import copy
import datetime
import json
import logging
import secrets
import time
import uuid
from collections import Counter
from collections.abc import Sequence
from typing import Any, Literal, cast

import sqlalchemy as sa
from redis.exceptions import LockNotOwnedError
from sqlalchemy import exists, func, select
from sqlalchemy.orm import Session
from werkzeug.exceptions import Forbidden, NotFound

from configs import dify_config
from core.db.session_factory import session_factory
from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
from core.helper.name_generator import generate_incremental_name
from core.model_manager import ModelManager
from core.rag.index_processor.constant.built_in_field import BuiltInField
from core.rag.index_processor.constant.index_type import IndexStructureType
from core.rag.retrieval.retrieval_methods import RetrievalMethod
from dify_graph.file import helpers as file_helpers
from dify_graph.model_runtime.entities.model_entities import ModelFeature, ModelType
from dify_graph.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from enums.cloud_plan import CloudPlan
from events.dataset_event import dataset_was_deleted
from events.document_event import document_was_deleted
from extensions.ext_database import db
from extensions.ext_redis import redis_client
from libs import helper
from libs.datetime_utils import naive_utc_now
from libs.login import current_user
from models import Account, TenantAccountRole
from models.dataset import (
    AppDatasetJoin,
    ChildChunk,
    Dataset,
    DatasetAutoDisableLog,
    DatasetCollectionBinding,
    DatasetPermission,
    DatasetPermissionEnum,
    DatasetProcessRule,
    DatasetQuery,
    Document,
    DocumentSegment,
    ExternalKnowledgeBindings,
    Pipeline,
    SegmentAttachmentBinding,
)
from models.model import UploadFile
from models.provider_ids import ModelProviderID
from models.source import DataSourceOauthBinding
from models.workflow import Workflow
from services.document_indexing_proxy.document_indexing_task_proxy import DocumentIndexingTaskProxy
from services.document_indexing_proxy.duplicate_document_indexing_task_proxy import DuplicateDocumentIndexingTaskProxy
from services.entities.knowledge_entities.knowledge_entities import (
    ChildChunkUpdateArgs,
    KnowledgeConfig,
    RerankingModel,
    RetrievalModel,
    SegmentUpdateArgs,
)
from services.entities.knowledge_entities.rag_pipeline_entities import (
    KnowledgeConfiguration,
    RagPipelineDatasetCreateEntity,
)
from services.errors.account import NoPermissionError
from services.errors.chunk import ChildChunkDeleteIndexError, ChildChunkIndexingError
from services.errors.dataset import DatasetNameDuplicateError
from services.errors.document import DocumentIndexingError
from services.errors.file import FileNotExistsError
from services.external_knowledge_service import ExternalDatasetService
from services.feature_service import FeatureModel, FeatureService
from services.file_service import FileService
from services.rag_pipeline.rag_pipeline import RagPipelineService
from services.tag_service import TagService
from services.vector_service import VectorService
from tasks.add_document_to_index_task import add_document_to_index_task
from tasks.batch_clean_document_task import batch_clean_document_task
from tasks.clean_notion_document_task import clean_notion_document_task
from tasks.deal_dataset_index_update_task import deal_dataset_index_update_task
from tasks.deal_dataset_vector_index_task import deal_dataset_vector_index_task
from tasks.delete_segment_from_index_task import delete_segment_from_index_task
from tasks.disable_segment_from_index_task import disable_segment_from_index_task
from tasks.disable_segments_from_index_task import disable_segments_from_index_task
from tasks.document_indexing_update_task import document_indexing_update_task
from tasks.enable_segments_to_index_task import enable_segments_to_index_task
from tasks.recover_document_indexing_task import recover_document_indexing_task
from tasks.regenerate_summary_index_task import regenerate_summary_index_task
from tasks.remove_document_from_index_task import remove_document_from_index_task
from tasks.retry_document_indexing_task import retry_document_indexing_task
from tasks.sync_website_document_indexing_task import sync_website_document_indexing_task

logger = logging.getLogger(__name__)


class DatasetService:
    @staticmethod
    def get_datasets(page, per_page, tenant_id=None, user=None, search=None, tag_ids=None, include_all=False):
        query = select(Dataset).where(Dataset.tenant_id == tenant_id).order_by(Dataset.created_at.desc(), Dataset.id)

        if user:
            # get permitted dataset ids
            dataset_permission = (
                db.session.query(DatasetPermission).filter_by(account_id=user.id, tenant_id=tenant_id).all()
            )
            permitted_dataset_ids = {dp.dataset_id for dp in dataset_permission} if dataset_permission else None

            if user.current_role == TenantAccountRole.DATASET_OPERATOR:
                # only show datasets that the user has permission to access
                # Check if permitted_dataset_ids is not empty to avoid WHERE false condition
                if permitted_dataset_ids and len(permitted_dataset_ids) > 0:
                    query = query.where(Dataset.id.in_(permitted_dataset_ids))
                else:
                    return [], 0
            else:
                if user.current_role != TenantAccountRole.OWNER or not include_all:
                    # show all datasets that the user has permission to access
                    # Check if permitted_dataset_ids is not empty to avoid WHERE false condition
                    if permitted_dataset_ids and len(permitted_dataset_ids) > 0:
                        query = query.where(
                            sa.or_(
                                Dataset.permission == DatasetPermissionEnum.ALL_TEAM,
                                sa.and_(
                                    Dataset.permission == DatasetPermissionEnum.ONLY_ME, Dataset.created_by == user.id
                                ),
                                sa.and_(
                                    Dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM,
                                    Dataset.id.in_(permitted_dataset_ids),
                                ),
                            )
                        )
                    else:
                        query = query.where(
                            sa.or_(
                                Dataset.permission == DatasetPermissionEnum.ALL_TEAM,
                                sa.and_(
                                    Dataset.permission == DatasetPermissionEnum.ONLY_ME, Dataset.created_by == user.id
                                ),
                            )
                        )
        else:
            # if no user, only show datasets that are shared with all team members
            query = query.where(Dataset.permission == DatasetPermissionEnum.ALL_TEAM)

        if search:
            escaped_search = helper.escape_like_pattern(search)
            query = query.where(Dataset.name.ilike(f"%{escaped_search}%", escape="\\"))

        # Check if tag_ids is not empty to avoid WHERE false condition
        if tag_ids and len(tag_ids) > 0:
            if tenant_id is not None:
                target_ids = TagService.get_target_ids_by_tag_ids(
                    "knowledge",
                    tenant_id,
                    tag_ids,
                )
            else:
                target_ids = []
            if target_ids and len(target_ids) > 0:
                query = query.where(Dataset.id.in_(target_ids))
            else:
                return [], 0

        datasets = db.paginate(select=query, page=page, per_page=per_page, max_per_page=100, error_out=False)

        return datasets.items, datasets.total

    @staticmethod
    def get_process_rules(dataset_id):
        # get the latest process rule
        dataset_process_rule = (
            db.session.query(DatasetProcessRule)
            .where(DatasetProcessRule.dataset_id == dataset_id)
            .order_by(DatasetProcessRule.created_at.desc())
            .limit(1)
            .one_or_none()
        )
        if dataset_process_rule:
            mode = dataset_process_rule.mode
            rules = dataset_process_rule.rules_dict
        else:
            mode = DocumentService.DEFAULT_RULES["mode"]
            rules = DocumentService.DEFAULT_RULES["rules"]
        return {"mode": mode, "rules": rules}

    @staticmethod
    def get_datasets_by_ids(ids, tenant_id):
        # Check if ids is not empty to avoid WHERE false condition
        if not ids or len(ids) == 0:
            return [], 0
        stmt = select(Dataset).where(Dataset.id.in_(ids), Dataset.tenant_id == tenant_id)

        datasets = db.paginate(select=stmt, page=1, per_page=len(ids), max_per_page=len(ids), error_out=False)

        return datasets.items, datasets.total

    @staticmethod
    def create_empty_dataset(
        tenant_id: str,
        name: str,
        description: str | None,
        indexing_technique: str | None,
        account: Account,
        permission: str | None = None,
        provider: str = "vendor",
        external_knowledge_api_id: str | None = None,
        external_knowledge_id: str | None = None,
        embedding_model_provider: str | None = None,
        embedding_model_name: str | None = None,
        retrieval_model: RetrievalModel | None = None,
        summary_index_setting: dict | None = None,
    ):
        # check if dataset name already exists
        if db.session.query(Dataset).filter_by(name=name, tenant_id=tenant_id).first():
            raise DatasetNameDuplicateError(f"Dataset with name {name} already exists.")
        embedding_model = None
        if indexing_technique == "high_quality":
            model_manager = ModelManager()
            if embedding_model_provider and embedding_model_name:
                # check if embedding model setting is valid
                DatasetService.check_embedding_model_setting(tenant_id, embedding_model_provider, embedding_model_name)
                embedding_model = model_manager.get_model_instance(
                    tenant_id=tenant_id,
                    provider=embedding_model_provider,
                    model_type=ModelType.TEXT_EMBEDDING,
                    model=embedding_model_name,
                )
            else:
                embedding_model = model_manager.get_default_model_instance(
                    tenant_id=tenant_id, model_type=ModelType.TEXT_EMBEDDING
                )
            if retrieval_model and retrieval_model.reranking_model:
                if (
                    retrieval_model.reranking_model.reranking_provider_name
                    and retrieval_model.reranking_model.reranking_model_name
                ):
                    # check if reranking model setting is valid
                    DatasetService.check_reranking_model_setting(
                        tenant_id,
                        retrieval_model.reranking_model.reranking_provider_name,
                        retrieval_model.reranking_model.reranking_model_name,
                    )
        dataset = Dataset(name=name, indexing_technique=indexing_technique)
        # dataset = Dataset(name=name, provider=provider, config=config)
        dataset.description = description
        dataset.created_by = account.id
        dataset.updated_by = account.id
        dataset.tenant_id = tenant_id
        dataset.embedding_model_provider = embedding_model.provider if embedding_model else None
        dataset.embedding_model = embedding_model.model_name if embedding_model else None
        dataset.retrieval_model = retrieval_model.model_dump() if retrieval_model else None
        dataset.permission = DatasetPermissionEnum(permission) if permission else DatasetPermissionEnum.ONLY_ME
        dataset.provider = provider
        if summary_index_setting is not None:
            dataset.summary_index_setting = summary_index_setting
        db.session.add(dataset)
        db.session.flush()

        if provider == "external" and external_knowledge_api_id:
            external_knowledge_api = ExternalDatasetService.get_external_knowledge_api(external_knowledge_api_id)
            if not external_knowledge_api:
                raise ValueError("External API template not found.")
            if external_knowledge_id is None:
                raise ValueError("external_knowledge_id is required")
            external_knowledge_binding = ExternalKnowledgeBindings(
                tenant_id=tenant_id,
                dataset_id=dataset.id,
                external_knowledge_api_id=external_knowledge_api_id,
                external_knowledge_id=external_knowledge_id,
                created_by=account.id,
            )
            db.session.add(external_knowledge_binding)

        db.session.commit()
        return dataset

    @staticmethod
    def create_empty_rag_pipeline_dataset(
        tenant_id: str,
        rag_pipeline_dataset_create_entity: RagPipelineDatasetCreateEntity,
    ):
        if rag_pipeline_dataset_create_entity.name:
            # check if dataset name already exists
            if (
                db.session.query(Dataset)
                .filter_by(name=rag_pipeline_dataset_create_entity.name, tenant_id=tenant_id)
                .first()
            ):
                raise DatasetNameDuplicateError(
                    f"Dataset with name {rag_pipeline_dataset_create_entity.name} already exists."
                )
        else:
            # generate a random name as Untitled 1 2 3 ...
            datasets = db.session.query(Dataset).filter_by(tenant_id=tenant_id).all()
            names = [dataset.name for dataset in datasets]
            rag_pipeline_dataset_create_entity.name = generate_incremental_name(
                names,
                "Untitled",
            )
        if not current_user or not current_user.id:
            raise ValueError("Current user or current user id not found")
        pipeline = Pipeline(
            tenant_id=tenant_id,
            name=rag_pipeline_dataset_create_entity.name,
            description=rag_pipeline_dataset_create_entity.description,
            created_by=current_user.id,
        )
        db.session.add(pipeline)
        db.session.flush()

        dataset = Dataset(
            tenant_id=tenant_id,
            name=rag_pipeline_dataset_create_entity.name,
            description=rag_pipeline_dataset_create_entity.description,
            permission=rag_pipeline_dataset_create_entity.permission,
            provider="vendor",
            runtime_mode="rag_pipeline",
            icon_info=rag_pipeline_dataset_create_entity.icon_info.model_dump(),
            created_by=current_user.id,
            pipeline_id=pipeline.id,
        )
        db.session.add(dataset)
        db.session.commit()
        return dataset

    @staticmethod
    def get_dataset(dataset_id) -> Dataset | None:
        dataset: Dataset | None = db.session.query(Dataset).filter_by(id=dataset_id).first()
        return dataset

    @staticmethod
    def check_doc_form(dataset: Dataset, doc_form: str):
        if dataset.doc_form and doc_form != dataset.doc_form:
            raise ValueError("doc_form is different from the dataset doc_form.")

