import logging import time import click from celery import shared_task from werkzeug.exceptions import NotFound from core.rag.datasource.vdb.vector_factory import Vector from core.rag.models.document import Document from extensions.ext_database import db from extensions.ext_redis import redis_client from models.dataset import Dataset from models.model import App, AppAnnotationSetting, MessageAnnotation from services.dataset_service import DatasetCollectionBindingService @shared_task(queue="dataset") def batch_import_annotations_task(job_id: str, content_list: list[dict], app_id: str, tenant_id: str, user_id: str): """ Add annotation to index. :param job_id: job_id :param content_list: content list :param app_id: app id :param tenant_id: tenant id :param user_id: user_id """ logging.info(click.style("Start batch import annotation: {}".format(job_id), fg="green")) start_at = time.perf_counter() indexing_cache_key = "app_annotation_batch_import_{}".format(str(job_id)) # get app info app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id, App.status == "normal").first() if app: try: documents = [] for content in content_list: annotation = MessageAnnotation( app_id=app.id, content=content["answer"], question=content["question"], account_id=user_id ) db.session.add(annotation) db.session.flush() document = Document( page_content=content["question"], metadata={"annotation_id": annotation.id, "app_id": app_id, "doc_id": annotation.id}, ) documents.append(document) # if annotation reply is enabled , batch add annotations' index app_annotation_setting = ( db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app_id).first() ) if app_annotation_setting: dataset_collection_binding = ( DatasetCollectionBindingService.get_dataset_collection_binding_by_id_and_type( app_annotation_setting.collection_binding_id, "annotation" ) ) if not dataset_collection_binding: raise NotFound("App annotation setting not found") dataset = Dataset( id=app_id, tenant_id=tenant_id, indexing_technique="high_quality", embedding_model_provider=dataset_collection_binding.provider_name, embedding_model=dataset_collection_binding.model_name, collection_binding_id=dataset_collection_binding.id, ) vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"]) vector.create(documents, duplicate_check=True) db.session.commit() redis_client.setex(indexing_cache_key, 600, "completed") end_at = time.perf_counter() logging.info( click.style( "Build index successful for batch import annotation: {} latency: {}".format( job_id, end_at - start_at ), fg="green", ) ) except Exception as e: db.session.rollback() redis_client.setex(indexing_cache_key, 600, "error") indexing_error_msg_key = "app_annotation_batch_import_error_msg_{}".format(str(job_id)) redis_client.setex(indexing_error_msg_key, 600, str(e)) logging.exception("Build index for batch import annotations failed")