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							- from flask_login import current_user
 
- from flask_restful import marshal, reqparse
 
- from werkzeug.exceptions import NotFound
 
- from controllers.service_api import api
 
- from controllers.service_api.app.error import ProviderNotInitializeError
 
- from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check
 
- from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 
- from core.model_manager import ModelManager
 
- from core.model_runtime.entities.model_entities import ModelType
 
- from extensions.ext_database import db
 
- from fields.segment_fields import segment_fields
 
- from models.dataset import Dataset, DocumentSegment
 
- from services.dataset_service import DatasetService, DocumentService, SegmentService
 
- class SegmentApi(DatasetApiResource):
 
-     """Resource for segments."""
 
-     @cloud_edition_billing_resource_check('vector_space', 'dataset')
 
-     def post(self, tenant_id, dataset_id, document_id):
 
-         """Create single segment."""
 
-         # check dataset
 
-         dataset_id = str(dataset_id)
 
-         tenant_id = str(tenant_id)
 
-         dataset = db.session.query(Dataset).filter(
 
-             Dataset.tenant_id == tenant_id,
 
-             Dataset.id == dataset_id
 
-         ).first()
 
-         if not dataset:
 
-             raise NotFound('Dataset not found.')
 
-         # check document
 
-         document_id = str(document_id)
 
-         document = DocumentService.get_document(dataset.id, document_id)
 
-         if not document:
 
-             raise NotFound('Document not found.')
 
-         # check embedding model setting
 
-         if dataset.indexing_technique == 'high_quality':
 
-             try:
 
-                 model_manager = ModelManager()
 
-                 model_manager.get_model_instance(
 
-                     tenant_id=current_user.current_tenant_id,
 
-                     provider=dataset.embedding_model_provider,
 
-                     model_type=ModelType.TEXT_EMBEDDING,
 
-                     model=dataset.embedding_model
 
-                 )
 
-             except LLMBadRequestError:
 
-                 raise ProviderNotInitializeError(
 
-                     "No Embedding Model available. Please configure a valid provider "
 
-                     "in the Settings -> Model Provider.")
 
-             except ProviderTokenNotInitError as ex:
 
-                 raise ProviderNotInitializeError(ex.description)
 
-         # validate args
 
-         parser = reqparse.RequestParser()
 
-         parser.add_argument('segments', type=list, required=False, nullable=True, location='json')
 
-         args = parser.parse_args()
 
-         for args_item in args['segments']:
 
-             SegmentService.segment_create_args_validate(args_item, document)
 
-         segments = SegmentService.multi_create_segment(args['segments'], document, dataset)
 
-         return {
 
-             'data': marshal(segments, segment_fields),
 
-             'doc_form': document.doc_form
 
-         }, 200
 
-     def get(self, tenant_id, dataset_id, document_id):
 
-         """Create single segment."""
 
-         # check dataset
 
-         dataset_id = str(dataset_id)
 
-         tenant_id = str(tenant_id)
 
-         dataset = db.session.query(Dataset).filter(
 
-             Dataset.tenant_id == tenant_id,
 
-             Dataset.id == dataset_id
 
-         ).first()
 
-         if not dataset:
 
-             raise NotFound('Dataset not found.')
 
-         # check document
 
-         document_id = str(document_id)
 
-         document = DocumentService.get_document(dataset.id, document_id)
 
-         if not document:
 
-             raise NotFound('Document not found.')
 
-         # check embedding model setting
 
-         if dataset.indexing_technique == 'high_quality':
 
-             try:
 
-                 model_manager = ModelManager()
 
-                 model_manager.get_model_instance(
 
-                     tenant_id=current_user.current_tenant_id,
 
-                     provider=dataset.embedding_model_provider,
 
-                     model_type=ModelType.TEXT_EMBEDDING,
 
-                     model=dataset.embedding_model
 
-                 )
 
-             except LLMBadRequestError:
 
-                 raise ProviderNotInitializeError(
 
-                     "No Embedding Model available. Please configure a valid provider "
 
-                     "in the Settings -> Model Provider.")
 