    @staticmethod
    def check_dataset_model_setting(dataset):
        if dataset.indexing_technique == "high_quality":
            try:
                model_manager = ModelManager()
                model_manager.get_model_instance(
                    tenant_id=dataset.tenant_id,
                    provider=dataset.embedding_model_provider,
                    model_type=ModelType.TEXT_EMBEDDING,
                    model=dataset.embedding_model,
                )
            except LLMBadRequestError:
                raise ValueError(
                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                )
            except ProviderTokenNotInitError as ex:
                raise ValueError(f"The dataset is unavailable, due to: {ex.description}")

    @staticmethod
    def check_embedding_model_setting(tenant_id: str, embedding_model_provider: str, embedding_model: str):
        try:
            model_manager = ModelManager()
            model_manager.get_model_instance(
                tenant_id=tenant_id,
                provider=embedding_model_provider,
                model_type=ModelType.TEXT_EMBEDDING,
                model=embedding_model,
            )
        except LLMBadRequestError:
            raise ValueError(
                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
            )
        except ProviderTokenNotInitError as ex:
            raise ValueError(ex.description)

    @staticmethod
    def check_is_multimodal_model(tenant_id: str, model_provider: str, model: str):
        try:
            model_manager = ModelManager()
            model_instance = model_manager.get_model_instance(
                tenant_id=tenant_id,
                provider=model_provider,
                model_type=ModelType.TEXT_EMBEDDING,
                model=model,
            )
            text_embedding_model = cast(TextEmbeddingModel, model_instance.model_type_instance)
            model_schema = text_embedding_model.get_model_schema(model_instance.model_name, model_instance.credentials)
            if not model_schema:
                raise ValueError("Model schema not found")
            if model_schema.features and ModelFeature.VISION in model_schema.features:
                return True
            else:
                return False
        except LLMBadRequestError:
            raise ValueError("No Model available. Please configure a valid provider in the Settings -> Model Provider.")

    @staticmethod
    def check_reranking_model_setting(tenant_id: str, reranking_model_provider: str, reranking_model: str):
        try:
            model_manager = ModelManager()
            model_manager.get_model_instance(
                tenant_id=tenant_id,
                provider=reranking_model_provider,
                model_type=ModelType.RERANK,
                model=reranking_model,
            )
        except LLMBadRequestError:
            raise ValueError(
                "No Rerank Model available. Please configure a valid provider in the Settings -> Model Provider."
            )
        except ProviderTokenNotInitError as ex:
            raise ValueError(ex.description)

    @staticmethod
    def update_dataset(dataset_id, data, user):
        """
        Update dataset configuration and settings.

        Args:
            dataset_id: The unique identifier of the dataset to update
            data: Dictionary containing the update data
            user: The user performing the update operation

        Returns:
            Dataset: The updated dataset object

        Raises:
            ValueError: If dataset not found or validation fails
            NoPermissionError: If user lacks permission to update the dataset
        """
        # Retrieve and validate dataset existence
        dataset = DatasetService.get_dataset(dataset_id)
        if not dataset:
            raise ValueError("Dataset not found")
            #  check if dataset name is exists
        if data.get("name") and data.get("name") != dataset.name:
            if DatasetService._has_dataset_same_name(
                tenant_id=dataset.tenant_id,
                dataset_id=dataset_id,
                name=data.get("name", dataset.name),
            ):
                raise ValueError("Dataset name already exists")

        # Verify user has permission to update this dataset
        DatasetService.check_dataset_permission(dataset, user)

        # Handle external dataset updates
        if dataset.provider == "external":
            return DatasetService._update_external_dataset(dataset, data, user)
        else:
            return DatasetService._update_internal_dataset(dataset, data, user)

    @staticmethod
    def _has_dataset_same_name(tenant_id: str, dataset_id: str, name: str):
        dataset = (
            db.session.query(Dataset)
            .where(
                Dataset.id != dataset_id,
                Dataset.name == name,
                Dataset.tenant_id == tenant_id,
            )
            .first()
        )
        return dataset is not None

    @staticmethod
    def _update_external_dataset(dataset, data, user):
        """
        Update external dataset configuration.

        Args:
            dataset: The dataset object to update
            data: Update data dictionary
            user: User performing the update

        Returns:
            Dataset: Updated dataset object
        """
        # Update retrieval model if provided
        external_retrieval_model = data.get("external_retrieval_model", None)
        if external_retrieval_model:
            dataset.retrieval_model = external_retrieval_model

        # Update summary index setting if provided
        summary_index_setting = data.get("summary_index_setting", None)
        if summary_index_setting is not None:
            dataset.summary_index_setting = summary_index_setting

        # Update basic dataset properties
        dataset.name = data.get("name", dataset.name)
        dataset.description = data.get("description", dataset.description)

        # Update permission if provided
        permission = data.get("permission")
        if permission:
            dataset.permission = permission

        # Validate and update external knowledge configuration
        external_knowledge_id = data.get("external_knowledge_id", None)
        external_knowledge_api_id = data.get("external_knowledge_api_id", None)

        if not external_knowledge_id:
            raise ValueError("External knowledge id is required.")
        if not external_knowledge_api_id:
            raise ValueError("External knowledge api id is required.")
        # Update metadata fields
        dataset.updated_by = user.id if user else None
        dataset.updated_at = naive_utc_now()
        db.session.add(dataset)

        # Update external knowledge binding
        DatasetService._update_external_knowledge_binding(dataset.id, external_knowledge_id, external_knowledge_api_id)

        # Commit changes to database
        db.session.commit()

        return dataset

    @staticmethod
    def _update_external_knowledge_binding(dataset_id, external_knowledge_id, external_knowledge_api_id):
        """
        Update external knowledge binding configuration.

        Args:
            dataset_id: Dataset identifier
            external_knowledge_id: External knowledge identifier
            external_knowledge_api_id: External knowledge API identifier
        """
        with Session(db.engine) as session:
            external_knowledge_binding = (
                session.query(ExternalKnowledgeBindings).filter_by(dataset_id=dataset_id).first()
            )

            if not external_knowledge_binding:
                raise ValueError("External knowledge binding not found.")

        # Update binding if values have changed
        if (
            external_knowledge_binding.external_knowledge_id != external_knowledge_id
            or external_knowledge_binding.external_knowledge_api_id != external_knowledge_api_id
        ):
            external_knowledge_binding.external_knowledge_id = external_knowledge_id
            external_knowledge_binding.external_knowledge_api_id = external_knowledge_api_id
            db.session.add(external_knowledge_binding)

    @staticmethod
    def _update_internal_dataset(dataset, data, user):
        """
        Update internal dataset configuration.

        Args:
            dataset: The dataset object to update
            data: Update data dictionary
            user: User performing the update

        Returns:
            Dataset: Updated dataset object
        """
        # Remove external-specific fields from update data
        data.pop("partial_member_list", None)
        data.pop("external_knowledge_api_id", None)
        data.pop("external_knowledge_id", None)
        data.pop("external_retrieval_model", None)

        # Filter out None values except for description field
        filtered_data = {k: v for k, v in data.items() if v is not None or k == "description"}

        # Handle indexing technique changes and embedding model updates
        action = DatasetService._handle_indexing_technique_change(dataset, data, filtered_data)

        # Add metadata fields
        filtered_data["updated_by"] = user.id
        filtered_data["updated_at"] = naive_utc_now()
        # update Retrieval model
        if data.get("retrieval_model"):
            filtered_data["retrieval_model"] = data["retrieval_model"]
        # update summary index setting
        if data.get("summary_index_setting"):
            filtered_data["summary_index_setting"] = data.get("summary_index_setting")
        # update icon info
        if data.get("icon_info"):
            filtered_data["icon_info"] = data.get("icon_info")

        # Update dataset in database
        db.session.query(Dataset).filter_by(id=dataset.id).update(filtered_data)
        db.session.commit()

        # Reload dataset to get updated values
        db.session.refresh(dataset)

        # update pipeline knowledge base node data
        DatasetService._update_pipeline_knowledge_base_node_data(dataset, user.id)

        # Trigger vector index task if indexing technique changed
        if action:
            deal_dataset_vector_index_task.delay(dataset.id, action)
            # If embedding_model changed, also regenerate summary vectors
            if action == "update":
                regenerate_summary_index_task.delay(
                    dataset.id,
                    regenerate_reason="embedding_model_changed",
                    regenerate_vectors_only=True,
                )

        # Note: summary_index_setting changes do not trigger automatic regeneration of existing summaries.
        # The new setting will only apply to:
        # 1. New documents added after the setting change
        # 2. Manual summary generation requests

        return dataset

    @staticmethod
    def _update_pipeline_knowledge_base_node_data(dataset: Dataset, updata_user_id: str):
        """
        Update pipeline knowledge base node data.
        """
        if dataset.runtime_mode != "rag_pipeline":
            return

        pipeline = db.session.query(Pipeline).filter_by(id=dataset.pipeline_id).first()
        if not pipeline:
            return

        try:
            rag_pipeline_service = RagPipelineService()
            published_workflow = rag_pipeline_service.get_published_workflow(pipeline)
            draft_workflow = rag_pipeline_service.get_draft_workflow(pipeline)

            # update knowledge nodes
            def update_knowledge_nodes(workflow_graph: str) -> str:
                """Update knowledge-index nodes in workflow graph."""
                data: dict[str, Any] = json.loads(workflow_graph)

                nodes = data.get("nodes", [])
                updated = False

                for node in nodes:
                    if node.get("data", {}).get("type") == "knowledge-index":
                        try:
                            knowledge_index_node_data = node.get("data", {})
                            knowledge_index_node_data["embedding_model"] = dataset.embedding_model
                            knowledge_index_node_data["embedding_model_provider"] = dataset.embedding_model_provider
                            knowledge_index_node_data["retrieval_model"] = dataset.retrieval_model
                            knowledge_index_node_data["chunk_structure"] = dataset.chunk_structure
                            knowledge_index_node_data["indexing_technique"] = dataset.indexing_technique  # pyright: ignore[reportAttributeAccessIssue]
                            knowledge_index_node_data["keyword_number"] = dataset.keyword_number
                            knowledge_index_node_data["summary_index_setting"] = dataset.summary_index_setting
                            node["data"] = knowledge_index_node_data
                            updated = True
                        except Exception:
                            logging.exception("Failed to update knowledge node")
                            continue

                if updated:
                    data["nodes"] = nodes
                    return json.dumps(data)
                return workflow_graph

            # Update published workflow
            if published_workflow:
                updated_graph = update_knowledge_nodes(published_workflow.graph)
                if updated_graph != published_workflow.graph:
                    # Create new workflow version
                    workflow = Workflow.new(
                        tenant_id=pipeline.tenant_id,
                        app_id=pipeline.id,
                        type=published_workflow.type,
                        version=str(datetime.datetime.now(datetime.UTC).replace(tzinfo=None)),
                        graph=updated_graph,
                        features=published_workflow.features,
                        created_by=updata_user_id,
                        environment_variables=published_workflow.environment_variables,
                        conversation_variables=published_workflow.conversation_variables,
                        rag_pipeline_variables=published_workflow.rag_pipeline_variables,
                        marked_name="",
                        marked_comment="",
                    )
                    db.session.add(workflow)

            # Update draft workflow
            if draft_workflow:
                updated_graph = update_knowledge_nodes(draft_workflow.graph)
                if updated_graph != draft_workflow.graph:
                    draft_workflow.graph = updated_graph
                    db.session.add(draft_workflow)

            # Commit all changes in one transaction
            db.session.commit()

        except Exception:
            logging.exception("Failed to update pipeline knowledge base node data")
            db.session.rollback()
            raise

    @staticmethod
    def _handle_indexing_technique_change(dataset, data, filtered_data):
        """
        Handle changes in indexing technique and configure embedding models accordingly.

