315 lines
14 KiB
Python
315 lines
14 KiB
Python
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"""Task for regenerating summary indexes when dataset settings change."""
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import logging
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import time
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from collections import defaultdict
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import click
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from celery import shared_task
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from sqlalchemy import or_, select
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from core.db.session_factory import session_factory
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from core.rag.index_processor.constant.index_type import IndexStructureType, IndexTechniqueType
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from models.dataset import Dataset, DocumentSegment, DocumentSegmentSummary
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from models.dataset import Document as DatasetDocument
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from services.summary_index_service import SummaryIndexService
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logger = logging.getLogger(__name__)
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@shared_task(queue="dataset_summary")
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def regenerate_summary_index_task(
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dataset_id: str,
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regenerate_reason: str = "summary_model_changed",
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regenerate_vectors_only: bool = False,
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):
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"""
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Regenerate summary indexes for all documents in a dataset.
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This task is triggered when:
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1. summary_index_setting model changes (regenerate_reason="summary_model_changed")
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- Regenerates summary content and vectors for all existing summaries
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2. embedding_model changes (regenerate_reason="embedding_model_changed")
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- Only regenerates vectors for existing summaries (keeps summary content)
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Args:
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dataset_id: Dataset ID
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regenerate_reason: Reason for regeneration ("summary_model_changed" or "embedding_model_changed")
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regenerate_vectors_only: If True, only regenerate vectors without regenerating summary content
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"""
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logger.info(
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click.style(
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f"Start regenerate summary index for dataset {dataset_id}, reason: {regenerate_reason}",
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fg="green",
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)
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)
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start_at = time.perf_counter()
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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 not dataset:
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logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
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return
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# Only regenerate summary index for high_quality indexing technique
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if dataset.indexing_technique != IndexTechniqueType.HIGH_QUALITY:
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logger.info(
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click.style(
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f"Skipping summary regeneration for dataset {dataset_id}: "
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f"indexing_technique is {dataset.indexing_technique}, not 'high_quality'",
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fg="cyan",
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)
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)
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return
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# Check if summary index is enabled (only for summary_model change)
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# For embedding_model change, we still re-vectorize existing summaries even if setting is disabled
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summary_index_setting = dataset.summary_index_setting
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if not regenerate_vectors_only:
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# For summary_model change, require summary_index_setting to be enabled
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if not summary_index_setting or not summary_index_setting.get("enable"):
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logger.info(
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click.style(
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f"Summary index is disabled for dataset {dataset_id}",
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fg="cyan",
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)
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)
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return
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total_segments_processed = 0
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total_segments_failed = 0
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if regenerate_vectors_only:
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# For embedding_model change: directly query all segments with existing summaries
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# Don't require document indexing_status == "completed"
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# Include summaries with status "completed" or "error" (if they have content)
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segments_with_summaries = session.execute(
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select(DocumentSegment, DocumentSegmentSummary)
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.join(
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DocumentSegmentSummary,
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DocumentSegment.id == DocumentSegmentSummary.chunk_id,
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)
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.join(
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DatasetDocument,
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DocumentSegment.document_id == DatasetDocument.id,
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)
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.where(
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DocumentSegment.dataset_id == dataset_id,
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DocumentSegment.status == "completed", # Segment must be completed
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DocumentSegment.enabled == True,
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DocumentSegmentSummary.dataset_id == dataset_id,
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DocumentSegmentSummary.summary_content.isnot(None), # Must have summary content
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# Include completed summaries or error summaries (with content)
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or_(
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DocumentSegmentSummary.status == "completed",
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DocumentSegmentSummary.status == "error",
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),
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DatasetDocument.enabled == True, # Document must be enabled
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DatasetDocument.archived == False, # Document must not be archived
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DatasetDocument.doc_form != IndexStructureType.QA_INDEX, # Skip qa_model documents
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)
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.order_by(DocumentSegment.document_id.asc(), DocumentSegment.position.asc())
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).all()
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if not segments_with_summaries:
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logger.info(
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click.style(
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f"No segments with summaries found for re-vectorization in dataset {dataset_id}",
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fg="cyan",
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)
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)
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return
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logger.info(
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"Found %s segments with summaries for re-vectorization in dataset %s",
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len(segments_with_summaries),
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dataset_id,
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)
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# Group by document for logging
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segments_by_document = defaultdict(list)
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for segment, summary_record in segments_with_summaries:
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segments_by_document[segment.document_id].append((segment, summary_record))
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logger.info(
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"Segments grouped into %s documents for re-vectorization",
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len(segments_by_document),
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)
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for document_id, segment_summary_pairs in segments_by_document.items():
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logger.info(
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"Re-vectorizing summaries for %s segments in document %s",
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len(segment_summary_pairs),
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document_id,
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)
