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refactor: use session factory instead of call db.session directly (#31198)
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
wangxiaolei
GitHub
@ -5,11 +5,11 @@ import click
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from celery import shared_task
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from sqlalchemy import select
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from core.db.session_factory import session_factory
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from core.rag.index_processor.constant.doc_type import DocType
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from core.rag.index_processor.constant.index_type import IndexStructureType
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from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
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from core.rag.models.document import AttachmentDocument, ChildDocument, Document
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from extensions.ext_database import db
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from models.dataset import Dataset, DocumentSegment
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from models.dataset import Document as DatasetDocument
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@ -27,160 +27,170 @@ def deal_dataset_vector_index_task(dataset_id: str, action: str):
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logger.info(click.style(f"Start deal dataset vector index: {dataset_id}", fg="green"))
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start_at = time.perf_counter()
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try:
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dataset = db.session.query(Dataset).filter_by(id=dataset_id).first()
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with session_factory.create_session() as session:
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try:
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dataset = session.query(Dataset).filter_by(id=dataset_id).first()
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if not dataset:
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raise Exception("Dataset not found")
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index_type = dataset.doc_form or IndexStructureType.PARAGRAPH_INDEX
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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if action == "remove":
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index_processor.clean(dataset, None, with_keywords=False)
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elif action == "add":
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dataset_documents = db.session.scalars(
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select(DatasetDocument).where(
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DatasetDocument.dataset_id == dataset_id,
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DatasetDocument.indexing_status == "completed",
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DatasetDocument.enabled == True,
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DatasetDocument.archived == False,
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)
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).all()
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if not dataset:
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raise Exception("Dataset not found")
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index_type = dataset.doc_form or IndexStructureType.PARAGRAPH_INDEX
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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if action == "remove":
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index_processor.clean(dataset, None, with_keywords=False)
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elif action == "add":
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dataset_documents = session.scalars(
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select(DatasetDocument).where(
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DatasetDocument.dataset_id == dataset_id,
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DatasetDocument.indexing_status == "completed",
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DatasetDocument.enabled == True,
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DatasetDocument.archived == False,
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)
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).all()
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if dataset_documents:
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dataset_documents_ids = [doc.id for doc in dataset_documents]
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db.session.query(DatasetDocument).where(DatasetDocument.id.in_(dataset_documents_ids)).update(
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{"indexing_status": "indexing"}, synchronize_session=False
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)
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db.session.commit()
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if dataset_documents:
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dataset_documents_ids = [doc.id for doc in dataset_documents]
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session.query(DatasetDocument).where(DatasetDocument.id.in_(dataset_documents_ids)).update(
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{"indexing_status": "indexing"}, synchronize_session=False
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)
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session.commit()
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for dataset_document in dataset_documents:
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try:
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# add from vector index
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segments = (
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db.session.query(DocumentSegment)
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.where(DocumentSegment.document_id == dataset_document.id, DocumentSegment.enabled == True)
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.order_by(DocumentSegment.position.asc())
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.all()
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)
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if segments:
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documents = []
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for segment in segments:
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document = Document(
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page_content=segment.content,
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metadata={
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"doc_id": segment.index_node_id,
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"doc_hash": segment.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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},
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for dataset_document in dataset_documents:
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try:
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# add from vector index
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segments = (
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session.query(DocumentSegment)
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.where(
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DocumentSegment.document_id == dataset_document.id,
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DocumentSegment.enabled == True,
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)
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documents.append(document)
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# save vector index
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index_processor.load(dataset, documents, with_keywords=False)
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db.session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "completed"}, synchronize_session=False
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)
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db.session.commit()
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except Exception as e:
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db.session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "error", "error": str(e)}, synchronize_session=False
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)
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db.session.commit()
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elif action == "update":
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dataset_documents = db.session.scalars(
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select(DatasetDocument).where(
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DatasetDocument.dataset_id == dataset_id,
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DatasetDocument.indexing_status == "completed",
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DatasetDocument.enabled == True,
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DatasetDocument.archived == False,
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)
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).all()
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# add new index
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if dataset_documents:
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# update document status
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dataset_documents_ids = [doc.id for doc in dataset_documents]
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db.session.query(DatasetDocument).where(DatasetDocument.id.in_(dataset_documents_ids)).update(
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{"indexing_status": "indexing"}, synchronize_session=False
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)
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db.session.commit()
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# clean index
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index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
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for dataset_document in dataset_documents:
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# update from vector index
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try:
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segments = (
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db.session.query(DocumentSegment)
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.where(DocumentSegment.document_id == dataset_document.id, DocumentSegment.enabled == True)
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.order_by(DocumentSegment.position.asc())
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.all()
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)
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if segments:
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documents = []
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multimodal_documents = []
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for segment in segments:
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document = Document(
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page_content=segment.content,
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metadata={
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"doc_id": segment.index_node_id,
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"doc_hash": segment.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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},
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)
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if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
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child_chunks = segment.get_child_chunks()
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if child_chunks:
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child_documents = []
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for child_chunk in child_chunks:
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child_document = ChildDocument(
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page_content=child_chunk.content,
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metadata={
