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feat: Optimize codes.
This commit is contained in:
FFXN
@ -6,7 +6,7 @@ import time
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import click
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from celery import shared_task
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from extensions.ext_database import db
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from core.db.session_factory import session_factory
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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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from services.summary_index_service import SummaryIndexService
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@ -37,76 +37,72 @@ def generate_summary_index_task(dataset_id: str, document_id: str, segment_ids:
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start_at = time.perf_counter()
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try:
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dataset = db.session.query(Dataset).where(Dataset.id == dataset_id).first()
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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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db.session.close()
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return
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with session_factory.create_session() as session:
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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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logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
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return
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document = db.session.query(DatasetDocument).where(DatasetDocument.id == document_id).first()
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if not document:
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logger.error(click.style(f"Document not found: {document_id}", fg="red"))
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db.session.close()
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return
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document = session.query(DatasetDocument).where(DatasetDocument.id == document_id).first()
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if not document:
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logger.error(click.style(f"Document not found: {document_id}", fg="red"))
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return
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# Only generate summary index for high_quality indexing technique
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if dataset.indexing_technique != "high_quality":
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# Only generate summary index for high_quality indexing technique
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if dataset.indexing_technique != "high_quality":
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logger.info(
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click.style(
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f"Skipping summary generation 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
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summary_index_setting = dataset.summary_index_setting
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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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# Determine if only parent chunks should be processed
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only_parent_chunks = dataset.chunk_structure == "parent_child_index"
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# Generate summaries
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summary_records = SummaryIndexService.generate_summaries_for_document(
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dataset=dataset,
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document=document,
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summary_index_setting=summary_index_setting,
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segment_ids=segment_ids,
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only_parent_chunks=only_parent_chunks,
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)
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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"Skipping summary generation 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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f"Summary index generation completed for document {document_id}: "
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f"{len(summary_records)} summaries generated, latency: {end_at - start_at}",
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fg="green",
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)
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)
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db.session.close()
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return
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# Check if summary index is enabled
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summary_index_setting = dataset.summary_index_setting
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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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db.session.close()
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return
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# Determine if only parent chunks should be processed
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only_parent_chunks = dataset.chunk_structure == "parent_child_index"
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# Generate summaries
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summary_records = SummaryIndexService.generate_summaries_for_document(
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dataset=dataset,
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document=document,
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summary_index_setting=summary_index_setting,
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segment_ids=segment_ids,
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only_parent_chunks=only_parent_chunks,
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)
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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"Summary index generation completed for document {document_id}: "
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f"{len(summary_records)} summaries generated, 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 as e:
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logger.exception("Failed to generate summary index for document %s", document_id)
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# Update document segments with error status if needed
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if segment_ids:
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db.session.query(DocumentSegment).filter(
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DocumentSegment.id.in_(segment_ids),
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DocumentSegment.dataset_id == dataset_id,
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).update(
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{
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DocumentSegment.error: f"Summary generation failed: {str(e)}",
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},
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synchronize_session=False,
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)
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db.session.commit()
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finally:
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db.session.close()
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with session_factory.create_session() as session:
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session.query(DocumentSegment).filter(
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DocumentSegment.id.in_(segment_ids),
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DocumentSegment.dataset_id == dataset_id,
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).update(
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{
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DocumentSegment.error: f"Summary generation failed: {str(e)}",
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},
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synchronize_session=False,
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)
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session.commit()
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@ -8,7 +8,7 @@ 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 extensions.ext_database import db
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from core.db.session_factory import session_factory
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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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@ -45,275 +45,271 @@ def regenerate_summary_index_task(
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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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if not dataset:
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logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
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db.session.close()
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return
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with session_factory.create_session() as session:
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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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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 != "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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db.session.close()
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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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# Only regenerate summary index for high_quality indexing technique
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if dataset.indexing_technique != "high_quality":
