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80 lines
3.3 KiB
Python
80 lines
3.3 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 models.dataset import Dataset, Document, DocumentSegment
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logger = logging.getLogger(__name__)
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@shared_task(queue="dataset")
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def clean_notion_document_task(document_ids: list[str], dataset_id: str):
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"""
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Clean document when document deleted.
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:param document_ids: document ids
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:param dataset_id: dataset id
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Usage: clean_notion_document_task.delay(document_ids, dataset_id)
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"""
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logger.info(click.style(f"Start clean document when import form notion document deleted: {dataset_id}", fg="green"))
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start_at = time.perf_counter()
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total_index_node_ids = []
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with session_factory.create_session() as session:
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dataset = session.scalar(select(Dataset).where(Dataset.id == dataset_id).limit(1))
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if not dataset:
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raise Exception("Document has no dataset")
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index_type = dataset.doc_form
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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document_delete_stmt = delete(Document).where(Document.id.in_(document_ids))
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session.execute(document_delete_stmt)
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for document_id in document_ids:
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segments = session.scalars(select(DocumentSegment).where(DocumentSegment.document_id == document_id)).all()
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total_index_node_ids.extend([segment.index_node_id for segment in segments])
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# Wrap vector / keyword index cleanup in try/except so that a transient
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# failure here (e.g. billing API hiccup propagated via FeatureService when
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# ``ModelManager`` is initialized inside ``Vector(dataset)``) does not abort
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# the task and leave the already-deleted documents' segments stranded in PG.
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# The Document rows are hard-deleted in the previous session block, so any
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# exception escaping this task would produce orphans that no later request
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# can reference back. Mirrors the pattern in ``clean_dataset_task``.
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try:
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with session_factory.create_session() as session:
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dataset = session.scalar(select(Dataset).where(Dataset.id == dataset_id).limit(1))
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if dataset:
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index_processor.clean(
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dataset, total_index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
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)
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except Exception:
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logger.exception(
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"Failed to clean vector / keyword index in clean_notion_document_task, "
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"dataset_id=%s, document_ids=%s, index_node_ids_count=%d. "
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"Continuing with segment deletion; vector orphans can be reaped later.",
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dataset_id,
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document_ids,
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len(total_index_node_ids),
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)
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with session_factory.create_session() as session, session.begin():
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segment_delete_stmt = delete(DocumentSegment).where(DocumentSegment.document_id.in_(document_ids))
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session.execute(segment_delete_stmt)
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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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"Clean document when import form notion document deleted end :: {} latency: {}".format(
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dataset_id, end_at - start_at
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),
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fg="green",
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)
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)
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