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"doc_id": child_chunk.index_node_id,
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"doc_hash": child_chunk.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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},
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)
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child_documents.append(child_document)
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document.children = child_documents
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if dataset.is_multimodal:
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for attachment in segment.attachments:
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multimodal_documents.append(
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AttachmentDocument(
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page_content=attachment["name"],
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metadata={
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"doc_id": attachment["id"],
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"doc_hash": "",
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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"doc_type": DocType.IMAGE,
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},
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)
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)
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documents.append(document)
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# save vector index
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index_processor.load(
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dataset, documents, multimodal_documents=multimodal_documents, with_keywords=False
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.order_by(DocumentSegment.position.asc())
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.all()
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)
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db.session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "completed"}, synchronize_session=False
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)
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db.session.commit()
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except Exception as e:
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db.session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "error", "error": str(e)}, synchronize_session=False
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)
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db.session.commit()
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else:
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# clean collection
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index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
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if segments:
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documents = []
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for segment in segments:
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document = Document(
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page_content=segment.content,
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metadata={
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"doc_id": segment.index_node_id,
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"doc_hash": segment.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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},
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)
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end_at = time.perf_counter()
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logger.info(click.style(f"Deal dataset vector index: {dataset_id} latency: {end_at - start_at}", fg="green"))
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except Exception:
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logger.exception("Deal dataset vector index failed")
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finally:
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db.session.close()
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documents.append(document)
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# save vector index
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index_processor.load(dataset, documents, with_keywords=False)
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session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "completed"}, synchronize_session=False
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)
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session.commit()
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except Exception as e:
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session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "error", "error": str(e)}, synchronize_session=False
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)
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session.commit()
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elif action == "update":
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dataset_documents = session.scalars(
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select(DatasetDocument).where(
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DatasetDocument.dataset_id == dataset_id,
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DatasetDocument.indexing_status == "completed",
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DatasetDocument.enabled == True,
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DatasetDocument.archived == False,
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)
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).all()
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# add new index
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if dataset_documents:
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# update document status
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dataset_documents_ids = [doc.id for doc in dataset_documents]
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session.query(DatasetDocument).where(DatasetDocument.id.in_(dataset_documents_ids)).update(
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{"indexing_status": "indexing"}, synchronize_session=False
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)
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session.commit()
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# clean index
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index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
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for dataset_document in dataset_documents:
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# update from vector index
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try:
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segments = (
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session.query(DocumentSegment)
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.where(
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DocumentSegment.document_id == dataset_document.id,
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DocumentSegment.enabled == True,
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)
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.order_by(DocumentSegment.position.asc())
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.all()
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)
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if segments:
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documents = []
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multimodal_documents = []
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for segment in segments:
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document = Document(
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page_content=segment.content,
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metadata={
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"doc_id": segment.index_node_id,
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"doc_hash": segment.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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},
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)
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if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
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child_chunks = segment.get_child_chunks()
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if child_chunks:
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child_documents = []
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for child_chunk in child_chunks:
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child_document = ChildDocument(
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page_content=child_chunk.content,
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metadata={
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"doc_id": child_chunk.index_node_id,
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"doc_hash": child_chunk.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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},
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)
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child_documents.append(child_document)
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document.children = child_documents
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if dataset.is_multimodal:
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for attachment in segment.attachments:
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multimodal_documents.append(
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AttachmentDocument(
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page_content=attachment["name"],
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metadata={
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"doc_id": attachment["id"],
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"doc_hash": "",
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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"doc_type": DocType.IMAGE,
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},
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)
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)
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documents.append(document)
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# save vector index
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index_processor.load(
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dataset, documents, multimodal_documents=multimodal_documents, with_keywords=False
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)
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session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "completed"}, synchronize_session=False
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)
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session.commit()
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except Exception as e:
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session.query(DatasetDocument).where(DatasetDocument.id == dataset_document.id).update(
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{"indexing_status": "error", "error": str(e)}, synchronize_session=False
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)
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session.commit()
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else:
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# clean collection
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index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
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end_at = time.perf_counter()
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logger.info(
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click.style(
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f"Deal dataset vector index: {dataset_id} latency: {end_at - start_at}",
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fg="green",
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)
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)
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except Exception:
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logger.exception("Deal dataset vector index failed")
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