dify

dify/api/tasks/annotation/batch_import_annotations_task.py

@@ -0,0 +1,94 @@
+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
+
+logger = logging.getLogger(__name__)
+
+
+@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 app_id: app id
+    :param tenant_id: tenant id
+    :param user_id: user_id
+
+    """
+    logger.info(click.style(f"Start batch import annotation: {job_id}", fg="green"))
+    start_at = time.perf_counter()
+    indexing_cache_key = f"app_annotation_batch_import_{str(job_id)}"
+    # get app info
+    app = db.session.query(App).where(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).where(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()
+            logger.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 = f"app_annotation_batch_import_error_msg_{str(job_id)}"
+            redis_client.setex(indexing_error_msg_key, 600, str(e))
+            logger.exception("Build index for batch import annotations failed")
+        finally:
+            db.session.close()