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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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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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db.session.close()
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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 = (
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db.session.query(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 != "qa_model", # 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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)
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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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db.session.close()
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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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db.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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db.session.add(summary_record)
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db.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 = 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_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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db.session.close()
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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 == "qa_model":
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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 = (
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db.session.query(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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)
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if not segments:
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continue
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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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"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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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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|
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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 = (
|
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session.query(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 != "qa_model", # 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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)
|
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|
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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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|
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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),
|
||||
)
|
||||
|
||||
for document_id, segment_summary_pairs in segments_by_document.items():
|
||||
logger.info(
|
||||
"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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|
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for segment in segments:
|
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summary_record = None
|
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for segment, summary_record in segment_summary_pairs:
|
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try:
|
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# Get existing summary record
|
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summary_record = (
|
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db.session.query(DocumentSegmentSummary)
|
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.filter_by(
|
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chunk_id=segment.id,
|
||||
dataset_id=dataset_id,
|
||||
)
|
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.first()
|
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)
|
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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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|
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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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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(
|
||||
"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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|
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# Regenerate both summary content and vectors (for summary_model change)
|
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SummaryIndexService.generate_and_vectorize_summary(segment, dataset, summary_index_setting)
|
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db.session.commit()
|
||||
# Re-vectorize with new embedding model
|
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SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
|
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session.commit()
|
||||
total_segments_processed += 1
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Failed to regenerate summary for segment %s: %s",
|
||||
"Failed to re-vectorize summary for segment %s: %s",
|
||||
segment.id,
|
||||
str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
total_segments_failed += 1
|
||||
# Update summary record with error status
|
||||
if summary_record:
|
||||
summary_record.status = "error"
|
||||
summary_record.error = f"Regeneration failed: {str(e)}"
|
||||
db.session.add(summary_record)
|
||||
db.session.commit()
|
||||
summary_record.status = "error"
|
||||
summary_record.error = f"Re-vectorization failed: {str(e)}"
|
||||
session.add(summary_record)
|
||||
session.commit()
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Failed to process document %s for summary regeneration: %s",
|
||||
dataset_document.id,
|
||||
str(e),
|
||||
exc_info=True,
|
||||
else:
|
||||
# For summary_model change: require document indexing_status == "completed"
|
||||
# Get all documents with completed indexing status
|
||||
dataset_documents = session.scalars(
|
||||
select(DatasetDocument).where(
|
||||
DatasetDocument.dataset_id == dataset_id,
|
||||
DatasetDocument.indexing_status == "completed",
|
||||
DatasetDocument.enabled == True,
|
||||
DatasetDocument.archived == False,
|
||||
)
|
||||
continue
|
||||
).all()
|
||||
|
||||
end_at = time.perf_counter()
|
||||
if regenerate_vectors_only:
|
||||
logger.info(
|
||||
click.style(
|
||||
f"Summary re-vectorization completed for dataset {dataset_id}: "
|
||||
f"{total_segments_processed} segments processed successfully, "
|
||||
f"{total_segments_failed} segments failed, "
|
||||
f"latency: {end_at - start_at:.2f}s",
|
||||
fg="green",
|
||||
if not dataset_documents:
|
||||
logger.info(
|
||||
click.style(
|
||||
f"No documents found for summary regeneration in dataset {dataset_id}",
|
||||
fg="cyan",
|
||||
)
|
||||
)
|
||||
return
|
||||
|
||||
logger.info(
|
||||
"Found %s documents for summary regeneration in dataset %s",
|
||||
len(dataset_documents),
|
||||
dataset_id,
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
click.style(
|
||||
f"Summary index regeneration completed for dataset {dataset_id}: "
|
||||
f"{total_segments_processed} segments processed successfully, "
|
||||
f"{total_segments_failed} segments failed, "
|
||||
f"latency: {end_at - start_at:.2f}s",
|
||||
fg="green",
|
||||
|
||||
for dataset_document in dataset_documents:
|
||||
# Skip qa_model documents
|
||||
if dataset_document.doc_form == "qa_model":
|
||||
continue
|
||||
|
||||
try:
|
||||
# Get all segments with existing summaries
|
||||
segments = (
|
||||
session.query(DocumentSegment)
|
||||
.join(
|
||||
DocumentSegmentSummary,
|
||||
DocumentSegment.id == DocumentSegmentSummary.chunk_id,
|
||||
)
|
||||
.where(
|
||||
DocumentSegment.document_id == dataset_document.id,
|
||||
DocumentSegment.dataset_id == dataset_id,
|
||||
DocumentSegment.status == "completed",
|
||||
DocumentSegment.enabled == True,
|
||||
DocumentSegmentSummary.dataset_id == dataset_id,
|
||||
)
|
||||
.order_by(DocumentSegment.position.asc())
|
||||
.all()
|
||||
)
|
||||
|
||||
if not segments:
|
||||
continue
|
||||
|
||||
logger.info(
|
||||
"Regenerating summaries for %s segments in document %s",
|
||||
len(segments),
|
||||
dataset_document.id,
|
||||
)
|
||||
|
||||
for segment in segments:
|
||||
summary_record = None
|
||||
try:
|
||||
# Get existing summary record
|
||||
summary_record = (
|
||||
session.query(DocumentSegmentSummary)
|
||||
.filter_by(
|
||||
chunk_id=segment.id,
|
||||
dataset_id=dataset_id,
|
||||
)
|
||||
.first()
|
||||
)
|
||||
|
||||
if not summary_record:
|
||||
logger.warning("Summary record not found for segment %s, skipping", segment.id)
|
||||
continue
|
||||
|
||||
# Regenerate both summary content and vectors (for summary_model change)
|
||||
SummaryIndexService.generate_and_vectorize_summary(
|
||||
segment, dataset, summary_index_setting
|
||||
)
|
||||
session.commit()
|
||||
total_segments_processed += 1
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Failed to regenerate summary for segment %s: %s",
|
||||
segment.id,
|
||||
str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
total_segments_failed += 1
|
||||
# Update summary record with error status
|
||||
if summary_record:
|
||||
summary_record.status = "error"
|
||||
summary_record.error = f"Regeneration failed: {str(e)}"
|
||||
session.add(summary_record)
|
||||
session.commit()
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Failed to process document %s for summary regeneration: %s",
|
||||
dataset_document.id,
|
||||
str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
continue
|
||||
|
||||
end_at = time.perf_counter()
|
||||
if regenerate_vectors_only:
|
||||
logger.info(
|
||||
click.style(
|
||||
f"Summary re-vectorization completed for dataset {dataset_id}: "
|
||||
f"{total_segments_processed} segments processed successfully, "
|
||||
f"{total_segments_failed} segments failed, "
|
||||
f"latency: {end_at - start_at:.2f}s",
|
||||
fg="green",
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
click.style(
|
||||
f"Summary index regeneration completed for dataset {dataset_id}: "
|
||||
f"{total_segments_processed} segments processed successfully, "
|
||||
f"{total_segments_failed} segments failed, "
|
||||
f"latency: {end_at - start_at:.2f}s",
|
||||
fg="green",
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Regenerate summary index failed for dataset %s", dataset_id)
|
||||
finally:
|
||||
db.session.close()
|
||||
|
||||
Reference in New Issue
Block a user