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