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chore(api/tasks): apply ruff reformatting (#7594)

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This commit is contained in:
Bowen Liang
2024-08-26 13:38:37 +08:00
committed by GitHub
parent 3be756eaed
commit 979422cdc6
29 changed files with 546 additions and 508 deletions
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33
api/tasks/annotation/update_annotation_to_index_task.py
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@@ -10,9 +10,10 @@ from models.dataset import Dataset
from services.dataset_service import DatasetCollectionBindingService
@shared_task(queue='dataset')
def update_annotation_to_index_task(annotation_id: str, question: str, tenant_id: str, app_id: str,
collection_binding_id: str):
@shared_task(queue="dataset")
def update_annotation_to_index_task(
annotation_id: str, question: str, tenant_id: str, app_id: str, collection_binding_id: str
):
"""
Update annotation to index.
:param annotation_id: annotation id
@@ -23,39 +24,35 @@ def update_annotation_to_index_task(annotation_id: str, question: str, tenant_id
Usage: clean_dataset_task.delay(dataset_id, tenant_id, indexing_technique, index_struct)
"""
logging.info(click.style('Start update index for annotation: {}'.format(annotation_id), fg='green'))
logging.info(click.style("Start update index for annotation: {}".format(annotation_id), fg="green"))
start_at = time.perf_counter()
try:
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding_by_id_and_type(
collection_binding_id,
'annotation'
collection_binding_id, "annotation"
)
dataset = Dataset(
id=app_id,
tenant_id=tenant_id,
indexing_technique='high_quality',
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
collection_binding_id=dataset_collection_binding.id,
)
document = Document(
page_content=question,
metadata={
"annotation_id": annotation_id,
"app_id": app_id,
"doc_id": annotation_id
}
page_content=question, metadata={"annotation_id": annotation_id, "app_id": app_id, "doc_id": annotation_id}
)
vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
vector.delete_by_metadata_field('annotation_id', annotation_id)
vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"])
vector.delete_by_metadata_field("annotation_id", annotation_id)
vector.add_texts([document])
end_at = time.perf_counter()
logging.info(
click.style(
'Build index successful for annotation: {} latency: {}'.format(annotation_id, end_at - start_at),
fg='green'))
"Build index successful for annotation: {} latency: {}".format(annotation_id, end_at - start_at),
fg="green",
)
)
except Exception:
logging.exception("Build index for annotation failed")