fix lint

langchain-ai · Mar 6, 2024 · a003ed0 · a003ed0
1 parent 0541ff4
commit a003ed0
Showing 1 changed file with 27 additions and 7 deletions.
diff --git a/libs/community/langchain_community/vectorstores/baiduvectordb.py b/libs/community/langchain_community/vectorstores/baiduvectordb.py
@@ -142,12 +142,30 @@ def _create_table(self, table_name: str) -> None:
                 efconstruction=self.table_params.params.get("efConstruction", 200),
             )
         fields = []
-        fields.append(schema.Field(self.field_id, self.mochowenum.FieldType.STRING, primary_key=True,
-            partition_key=True, auto_increment=False, not_null=True))
-        fields.append(schema.Field(self.field_vector, self.mochowenum.FieldType.FLOAT_VECTOR,
-            dimension=self.table_params.dimension))
+        fields.append(
+            schema.Field(
+                self.field_id, 
+                self.mochowenum.FieldType.STRING, 
+                primary_key=True,
+                partition_key=True, 
+                auto_increment=False, 
+                not_null=True
+            )
+        )
+        fields.append(
+            schema.Field(
+                self.field_vector, 
+                self.mochowenum.FieldType.FLOAT_VECTOR,
+                dimension=self.table_params.dimension
+            )
+        )
         fields.append(schema.Field(self.field_text, self.mochowenum.FieldType.STRING))
-        fields.append(schema.Field(self.field_metadata, self.mochowenum.FieldType.STRING))
+        fields.append(
+            schema.Field(
+                self.field_metadata, 
+                self.mochowenum.FieldType.STRING
+            )
+        )
         indexes = []
         indexes.append(schema.VectorIndex(index_name=self.index_vector,
             index_type=index_type,
@@ -322,7 +340,8 @@ def _similarity_search_with_score(
                 meta = row_data.get(self.field_metadata)
                 if meta is not None:
                     meta = json.loads(meta)
-                doc = Document(page_content=row_data.get(self.field_text), metadata=meta)  # type: ignore[arg-type]
+                doc = Document(page_content=row_data.get(self.field_text),
+                               metadata=meta)
                 pair = (doc, result.get("distance", 0.0))
                 ret.append(pair)
         return ret
@@ -379,7 +398,8 @@ def _max_marginal_relevance_search(
                 meta = row_data.get(self.field_metadata)
                 if meta is not None:
                     meta = json.loads(meta)
-                doc = Document(page_content=row_data.get(self.field_text), metadata=meta)  # type: ignore[arg-type]
+                doc = Document(page_content=row_data.get(self.field_text), 
+                               metadata=meta)
                 documents.append(doc)
                 ordered_result_embeddings.append(row_data.get(self.field_vector))
         # Get the new order of results.