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https://github.com/langgenius/dify.git
synced 2026-02-22 19:15:47 +08:00
fix: fix summary index bug.
This commit is contained in:
FFXN
@ -2,10 +2,11 @@
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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 select
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from sqlalchemy import and_, or_, select
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from extensions.ext_database import db
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from models.dataset import Dataset, DocumentSegment, DocumentSegmentSummary
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@ -62,158 +63,254 @@ def regenerate_summary_index_task(
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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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# 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 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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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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)
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db.session.close()
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return
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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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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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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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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:
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continue
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if not segments_with_summaries:
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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"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 in segments:
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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,
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dataset_id=dataset_id,
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)
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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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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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if regenerate_vectors_only:
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# Only regenerate vectors (for embedding_model change)
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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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else:
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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()
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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 regenerate summary for segment %s: %s",
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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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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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db.session.add(summary_record)
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db.session.commit()
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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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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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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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continue
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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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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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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,
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dataset_id=dataset_id,
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)
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.first()
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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(segment, dataset, summary_index_setting)
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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 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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db.session.add(summary_record)
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db.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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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"total documents: {len(dataset_documents)}, "
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f"latency: {end_at - start_at:.2f}s",
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fg="green",
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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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)
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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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