fix: dataset switch indexing_technique invalid

api/core/index/index.py

@@ -10,9 +10,9 @@ from models.dataset import Dataset
 
 class IndexBuilder:
     @classmethod
-    def get_index(cls, dataset: Dataset, indexing_technique: str):
+    def get_index(cls, dataset: Dataset, indexing_technique: str, ignore_high_quality_check: bool = False):
         if indexing_technique == "high_quality":
-            if dataset.indexing_technique != 'high_quality':
+            if not ignore_high_quality_check and dataset.indexing_technique != 'high_quality':
                 return None
 
             model_credentials = LLMBuilder.get_model_credentials(

api/tasks/deal_dataset_vector_index_task.py

@@ -26,18 +26,26 @@ def deal_dataset_vector_index_task(dataset_id: str, action: str):
         dataset = Dataset.query.filter_by(
             id=dataset_id
         ).first()
+
         if not dataset:
             raise Exception('Dataset not found')
-        dataset_documents = DatasetDocument.query.filter_by(dataset_id=dataset_id).all()
+
+        if action == "remove":
+            index = IndexBuilder.get_index(dataset, 'high_quality', ignore_high_quality_check=True)
+            index.delete()
+        elif action == "add":
+            dataset_documents = db.session.query(DatasetDocument).filter(
+                DatasetDocument.dataset_id == dataset_id,
+                DatasetDocument.indexing_status == 'completed',
+                DatasetDocument.enabled == True,
+                DatasetDocument.archived == False,
+            ).all()
+
             if dataset_documents:
                 # save vector index
-            index = IndexBuilder.get_index(dataset, 'high_quality')
-            if index:
+                index = IndexBuilder.get_index(dataset, 'high_quality', ignore_high_quality_check=True)
                 for dataset_document in dataset_documents:
                     # delete from vector index
-                    if action == "remove":
-                        index.delete_by_document_id(dataset_document.id)
-                    elif action == "add":
                     segments = db.session.query(DocumentSegment).filter(
                         DocumentSegment.document_id == dataset_document.id,
                         DocumentSegment.enabled == True
@@ -58,10 +66,7 @@ def deal_dataset_vector_index_task(dataset_id: str, action: str):
                         documents.append(document)
 
                     # save vector index
-                        index.add_texts(
-                            documents,
-                            duplicate_check=True
-                        )
+                    index.add_texts(documents)
 
         end_at = time.perf_counter()
         logging.info(