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盐粒 Yanli
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125 lines
5.5 KiB
Python
125 lines
5.5 KiB
Python
import logging
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import time
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import click
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from celery import shared_task
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from sqlalchemy import delete, select
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from core.db.session_factory import session_factory
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from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
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from core.tools.utils.web_reader_tool import get_image_upload_file_ids
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from extensions.ext_storage import storage
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from models.dataset import Dataset, DatasetMetadataBinding, DocumentSegment, SegmentAttachmentBinding
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from models.model import UploadFile
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logger = logging.getLogger(__name__)
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@shared_task(queue="dataset")
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def clean_document_task(document_id: str, dataset_id: str, doc_form: str, file_id: str | None):
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"""
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Clean document when document deleted.
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:param document_id: document id
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:param dataset_id: dataset id
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:param doc_form: doc_form
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:param file_id: file id
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Usage: clean_document_task.delay(document_id, dataset_id)
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"""
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logger.info(click.style(f"Start clean document when document deleted: {document_id}", fg="green"))
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start_at = time.perf_counter()
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with session_factory.create_session() as session:
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try:
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dataset = session.query(Dataset).where(Dataset.id == dataset_id).first()
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if not dataset:
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raise Exception("Document has no dataset")
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segments = session.scalars(select(DocumentSegment).where(DocumentSegment.document_id == document_id)).all()
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# Use JOIN to fetch attachments with bindings in a single query
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attachments_with_bindings = session.execute(
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select(SegmentAttachmentBinding, UploadFile)
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.join(UploadFile, UploadFile.id == SegmentAttachmentBinding.attachment_id)
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.where(
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SegmentAttachmentBinding.tenant_id == dataset.tenant_id,
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SegmentAttachmentBinding.dataset_id == dataset_id,
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SegmentAttachmentBinding.document_id == document_id,
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)
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).all()
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# check segment is exist
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if segments:
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index_node_ids = [segment.index_node_id for segment in segments]
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index_processor = IndexProcessorFactory(doc_form).init_index_processor()
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index_processor.clean(
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dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
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)
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for segment in segments:
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image_upload_file_ids = get_image_upload_file_ids(segment.content)
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image_files = session.scalars(
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select(UploadFile).where(UploadFile.id.in_(image_upload_file_ids))
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).all()
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for image_file in image_files:
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if image_file is None:
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continue
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try:
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storage.delete(image_file.key)
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except Exception:
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logger.exception(
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"Delete image_files failed when storage deleted, \
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image_upload_file_is: %s",
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image_file.id,
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)
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image_file_delete_stmt = delete(UploadFile).where(UploadFile.id.in_(image_upload_file_ids))
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session.execute(image_file_delete_stmt)
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session.delete(segment)
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session.commit()
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if file_id:
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file = session.query(UploadFile).where(UploadFile.id == file_id).first()
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if file:
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try:
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storage.delete(file.key)
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except Exception:
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logger.exception("Delete file failed when document deleted, file_id: %s", file_id)
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session.delete(file)
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# delete segment attachments
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if attachments_with_bindings:
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attachment_ids = [attachment_file.id for _, attachment_file in attachments_with_bindings]
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binding_ids = [binding.id for binding, _ in attachments_with_bindings]
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for binding, attachment_file in attachments_with_bindings:
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try:
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storage.delete(attachment_file.key)
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except Exception:
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logger.exception(
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"Delete attachment_file failed when storage deleted, \
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attachment_file_id: %s",
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binding.attachment_id,
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)
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attachment_file_delete_stmt = delete(UploadFile).where(UploadFile.id.in_(attachment_ids))
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session.execute(attachment_file_delete_stmt)
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binding_delete_stmt = delete(SegmentAttachmentBinding).where(
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SegmentAttachmentBinding.id.in_(binding_ids)
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)
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session.execute(binding_delete_stmt)
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# delete dataset metadata binding
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session.query(DatasetMetadataBinding).where(
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DatasetMetadataBinding.dataset_id == dataset_id,
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DatasetMetadataBinding.document_id == document_id,
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).delete()
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session.commit()
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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"Cleaned document when document deleted: {document_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("Cleaned document when document deleted failed")
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