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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 tenant_id: tenant id
:param app_id: app 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")
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