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Merge branch 'main' into feat/external-knowledge
# Conflicts: # api/core/rag/datasource/retrieval_service.py # api/models/dataset.py # api/services/dataset_service.py
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@ -18,7 +18,7 @@ from core.model_runtime.entities.model_entities import ModelType
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from core.provider_manager import ProviderManager
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from core.rag.datasource.vdb.vector_type import VectorType
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from core.rag.extractor.entity.extract_setting import ExtractSetting
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from core.rag.retrieval.retrival_methods import RetrievalMethod
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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from extensions.ext_database import db
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from fields.app_fields import related_app_list
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from fields.dataset_fields import dataset_detail_fields, dataset_query_detail_fields
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@ -422,7 +422,7 @@ class DatasetIndexingEstimateApi(Resource):
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)
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except LLMBadRequestError:
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raise ProviderNotInitializeError(
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"No Embedding Model available. Please configure a valid provider " "in the Settings -> Model Provider."
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"No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
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)
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except ProviderTokenNotInitError as ex:
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raise ProviderNotInitializeError(ex.description)
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@ -573,12 +573,7 @@ class DatasetApiBaseUrlApi(Resource):
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@login_required
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@account_initialization_required
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def get(self):
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return {
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"api_base_url": (
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dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL else request.host_url.rstrip("/")
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)
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+ "/v1"
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}
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return {"api_base_url": (dify_config.SERVICE_API_URL or request.host_url.rstrip("/")) + "/v1"}
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class DatasetRetrievalSettingApi(Resource):
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@ -302,6 +302,8 @@ class DatasetInitApi(Resource):
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"doc_language", type=str, default="English", required=False, nullable=False, location="json"
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)
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parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
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parser.add_argument("embedding_model", type=str, required=False, nullable=True, location="json")
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parser.add_argument("embedding_model_provider", type=str, required=False, nullable=True, location="json")
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args = parser.parse_args()
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# The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
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@ -309,6 +311,8 @@ class DatasetInitApi(Resource):
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raise Forbidden()
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if args["indexing_technique"] == "high_quality":
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if args["embedding_model"] is None or args["embedding_model_provider"] is None:
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raise ValueError("embedding model and embedding model provider are required for high quality indexing.")
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try:
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model_manager = ModelManager()
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model_manager.get_default_model_instance(
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@ -350,7 +354,7 @@ class DocumentIndexingEstimateApi(DocumentResource):
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document_id = str(document_id)
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document = self.get_document(dataset_id, document_id)
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if document.indexing_status in ["completed", "error"]:
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if document.indexing_status in {"completed", "error"}:
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raise DocumentAlreadyFinishedError()
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data_process_rule = document.dataset_process_rule
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@ -417,7 +421,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
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info_list = []
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extract_settings = []
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for document in documents:
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if document.indexing_status in ["completed", "error"]:
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if document.indexing_status in {"completed", "error"}:
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raise DocumentAlreadyFinishedError()
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data_source_info = document.data_source_info_dict
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# format document files info
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@ -661,7 +665,7 @@ class DocumentProcessingApi(DocumentResource):
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db.session.commit()
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elif action == "resume":
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if document.indexing_status not in ["paused", "error"]:
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if document.indexing_status not in {"paused", "error"}:
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raise InvalidActionError("Document not in paused or error state.")
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document.paused_by = None
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