| dataset.retrieval_model = document_data.get("retrieval_model") or default_retrieval_model | dataset.retrieval_model = document_data.get("retrieval_model") or default_retrieval_model | ||||
| documents = [] | documents = [] | ||||
| batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999)) | |||||
| if document_data.get("original_document_id"): | if document_data.get("original_document_id"): | ||||
| document = DocumentService.update_document_with_dataset_id(dataset, document_data, account) | document = DocumentService.update_document_with_dataset_id(dataset, document_data, account) | ||||
| documents.append(document) | documents.append(document) | ||||
| batch = document.batch | |||||
| else: | else: | ||||
| batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999)) | |||||
| # save process rule | # save process rule | ||||
| if not dataset_process_rule: | if not dataset_process_rule: | ||||
| process_rule = document_data["process_rule"] | process_rule = document_data["process_rule"] | ||||
| if duplicate_document_ids: | if duplicate_document_ids: | ||||
| duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids) | duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids) | ||||
| return documents, batch | |||||
| return documents, batch | |||||
| @staticmethod | @staticmethod | ||||
| def check_documents_upload_quota(count: int, features: FeatureModel): | def check_documents_upload_quota(count: int, features: FeatureModel): |