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76 lines
2.8 KiB
Python
76 lines
2.8 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 llama_index.data_structs.node_v2 import DocumentRelationship, Node
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from core.index.vector_index import VectorIndex
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from extensions.ext_database import db
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from models.dataset import DocumentSegment, Document, Dataset
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@shared_task
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def deal_dataset_vector_index_task(dataset_id: str, action: str):
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"""
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Async deal dataset from index
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:param dataset_id: dataset_id
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:param action: action
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Usage: deal_dataset_vector_index_task.delay(dataset_id, action)
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"""
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logging.info(click.style('Start deal dataset vector index: {}'.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.query.filter_by(
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id=dataset_id
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).first()
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if not dataset:
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raise Exception('Dataset not found')
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documents = Document.query.filter_by(dataset_id=dataset_id).all()
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if documents:
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vector_index = VectorIndex(dataset=dataset)
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for document in documents:
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# delete from vector index
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if action == "remove":
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vector_index.del_doc(document.id)
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elif action == "add":
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segments = db.session.query(DocumentSegment).filter(
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DocumentSegment.document_id == document.id,
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DocumentSegment.enabled == True
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) .order_by(DocumentSegment.position.asc()).all()
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nodes = []
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previous_node = None
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for segment in segments:
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relationships = {
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DocumentRelationship.SOURCE: document.id
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}
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if previous_node:
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relationships[DocumentRelationship.PREVIOUS] = previous_node.doc_id
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previous_node.relationships[DocumentRelationship.NEXT] = segment.index_node_id
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node = Node(
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doc_id=segment.index_node_id,
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doc_hash=segment.index_node_hash,
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text=segment.content,
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extra_info=None,
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node_info=None,
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relationships=relationships
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)
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previous_node = node
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nodes.append(node)
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# save vector index
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vector_index.add_nodes(
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nodes=nodes,
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duplicate_check=True
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)
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end_at = time.perf_counter()
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logging.info(
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click.style('Deal dataset vector index: {} latency: {}'.format(dataset_id, end_at - start_at), fg='green'))
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except Exception:
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logging.exception("Deal dataset vector index failed")
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