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import logging
import time
import click
from celery import shared_task # type: ignore
from core.rag.index_processor.constant.index_type import IndexType
from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
from core.rag.models.document import ChildDocument, Document
from extensions.ext_database import db
from models.dataset import Dataset, DocumentSegment
from models.dataset import Document as DatasetDocument
@shared_task(queue="dataset")
def deal_dataset_vector_index_task(dataset_id: str, action: str):
"""
Async deal dataset from index
:param dataset_id: dataset_id
:param action: action
Usage: deal_dataset_vector_index_task.delay(dataset_id, action)
"""
logging.info(click.style("Start deal dataset vector index: {}".format(dataset_id), fg="green"))
start_at = time.perf_counter()
try:
dataset = Dataset.query.filter_by(id=dataset_id).first()
if not dataset:
raise Exception("Dataset not found")
index_type = dataset.doc_form or IndexType.PARAGRAPH_INDEX
index_processor = IndexProcessorFactory(index_type).init_index_processor()
if action == "remove":
index_processor.clean(dataset, None, with_keywords=False)
elif action == "add":
dataset_documents = (
db.session.query(DatasetDocument)
.filter(
DatasetDocument.dataset_id == dataset_id,
DatasetDocument.indexing_status == "completed",
DatasetDocument.enabled == True,
DatasetDocument.archived == False,
)
.all()
)
if dataset_documents:
dataset_documents_ids = [doc.id for doc in dataset_documents]
db.session.query(DatasetDocument).filter(DatasetDocument.id.in_(dataset_documents_ids)).update(
{"indexing_status": "indexing"}, synchronize_session=False
)
db.session.commit()
for dataset_document in dataset_documents:
try:
# add from vector index
segments = (
db.session.query(DocumentSegment)
.filter(DocumentSegment.document_id == dataset_document.id, DocumentSegment.enabled == True)
.order_by(DocumentSegment.position.asc())
.all()
)
if segments:
documents = []
for segment in segments:
document = Document(
page_content=segment.content,
metadata={
"doc_id": segment.index_node_id,
"doc_hash": segment.index_node_hash,
"document_id": segment.document_id,
"dataset_id": segment.dataset_id,
},
)
documents.append(document)
# save vector index
index_processor.load(dataset, documents, with_keywords=False)
db.session.query(DatasetDocument).filter(DatasetDocument.id == dataset_document.id).update(
{"indexing_status": "completed"}, synchronize_session=False
)
db.session.commit()
except Exception as e:
db.session.query(DatasetDocument).filter(DatasetDocument.id == dataset_document.id).update(
{"indexing_status": "error", "error": str(e)}, synchronize_session=False
)
db.session.commit()
elif action == "update":
dataset_documents = (
db.session.query(DatasetDocument)
.filter(
DatasetDocument.dataset_id == dataset_id,
DatasetDocument.indexing_status == "completed",
DatasetDocument.enabled == True,
DatasetDocument.archived == False,
)
.all()
)
# add new index
if dataset_documents:
# update document status
dataset_documents_ids = [doc.id for doc in dataset_documents]
db.session.query(DatasetDocument).filter(DatasetDocument.id.in_(dataset_documents_ids)).update(
{"indexing_status": "indexing"}, synchronize_session=False
)
db.session.commit()
# clean index
index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
for dataset_document in dataset_documents:
# update from vector index
try:
segments = (
db.session.query(DocumentSegment)
.filter(DocumentSegment.document_id == dataset_document.id, DocumentSegment.enabled == True)
.order_by(DocumentSegment.position.asc())
.all()
)
if segments:
documents = []
for segment in segments:
document = Document(
page_content=segment.content,
metadata={
"doc_id": segment.index_node_id,
"doc_hash": segment.index_node_hash,
"document_id": segment.document_id,
"dataset_id": segment.dataset_id,
},
)
if dataset_document.doc_form == IndexType.PARENT_CHILD_INDEX:
child_chunks = segment.child_chunks
if child_chunks:
child_documents = []
for child_chunk in child_chunks:
child_document = ChildDocument(
page_content=child_chunk.content,
metadata={
"doc_id": child_chunk.index_node_id,
"doc_hash": child_chunk.index_node_hash,
"document_id": segment.document_id,
"dataset_id": segment.dataset_id,
},
)
child_documents.append(child_document)
document.children = child_documents
documents.append(document)
# save vector index
index_processor.load(dataset, documents, with_keywords=False)
db.session.query(DatasetDocument).filter(DatasetDocument.id == dataset_document.id).update(
{"indexing_status": "completed"}, synchronize_session=False
)
db.session.commit()
except Exception as e:
db.session.query(DatasetDocument).filter(DatasetDocument.id == dataset_document.id).update(
{"indexing_status": "error", "error": str(e)}, synchronize_session=False
)
db.session.commit()
else:
# clean collection
index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
end_at = time.perf_counter()
logging.info(
click.style("Deal dataset vector index: {} latency: {}".format(dataset_id, end_at - start_at), fg="green")
)
except Exception:
logging.exception("Deal dataset vector index failed")
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