fix(batch_create_segment_to_index_task): count max_position in memory. (#12929)

api/models/dataset.py

@@ -13,6 +13,7 @@ from typing import Any, cast
 
 from sqlalchemy import func
 from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped
 
 from configs import dify_config
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
@@ -515,7 +516,7 @@ class DocumentSegment(db.Model):  # type: ignore[name-defined]
     tenant_id = db.Column(StringUUID, nullable=False)
     dataset_id = db.Column(StringUUID, nullable=False)
     document_id = db.Column(StringUUID, nullable=False)
-    position = db.Column(db.Integer, nullable=False)
+    position: Mapped[int]
     content = db.Column(db.Text, nullable=False)
     answer = db.Column(db.Text, nullable=True)
     word_count = db.Column(db.Integer, nullable=False)

api/tasks/batch_create_segment_to_index_task.py

@@ -5,7 +5,8 @@ import uuid
 
 import click
 from celery import shared_task  # type: ignore
-from sqlalchemy import func
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
 
 from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
@@ -18,7 +19,12 @@ from services.vector_service import VectorService
 
 @shared_task(queue="dataset")
 def batch_create_segment_to_index_task(
-    job_id: str, content: list, dataset_id: str, document_id: str, tenant_id: str, user_id: str
+    job_id: str,
+    content: list,
+    dataset_id: str,
+    document_id: str,
+    tenant_id: str,
+    user_id: str,
 ):
     """
     Async batch create segment to index
@@ -37,15 +43,20 @@ def batch_create_segment_to_index_task(
     indexing_cache_key = "segment_batch_import_{}".format(job_id)
 
     try:
-        dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+        with Session(db.engine) as session:
+            dataset = session.get(Dataset, dataset_id)
             if not dataset:
                 raise ValueError("Dataset not exist.")
 
-        dataset_document = db.session.query(Document).filter(Document.id == document_id).first()
+            dataset_document = session.get(Document, document_id)
             if not dataset_document:
                 raise ValueError("Document not exist.")
 
-        if not dataset_document.enabled or dataset_document.archived or dataset_document.indexing_status != "completed":
+            if (
+                not dataset_document.enabled
+                or dataset_document.archived
+                or dataset_document.indexing_status != "completed"
+            ):
                 raise ValueError("Document is not available.")
             document_segments = []
             embedding_model = None
@@ -58,25 +69,24 @@ def batch_create_segment_to_index_task(
                     model=dataset.embedding_model,
                 )
             word_count_change = 0
-        segments_to_insert: list[str] = []  # Explicitly type hint the list as List[str]
+            segments_to_insert: list[str] = []
+            max_position_stmt = select(func.max(DocumentSegment.position)).where(
+                DocumentSegment.document_id == dataset_document.id
+            )
+            max_position = session.scalar(max_position_stmt) or 1
             for segment in content:
                 content_str = segment["content"]
                 doc_id = str(uuid.uuid4())
                 segment_hash = helper.generate_text_hash(content_str)
                 # calc embedding use tokens
                 tokens = embedding_model.get_text_embedding_num_tokens(texts=[content_str]) if embedding_model else 0
-            max_position = (
-                db.session.query(func.max(DocumentSegment.position))
-                .filter(DocumentSegment.document_id == dataset_document.id)
-                .scalar()
-            )
                 segment_document = DocumentSegment(
                     tenant_id=tenant_id,
                     dataset_id=dataset_id,
                     document_id=document_id,
                     index_node_id=doc_id,
                     index_node_hash=segment_hash,
-                position=max_position + 1 if max_position else 1,
+                    position=max_position,
                     content=content_str,
                     word_count=len(content_str),
                     tokens=tokens,
@@ -85,23 +95,28 @@ def batch_create_segment_to_index_task(
                     status="completed",
                     completed_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
                 )
+                max_position += 1
                 if dataset_document.doc_form == "qa_model":
                     segment_document.answer = segment["answer"]
                     segment_document.word_count += len(segment["answer"])
                 word_count_change += segment_document.word_count
-            db.session.add(segment_document)
+                session.add(segment_document)
                 document_segments.append(segment_document)
                 segments_to_insert.append(str(segment))  # Cast to string if needed
             # update document word count
             dataset_document.word_count += word_count_change
-        db.session.add(dataset_document)
+            session.add(dataset_document)
             # add index to db
             VectorService.create_segments_vector(None, document_segments, dataset, dataset_document.doc_form)
-        db.session.commit()
+            session.commit()
+
         redis_client.setex(indexing_cache_key, 600, "completed")
         end_at = time.perf_counter()
         logging.info(
-            click.style("Segment batch created job: {} latency: {}".format(job_id, end_at - start_at), fg="green")
+            click.style(
+                "Segment batch created job: {} latency: {}".format(job_id, end_at - start_at),
+                fg="green",
+            )
         )
     except Exception as e:
         logging.exception("Segments batch created index failed")