dify/api/controllers/service_api/dataset/dataset.py

from flask import request
from flask_restful import marshal, reqparse
from werkzeug.exceptions import NotFound

import services.dataset_service
from controllers.service_api import api
from controllers.service_api.dataset.error import DatasetInUseError, DatasetNameDuplicateError
from controllers.service_api.wraps import DatasetApiResource
from core.model_runtime.entities.model_entities import ModelType
from core.provider_manager import ProviderManager
from fields.dataset_fields import dataset_detail_fields
from libs.login import current_user
from models.dataset import Dataset, DatasetPermissionEnum
from services.dataset_service import DatasetService


def _validate_name(name):
    if not name or len(name) < 1 or len(name) > 40:
        raise ValueError("Name must be between 1 to 40 characters.")
    return name


class DatasetListApi(DatasetApiResource):
    """Resource for datasets."""

    def get(self, tenant_id):
        """Resource for getting datasets."""

        page = request.args.get("page", default=1, type=int)
        limit = request.args.get("limit", default=20, type=int)
        # provider = request.args.get("provider", default="vendor")
        search = request.args.get("keyword", default=None, type=str)
        tag_ids = request.args.getlist("tag_ids")

        datasets, total = DatasetService.get_datasets(page, limit, tenant_id, current_user, search, tag_ids)
        # check embedding setting
        provider_manager = ProviderManager()
        configurations = provider_manager.get_configurations(tenant_id=current_user.current_tenant_id)

        embedding_models = configurations.get_models(model_type=ModelType.TEXT_EMBEDDING, only_active=True)

        model_names = []
        for embedding_model in embedding_models:
            model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}")

        data = marshal(datasets, dataset_detail_fields)
        for item in data:
            if item["indexing_technique"] == "high_quality":
                item_model = f"{item['embedding_model']}:{item['embedding_model_provider']}"
                if item_model in model_names:
                    item["embedding_available"] = True
                else:
                    item["embedding_available"] = False
            else:
                item["embedding_available"] = True
        response = {"data": data, "has_more": len(datasets) == limit, "limit": limit, "total": total, "page": page}
        return response, 200

    def post(self, tenant_id):
        """Resource for creating datasets."""
        parser = reqparse.RequestParser()
        parser.add_argument(
            "name",
            nullable=False,
            required=True,
            help="type is required. Name must be between 1 to 40 characters.",
            type=_validate_name,
        )
        parser.add_argument(
            "description",
            type=str,
            nullable=True,
            required=False,
            default="",
        )
        parser.add_argument(
            "indexing_technique",
            type=str,
            location="json",
            choices=Dataset.INDEXING_TECHNIQUE_LIST,
            help="Invalid indexing technique.",
        )
        parser.add_argument(
            "permission",
            type=str,
            location="json",
            choices=(DatasetPermissionEnum.ONLY_ME, DatasetPermissionEnum.ALL_TEAM, DatasetPermissionEnum.PARTIAL_TEAM),
            help="Invalid permission.",
            required=False,
            nullable=False,
        )
        parser.add_argument(
            "external_knowledge_api_id",
            type=str,
            nullable=True,
            required=False,
            default="_validate_name",
        )
        parser.add_argument(
            "provider",
            type=str,
            nullable=True,
            required=False,
            default="vendor",
        )
        parser.add_argument(
            "external_knowledge_id",
            type=str,
            nullable=True,
            required=False,
        )
        args = parser.parse_args()

        try:
            dataset = DatasetService.create_empty_dataset(
                tenant_id=tenant_id,
                name=args["name"],
                description=args["description"],
                indexing_technique=args["indexing_technique"],
                account=current_user,
                permission=args["permission"],
                provider=args["provider"],
                external_knowledge_api_id=args["external_knowledge_api_id"],
                external_knowledge_id=args["external_knowledge_id"],
            )
        except services.errors.dataset.DatasetNameDuplicateError:
            raise DatasetNameDuplicateError()

        return marshal(dataset, dataset_detail_fields), 200


class DatasetApi(DatasetApiResource):
    """Resource for dataset."""

    def delete(self, _, dataset_id):
        """
        Deletes a dataset given its ID.

        Args:
            dataset_id (UUID): The ID of the dataset to be deleted.

        Returns:
            dict: A dictionary with a key 'result' and a value 'success'
                  if the dataset was successfully deleted. Omitted in HTTP response.
            int: HTTP status code 204 indicating that the operation was successful.

        Raises:
            NotFound: If the dataset with the given ID does not exist.
        """

        dataset_id_str = str(dataset_id)

        try:
            if DatasetService.delete_dataset(dataset_id_str, current_user):
                return {"result": "success"}, 204
            else:
                raise NotFound("Dataset not found.")
        except services.errors.dataset.DatasetInUseError:
            raise DatasetInUseError()


api.add_resource(DatasetListApi, "/datasets")
api.add_resource(DatasetApi, "/datasets/<uuid:dataset_id>")