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107 lines
4.3 KiB
Python
107 lines
4.3 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
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from werkzeug.exceptions import NotFound
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from core.rag.datasource.vdb.vector_factory import Vector
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from core.rag.models.document import Document
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from extensions.ext_database import db
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from extensions.ext_redis import redis_client
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from models.dataset import Dataset
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from models.model import App, AppAnnotationSetting, MessageAnnotation
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from services.dataset_service import DatasetCollectionBindingService
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@shared_task(queue='dataset')
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def enable_annotation_reply_task(job_id: str, app_id: str, user_id: str, tenant_id: str, score_threshold: float,
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embedding_provider_name: str, embedding_model_name: str):
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"""
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Async enable annotation reply task
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"""
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logging.info(click.style('Start add app annotation to index: {}'.format(app_id), fg='green'))
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start_at = time.perf_counter()
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# get app info
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app = db.session.query(App).filter(
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App.id == app_id,
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App.tenant_id == tenant_id,
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App.status == 'normal'
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).first()
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if not app:
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raise NotFound("App not found")
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annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app_id).all()
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enable_app_annotation_key = 'enable_app_annotation_{}'.format(str(app_id))
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enable_app_annotation_job_key = 'enable_app_annotation_job_{}'.format(str(job_id))
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try:
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documents = []
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dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
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embedding_provider_name,
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embedding_model_name,
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'annotation'
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)
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annotation_setting = db.session.query(AppAnnotationSetting).filter(
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AppAnnotationSetting.app_id == app_id).first()
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if annotation_setting:
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annotation_setting.score_threshold = score_threshold
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annotation_setting.collection_binding_id = dataset_collection_binding.id
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annotation_setting.updated_user_id = user_id
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annotation_setting.updated_at = datetime.datetime.utcnow()
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db.session.add(annotation_setting)
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else:
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new_app_annotation_setting = AppAnnotationSetting(
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app_id=app_id,
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score_threshold=score_threshold,
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collection_binding_id=dataset_collection_binding.id,
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created_user_id=user_id,
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updated_user_id=user_id
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)
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db.session.add(new_app_annotation_setting)
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dataset = Dataset(
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id=app_id,
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tenant_id=tenant_id,
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indexing_technique='high_quality',
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embedding_model_provider=embedding_provider_name,
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embedding_model=embedding_model_name,
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collection_binding_id=dataset_collection_binding.id
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)
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if annotations:
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for annotation in annotations:
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document = Document(
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page_content=annotation.question,
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metadata={
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"annotation_id": annotation.id,
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"app_id": app_id,
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"doc_id": annotation.id
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}
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)
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documents.append(document)
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vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
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try:
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vector.delete_by_metadata_field('app_id', app_id)
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except Exception as e:
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logging.info(
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click.style('Delete annotation index error: {}'.format(str(e)),
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fg='red'))
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vector.add_texts(documents)
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db.session.commit()
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redis_client.setex(enable_app_annotation_job_key, 600, 'completed')
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end_at = time.perf_counter()
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logging.info(
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click.style('App annotations added to index: {} latency: {}'.format(app_id, end_at - start_at),
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fg='green'))
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except Exception as e:
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logging.exception("Annotation batch created index failed:{}".format(str(e)))
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redis_client.setex(enable_app_annotation_job_key, 600, 'error')
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enable_app_annotation_error_key = 'enable_app_annotation_error_{}'.format(str(job_id))
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redis_client.setex(enable_app_annotation_error_key, 600, str(e))
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db.session.rollback()
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finally:
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redis_client.delete(enable_app_annotation_key)
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