        Args:
            dataset: Current dataset object
            data: Update data dictionary
            filtered_data: Filtered update data

        Returns:
            str: Action to perform ('add', 'remove', 'update', or None)
        """
        if "indexing_technique" not in data:
            return None
        if dataset.indexing_technique != data["indexing_technique"]:
            if data["indexing_technique"] == "economy":
                # Remove embedding model configuration for economy mode
                filtered_data["embedding_model"] = None
                filtered_data["embedding_model_provider"] = None
                filtered_data["collection_binding_id"] = None
                return "remove"
            elif data["indexing_technique"] == "high_quality":
                # Configure embedding model for high quality mode
                DatasetService._configure_embedding_model_for_high_quality(data, filtered_data)
                return "add"
        else:
            # Handle embedding model updates when indexing technique remains the same
            return DatasetService._handle_embedding_model_update_when_technique_unchanged(dataset, data, filtered_data)
        return None

    @staticmethod
    def _configure_embedding_model_for_high_quality(data, filtered_data):
        """
        Configure embedding model settings for high quality indexing.

        Args:
            data: Update data dictionary
            filtered_data: Filtered update data to modify
        """
        # assert isinstance(current_user, Account) and current_user.current_tenant_id is not None
        try:
            model_manager = ModelManager()
            assert isinstance(current_user, Account)
            assert current_user.current_tenant_id is not None
            embedding_model = model_manager.get_model_instance(
                tenant_id=current_user.current_tenant_id,
                provider=data["embedding_model_provider"],
                model_type=ModelType.TEXT_EMBEDDING,
                model=data["embedding_model"],
            )
            embedding_model_name = embedding_model.model_name
            filtered_data["embedding_model"] = embedding_model_name
            filtered_data["embedding_model_provider"] = embedding_model.provider
            dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
                embedding_model.provider,
                embedding_model_name,
            )
            filtered_data["collection_binding_id"] = dataset_collection_binding.id
        except LLMBadRequestError:
            raise ValueError(
                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
            )
        except ProviderTokenNotInitError as ex:
            raise ValueError(ex.description)

    @staticmethod
    def _handle_embedding_model_update_when_technique_unchanged(dataset, data, filtered_data):
        """
        Handle embedding model updates when indexing technique remains the same.

        Args:
            dataset: Current dataset object
            data: Update data dictionary
            filtered_data: Filtered update data to modify

        Returns:
            str: Action to perform ('update' or None)
        """
        # Skip embedding model checks if not provided in the update request
        if (
            "embedding_model_provider" not in data
            or "embedding_model" not in data
            or not data.get("embedding_model_provider")
            or not data.get("embedding_model")
        ):
            DatasetService._preserve_existing_embedding_settings(dataset, filtered_data)
            return None
        else:
            return DatasetService._update_embedding_model_settings(dataset, data, filtered_data)

    @staticmethod
    def _preserve_existing_embedding_settings(dataset, filtered_data):
        """
        Preserve existing embedding model settings when not provided in update.

        Args:
            dataset: Current dataset object
            filtered_data: Filtered update data to modify
        """
        # If the dataset already has embedding model settings, use those
        if dataset.embedding_model_provider and dataset.embedding_model:
            filtered_data["embedding_model_provider"] = dataset.embedding_model_provider
            filtered_data["embedding_model"] = dataset.embedding_model
            # If collection_binding_id exists, keep it too
            if dataset.collection_binding_id:
                filtered_data["collection_binding_id"] = dataset.collection_binding_id
        # Otherwise, don't try to update embedding model settings at all
        # Remove these fields from filtered_data if they exist but are None/empty
        if "embedding_model_provider" in filtered_data and not filtered_data["embedding_model_provider"]:
            del filtered_data["embedding_model_provider"]
        if "embedding_model" in filtered_data and not filtered_data["embedding_model"]:
            del filtered_data["embedding_model"]

    @staticmethod
    def _update_embedding_model_settings(dataset, data, filtered_data):
        """
        Update embedding model settings with new values.

        Args:
            dataset: Current dataset object
            data: Update data dictionary
            filtered_data: Filtered update data to modify

        Returns:
            str: Action to perform ('update' or None)
        """
        try:
            # Compare current and new model provider settings
            current_provider_str = (
                str(ModelProviderID(dataset.embedding_model_provider)) if dataset.embedding_model_provider else None
            )
            new_provider_str = (
                str(ModelProviderID(data["embedding_model_provider"])) if data["embedding_model_provider"] else None
            )

            # Only update if values are different
            if current_provider_str != new_provider_str or data["embedding_model"] != dataset.embedding_model:
                DatasetService._apply_new_embedding_settings(dataset, data, filtered_data)
                return "update"
        except LLMBadRequestError:
            raise ValueError(
                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
            )
        except ProviderTokenNotInitError as ex:
            raise ValueError(ex.description)
        return None

    @staticmethod
    def _apply_new_embedding_settings(dataset, data, filtered_data):
        """
        Apply new embedding model settings to the dataset.

        Args:
            dataset: Current dataset object
            data: Update data dictionary
            filtered_data: Filtered update data to modify
        """
        # assert isinstance(current_user, Account) and current_user.current_tenant_id is not None

        model_manager = ModelManager()
        try:
            assert isinstance(current_user, Account)
            assert current_user.current_tenant_id is not None
            embedding_model = model_manager.get_model_instance(
                tenant_id=current_user.current_tenant_id,
                provider=data["embedding_model_provider"],
                model_type=ModelType.TEXT_EMBEDDING,
                model=data["embedding_model"],
            )
        except ProviderTokenNotInitError:
            # If we can't get the embedding model, preserve existing settings
            logger.warning(
                "Failed to initialize embedding model %s/%s, preserving existing settings",
                data["embedding_model_provider"],
                data["embedding_model"],
            )
            if dataset.embedding_model_provider and dataset.embedding_model:
                filtered_data["embedding_model_provider"] = dataset.embedding_model_provider
                filtered_data["embedding_model"] = dataset.embedding_model
                if dataset.collection_binding_id:
                    filtered_data["collection_binding_id"] = dataset.collection_binding_id
            # Skip the rest of the embedding model update
            return

        # Apply new embedding model settings
        embedding_model_name = embedding_model.model_name
        filtered_data["embedding_model"] = embedding_model_name
        filtered_data["embedding_model_provider"] = embedding_model.provider
        dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
            embedding_model.provider,
            embedding_model_name,
        )
        filtered_data["collection_binding_id"] = dataset_collection_binding.id

    @staticmethod
    def _check_summary_index_setting_model_changed(dataset: Dataset, data: dict[str, Any]) -> bool:
        """
        Check if summary_index_setting model (model_name or model_provider_name) has changed.

        Args:
            dataset: Current dataset object
            data: Update data dictionary

        Returns:
            bool: True if summary model changed, False otherwise
        """
        # Check if summary_index_setting is being updated
        if "summary_index_setting" not in data or data.get("summary_index_setting") is None:
            return False

        new_summary_setting = data.get("summary_index_setting")
        old_summary_setting = dataset.summary_index_setting

        # If new setting is disabled, no need to regenerate
        if not new_summary_setting or not new_summary_setting.get("enable"):
            return False

        # If old setting doesn't exist, no need to regenerate (no existing summaries to regenerate)
        # Note: This task only regenerates existing summaries, not generates new ones
        if not old_summary_setting:
            return False

        # Compare model_name and model_provider_name
        old_model_name = old_summary_setting.get("model_name")
        old_model_provider = old_summary_setting.get("model_provider_name")
        new_model_name = new_summary_setting.get("model_name")
        new_model_provider = new_summary_setting.get("model_provider_name")

        # Check if model changed
        if old_model_name != new_model_name or old_model_provider != new_model_provider:
            logger.info(
                "Summary index setting model changed for dataset %s: old=%s/%s, new=%s/%s",
                dataset.id,
                old_model_provider,
                old_model_name,
                new_model_provider,
                new_model_name,
            )
            return True

        return False

    @staticmethod
    def update_rag_pipeline_dataset_settings(
        session: Session, dataset: Dataset, knowledge_configuration: KnowledgeConfiguration, has_published: bool = False
    ):
        if not current_user or not current_user.current_tenant_id:
            raise ValueError("Current user or current tenant not found")
        dataset = session.merge(dataset)
        if not has_published:
            dataset.chunk_structure = knowledge_configuration.chunk_structure
            dataset.indexing_technique = knowledge_configuration.indexing_technique
            if knowledge_configuration.indexing_technique == "high_quality":
                model_manager = ModelManager()
                embedding_model = model_manager.get_model_instance(
                    tenant_id=current_user.current_tenant_id,  # ignore type error
                    provider=knowledge_configuration.embedding_model_provider or "",
                    model_type=ModelType.TEXT_EMBEDDING,
                    model=knowledge_configuration.embedding_model or "",
                )
                is_multimodal = DatasetService.check_is_multimodal_model(
                    current_user.current_tenant_id,
                    knowledge_configuration.embedding_model_provider,
                    knowledge_configuration.embedding_model,
                )
                dataset.is_multimodal = is_multimodal
                embedding_model_name = embedding_model.model_name
                dataset.embedding_model = embedding_model_name
                dataset.embedding_model_provider = embedding_model.provider
                dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
                    embedding_model.provider,
                    embedding_model_name,
                )
                dataset.collection_binding_id = dataset_collection_binding.id
            elif knowledge_configuration.indexing_technique == "economy":
                dataset.keyword_number = knowledge_configuration.keyword_number
            else:
                raise ValueError("Invalid index method")
            dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
            # Update summary_index_setting if provided
            if knowledge_configuration.summary_index_setting is not None:
                dataset.summary_index_setting = knowledge_configuration.summary_index_setting
            session.add(dataset)
        else:
            if dataset.chunk_structure and dataset.chunk_structure != knowledge_configuration.chunk_structure:
                raise ValueError("Chunk structure is not allowed to be updated.")
            action = None
            if dataset.indexing_technique != knowledge_configuration.indexing_technique:
                # if update indexing_technique
                if knowledge_configuration.indexing_technique == "economy":
                    raise ValueError("Knowledge base indexing technique is not allowed to be updated to economy.")
                elif knowledge_configuration.indexing_technique == "high_quality":
                    action = "add"
                    # get embedding model setting
                    try:
                        model_manager = ModelManager()
                        embedding_model = model_manager.get_model_instance(
                            tenant_id=current_user.current_tenant_id,
                            provider=knowledge_configuration.embedding_model_provider,
                            model_type=ModelType.TEXT_EMBEDDING,
                            model=knowledge_configuration.embedding_model,
                        )
                        embedding_model_name = embedding_model.model_name
                        dataset.embedding_model = embedding_model_name
                        dataset.embedding_model_provider = embedding_model.provider
                        dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
                            embedding_model.provider,
                            embedding_model_name,
                        )
                        is_multimodal = DatasetService.check_is_multimodal_model(
                            current_user.current_tenant_id,
                            knowledge_configuration.embedding_model_provider,
                            knowledge_configuration.embedding_model,
                        )
                        dataset.is_multimodal = is_multimodal
                        dataset.collection_binding_id = dataset_collection_binding.id
                        dataset.indexing_technique = knowledge_configuration.indexing_technique
                    except LLMBadRequestError:
                        raise ValueError(
                            "No Embedding Model available. Please configure a valid provider "
                            "in the Settings -> Model Provider."
                        )
                    except ProviderTokenNotInitError as ex:
                        raise ValueError(ex.description)
            else:
                # add default plugin id to both setting sets, to make sure the plugin model provider is consistent
                # Skip embedding model checks if not provided in the update request
                if dataset.indexing_technique == "high_quality":
                    skip_embedding_update = False
                    try:
                        # Handle existing model provider
                        plugin_model_provider = dataset.embedding_model_provider
                        plugin_model_provider_str = None
                        if plugin_model_provider:
                            plugin_model_provider_str = str(ModelProviderID(plugin_model_provider))

