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106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
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 core.rag.index_processor.index_processor_factory import IndexProcessorFactory
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from extensions.ext_database import db
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from extensions.ext_storage import storage
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from models.dataset import (
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AppDatasetJoin,
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Dataset,
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DatasetProcessRule,
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DatasetQuery,
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Document,
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DocumentSegment,
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)
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from models.model import UploadFile
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# Add import statement for ValueError
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@shared_task(queue="dataset")
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def clean_dataset_task(
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dataset_id: str,
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tenant_id: str,
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indexing_technique: str,
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index_struct: str,
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collection_binding_id: str,
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doc_form: str,
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):
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"""
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Clean dataset when dataset deleted.
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:param dataset_id: dataset id
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:param tenant_id: tenant id
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:param indexing_technique: indexing technique
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:param index_struct: index struct dict
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:param collection_binding_id: collection binding id
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:param doc_form: dataset form
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Usage: clean_dataset_task.delay(dataset_id, tenant_id, indexing_technique, index_struct)
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"""
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logging.info(click.style("Start clean dataset when dataset deleted: {}".format(dataset_id), fg="green"))
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start_at = time.perf_counter()
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try:
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dataset = Dataset(
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id=dataset_id,
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tenant_id=tenant_id,
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indexing_technique=indexing_technique,
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index_struct=index_struct,
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collection_binding_id=collection_binding_id,
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)
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documents = db.session.query(Document).filter(Document.dataset_id == dataset_id).all()
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segments = db.session.query(DocumentSegment).filter(DocumentSegment.dataset_id == dataset_id).all()
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if documents is None or len(documents) == 0:
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logging.info(click.style("No documents found for dataset: {}".format(dataset_id), fg="green"))
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else:
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logging.info(click.style("Cleaning documents for dataset: {}".format(dataset_id), fg="green"))
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# Specify the index type before initializing the index processor
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if doc_form is None:
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raise ValueError("Index type must be specified.")
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index_processor = IndexProcessorFactory(doc_form).init_index_processor()
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index_processor.clean(dataset, None)
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for document in documents:
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db.session.delete(document)
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for segment in segments:
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db.session.delete(segment)
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db.session.query(DatasetProcessRule).filter(DatasetProcessRule.dataset_id == dataset_id).delete()
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db.session.query(DatasetQuery).filter(DatasetQuery.dataset_id == dataset_id).delete()
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db.session.query(AppDatasetJoin).filter(AppDatasetJoin.dataset_id == dataset_id).delete()
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# delete files
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if documents:
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for document in documents:
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try:
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if document.data_source_type == "upload_file":
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if document.data_source_info:
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data_source_info = document.data_source_info_dict
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if data_source_info and "upload_file_id" in data_source_info:
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file_id = data_source_info["upload_file_id"]
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file = (
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db.session.query(UploadFile)
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.filter(UploadFile.tenant_id == document.tenant_id, UploadFile.id == file_id)
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.first()
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)
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if not file:
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continue
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storage.delete(file.key)
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db.session.delete(file)
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except Exception:
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continue
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db.session.commit()
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end_at = time.perf_counter()
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logging.info(
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click.style(
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"Cleaned dataset when dataset deleted: {} latency: {}".format(dataset_id, end_at - start_at), fg="green"
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)
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)
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except Exception:
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logging.exception("Cleaned dataset when dataset deleted failed")
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