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91 lines
3.7 KiB
Python
91 lines
3.7 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 # type: ignore
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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 batch_import_annotations_task(job_id: str, content_list: list[dict], app_id: str, tenant_id: str, user_id: str):
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"""
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Add annotation to index.
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:param job_id: job_id
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:param content_list: content list
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:param app_id: app id
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:param tenant_id: tenant id
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:param user_id: user_id
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"""
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logging.info(click.style("Start batch import annotation: {}".format(job_id), fg="green"))
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start_at = time.perf_counter()
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indexing_cache_key = "app_annotation_batch_import_{}".format(str(job_id))
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# get app info
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app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id, App.status == "normal").first()
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if app:
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try:
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documents = []
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for content in content_list:
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annotation = MessageAnnotation(
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app_id=app.id, content=content["answer"], question=content["question"], account_id=user_id
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)
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db.session.add(annotation)
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db.session.flush()
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document = Document(
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page_content=content["question"],
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metadata={"annotation_id": annotation.id, "app_id": app_id, "doc_id": annotation.id},
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)
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documents.append(document)
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# if annotation reply is enabled , batch add annotations' index
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app_annotation_setting = (
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db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app_id).first()
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)
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if app_annotation_setting:
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dataset_collection_binding = (
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DatasetCollectionBindingService.get_dataset_collection_binding_by_id_and_type(
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app_annotation_setting.collection_binding_id, "annotation"
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)
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)
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if not dataset_collection_binding:
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raise NotFound("App annotation setting not found")
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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=dataset_collection_binding.provider_name,
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embedding_model=dataset_collection_binding.model_name,
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collection_binding_id=dataset_collection_binding.id,
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)
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vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"])
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vector.create(documents, duplicate_check=True)
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db.session.commit()
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redis_client.setex(indexing_cache_key, 600, "completed")
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end_at = time.perf_counter()
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logging.info(
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click.style(
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"Build index successful for batch import annotation: {} latency: {}".format(
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job_id, end_at - start_at
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),
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fg="green",
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)
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)
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except Exception as e:
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db.session.rollback()
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redis_client.setex(indexing_cache_key, 600, "error")
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indexing_error_msg_key = "app_annotation_batch_import_error_msg_{}".format(str(job_id))
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redis_client.setex(indexing_error_msg_key, 600, str(e))
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logging.exception("Build index for batch import annotations failed")
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