                        # Handle new model provider from request
                        new_plugin_model_provider = knowledge_configuration.embedding_model_provider
                        new_plugin_model_provider_str = None
                        if new_plugin_model_provider:
                            new_plugin_model_provider_str = str(ModelProviderID(new_plugin_model_provider))

                        # Only update embedding model if both values are provided and different from current
                        if (
                            plugin_model_provider_str != new_plugin_model_provider_str
                            or knowledge_configuration.embedding_model != dataset.embedding_model
                        ):
                            action = "update"
                            model_manager = ModelManager()
                            embedding_model = None
                            try:
                                embedding_model = model_manager.get_model_instance(
                                    tenant_id=current_user.current_tenant_id,
                                    provider=knowledge_configuration.embedding_model_provider,
                                    model_type=ModelType.TEXT_EMBEDDING,
                                    model=knowledge_configuration.embedding_model,
                                )
                            except ProviderTokenNotInitError:
                                # If we can't get the embedding model, skip updating it
                                # and keep the existing settings if available
                                # Skip the rest of the embedding model update
                                skip_embedding_update = True
                            if not skip_embedding_update:
                                if embedding_model:
                                    embedding_model_name = embedding_model.model_name
                                    dataset.embedding_model = embedding_model_name
                                    dataset.embedding_model_provider = embedding_model.provider
                                    dataset_collection_binding = (
                                        DatasetCollectionBindingService.get_dataset_collection_binding(
                                            embedding_model.provider,
                                            embedding_model_name,
                                        )
                                    )
                                    dataset.collection_binding_id = dataset_collection_binding.id
                                    is_multimodal = DatasetService.check_is_multimodal_model(
                                        current_user.current_tenant_id,
                                        knowledge_configuration.embedding_model_provider,
                                        knowledge_configuration.embedding_model,
                                    )
                                    dataset.is_multimodal = is_multimodal
                    except LLMBadRequestError:
                        raise ValueError(
                            "No Embedding Model available. Please configure a valid provider "
                            "in the Settings -> Model Provider."
                        )
                    except ProviderTokenNotInitError as ex:
                        raise ValueError(ex.description)
                elif dataset.indexing_technique == "economy":
                    if dataset.keyword_number != knowledge_configuration.keyword_number:
                        dataset.keyword_number = knowledge_configuration.keyword_number
            dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
            # Update summary_index_setting if provided
            if knowledge_configuration.summary_index_setting is not None:
                dataset.summary_index_setting = knowledge_configuration.summary_index_setting
            session.add(dataset)
            session.commit()
            if action:
                deal_dataset_index_update_task.delay(dataset.id, action)

    @staticmethod
    def delete_dataset(dataset_id, user):
        dataset = DatasetService.get_dataset(dataset_id)

        if dataset is None:
            return False

        DatasetService.check_dataset_permission(dataset, user)

        dataset_was_deleted.send(dataset)

        db.session.delete(dataset)
        db.session.commit()
        return True

    @staticmethod
    def dataset_use_check(dataset_id) -> bool:
        stmt = select(exists().where(AppDatasetJoin.dataset_id == dataset_id))
        return db.session.execute(stmt).scalar_one()

    @staticmethod
    def check_dataset_permission(dataset, user):
        if dataset.tenant_id != user.current_tenant_id:
            logger.debug("User %s does not have permission to access dataset %s", user.id, dataset.id)
            raise NoPermissionError("You do not have permission to access this dataset.")
        if user.current_role != TenantAccountRole.OWNER:
            if dataset.permission == DatasetPermissionEnum.ONLY_ME and dataset.created_by != user.id:
                logger.debug("User %s does not have permission to access dataset %s", user.id, dataset.id)
                raise NoPermissionError("You do not have permission to access this dataset.")
            if dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM:
                # For partial team permission, user needs explicit permission or be the creator
                if dataset.created_by != user.id:
                    user_permission = (
                        db.session.query(DatasetPermission).filter_by(dataset_id=dataset.id, account_id=user.id).first()
                    )
                    if not user_permission:
                        logger.debug("User %s does not have permission to access dataset %s", user.id, dataset.id)
                        raise NoPermissionError("You do not have permission to access this dataset.")

    @staticmethod
    def check_dataset_operator_permission(user: Account | None = None, dataset: Dataset | None = None):
        if not dataset:
            raise ValueError("Dataset not found")

        if not user:
            raise ValueError("User not found")

        if user.current_role != TenantAccountRole.OWNER:
            if dataset.permission == DatasetPermissionEnum.ONLY_ME:
                if dataset.created_by != user.id:
                    raise NoPermissionError("You do not have permission to access this dataset.")

            elif dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM:
                if not any(
                    dp.dataset_id == dataset.id
                    for dp in db.session.query(DatasetPermission).filter_by(account_id=user.id).all()
                ):
                    raise NoPermissionError("You do not have permission to access this dataset.")

    @staticmethod
    def get_dataset_queries(dataset_id: str, page: int, per_page: int):
        stmt = select(DatasetQuery).filter_by(dataset_id=dataset_id).order_by(db.desc(DatasetQuery.created_at))

        dataset_queries = db.paginate(select=stmt, page=page, per_page=per_page, max_per_page=100, error_out=False)

        return dataset_queries.items, dataset_queries.total

    @staticmethod
    def get_related_apps(dataset_id: str):
        return (
            db.session.query(AppDatasetJoin)
            .where(AppDatasetJoin.dataset_id == dataset_id)
            .order_by(db.desc(AppDatasetJoin.created_at))
            .all()
        )

    @staticmethod
    def update_dataset_api_status(dataset_id: str, status: bool):
        dataset = DatasetService.get_dataset(dataset_id)
        if dataset is None:
            raise NotFound("Dataset not found.")
        dataset.enable_api = status
        if not current_user or not current_user.id:
            raise ValueError("Current user or current user id not found")
        dataset.updated_by = current_user.id
        dataset.updated_at = naive_utc_now()
        db.session.commit()

    @staticmethod
    def get_dataset_auto_disable_logs(dataset_id: str):
        assert isinstance(current_user, Account)
        assert current_user.current_tenant_id is not None
        features = FeatureService.get_features(current_user.current_tenant_id)
        if not features.billing.enabled or features.billing.subscription.plan == CloudPlan.SANDBOX:
            return {
                "document_ids": [],
                "count": 0,
            }
        # get recent 30 days auto disable logs
        start_date = datetime.datetime.now() - datetime.timedelta(days=30)
        dataset_auto_disable_logs = db.session.scalars(
            select(DatasetAutoDisableLog).where(
                DatasetAutoDisableLog.dataset_id == dataset_id,
                DatasetAutoDisableLog.created_at >= start_date,
            )
        ).all()
        if dataset_auto_disable_logs:
            return {
                "document_ids": [log.document_id for log in dataset_auto_disable_logs],
                "count": len(dataset_auto_disable_logs),
            }
        return {
            "document_ids": [],
            "count": 0,
        }


class DocumentService:
    DEFAULT_RULES: dict[str, Any] = {
        "mode": "custom",
        "rules": {
            "pre_processing_rules": [
                {"id": "remove_extra_spaces", "enabled": True},
                {"id": "remove_urls_emails", "enabled": False},
            ],
            "segmentation": {"delimiter": "\n", "max_tokens": 1024, "chunk_overlap": 50},
        },
        "limits": {
            "indexing_max_segmentation_tokens_length": dify_config.INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH,
        },
    }

    DISPLAY_STATUS_ALIASES: dict[str, str] = {
        "active": "available",
        "enabled": "available",
    }

    _INDEXING_STATUSES: tuple[str, ...] = ("parsing", "cleaning", "splitting", "indexing")

    DISPLAY_STATUS_FILTERS: dict[str, tuple[Any, ...]] = {
        "queuing": (Document.indexing_status == "waiting",),
        "indexing": (
            Document.indexing_status.in_(_INDEXING_STATUSES),
            Document.is_paused.is_not(True),
        ),
        "paused": (
            Document.indexing_status.in_(_INDEXING_STATUSES),
            Document.is_paused.is_(True),
        ),
        "error": (Document.indexing_status == "error",),
        "available": (
            Document.indexing_status == "completed",
            Document.archived.is_(False),
            Document.enabled.is_(True),
        ),
        "disabled": (
            Document.indexing_status == "completed",
            Document.archived.is_(False),
            Document.enabled.is_(False),
        ),
        "archived": (
            Document.indexing_status == "completed",
            Document.archived.is_(True),
        ),
    }
    DOCUMENT_BATCH_DOWNLOAD_ZIP_FILENAME_EXTENSION = ".zip"

    @classmethod
    def normalize_display_status(cls, status: str | None) -> str | None:
        if not status:
            return None
        normalized = status.lower()
        normalized = cls.DISPLAY_STATUS_ALIASES.get(normalized, normalized)
        return normalized if normalized in cls.DISPLAY_STATUS_FILTERS else None

    @classmethod
    def build_display_status_filters(cls, status: str | None) -> tuple[Any, ...]:
        normalized = cls.normalize_display_status(status)
        if not normalized:
            return ()
        return cls.DISPLAY_STATUS_FILTERS[normalized]

    @classmethod
    def apply_display_status_filter(cls, query, status: str | None):
        filters = cls.build_display_status_filters(status)
        if not filters:
            return query
        return query.where(*filters)

    DOCUMENT_METADATA_SCHEMA: dict[str, Any] = {
        "book": {
            "title": str,
            "language": str,
            "author": str,
            "publisher": str,
            "publication_date": str,
            "isbn": str,
            "category": str,
        },
        "web_page": {
            "title": str,
            "url": str,
            "language": str,
            "publish_date": str,
            "author/publisher": str,
            "topic/keywords": str,
            "description": str,
        },
        "paper": {
            "title": str,
            "language": str,
            "author": str,
            "publish_date": str,
            "journal/conference_name": str,
            "volume/issue/page_numbers": str,
            "doi": str,
            "topic/keywords": str,
            "abstract": str,
        },
        "social_media_post": {
            "platform": str,
            "author/username": str,
            "publish_date": str,
            "post_url": str,
            "topic/tags": str,
        },
        "wikipedia_entry": {
            "title": str,
            "language": str,
            "web_page_url": str,
            "last_edit_date": str,
            "editor/contributor": str,
            "summary/introduction": str,
        },
        "personal_document": {
            "title": str,
            "author": str,
            "creation_date": str,
            "last_modified_date": str,
            "document_type": str,
            "tags/category": str,
        },
        "business_document": {
            "title": str,
            "author": str,
            "creation_date": str,
            "last_modified_date": str,
            "document_type": str,
            "department/team": str,
        },
        "im_chat_log": {
            "chat_platform": str,
            "chat_participants/group_name": str,
            "start_date": str,
            "end_date": str,
            "summary": str,
        },
        "synced_from_notion": {
            "title": str,
            "language": str,
            "author/creator": str,
            "creation_date": str,
            "last_modified_date": str,
            "notion_page_link": str,
            "category/tags": str,
            "description": str,
        },
        "synced_from_github": {
            "repository_name": str,
            "repository_description": str,
            "repository_owner/organization": str,
            "code_filename": str,
            "code_file_path": str,
            "programming_language": str,
            "github_link": str,
            "open_source_license": str,
            "commit_date": str,
            "commit_author": str,
        },
        "others": dict,
    }

    @staticmethod
    def get_document(dataset_id: str, document_id: str | None = None) -> Document | None:
        if document_id:
            document = (
                db.session.query(Document).where(Document.id == document_id, Document.dataset_id == dataset_id).first()
            )
            return document
        else:
            return None

    @staticmethod
    def get_documents_by_ids(dataset_id: str, document_ids: Sequence[str]) -> Sequence[Document]:
        """Fetch documents for a dataset in a single batch query."""
        if not document_ids:
            return []
        document_id_list: list[str] = [str(document_id) for document_id in document_ids]
        # Fetch all requested documents in one query to avoid N+1 lookups.
        documents: Sequence[Document] = db.session.scalars(
            select(Document).where(
                Document.dataset_id == dataset_id,
                Document.id.in_(document_id_list),
            )
        ).all()
        return documents

    @staticmethod
    def update_documents_need_summary(dataset_id: str, document_ids: Sequence[str], need_summary: bool = True) -> int:
        """
        Update need_summary field for multiple documents.

