dify/api/controllers/console/datasets/datasets_segments.py

import uuid
from datetime import datetime, timezone

import pandas as pd
from flask import request
from flask_login import current_user
from flask_restful import Resource, marshal, reqparse
from werkzeug.exceptions import Forbidden, NotFound

import services
from controllers.console import api
from controllers.console.app.error import ProviderNotInitializeError
from controllers.console.datasets.error import InvalidActionError, NoFileUploadedError, TooManyFilesError
from controllers.console.setup import setup_required
from controllers.console.wraps import (
    account_initialization_required,
    cloud_edition_billing_knowledge_limit_check,
    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 extensions.ext_redis import redis_client
from fields.segment_fields import segment_fields
from libs.login import login_required
from models.dataset import DocumentSegment
from services.dataset_service import DatasetService, DocumentService, SegmentService
from tasks.batch_create_segment_to_index_task import batch_create_segment_to_index_task
from tasks.disable_segment_from_index_task import disable_segment_from_index_task
from tasks.enable_segment_to_index_task import enable_segment_to_index_task


class DatasetDocumentSegmentListApi(Resource):
    @setup_required
    @login_required
    @account_initialization_required
    def get(self, dataset_id, document_id):
        dataset_id = str(dataset_id)
        document_id = str(document_id)
        dataset = DatasetService.get_dataset(dataset_id)
        if not dataset:
            raise NotFound('Dataset not found.')

        try:
            DatasetService.check_dataset_permission(dataset, current_user)
        except services.errors.account.NoPermissionError as e:
            raise Forbidden(str(e))

        document = DocumentService.get_document(dataset_id, document_id)

        if not document:
            raise NotFound('Document not found.')

        parser = reqparse.RequestParser()
        parser.add_argument('last_id', type=str, default=None, location='args')
        parser.add_argument('limit', type=int, default=20, location='args')
        parser.add_argument('status', type=str,
                            action='append', default=[], location='args')
        parser.add_argument('hit_count_gte', type=int,
                            default=None, location='args')
        parser.add_argument('enabled', type=str, default='all', location='args')
        parser.add_argument('keyword', type=str, default=None, location='args')
        args = parser.parse_args()

        last_id = args['last_id']
        limit = min(args['limit'], 100)
        status_list = args['status']
        hit_count_gte = args['hit_count_gte']
        keyword = args['keyword']

        query = DocumentSegment.query.filter(
            DocumentSegment.document_id == str(document_id),
            DocumentSegment.tenant_id == current_user.current_tenant_id
        )

        if last_id is not None:
            last_segment = DocumentSegment.query.get(str(last_id))
            if last_segment:
                query = query.filter(
                    DocumentSegment.position > last_segment.position)
            else:
                return {'data': [], 'has_more': False, 'limit': limit}, 200

        if status_list:
            query = query.filter(DocumentSegment.status.in_(status_list))

        if hit_count_gte is not None:
            query = query.filter(DocumentSegment.hit_count >= hit_count_gte)

        if keyword:
            query = query.where(DocumentSegment.content.ilike(f'%{keyword}%'))

        if args['enabled'].lower() != 'all':
            if args['enabled'].lower() == 'true':
                query = query.filter(DocumentSegment.enabled == True)
            elif args['enabled'].lower() == 'false':
                query = query.filter(DocumentSegment.enabled == False)

        total = query.count()
        segments = query.order_by(DocumentSegment.position).limit(limit + 1).all()

        has_more = False
        if len(segments) > limit:
            has_more = True
            segments = segments[:-1]

        return {
            'data': marshal(segments, segment_fields),
            'doc_form': document.doc_form,
            'has_more': has_more,
            'limit': limit,
            'total': total
        }, 200


class DatasetDocumentSegmentApi(Resource):
    @setup_required
    @login_required
    @account_initialization_required
    @cloud_edition_billing_resource_check('vector_space')
    def patch(self, dataset_id, segment_id, action):
        dataset_id = str(dataset_id)
        dataset = DatasetService.get_dataset(dataset_id)
        if not dataset:
            raise NotFound('Dataset not found.')
        # check user's model setting
        DatasetService.check_dataset_model_setting(dataset)
        # The role of the current user in the ta table must be admin, owner, or editor
        if not current_user.is_editor:
            raise Forbidden()

        try:
            DatasetService.check_dataset_permission(dataset, current_user)
        except services.errors.account.NoPermissionError as e:
            raise Forbidden(str(e))
        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)

        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.')

        if segment.status != 'completed':
            raise NotFound('Segment is not completed, enable or disable function is not allowed')

        document_indexing_cache_key = 'document_{}_indexing'.format(segment.document_id)
        cache_result = redis_client.get(document_indexing_cache_key)
        if cache_result is not None:
            raise InvalidActionError("Document is being indexed, please try again later")

        indexing_cache_key = 'segment_{}_indexing'.format(segment.id)
        cache_result = redis_client.get(indexing_cache_key)
        if cache_result is not None:
            raise InvalidActionError("Segment is being indexed, please try again later")

        if action == "enable":
            if segment.enabled:
                raise InvalidActionError("Segment is already enabled.")

            segment.enabled = True
            segment.disabled_at = None
            segment.disabled_by = None
            db.session.commit()

            # Set cache to prevent indexing the same segment multiple times
            redis_client.setex(indexing_cache_key, 600, 1)

            enable_segment_to_index_task.delay(segment.id)

            return {'result': 'success'}, 200
        elif action == "disable":
            if not segment.enabled:
                raise InvalidActionError("Segment is already disabled.")

            segment.enabled = False
            segment.disabled_at = datetime.now(timezone.utc).replace(tzinfo=None)
            segment.disabled_by = current_user.id
            db.session.commit()

