dify/api/tasks/annotation/batch_import_annotations_task.py

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")