        This method handles the case where documents were created when summary_index_setting was disabled,
        and need to be updated when summary_index_setting is later enabled.

        Args:
            dataset_id: Dataset ID
            document_ids: List of document IDs to update
            need_summary: Value to set for need_summary field (default: True)

        Returns:
            Number of documents updated
        """
        if not document_ids:
            return 0

        document_id_list: list[str] = [str(document_id) for document_id in document_ids]

        with session_factory.create_session() as session:
            updated_count = (
                session.query(Document)
                .filter(
                    Document.id.in_(document_id_list),
                    Document.dataset_id == dataset_id,
                    Document.doc_form != "qa_model",  # Skip qa_model documents
                )
                .update({Document.need_summary: need_summary}, synchronize_session=False)
            )
            session.commit()
            logger.info(
                "Updated need_summary to %s for %d documents in dataset %s",
                need_summary,
                updated_count,
                dataset_id,
            )
            return updated_count

    @staticmethod
    def get_document_download_url(document: Document) -> str:
        """
        Return a signed download URL for an upload-file document.
        """
        upload_file = DocumentService._get_upload_file_for_upload_file_document(document)
        return file_helpers.get_signed_file_url(upload_file_id=upload_file.id, as_attachment=True)

    @staticmethod
    def enrich_documents_with_summary_index_status(
        documents: Sequence[Document],
        dataset: Dataset,
        tenant_id: str,
    ) -> None:
        """
        Enrich documents with summary_index_status based on dataset summary index settings.

        This method calculates and sets the summary_index_status for each document that needs summary.
        Documents that don't need summary or when summary index is disabled will have status set to None.

        Args:
            documents: List of Document instances to enrich
            dataset: Dataset instance containing summary_index_setting
            tenant_id: Tenant ID for summary status lookup
        """
        # Check if dataset has summary index enabled
        has_summary_index = dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True

        # Filter documents that need summary calculation
        documents_need_summary = [doc for doc in documents if doc.need_summary is True]
        document_ids_need_summary = [str(doc.id) for doc in documents_need_summary]

        # Calculate summary_index_status for documents that need summary (only if dataset summary index is enabled)
        summary_status_map: dict[str, str | None] = {}
        if has_summary_index and document_ids_need_summary:
            from services.summary_index_service import SummaryIndexService

            summary_status_map = SummaryIndexService.get_documents_summary_index_status(
                document_ids=document_ids_need_summary,
                dataset_id=dataset.id,
                tenant_id=tenant_id,
            )

        # Add summary_index_status to each document
        for document in documents:
            if has_summary_index and document.need_summary is True:
                # Get status from map, default to None (not queued yet)
                document.summary_index_status = summary_status_map.get(str(document.id))  # type: ignore[attr-defined]
            else:
                # Return null if summary index is not enabled or document doesn't need summary
                document.summary_index_status = None  # type: ignore[attr-defined]

    @staticmethod
    def prepare_document_batch_download_zip(
        *,
        dataset_id: str,
        document_ids: Sequence[str],
        tenant_id: str,
        current_user: Account,
    ) -> tuple[list[UploadFile], str]:
        """
        Resolve upload files for batch ZIP downloads and generate a client-visible filename.
        """
        dataset = DatasetService.get_dataset(dataset_id)
        if not dataset:
            raise NotFound("Dataset not found.")
        try:
            DatasetService.check_dataset_permission(dataset, current_user)
        except NoPermissionError as e:
            raise Forbidden(str(e))

        upload_files_by_document_id = DocumentService._get_upload_files_by_document_id_for_zip_download(
            dataset_id=dataset_id,
            document_ids=document_ids,
            tenant_id=tenant_id,
        )
        upload_files = [upload_files_by_document_id[document_id] for document_id in document_ids]
        download_name = DocumentService._generate_document_batch_download_zip_filename()
        return upload_files, download_name

    @staticmethod
    def _generate_document_batch_download_zip_filename() -> str:
        """
        Generate a random attachment filename for the batch download ZIP.
        """
        return f"{uuid.uuid4().hex}{DocumentService.DOCUMENT_BATCH_DOWNLOAD_ZIP_FILENAME_EXTENSION}"

    @staticmethod
    def _get_upload_file_id_for_upload_file_document(
        document: Document,
        *,
        invalid_source_message: str,
        missing_file_message: str,
    ) -> str:
        """
        Normalize and validate `Document -> UploadFile` linkage for download flows.
        """
        if document.data_source_type != "upload_file":
            raise NotFound(invalid_source_message)

        data_source_info: dict[str, Any] = document.data_source_info_dict or {}
        upload_file_id: str | None = data_source_info.get("upload_file_id")
        if not upload_file_id:
            raise NotFound(missing_file_message)

        return str(upload_file_id)

    @staticmethod
    def _get_upload_file_for_upload_file_document(document: Document) -> UploadFile:
        """
        Load the `UploadFile` row for an upload-file document.
        """
        upload_file_id = DocumentService._get_upload_file_id_for_upload_file_document(
            document,
            invalid_source_message="Document does not have an uploaded file to download.",
            missing_file_message="Uploaded file not found.",
        )
        upload_files_by_id = FileService.get_upload_files_by_ids(document.tenant_id, [upload_file_id])
        upload_file = upload_files_by_id.get(upload_file_id)
        if not upload_file:
            raise NotFound("Uploaded file not found.")
        return upload_file

    @staticmethod
    def _get_upload_files_by_document_id_for_zip_download(
        *,
        dataset_id: str,
        document_ids: Sequence[str],
        tenant_id: str,
    ) -> dict[str, UploadFile]:
        """
        Batch load upload files keyed by document id for ZIP downloads.
        """
        document_id_list: list[str] = [str(document_id) for document_id in document_ids]

        documents = DocumentService.get_documents_by_ids(dataset_id, document_id_list)
        documents_by_id: dict[str, Document] = {str(document.id): document for document in documents}

        missing_document_ids: set[str] = set(document_id_list) - set(documents_by_id.keys())
        if missing_document_ids:
            raise NotFound("Document not found.")

        upload_file_ids: list[str] = []
        upload_file_ids_by_document_id: dict[str, str] = {}
        for document_id, document in documents_by_id.items():
            if document.tenant_id != tenant_id:
                raise Forbidden("No permission.")

            upload_file_id = DocumentService._get_upload_file_id_for_upload_file_document(
                document,
                invalid_source_message="Only uploaded-file documents can be downloaded as ZIP.",
                missing_file_message="Only uploaded-file documents can be downloaded as ZIP.",
            )
            upload_file_ids.append(upload_file_id)
            upload_file_ids_by_document_id[document_id] = upload_file_id

        upload_files_by_id = FileService.get_upload_files_by_ids(tenant_id, upload_file_ids)
        missing_upload_file_ids: set[str] = set(upload_file_ids) - set(upload_files_by_id.keys())
        if missing_upload_file_ids:
            raise NotFound("Only uploaded-file documents can be downloaded as ZIP.")

        return {
            document_id: upload_files_by_id[upload_file_id]
            for document_id, upload_file_id in upload_file_ids_by_document_id.items()
        }

    @staticmethod
    def get_document_by_id(document_id: str) -> Document | None:
        document = db.session.query(Document).where(Document.id == document_id).first()

        return document

    @staticmethod
    def get_document_by_ids(document_ids: list[str]) -> Sequence[Document]:
        documents = db.session.scalars(
            select(Document).where(
                Document.id.in_(document_ids),
                Document.enabled == True,
                Document.indexing_status == "completed",
                Document.archived == False,
            )
        ).all()
        return documents

    @staticmethod
    def get_document_by_dataset_id(dataset_id: str) -> Sequence[Document]:
        documents = db.session.scalars(
            select(Document).where(
                Document.dataset_id == dataset_id,
                Document.enabled == True,
            )
        ).all()

        return documents

    @staticmethod
    def get_working_documents_by_dataset_id(dataset_id: str) -> Sequence[Document]:
        documents = db.session.scalars(
            select(Document).where(
                Document.dataset_id == dataset_id,
                Document.enabled == True,
                Document.indexing_status == "completed",
                Document.archived == False,
            )
        ).all()

        return documents

    @staticmethod
    def get_error_documents_by_dataset_id(dataset_id: str) -> Sequence[Document]:
        documents = db.session.scalars(
            select(Document).where(Document.dataset_id == dataset_id, Document.indexing_status.in_(["error", "paused"]))
        ).all()
        return documents

    @staticmethod
    def get_batch_documents(dataset_id: str, batch: str) -> Sequence[Document]:
        assert isinstance(current_user, Account)
        documents = db.session.scalars(
            select(Document).where(
                Document.batch == batch,
                Document.dataset_id == dataset_id,
                Document.tenant_id == current_user.current_tenant_id,
            )
        ).all()

        return documents

    @staticmethod
    def get_document_file_detail(file_id: str):
        file_detail = db.session.query(UploadFile).where(UploadFile.id == file_id).one_or_none()
        return file_detail

    @staticmethod
    def check_archived(document):
        if document.archived:
            return True
        else:
            return False

    @staticmethod
    def delete_document(document):
        # trigger document_was_deleted signal
        file_id = None
        if document.data_source_type == "upload_file":
            if document.data_source_info:
                data_source_info = document.data_source_info_dict
                if data_source_info and "upload_file_id" in data_source_info:
                    file_id = data_source_info["upload_file_id"]
        document_was_deleted.send(
            document.id, dataset_id=document.dataset_id, doc_form=document.doc_form, file_id=file_id
        )

        db.session.delete(document)
        db.session.commit()

    @staticmethod
    def delete_documents(dataset: Dataset, document_ids: list[str]):
        # Check if document_ids is not empty to avoid WHERE false condition
        if not document_ids or len(document_ids) == 0:
            return
        documents = db.session.scalars(select(Document).where(Document.id.in_(document_ids))).all()
        file_ids = [
            document.data_source_info_dict.get("upload_file_id", "")
            for document in documents
            if document.data_source_type == "upload_file" and document.data_source_info_dict
        ]

        # Delete documents first, then dispatch cleanup task after commit
        # to avoid deadlock between main transaction and async task
        for document in documents:
            db.session.delete(document)
        db.session.commit()

        # Dispatch cleanup task after commit to avoid lock contention
        # Task cleans up segments, files, and vector indexes
        if dataset.doc_form is not None:
            batch_clean_document_task.delay(document_ids, dataset.id, dataset.doc_form, file_ids)

    @staticmethod
    def rename_document(dataset_id: str, document_id: str, name: str) -> Document:
        assert isinstance(current_user, Account)

        dataset = DatasetService.get_dataset(dataset_id)
        if not dataset:
            raise ValueError("Dataset not found.")

        document = DocumentService.get_document(dataset_id, document_id)

        if not document:
            raise ValueError("Document not found.")

