File size: 4,972 Bytes
20f348c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import logging
import time
import click
from celery import shared_task # type: ignore
from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
from core.tools.utils.web_reader_tool import get_image_upload_file_ids
from extensions.ext_database import db
from extensions.ext_storage import storage
from models.dataset import (
AppDatasetJoin,
Dataset,
DatasetProcessRule,
DatasetQuery,
Document,
DocumentSegment,
)
from models.model import UploadFile
# Add import statement for ValueError
@shared_task(queue="dataset")
def clean_dataset_task(
dataset_id: str,
tenant_id: str,
indexing_technique: str,
index_struct: str,
collection_binding_id: str,
doc_form: str,
):
"""
Clean dataset when dataset deleted.
:param dataset_id: dataset id
:param tenant_id: tenant id
:param indexing_technique: indexing technique
:param index_struct: index struct dict
:param collection_binding_id: collection binding id
:param doc_form: dataset form
Usage: clean_dataset_task.delay(dataset_id, tenant_id, indexing_technique, index_struct)
"""
logging.info(click.style("Start clean dataset when dataset deleted: {}".format(dataset_id), fg="green"))
start_at = time.perf_counter()
try:
dataset = Dataset(
id=dataset_id,
tenant_id=tenant_id,
indexing_technique=indexing_technique,
index_struct=index_struct,
collection_binding_id=collection_binding_id,
)
documents = db.session.query(Document).filter(Document.dataset_id == dataset_id).all()
segments = db.session.query(DocumentSegment).filter(DocumentSegment.dataset_id == dataset_id).all()
if documents is None or len(documents) == 0:
logging.info(click.style("No documents found for dataset: {}".format(dataset_id), fg="green"))
else:
logging.info(click.style("Cleaning documents for dataset: {}".format(dataset_id), fg="green"))
# Specify the index type before initializing the index processor
if doc_form is None:
raise ValueError("Index type must be specified.")
index_processor = IndexProcessorFactory(doc_form).init_index_processor()
index_processor.clean(dataset, None, with_keywords=True, delete_child_chunks=True)
for document in documents:
db.session.delete(document)
for segment in segments:
image_upload_file_ids = get_image_upload_file_ids(segment.content)
for upload_file_id in image_upload_file_ids:
image_file = db.session.query(UploadFile).filter(UploadFile.id == upload_file_id).first()
if image_file is None:
continue
try:
storage.delete(image_file.key)
except Exception:
logging.exception(
"Delete image_files failed when storage deleted, \
image_upload_file_is: {}".format(upload_file_id)
)
db.session.delete(image_file)
db.session.delete(segment)
db.session.query(DatasetProcessRule).filter(DatasetProcessRule.dataset_id == dataset_id).delete()
db.session.query(DatasetQuery).filter(DatasetQuery.dataset_id == dataset_id).delete()
db.session.query(AppDatasetJoin).filter(AppDatasetJoin.dataset_id == dataset_id).delete()
# delete files
if documents:
for document in documents:
try:
if document.data_source_type == "upload_file":
if document.data_source_info:
data_source_info = document.data_source_info_dict
if data_source_info and "upload_file_id" in data_source_info:
file_id = data_source_info["upload_file_id"]
file = (
db.session.query(UploadFile)
.filter(UploadFile.tenant_id == document.tenant_id, UploadFile.id == file_id)
.first()
)
if not file:
continue
storage.delete(file.key)
db.session.delete(file)
except Exception:
continue
db.session.commit()
end_at = time.perf_counter()
logging.info(
click.style(
"Cleaned dataset when dataset deleted: {} latency: {}".format(dataset_id, end_at - start_at), fg="green"
)
)
except Exception:
logging.exception("Cleaned dataset when dataset deleted failed")
|