-             except ProviderTokenNotInitError as ex:
 
-                 raise ProviderNotInitializeError(ex.description)
 
-         parser = reqparse.RequestParser()
 
-         parser.add_argument('status', type=str,
 
-                             action='append', default=[], location='args')
 
-         parser.add_argument('keyword', type=str, default=None, location='args')
 
-         args = parser.parse_args()
 
-         status_list = args['status']
 
-         keyword = args['keyword']
 
-         query = DocumentSegment.query.filter(
 
-             DocumentSegment.document_id == str(document_id),
 
-             DocumentSegment.tenant_id == current_user.current_tenant_id
 
-         )
 
-         if status_list:
 
-             query = query.filter(DocumentSegment.status.in_(status_list))
 
-         if keyword:
 
-             query = query.where(DocumentSegment.content.ilike(f'%{keyword}%'))
 
-         total = query.count()
 
-         segments = query.order_by(DocumentSegment.position).all()
 
-         return {
 
-             'data': marshal(segments, segment_fields),
 
-             'doc_form': document.doc_form,
 
-             'total': total
 
-         }, 200
 
- class DatasetSegmentApi(DatasetApiResource):
 
-     def delete(self, tenant_id, dataset_id, document_id, segment_id):
 
-         # check dataset
 
-         dataset_id = str(dataset_id)
 
-         tenant_id = str(tenant_id)
 
-         dataset = db.session.query(Dataset).filter(
 
-             Dataset.tenant_id == tenant_id,
 
-             Dataset.id == dataset_id
 
-         ).first()
 
-         if not dataset:
 
-             raise NotFound('Dataset not found.')
 
-         # check user's model setting
 
-         DatasetService.check_dataset_model_setting(dataset)
 
-         # check document
 
-         document_id = str(document_id)
 
-         document = DocumentService.get_document(dataset_id, document_id)
 
-         if not document:
 
-             raise NotFound('Document not found.')
 
-         # check segment
 
-         segment = DocumentSegment.query.filter(
 
-             DocumentSegment.id == str(segment_id),
 
-             DocumentSegment.tenant_id == current_user.current_tenant_id
 
-         ).first()
 
-         if not segment:
 
-             raise NotFound('Segment not found.')
 
-         SegmentService.delete_segment(segment, document, dataset)
 
-         return {'result': 'success'}, 200
 
-     @cloud_edition_billing_resource_check('vector_space', 'dataset')
 
-     def post(self, tenant_id, dataset_id, document_id, segment_id):
 
-         # check dataset
 
-         dataset_id = str(dataset_id)
 
-         tenant_id = str(tenant_id)
 
-         dataset = db.session.query(Dataset).filter(
 
-             Dataset.tenant_id == tenant_id,
 
-             Dataset.id == dataset_id
 
-         ).first()
 
-         if not dataset:
 
-             raise NotFound('Dataset not found.')
 
-         # check user's model setting
 
-         DatasetService.check_dataset_model_setting(dataset)
 
-         # check document
 
-         document_id = str(document_id)
 
-         document = DocumentService.get_document(dataset_id, document_id)
 
-         if not document:
 
-             raise NotFound('Document not found.')
 
-         if dataset.indexing_technique == 'high_quality':
 
-             # check embedding model setting
 
-             try:
 
-                 model_manager = ModelManager()
 
-                 model_manager.get_model_instance(
 
-                     tenant_id=current_user.current_tenant_id,
 
-                     provider=dataset.embedding_model_provider,
 
-                     model_type=ModelType.TEXT_EMBEDDING,
 
-                     model=dataset.embedding_model
 
-                 )
 
-             except LLMBadRequestError:
 
-                 raise ProviderNotInitializeError(
 
-                     "No Embedding Model available. Please configure a valid provider "
 
-                     "in the Settings -> Model Provider.")
 
-             except ProviderTokenNotInitError as ex:
 
-                 raise ProviderNotInitializeError(ex.description)
 
-             # check segment
 
-         segment_id = str(segment_id)
 
-         segment = DocumentSegment.query.filter(
 
-             DocumentSegment.id == str(segment_id),
 
-             DocumentSegment.tenant_id == current_user.current_tenant_id
 
-         ).first()
 
-         if not segment:
 
-             raise NotFound('Segment not found.')
 
-         # validate args
 
-         parser = reqparse.RequestParser()
 
-         parser.add_argument('segments', type=dict, required=False, nullable=True, location='json')
 
-         args = parser.parse_args()
 
-         SegmentService.segment_create_args_validate(args, document)
 
-         segment = SegmentService.update_segment(args, segment, document, dataset)
 
-         return {
 
-             'data': marshal(segment, segment_fields),
 
-             'doc_form': document.doc_form
 
-         }, 200
 
- api.add_resource(SegmentApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments')
 
- api.add_resource(DatasetSegmentApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>')
 
 
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