        if document.tenant_id != current_user.current_tenant_id:
            raise ValueError("No permission.")

        if dataset.built_in_field_enabled:
            if document.doc_metadata:
                doc_metadata = copy.deepcopy(document.doc_metadata)
                doc_metadata[BuiltInField.document_name] = name
                document.doc_metadata = doc_metadata

        document.name = name
        db.session.add(document)
        if document.data_source_info_dict and "upload_file_id" in document.data_source_info_dict:
            db.session.query(UploadFile).where(
                UploadFile.id == document.data_source_info_dict["upload_file_id"]
            ).update({UploadFile.name: name})

        db.session.commit()

        return document

    @staticmethod
    def pause_document(document):
        if document.indexing_status not in {"waiting", "parsing", "cleaning", "splitting", "indexing"}:
            raise DocumentIndexingError()
        # update document to be paused
        assert current_user is not None
        document.is_paused = True
        document.paused_by = current_user.id
        document.paused_at = naive_utc_now()

        db.session.add(document)
        db.session.commit()
        # set document paused flag
        indexing_cache_key = f"document_{document.id}_is_paused"
        redis_client.setnx(indexing_cache_key, "True")

    @staticmethod
    def recover_document(document):
        if not document.is_paused:
            raise DocumentIndexingError()
        # update document to be recover
        document.is_paused = False
        document.paused_by = None
        document.paused_at = None

        db.session.add(document)
        db.session.commit()
        # delete paused flag
        indexing_cache_key = f"document_{document.id}_is_paused"
        redis_client.delete(indexing_cache_key)
        # trigger async task
        recover_document_indexing_task.delay(document.dataset_id, document.id)

    @staticmethod
    def retry_document(dataset_id: str, documents: list[Document]):
        for document in documents:
            # add retry flag
            retry_indexing_cache_key = f"document_{document.id}_is_retried"
            cache_result = redis_client.get(retry_indexing_cache_key)
            if cache_result is not None:
                raise ValueError("Document is being retried, please try again later")
            # retry document indexing
            document.indexing_status = "waiting"
            db.session.add(document)
            db.session.commit()

            redis_client.setex(retry_indexing_cache_key, 600, 1)
        # trigger async task
        document_ids = [document.id for document in documents]
        if not current_user or not current_user.id:
            raise ValueError("Current user or current user id not found")
        retry_document_indexing_task.delay(dataset_id, document_ids, current_user.id)

    @staticmethod
    def sync_website_document(dataset_id: str, document: Document):
        # add sync flag
        sync_indexing_cache_key = f"document_{document.id}_is_sync"
        cache_result = redis_client.get(sync_indexing_cache_key)
        if cache_result is not None:
            raise ValueError("Document is being synced, please try again later")
        # sync document indexing
        document.indexing_status = "waiting"
        data_source_info = document.data_source_info_dict
        if data_source_info:
            data_source_info["mode"] = "scrape"
            document.data_source_info = json.dumps(data_source_info, ensure_ascii=False)
        db.session.add(document)
        db.session.commit()

        redis_client.setex(sync_indexing_cache_key, 600, 1)

        sync_website_document_indexing_task.delay(dataset_id, document.id)

    @staticmethod
    def get_documents_position(dataset_id):
        document = (
            db.session.query(Document).filter_by(dataset_id=dataset_id).order_by(Document.position.desc()).first()
        )
        if document:
            return document.position + 1
        else:
            return 1

    @staticmethod
    def save_document_with_dataset_id(
        dataset: Dataset,
        knowledge_config: KnowledgeConfig,
        account: Account | Any,
        dataset_process_rule: DatasetProcessRule | None = None,
        created_from: str = "web",
    ) -> tuple[list[Document], str]:
        # check doc_form
        DatasetService.check_doc_form(dataset, knowledge_config.doc_form)
        # check document limit
        assert isinstance(current_user, Account)
        assert current_user.current_tenant_id is not None

        features = FeatureService.get_features(current_user.current_tenant_id)

        if features.billing.enabled:
            if not knowledge_config.original_document_id:
                count = 0
                if knowledge_config.data_source:
                    if knowledge_config.data_source.info_list.data_source_type == "upload_file":
                        if not knowledge_config.data_source.info_list.file_info_list:
                            raise ValueError("File source info is required")
                        upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids
                        count = len(upload_file_list)
                    elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
                        notion_info_list = knowledge_config.data_source.info_list.notion_info_list or []
                        for notion_info in notion_info_list:
                            count = count + len(notion_info.pages)
                    elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
                        website_info = knowledge_config.data_source.info_list.website_info_list
                        assert website_info
                        count = len(website_info.urls)
                    batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)

                    if features.billing.subscription.plan == CloudPlan.SANDBOX and count > 1:
                        raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
                    if count > batch_upload_limit:
                        raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")

                    DocumentService.check_documents_upload_quota(count, features)

        # if dataset is empty, update dataset data_source_type
        if not dataset.data_source_type and knowledge_config.data_source:
            dataset.data_source_type = knowledge_config.data_source.info_list.data_source_type

        if not dataset.indexing_technique:
            if knowledge_config.indexing_technique not in Dataset.INDEXING_TECHNIQUE_LIST:
                raise ValueError("Indexing technique is invalid")

            dataset.indexing_technique = knowledge_config.indexing_technique
            if knowledge_config.indexing_technique == "high_quality":
                model_manager = ModelManager()
                if knowledge_config.embedding_model and knowledge_config.embedding_model_provider:
                    dataset_embedding_model = knowledge_config.embedding_model
                    dataset_embedding_model_provider = knowledge_config.embedding_model_provider
                else:
                    embedding_model = model_manager.get_default_model_instance(
                        tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
                    )
                    dataset_embedding_model = embedding_model.model_name
                    dataset_embedding_model_provider = embedding_model.provider
                dataset.embedding_model = dataset_embedding_model
                dataset.embedding_model_provider = dataset_embedding_model_provider
                dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
                    dataset_embedding_model_provider, dataset_embedding_model
                )
                dataset.collection_binding_id = dataset_collection_binding.id
                if not dataset.retrieval_model:
                    default_retrieval_model = {
                        "search_method": RetrievalMethod.SEMANTIC_SEARCH,
                        "reranking_enable": False,
                        "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
                        "top_k": 4,
                        "score_threshold_enabled": False,
                    }

                    dataset.retrieval_model = (
                        knowledge_config.retrieval_model.model_dump()
                        if knowledge_config.retrieval_model
                        else default_retrieval_model
                    )

        documents = []
        if knowledge_config.original_document_id:
            document = DocumentService.update_document_with_dataset_id(dataset, knowledge_config, account)
            documents.append(document)
            batch = document.batch
        else:
            # When creating new documents, data_source must be provided
            if not knowledge_config.data_source:
                raise ValueError("Data source is required when creating new documents")

            batch = time.strftime("%Y%m%d%H%M%S") + str(100000 + secrets.randbelow(exclusive_upper_bound=900000))
            # save process rule
            if not dataset_process_rule:
                process_rule = knowledge_config.process_rule
                if process_rule:
                    if process_rule.mode in ("custom", "hierarchical"):
                        if process_rule.rules:
                            dataset_process_rule = DatasetProcessRule(
                                dataset_id=dataset.id,
                                mode=process_rule.mode,
                                rules=process_rule.rules.model_dump_json() if process_rule.rules else None,
                                created_by=account.id,
                            )
                        else:
                            dataset_process_rule = dataset.latest_process_rule
                            if not dataset_process_rule:
                                raise ValueError("No process rule found.")
                    elif process_rule.mode == "automatic":
                        dataset_process_rule = DatasetProcessRule(
                            dataset_id=dataset.id,
                            mode=process_rule.mode,
                            rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
                            created_by=account.id,
                        )
                    else:
                        logger.warning(
                            "Invalid process rule mode: %s, can not find dataset process rule",
                            process_rule.mode,
                        )
                        return [], ""
                    db.session.add(dataset_process_rule)
                    db.session.flush()
                else:
                    # Fallback when no process_rule provided in knowledge_config:
                    # 1) reuse dataset.latest_process_rule if present
                    # 2) otherwise create an automatic rule
                    dataset_process_rule = getattr(dataset, "latest_process_rule", None)
                    if not dataset_process_rule:
                        dataset_process_rule = DatasetProcessRule(
                            dataset_id=dataset.id,
                            mode="automatic",
                            rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
                            created_by=account.id,
                        )
                        db.session.add(dataset_process_rule)
                        db.session.flush()
            lock_name = f"add_document_lock_dataset_id_{dataset.id}"
            try:
                with redis_client.lock(lock_name, timeout=600):
                    assert dataset_process_rule
                    position = DocumentService.get_documents_position(dataset.id)
                    document_ids = []
                    duplicate_document_ids = []
                    if knowledge_config.data_source.info_list.data_source_type == "upload_file":
                        if not knowledge_config.data_source.info_list.file_info_list:
                            raise ValueError("File source info is required")
                        upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids
                        files = (
                            db.session.query(UploadFile)
                            .where(
                                UploadFile.tenant_id == dataset.tenant_id,
                                UploadFile.id.in_(upload_file_list),
                            )
                            .all()
                        )
                        if len(files) != len(set(upload_file_list)):
                            raise FileNotExistsError("One or more files not found.")

                        file_names = [file.name for file in files]
                        db_documents = (
                            db.session.query(Document)
                            .where(
                                Document.dataset_id == dataset.id,
                                Document.tenant_id == current_user.current_tenant_id,
                                Document.data_source_type == "upload_file",
                                Document.enabled == True,
                                Document.name.in_(file_names),
                            )
                            .all()
                        )
                        documents_map = {document.name: document for document in db_documents}
                        for file in files:
                            data_source_info: dict[str, str | bool] = {
                                "upload_file_id": file.id,
                            }
                            document = documents_map.get(file.name)
                            if knowledge_config.duplicate and document:
                                document.dataset_process_rule_id = dataset_process_rule.id
                                document.updated_at = naive_utc_now()
                                document.created_from = created_from
                                document.doc_form = knowledge_config.doc_form
                                document.doc_language = knowledge_config.doc_language
                                document.data_source_info = json.dumps(data_source_info)
                                document.batch = batch
                                document.indexing_status = "waiting"
                                db.session.add(document)
                                documents.append(document)
                                duplicate_document_ids.append(document.id)
                                continue
                            else:
                                document = DocumentService.build_document(
                                    dataset,
                                    dataset_process_rule.id,
                                    knowledge_config.data_source.info_list.data_source_type,
                                    knowledge_config.doc_form,
                                    knowledge_config.doc_language,
                                    data_source_info,
                                    created_from,
                                    position,
                                    account,
                                    file.name,
                                    batch,
                                )
                                db.session.add(document)
                                db.session.flush()
                                document_ids.append(document.id)
                                documents.append(document)
                                position += 1
                    elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
                        notion_info_list = knowledge_config.data_source.info_list.notion_info_list  # type: ignore
                        if not notion_info_list:
                            raise ValueError("No notion info list found.")
                        exist_page_ids = []
                        exist_document = {}
                        documents = (
                            db.session.query(Document)
                            .filter_by(
                                dataset_id=dataset.id,
                                tenant_id=current_user.current_tenant_id,
                                data_source_type="notion_import",
                                enabled=True,
                            )
                            .all()
                        )
                        if documents:
                            for document in documents:
                                data_source_info = json.loads(document.data_source_info)
                                exist_page_ids.append(data_source_info["notion_page_id"])
                                exist_document[data_source_info["notion_page_id"]] = document.id
                        for notion_info in notion_info_list:
                            workspace_id = notion_info.workspace_id
                            for page in notion_info.pages:
                                if page.page_id not in exist_page_ids:
                                    data_source_info = {
                                        "credential_id": notion_info.credential_id,
                                        "notion_workspace_id": workspace_id,
                                        "notion_page_id": page.page_id,
                                        "notion_page_icon": page.page_icon.model_dump() if page.page_icon else None,  # type: ignore
                                        "type": page.type,
                                    }
                                    # Truncate page name to 255 characters to prevent DB field length errors
                                    truncated_page_name = page.page_name[:255] if page.page_name else "nopagename"
                                    document = DocumentService.build_document(
                                        dataset,
                                        dataset_process_rule.id,
                                        knowledge_config.data_source.info_list.data_source_type,
                                        knowledge_config.doc_form,
                                        knowledge_config.doc_language,
                                        data_source_info,
                                        created_from,
                                        position,
                                        account,
                                        truncated_page_name,
                                        batch,
                                    )
                                    db.session.add(document)
                                    db.session.flush()
                                    document_ids.append(document.id)
                                    documents.append(document)
                                    position += 1
                                else:
                                    exist_document.pop(page.page_id)
                        # delete not selected documents
                        if len(exist_document) > 0:
                            clean_notion_document_task.delay(list(exist_document.values()), dataset.id)
                    elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
                        website_info = knowledge_config.data_source.info_list.website_info_list
                        if not website_info:
                            raise ValueError("No website info list found.")
                        urls = website_info.urls
                        for url in urls:
                            data_source_info = {
                                "url": url,
                                "provider": website_info.provider,
                                "job_id": website_info.job_id,
                                "only_main_content": website_info.only_main_content,
                                "mode": "crawl",
                            }
                            if len(url) > 255:
                                document_name = url[:200] + "..."
                            else:
                                document_name = url
                            document = DocumentService.build_document(
                                dataset,
                                dataset_process_rule.id,
                                knowledge_config.data_source.info_list.data_source_type,
                                knowledge_config.doc_form,
                                knowledge_config.doc_language,
                                data_source_info,
                                created_from,
                                position,
                                account,
                                document_name,
                                batch,
                            )
                            db.session.add(document)
                            db.session.flush()
                            document_ids.append(document.id)
                            documents.append(document)
                            position += 1
                    db.session.commit()

