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import datetime
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import logging
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import time
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import click
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from celery import shared_task
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from flask import current_app
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from core.indexing_runner import DocumentIsPausedException, IndexingRunner
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from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
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from extensions.ext_database import db
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from models.dataset import Dataset, Document, DocumentSegment
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from services.feature_service import FeatureService
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@shared_task(queue='dataset')
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def duplicate_document_indexing_task(dataset_id: str, document_ids: list):
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"""
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Async process document
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:param dataset_id:
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:param document_ids:
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Usage: duplicate_document_indexing_task.delay(dataset_id, document_id)
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"""
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documents = []
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start_at = time.perf_counter()
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dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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features = FeatureService.get_features(dataset.tenant_id)
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try:
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if features.billing.enabled:
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vector_space = features.vector_space
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count = len(document_ids)
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batch_upload_limit = int(current_app.config['BATCH_UPLOAD_LIMIT'])
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if count > batch_upload_limit:
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raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
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if 0 < vector_space.limit <= vector_space.size:
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raise ValueError("Your total number of documents plus the number of uploads have over the limit of "
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"your subscription.")
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except Exception as e:
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for document_id in document_ids:
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document = db.session.query(Document).filter(
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Document.id == document_id,
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Document.dataset_id == dataset_id
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).first()
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if document:
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document.indexing_status = 'error'
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document.error = str(e)
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document.stopped_at = datetime.datetime.utcnow()
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db.session.add(document)
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db.session.commit()
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return
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for document_id in document_ids:
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logging.info(click.style('Start process document: {}'.format(document_id), fg='green'))
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document = db.session.query(Document).filter(
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Document.id == document_id,
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Document.dataset_id == dataset_id
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).first()
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if document:
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index_type = document.doc_form
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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segments = db.session.query(DocumentSegment).filter(DocumentSegment.document_id == document_id).all()
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if segments:
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index_node_ids = [segment.index_node_id for segment in segments]
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index_processor.clean(dataset, index_node_ids)
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for segment in segments:
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db.session.delete(segment)
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db.session.commit()
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document.indexing_status = 'parsing'
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document.processing_started_at = datetime.datetime.utcnow()
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documents.append(document)
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db.session.add(document)
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db.session.commit()
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try:
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indexing_runner = IndexingRunner()
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indexing_runner.run(documents)
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end_at = time.perf_counter()
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logging.info(click.style('Processed dataset: {} latency: {}'.format(dataset_id, end_at - start_at), fg='green'))
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except DocumentIsPausedException as ex:
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logging.info(click.style(str(ex), fg='yellow'))
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except Exception:
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pass
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