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File size: 4,245 Bytes
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import datetime
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 enable_annotation_reply_task(
job_id: str,
app_id: str,
user_id: str,
tenant_id: str,
score_threshold: float,
embedding_provider_name: str,
embedding_model_name: str,
):
"""
Async enable annotation reply task
"""
logging.info(click.style("Start add app annotation to index: {}".format(app_id), fg="green"))
start_at = time.perf_counter()
# get app info
app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id, App.status == "normal").first()
if not app:
raise NotFound("App not found")
annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app_id).all()
enable_app_annotation_key = "enable_app_annotation_{}".format(str(app_id))
enable_app_annotation_job_key = "enable_app_annotation_job_{}".format(str(job_id))
try:
documents = []
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
embedding_provider_name, embedding_model_name, "annotation"
)
annotation_setting = (
db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app_id).first()
)
if annotation_setting:
annotation_setting.score_threshold = score_threshold
annotation_setting.collection_binding_id = dataset_collection_binding.id
annotation_setting.updated_user_id = user_id
annotation_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
db.session.add(annotation_setting)
else:
new_app_annotation_setting = AppAnnotationSetting(
app_id=app_id,
score_threshold=score_threshold,
collection_binding_id=dataset_collection_binding.id,
created_user_id=user_id,
updated_user_id=user_id,
)
db.session.add(new_app_annotation_setting)
dataset = Dataset(
id=app_id,
tenant_id=tenant_id,
indexing_technique="high_quality",
embedding_model_provider=embedding_provider_name,
embedding_model=embedding_model_name,
collection_binding_id=dataset_collection_binding.id,
)
if annotations:
for annotation in annotations:
document = Document(
page_content=annotation.question,
metadata={"annotation_id": annotation.id, "app_id": app_id, "doc_id": annotation.id},
)
documents.append(document)
vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"])
try:
vector.delete_by_metadata_field("app_id", app_id)
except Exception as e:
logging.info(click.style("Delete annotation index error: {}".format(str(e)), fg="red"))
vector.create(documents)
db.session.commit()
redis_client.setex(enable_app_annotation_job_key, 600, "completed")
end_at = time.perf_counter()
logging.info(
click.style("App annotations added to index: {} latency: {}".format(app_id, end_at - start_at), fg="green")
)
except Exception as e:
logging.exception("Annotation batch created index failed:{}".format(str(e)))
redis_client.setex(enable_app_annotation_job_key, 600, "error")
enable_app_annotation_error_key = "enable_app_annotation_error_{}".format(str(job_id))
redis_client.setex(enable_app_annotation_error_key, 600, str(e))
db.session.rollback()
finally:
redis_client.delete(enable_app_annotation_key)
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