                    # trigger async task
                    if document_ids:
                        DocumentIndexingTaskProxy(dataset.tenant_id, dataset.id, document_ids).delay()
                    if duplicate_document_ids:
                        DuplicateDocumentIndexingTaskProxy(
                            dataset.tenant_id, dataset.id, duplicate_document_ids
                        ).delay()
                    # Note: Summary index generation is triggered in document_indexing_task after indexing completes
                    # to ensure segments are available. See tasks/document_indexing_task.py
            except LockNotOwnedError:
                pass

        return documents, batch

    # @staticmethod
    # def save_document_with_dataset_id(
    #     dataset: Dataset,
    #     knowledge_config: KnowledgeConfig,
    #     account: Account | Any,
    #     dataset_process_rule: Optional[DatasetProcessRule] = None,
    #     created_from: str = "web",
    # ):
    #     # check document limit
    #     features = FeatureService.get_features(current_user.current_tenant_id)

    #     if features.billing.enabled:
    #         if not knowledge_config.original_document_id:
    #             count = 0
    #             if knowledge_config.data_source:
    #                 if knowledge_config.data_source.info_list.data_source_type == "upload_file":
    #                     upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids
    # # type: ignore
    #                     count = len(upload_file_list)
    #                 elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
    #                     notion_info_list = knowledge_config.data_source.info_list.notion_info_list
    #                     for notion_info in notion_info_list:  # type: ignore
    #                         count = count + len(notion_info.pages)
    #                 elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
    #                     website_info = knowledge_config.data_source.info_list.website_info_list
    #                     count = len(website_info.urls)  # type: ignore
    #                 batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)

    #                 if features.billing.subscription.plan == CloudPlan.SANDBOX and count > 1:
    #                     raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
    #                 if count > batch_upload_limit:
    #                     raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")

    #                 DocumentService.check_documents_upload_quota(count, features)

    #     # if dataset is empty, update dataset data_source_type
    #     if not dataset.data_source_type:
    #         dataset.data_source_type = knowledge_config.data_source.info_list.data_source_type  # type: ignore

    #     if not dataset.indexing_technique:
    #         if knowledge_config.indexing_technique not in Dataset.INDEXING_TECHNIQUE_LIST:
    #             raise ValueError("Indexing technique is invalid")

    #         dataset.indexing_technique = knowledge_config.indexing_technique
    #         if knowledge_config.indexing_technique == "high_quality":
    #             model_manager = ModelManager()
    #             if knowledge_config.embedding_model and knowledge_config.embedding_model_provider:
    #                 dataset_embedding_model = knowledge_config.embedding_model
    #                 dataset_embedding_model_provider = knowledge_config.embedding_model_provider
    #             else:
    #                 embedding_model = model_manager.get_default_model_instance(
    #                     tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
    #                 )
    #                 dataset_embedding_model = embedding_model.model
    #                 dataset_embedding_model_provider = embedding_model.provider
    #             dataset.embedding_model = dataset_embedding_model
    #             dataset.embedding_model_provider = dataset_embedding_model_provider
    #             dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
    #                 dataset_embedding_model_provider, dataset_embedding_model
    #             )
    #             dataset.collection_binding_id = dataset_collection_binding.id
    #             if not dataset.retrieval_model:
    #                 default_retrieval_model = {
    #                     "search_method": RetrievalMethod.SEMANTIC_SEARCH,
    #                     "reranking_enable": False,
    #                     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
    #                     "top_k": 2,
    #                     "score_threshold_enabled": False,
    #                 }

    #                 dataset.retrieval_model = (
    #                     knowledge_config.retrieval_model.model_dump()
    #                     if knowledge_config.retrieval_model
    #                     else default_retrieval_model
    #                 )  # type: ignore

    #     documents = []
    #     if knowledge_config.original_document_id:
    #         document = DocumentService.update_document_with_dataset_id(dataset, knowledge_config, account)
    #         documents.append(document)
    #         batch = document.batch
    #     else:
    #         batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999))
    #         # save process rule
    #         if not dataset_process_rule:
    #             process_rule = knowledge_config.process_rule
    #             if process_rule:
    #                 if process_rule.mode in ("custom", "hierarchical"):
    #                     dataset_process_rule = DatasetProcessRule(
    #                         dataset_id=dataset.id,
    #                         mode=process_rule.mode,
    #                         rules=process_rule.rules.model_dump_json() if process_rule.rules else None,
    #                         created_by=account.id,
    #                     )
    #                 elif process_rule.mode == "automatic":
    #                     dataset_process_rule = DatasetProcessRule(
    #                         dataset_id=dataset.id,
    #                         mode=process_rule.mode,
    #                         rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
    #                         created_by=account.id,
    #                     )
    #                 else:
    #                     logging.warn(
    #                         f"Invalid process rule mode: {process_rule.mode}, can not find dataset process rule"
    #                     )
    #                     return
    #                 db.session.add(dataset_process_rule)
    #                 db.session.commit()
    #         lock_name = "add_document_lock_dataset_id_{}".format(dataset.id)
    #         with redis_client.lock(lock_name, timeout=600):
    #             position = DocumentService.get_documents_position(dataset.id)
    #             document_ids = []
    #             duplicate_document_ids = []
    #             if knowledge_config.data_source.info_list.data_source_type == "upload_file":  # type: ignore
    #                 upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids  # type: ignore
    #                 for file_id in upload_file_list:
    #                     file = (
    #                         db.session.query(UploadFile)
    #                         .filter(UploadFile.tenant_id == dataset.tenant_id, UploadFile.id == file_id)
    #                         .first()
    #                     )

    #                     # raise error if file not found
    #                     if not file:
    #                         raise FileNotExistsError()

    #                     file_name = file.name
    #                     data_source_info = {
    #                         "upload_file_id": file_id,
    #                     }
    #                     # check duplicate
    #                     if knowledge_config.duplicate:
    #                         document = Document.query.filter_by(
    #                             dataset_id=dataset.id,
    #                             tenant_id=current_user.current_tenant_id,
    #                             data_source_type="upload_file",
    #                             enabled=True,
    #                             name=file_name,
    #                         ).first()
    #                         if document:
    #                             document.dataset_process_rule_id = dataset_process_rule.id  # type: ignore
    #                             document.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
    #                             document.created_from = created_from
    #                             document.doc_form = knowledge_config.doc_form
    #                             document.doc_language = knowledge_config.doc_language
    #                             document.data_source_info = json.dumps(data_source_info)
    #                             document.batch = batch
    #                             document.indexing_status = "waiting"
    #                             db.session.add(document)
    #                             documents.append(document)
    #                             duplicate_document_ids.append(document.id)
    #                             continue
    #                     document = DocumentService.build_document(
    #                         dataset,
    #                         dataset_process_rule.id,  # type: ignore
    #                         knowledge_config.data_source.info_list.data_source_type,  # type: ignore
    #                         knowledge_config.doc_form,
    #                         knowledge_config.doc_language,
    #                         data_source_info,
    #                         created_from,
    #                         position,
    #                         account,
    #                         file_name,
    #                         batch,
    #                     )
    #                     db.session.add(document)
    #                     db.session.flush()
    #                     document_ids.append(document.id)
    #                     documents.append(document)
    #                     position += 1
    #             elif knowledge_config.data_source.info_list.data_source_type == "notion_import":  # type: ignore
    #                 notion_info_list = knowledge_config.data_source.info_list.notion_info_list  # type: ignore
    #                 if not notion_info_list:
    #                     raise ValueError("No notion info list found.")
    #                 exist_page_ids = []
    #                 exist_document = {}
    #                 documents = Document.query.filter_by(
    #                     dataset_id=dataset.id,
    #                     tenant_id=current_user.current_tenant_id,
    #                     data_source_type="notion_import",
    #                     enabled=True,
    #                 ).all()
    #                 if documents:
    #                     for document in documents:
    #                         data_source_info = json.loads(document.data_source_info)
    #                         exist_page_ids.append(data_source_info["notion_page_id"])
    #                         exist_document[data_source_info["notion_page_id"]] = document.id
    #                 for notion_info in notion_info_list:
    #                     workspace_id = notion_info.workspace_id
    #                     data_source_binding = DataSourceOauthBinding.query.filter(
    #                         sa.and_(
    #                             DataSourceOauthBinding.tenant_id == current_user.current_tenant_id,
    #                             DataSourceOauthBinding.provider == "notion",
    #                             DataSourceOauthBinding.disabled == False,
    #                             DataSourceOauthBinding.source_info["workspace_id"] == f'"{workspace_id}"',
    #                         )
    #                     ).first()
    #                     if not data_source_binding:
    #                         raise ValueError("Data source binding not found.")
    #                     for page in notion_info.pages:
    #                         if page.page_id not in exist_page_ids:
    #                             data_source_info = {
    #                                 "notion_workspace_id": workspace_id,
    #                                 "notion_page_id": page.page_id,
    #                                 "notion_page_icon": page.page_icon.model_dump() if page.page_icon else None,
    #                                 "type": page.type,
    #                             }
    #                             # Truncate page name to 255 characters to prevent DB field length errors
    #                             truncated_page_name = page.page_name[:255] if page.page_name else "nopagename"
    #                             document = DocumentService.build_document(
    #                                 dataset,
    #                                 dataset_process_rule.id,  # type: ignore
    #                                 knowledge_config.data_source.info_list.data_source_type,  # type: ignore
    #                                 knowledge_config.doc_form,
    #                                 knowledge_config.doc_language,
    #                                 data_source_info,
    #                                 created_from,
    #                                 position,
    #                                 account,
    #                                 truncated_page_name,
    #                                 batch,
    #                             )
    #                             db.session.add(document)
    #                             db.session.flush()
    #                             document_ids.append(document.id)
    #                             documents.append(document)
    #                             position += 1
    #                         else:
    #                             exist_document.pop(page.page_id)
    #                 # delete not selected documents
    #                 if len(exist_document) > 0:
    #                     clean_notion_document_task.delay(list(exist_document.values()), dataset.id)
    #             elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":  # type: ignore
    #                 website_info = knowledge_config.data_source.info_list.website_info_list  # type: ignore
    #                 if not website_info:
    #                     raise ValueError("No website info list found.")
    #                 urls = website_info.urls
    #                 for url in urls:
    #                     data_source_info = {
    #                         "url": url,
    #                         "provider": website_info.provider,
    #                         "job_id": website_info.job_id,
    #                         "only_main_content": website_info.only_main_content,
    #                         "mode": "crawl",
    #                     }
    #                     if len(url) > 255:
    #                         document_name = url[:200] + "..."
    #                     else:
    #                         document_name = url
    #                     document = DocumentService.build_document(
    #                         dataset,
    #                         dataset_process_rule.id,  # type: ignore
    #                         knowledge_config.data_source.info_list.data_source_type,  # type: ignore
    #                         knowledge_config.doc_form,
    #                         knowledge_config.doc_language,
    #                         data_source_info,
    #                         created_from,
    #                         position,
    #                         account,
    #                         document_name,
    #                         batch,
    #                     )
    #                     db.session.add(document)
    #                     db.session.flush()
    #                     document_ids.append(document.id)
    #                     documents.append(document)
    #                     position += 1
    #             db.session.commit()