            # Set cache to prevent indexing the same segment multiple times
            redis_client.setex(indexing_cache_key, 600, 1)

            disable_segment_from_index_task.delay(segment.id)

            return {'result': 'success'}, 200
        else:
            raise InvalidActionError()


class DatasetDocumentSegmentAddApi(Resource):
    @setup_required
    @login_required
    @account_initialization_required
    @cloud_edition_billing_resource_check('vector_space')
    @cloud_edition_billing_knowledge_limit_check('add_segment')
    def post(self, dataset_id, document_id):
        # check dataset
        dataset_id = str(dataset_id)
        dataset = DatasetService.get_dataset(dataset_id)
        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.')
        # The role of the current user in the ta table must be admin or owner
        if not current_user.is_admin_or_owner:
            raise Forbidden()
        # 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)
        try:
            DatasetService.check_dataset_permission(dataset, current_user)
        except services.errors.account.NoPermissionError as e:
            raise Forbidden(str(e))
        # validate args
        parser = reqparse.RequestParser()
        parser.add_argument('content', type=str, required=True, nullable=False, location='json')
        parser.add_argument('answer', type=str, required=False, nullable=True, location='json')
        parser.add_argument('keywords', type=list, required=False, nullable=True, location='json')
        args = parser.parse_args()
        SegmentService.segment_create_args_validate(args, document)
        segment = SegmentService.create_segment(args, document, dataset)
        return {
            'data': marshal(segment, segment_fields),
            'doc_form': document.doc_form
        }, 200


class DatasetDocumentSegmentUpdateApi(Resource):
    @setup_required
    @login_required
    @account_initialization_required
    @cloud_edition_billing_resource_check('vector_space')
    def patch(self, dataset_id, document_id, segment_id):
        # check dataset
        dataset_id = str(dataset_id)
        dataset = DatasetService.get_dataset(dataset_id)
        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.')
        # The role of the current user in the ta table must be admin, owner, or editor
        if not current_user.is_editor:
            raise Forbidden()
        try:
            DatasetService.check_dataset_permission(dataset, current_user)
        except services.errors.account.NoPermissionError as e:
            raise Forbidden(str(e))
        # validate args
        parser = reqparse.RequestParser()
        parser.add_argument('content', type=str, required=True, nullable=False, location='json')
        parser.add_argument('answer', type=str, required=False, nullable=True, location='json')
        parser.add_argument('keywords', type=list, 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

    @setup_required
    @login_required
    @account_initialization_required
    def delete(self, dataset_id, document_id, segment_id):
        # check dataset
        dataset_id = str(dataset_id)
        dataset = DatasetService.get_dataset(dataset_id)
        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_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.')
        # The role of the current user in the ta table must be admin or owner
        if not current_user.is_admin_or_owner:
            raise Forbidden()
        try:
            DatasetService.check_dataset_permission(dataset, current_user)
        except services.errors.account.NoPermissionError as e:
            raise Forbidden(str(e))
        SegmentService.delete_segment(segment, document, dataset)
        return {'result': 'success'}, 200


class DatasetDocumentSegmentBatchImportApi(Resource):
    @setup_required
    @login_required
    @account_initialization_required
    @cloud_edition_billing_resource_check('vector_space')
    @cloud_edition_billing_knowledge_limit_check('add_segment')
    def post(self, dataset_id, document_id):
        # check dataset
        dataset_id = str(dataset_id)
        dataset = DatasetService.get_dataset(dataset_id)
        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.')
        # get file from request
        file = request.files['file']
        # check file
        if 'file' not in request.files:
            raise NoFileUploadedError()

        if len(request.files) > 1:
            raise TooManyFilesError()
        # check file type
        if not file.filename.endswith('.csv'):
            raise ValueError("Invalid file type. Only CSV files are allowed")

        try:
            # Skip the first row
            df = pd.read_csv(file)
            result = []
            for index, row in df.iterrows():
                if document.doc_form == 'qa_model':
                    data = {'content': row[0], 'answer': row[1]}
                else:
                    data = {'content': row[0]}
                result.append(data)
            if len(result) == 0:
                raise ValueError("The CSV file is empty.")
            # async job
            job_id = str(uuid.uuid4())
            indexing_cache_key = 'segment_batch_import_{}'.format(str(job_id))
            # send batch add segments task
            redis_client.setnx(indexing_cache_key, 'waiting')
            batch_create_segment_to_index_task.delay(str(job_id), result, dataset_id, document_id,
                                                     current_user.current_tenant_id, current_user.id)
        except Exception as e:
            return {'error': str(e)}, 500
        return {
            'job_id': job_id,
            'job_status': 'waiting'
        }, 200

    @setup_required
    @login_required
    @account_initialization_required
    def get(self, job_id):
        job_id = str(job_id)
        indexing_cache_key = 'segment_batch_import_{}'.format(job_id)
        cache_result = redis_client.get(indexing_cache_key)
        if cache_result is None:
            raise ValueError("The job is not exist.")

        return {
            'job_id': job_id,
            'job_status': cache_result.decode()
        }, 200


api.add_resource(DatasetDocumentSegmentListApi,
                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments')
api.add_resource(DatasetDocumentSegmentApi,
                 '/datasets/<uuid:dataset_id>/segments/<uuid:segment_id>/<string:action>')
api.add_resource(DatasetDocumentSegmentAddApi,
                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segment')
api.add_resource(DatasetDocumentSegmentUpdateApi,
                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>')
api.add_resource(DatasetDocumentSegmentBatchImportApi,
                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/batch_import',
                 '/datasets/batch_import_status/<uuid:job_id>')