding_batch(
+                    [s["combined_sentence"] for s in sentences],
+                    show_progress=show_progress,
+                )
             )
 
             for i, embedding in enumerate(combined_sentence_embeddings):