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105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
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 # type: ignore
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from configs import dify_config
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from core.indexing_runner import DocumentIsPausedError, 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_ids)
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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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if dataset is None:
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logging.info(click.style("Dataset not found: {}".format(dataset_id), fg="red"))
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db.session.close()
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return
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# check document limit
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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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if features.billing.subscription.plan == "sandbox" and count > 1:
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raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
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batch_upload_limit = int(dify_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(
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"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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)
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except Exception as e:
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for document_id in document_ids:
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document = (
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db.session.query(Document).filter(Document.id == document_id, Document.dataset_id == dataset_id).first()
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)
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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.now(datetime.UTC).replace(tzinfo=None)
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db.session.add(document)
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db.session.commit()
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return
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finally:
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db.session.close()
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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 = (
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db.session.query(Document).filter(Document.id == document_id, Document.dataset_id == dataset_id).first()
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)
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if document:
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# clean old data
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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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# delete from vector index
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index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
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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.now(datetime.UTC).replace(tzinfo=None)
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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 DocumentIsPausedError as ex:
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logging.info(click.style(str(ex), fg="yellow"))
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except Exception:
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logging.exception("duplicate_document_indexing_task failed, dataset_id: {}".format(dataset_id))
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finally:
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db.session.close()
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