diff --git a/api/tasks/duplicate_document_indexing_task.py b/api/tasks/duplicate_document_indexing_task.py
index 88e8697d1..955e898ec 100644
--- a/api/tasks/duplicate_document_indexing_task.py
+++ b/api/tasks/duplicate_document_indexing_task.py
@@ -27,73 +27,73 @@ def duplicate_document_indexing_task(dataset_id: str, document_ids: list):
     documents = []
     start_at = time.perf_counter()
 
-    dataset = db.session.query(Dataset).where(Dataset.id == dataset_id).first()
-    if dataset is None:
-        logger.info(click.style(f"Dataset not found: {dataset_id}", fg="red"))
-        db.session.close()
-        return
-
-    # check document limit
-    features = FeatureService.get_features(dataset.tenant_id)
     try:
-        if features.billing.enabled:
-            vector_space = features.vector_space
-            count = len(document_ids)
-            if features.billing.subscription.plan == "sandbox" and count > 1:
-                raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
-            batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)
-            if count > batch_upload_limit:
-                raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
-            if 0 < vector_space.limit <= vector_space.size:
-                raise ValueError(
-                    "Your total number of documents plus the number of uploads have over the limit of "
-                    "your subscription."
+        dataset = db.session.query(Dataset).where(Dataset.id == dataset_id).first()
+        if dataset is None:
+            logger.info(click.style(f"Dataset not found: {dataset_id}", fg="red"))
+            db.session.close()
+            return
+
+        # check document limit
+        features = FeatureService.get_features(dataset.tenant_id)
+        try:
+            if features.billing.enabled:
+                vector_space = features.vector_space
+                count = len(document_ids)
+                if features.billing.subscription.plan == "sandbox" and count > 1:
+                    raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
+                batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)
+                if count > batch_upload_limit:
+                    raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
+                if 0 < vector_space.limit <= vector_space.size:
+                    raise ValueError(
+                        "Your total number of documents plus the number of uploads have over the limit of "
+                        "your subscription."
+                    )
+        except Exception as e:
+            for document_id in document_ids:
+                document = (
+                    db.session.query(Document)
+                    .where(Document.id == document_id, Document.dataset_id == dataset_id)
+                    .first()
                 )
-    except Exception as e:
+                if document:
+                    document.indexing_status = "error"
+                    document.error = str(e)
+                    document.stopped_at = naive_utc_now()
+                    db.session.add(document)
+            db.session.commit()
+            return
+
         for document_id in document_ids:
+            logger.info(click.style(f"Start process document: {document_id}", fg="green"))
+
             document = (
                 db.session.query(Document).where(Document.id == document_id, Document.dataset_id == dataset_id).first()
             )
+
             if document:
-                document.indexing_status = "error"
-                document.error = str(e)
-                document.stopped_at = naive_utc_now()
+                # clean old data
+                index_type = document.doc_form
+                index_processor = IndexProcessorFactory(index_type).init_index_processor()
+
+                segments = db.session.query(DocumentSegment).where(DocumentSegment.document_id == document_id).all()
+                if segments:
+                    index_node_ids = [segment.index_node_id for segment in segments]
+
+                    # delete from vector index
+                    index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
+
+                    for segment in segments:
+                        db.session.delete(segment)
+                    db.session.commit()
+
+                document.indexing_status = "parsing"
+                document.processing_started_at = naive_utc_now()
+                documents.append(document)
                 db.session.add(document)
         db.session.commit()
-        return
-    finally:
-        db.session.close()
 
-    for document_id in document_ids:
-        logger.info(click.style(f"Start process document: {document_id}", fg="green"))
-
-        document = (
-            db.session.query(Document).where(Document.id == document_id, Document.dataset_id == dataset_id).first()
-        )
-
-        if document:
-            # clean old data
-            index_type = document.doc_form
-            index_processor = IndexProcessorFactory(index_type).init_index_processor()
-
-            segments = db.session.query(DocumentSegment).where(DocumentSegment.document_id == document_id).all()
-            if segments:
-                index_node_ids = [segment.index_node_id for segment in segments]
-
-                # delete from vector index
-                index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
-
-                for segment in segments:
-                    db.session.delete(segment)
-                db.session.commit()
-
-            document.indexing_status = "parsing"
-            document.processing_started_at = naive_utc_now()
-            documents.append(document)
-            db.session.add(document)
-    db.session.commit()
-
-    try:
         indexing_runner = IndexingRunner()
         indexing_runner.run(documents)
         end_at = time.perf_counter()