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for segment, summary_record in segment_summary_pairs:
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try:
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# Delete old vector
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if summary_record.summary_index_node_id:
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try:
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from core.rag.datasource.vdb.vector_factory import Vector
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vector = Vector(dataset)
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vector.delete_by_ids([summary_record.summary_index_node_id])
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except Exception as e:
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logger.warning(
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"Failed to delete old summary vector for segment %s: %s",
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segment.id,
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str(e),
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)
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# Re-vectorize with new embedding model
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SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
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session.commit()
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total_segments_processed += 1
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except Exception as e:
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logger.error(
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"Failed to re-vectorize summary for segment %s: %s",
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segment.id,
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str(e),
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exc_info=True,
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)
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total_segments_failed += 1
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# Update summary record with error status
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summary_record.status = "error"
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summary_record.error = f"Re-vectorization failed: {str(e)}"
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session.add(summary_record)
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session.commit()
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continue
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else:
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# For summary_model change: require document indexing_status == "completed"
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# Get all documents with completed indexing status
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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 not dataset_documents:
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logger.info(
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click.style(
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f"No documents found for summary regeneration in dataset {dataset_id}",
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fg="cyan",
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)
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)
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return
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logger.info(
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"Found %s documents for summary regeneration in dataset %s",
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len(dataset_documents),
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dataset_id,
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)
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for dataset_document in dataset_documents:
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# Skip qa_model documents
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if dataset_document.doc_form == IndexStructureType.QA_INDEX:
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continue
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try:
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# Get all segments with existing summaries
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segments = session.scalars(
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select(DocumentSegment)
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.join(
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DocumentSegmentSummary,
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DocumentSegment.id == DocumentSegmentSummary.chunk_id,
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)
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.where(
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DocumentSegment.document_id == dataset_document.id,
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DocumentSegment.dataset_id == dataset_id,
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DocumentSegment.status == "completed",
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DocumentSegment.enabled == True,
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DocumentSegmentSummary.dataset_id == dataset_id,
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)
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.order_by(DocumentSegment.position.asc())
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).all()
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if not segments:
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continue
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logger.info(
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"Regenerating summaries for %s segments in document %s",
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len(segments),
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dataset_document.id,
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)
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for segment in segments:
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summary_record = None
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try:
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# Get existing summary record
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summary_record = session.scalar(
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select(DocumentSegmentSummary)
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.where(
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DocumentSegmentSummary.chunk_id == segment.id,
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DocumentSegmentSummary.dataset_id == dataset_id,
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)
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.limit(1)
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)
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if not summary_record:
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logger.warning("Summary record not found for segment %s, skipping", segment.id)
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continue
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# Regenerate both summary content and vectors (for summary_model change)
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SummaryIndexService.generate_and_vectorize_summary(
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segment, dataset, summary_index_setting
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)
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session.commit()
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total_segments_processed += 1
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except Exception as e:
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logger.error(
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"Failed to regenerate summary for segment %s: %s",
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segment.id,
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str(e),
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exc_info=True,
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)
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total_segments_failed += 1
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# Update summary record with error status
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if summary_record:
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summary_record.status = "error"
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summary_record.error = f"Regeneration failed: {str(e)}"
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session.add(summary_record)
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session.commit()
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continue
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except Exception as e:
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logger.error(
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"Failed to process document %s for summary regeneration: %s",
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dataset_document.id,
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str(e),
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exc_info=True,
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)
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continue
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end_at = time.perf_counter()
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if regenerate_vectors_only:
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logger.info(
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click.style(
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f"Summary re-vectorization completed for dataset {dataset_id}: "
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f"{total_segments_processed} segments processed successfully, "
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f"{total_segments_failed} segments failed, "
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f"latency: {end_at - start_at:.2f}s",
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fg="green",
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)
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)
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else:
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logger.info(
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click.style(
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f"Summary index regeneration completed for dataset {dataset_id}: "
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f"{total_segments_processed} segments processed successfully, "
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f"{total_segments_failed} segments failed, "
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f"latency: {end_at - start_at:.2f}s",
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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("Regenerate summary index failed for dataset %s", dataset_id)
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