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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 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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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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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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