    #             # trigger async task
    #             if document_ids:
    #                 document_indexing_task.delay(dataset.id, document_ids)
    #             if duplicate_document_ids:
    #                 duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids)

    #     return documents, batch

    @staticmethod
    def check_documents_upload_quota(count: int, features: FeatureModel):
        can_upload_size = features.documents_upload_quota.limit - features.documents_upload_quota.size
        if count > can_upload_size:
            raise ValueError(
                f"You have reached the limit of your subscription. Only {can_upload_size} documents can be uploaded."
            )

    @staticmethod
    def build_document(
        dataset: Dataset,
        process_rule_id: str | None,
        data_source_type: str,
        document_form: str,
        document_language: str,
        data_source_info: dict,
        created_from: str,
        position: int,
        account: Account,
        name: str,
        batch: str,
    ):
        # Set need_summary based on dataset's summary_index_setting
        need_summary = False
        if dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True:
            need_summary = True

        document = Document(
            tenant_id=dataset.tenant_id,
            dataset_id=dataset.id,
            position=position,
            data_source_type=data_source_type,
            data_source_info=json.dumps(data_source_info),
            dataset_process_rule_id=process_rule_id,
            batch=batch,
            name=name,
            created_from=created_from,
            created_by=account.id,
            doc_form=document_form,
            doc_language=document_language,
            need_summary=need_summary,
        )
        doc_metadata = {}
        if dataset.built_in_field_enabled:
            doc_metadata = {
                BuiltInField.document_name: name,
                BuiltInField.uploader: account.name,
                BuiltInField.upload_date: datetime.datetime.now(datetime.UTC).strftime("%Y-%m-%d %H:%M:%S"),
                BuiltInField.last_update_date: datetime.datetime.now(datetime.UTC).strftime("%Y-%m-%d %H:%M:%S"),
                BuiltInField.source: data_source_type,
            }
        if doc_metadata:
            document.doc_metadata = doc_metadata
        return document

    @staticmethod
    def get_tenant_documents_count():
        assert isinstance(current_user, Account)

        documents_count = (
            db.session.query(Document)
            .where(
                Document.completed_at.isnot(None),
                Document.enabled == True,
                Document.archived == False,
                Document.tenant_id == current_user.current_tenant_id,
            )
            .count()
        )
        return documents_count

    @staticmethod
    def update_document_with_dataset_id(
        dataset: Dataset,
        document_data: KnowledgeConfig,
        account: Account,
        dataset_process_rule: DatasetProcessRule | None = None,
        created_from: str = "web",
    ):
        assert isinstance(current_user, Account)

        DatasetService.check_dataset_model_setting(dataset)
        document = DocumentService.get_document(dataset.id, document_data.original_document_id)
        if document is None:
            raise NotFound("Document not found")
        if document.display_status != "available":
            raise ValueError("Document is not available")
        # save process rule
        if document_data.process_rule:
            process_rule = document_data.process_rule
            if process_rule.mode in {"custom", "hierarchical"}:
                dataset_process_rule = DatasetProcessRule(
                    dataset_id=dataset.id,
                    mode=process_rule.mode,
                    rules=process_rule.rules.model_dump_json() if process_rule.rules else None,
                    created_by=account.id,
                )
            elif process_rule.mode == "automatic":
                dataset_process_rule = DatasetProcessRule(
                    dataset_id=dataset.id,
                    mode=process_rule.mode,
                    rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
                    created_by=account.id,
                )
            if dataset_process_rule is not None:
                db.session.add(dataset_process_rule)
                db.session.commit()
                document.dataset_process_rule_id = dataset_process_rule.id
        # update document data source
        if document_data.data_source:
            file_name = ""
            data_source_info: dict[str, str | bool] = {}
            if document_data.data_source.info_list.data_source_type == "upload_file":
                if not document_data.data_source.info_list.file_info_list:
                    raise ValueError("No file info list found.")
                upload_file_list = document_data.data_source.info_list.file_info_list.file_ids
                for file_id in upload_file_list:
                    file = (
                        db.session.query(UploadFile)
                        .where(UploadFile.tenant_id == dataset.tenant_id, UploadFile.id == file_id)
                        .first()
                    )

                    # raise error if file not found
                    if not file:
                        raise FileNotExistsError()

                    file_name = file.name
                    data_source_info = {
                        "upload_file_id": file_id,
                    }
            elif document_data.data_source.info_list.data_source_type == "notion_import":
                if not document_data.data_source.info_list.notion_info_list:
                    raise ValueError("No notion info list found.")
                notion_info_list = document_data.data_source.info_list.notion_info_list
                for notion_info in notion_info_list:
                    workspace_id = notion_info.workspace_id
                    data_source_binding = (
                        db.session.query(DataSourceOauthBinding)
                        .where(
                            sa.and_(
                                DataSourceOauthBinding.tenant_id == current_user.current_tenant_id,
                                DataSourceOauthBinding.provider == "notion",
                                DataSourceOauthBinding.disabled == False,
                                DataSourceOauthBinding.source_info["workspace_id"] == f'"{workspace_id}"',
                            )
                        )
                        .first()
                    )
                    if not data_source_binding:
                        raise ValueError("Data source binding not found.")
                    for page in notion_info.pages:
                        data_source_info = {
                            "credential_id": notion_info.credential_id,
                            "notion_workspace_id": workspace_id,
                            "notion_page_id": page.page_id,
                            "notion_page_icon": page.page_icon.model_dump() if page.page_icon else None,  # type: ignore
                            "type": page.type,
                        }
            elif document_data.data_source.info_list.data_source_type == "website_crawl":
                website_info = document_data.data_source.info_list.website_info_list
                if website_info:
                    urls = website_info.urls
                    for url in urls:
                        data_source_info = {
                            "url": url,
                            "provider": website_info.provider,
                            "job_id": website_info.job_id,
                            "only_main_content": website_info.only_main_content,
                            "mode": "crawl",
                        }
            document.data_source_type = document_data.data_source.info_list.data_source_type
            document.data_source_info = json.dumps(data_source_info)
            document.name = file_name

        # update document name
        if document_data.name:
            document.name = document_data.name
        # update document to be waiting
        document.indexing_status = "waiting"
        document.completed_at = None
        document.processing_started_at = None
        document.parsing_completed_at = None
        document.cleaning_completed_at = None
        document.splitting_completed_at = None
        document.updated_at = naive_utc_now()
        document.created_from = created_from
        document.doc_form = document_data.doc_form
        db.session.add(document)
        db.session.commit()
        # update document segment

        db.session.query(DocumentSegment).filter_by(document_id=document.id).update(
            {DocumentSegment.status: "re_segment"}
        )
        db.session.commit()
        # trigger async task
        document_indexing_update_task.delay(document.dataset_id, document.id)
        return document

    @staticmethod
    def save_document_without_dataset_id(tenant_id: str, knowledge_config: KnowledgeConfig, account: Account):
        assert isinstance(current_user, Account)
        assert current_user.current_tenant_id is not None
        assert knowledge_config.data_source

        features = FeatureService.get_features(current_user.current_tenant_id)

        if features.billing.enabled:
            count = 0
            if knowledge_config.data_source.info_list.data_source_type == "upload_file":
                upload_file_list = (
                    knowledge_config.data_source.info_list.file_info_list.file_ids
                    if knowledge_config.data_source.info_list.file_info_list
                    else []
                )
                count = len(upload_file_list)
            elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
                notion_info_list = knowledge_config.data_source.info_list.notion_info_list
                if notion_info_list:
                    for notion_info in notion_info_list:
                        count = count + len(notion_info.pages)
            elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
                website_info = knowledge_config.data_source.info_list.website_info_list
                if website_info:
                    count = len(website_info.urls)
            if features.billing.subscription.plan == CloudPlan.SANDBOX and count > 1:
                raise ValueError("Your current plan does not support batch upload, please upgrade your plan.")
            batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)
            if count > batch_upload_limit:
                raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")

            DocumentService.check_documents_upload_quota(count, features)

        dataset_collection_binding_id = None
        retrieval_model = None
        if knowledge_config.indexing_technique == "high_quality":
            assert knowledge_config.embedding_model_provider
            assert knowledge_config.embedding_model
            dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
                knowledge_config.embedding_model_provider,
                knowledge_config.embedding_model,
            )
            dataset_collection_binding_id = dataset_collection_binding.id
        if knowledge_config.retrieval_model:
            retrieval_model = knowledge_config.retrieval_model
        else:
            retrieval_model = RetrievalModel(
                search_method=RetrievalMethod.SEMANTIC_SEARCH,
                reranking_enable=False,
                reranking_model=RerankingModel(reranking_provider_name="", reranking_model_name=""),
                top_k=4,
                score_threshold_enabled=False,
            )
        # save dataset
        dataset = Dataset(
            tenant_id=tenant_id,
            name="",
            data_source_type=knowledge_config.data_source.info_list.data_source_type,
            indexing_technique=knowledge_config.indexing_technique,
            created_by=account.id,
            embedding_model=knowledge_config.embedding_model,
            embedding_model_provider=knowledge_config.embedding_model_provider,
            collection_binding_id=dataset_collection_binding_id,
            retrieval_model=retrieval_model.model_dump() if retrieval_model else None,
            summary_index_setting=knowledge_config.summary_index_setting,
            is_multimodal=knowledge_config.is_multimodal,
        )

        db.session.add(dataset)
        db.session.flush()

        documents, batch = DocumentService.save_document_with_dataset_id(dataset, knowledge_config, account)

        cut_length = 18
        cut_name = documents[0].name[:cut_length]
        dataset.name = cut_name + "..."
        dataset.description = "useful for when you want to answer queries about the " + documents[0].name
        db.session.commit()

        return dataset, documents, batch

    @classmethod
    def document_create_args_validate(cls, knowledge_config: KnowledgeConfig):
        if not knowledge_config.data_source and not knowledge_config.process_rule:
            raise ValueError("Data source or Process rule is required")
        else:
            if knowledge_config.data_source:
                DocumentService.data_source_args_validate(knowledge_config)
            if knowledge_config.process_rule:
                DocumentService.process_rule_args_validate(knowledge_config)

    @classmethod
    def data_source_args_validate(cls, knowledge_config: KnowledgeConfig):
        if not knowledge_config.data_source:
            raise ValueError("Data source is required")

        if knowledge_config.data_source.info_list.data_source_type not in Document.DATA_SOURCES:
            raise ValueError("Data source type is invalid")

        if not knowledge_config.data_source.info_list:
            raise ValueError("Data source info is required")

        if knowledge_config.data_source.info_list.data_source_type == "upload_file":
            if not knowledge_config.data_source.info_list.file_info_list:
                raise ValueError("File source info is required")
        if knowledge_config.data_source.info_list.data_source_type == "notion_import":
            if not knowledge_config.data_source.info_list.notion_info_list:
                raise ValueError("Notion source info is required")
        if knowledge_config.data_source.info_list.data_source_type == "website_crawl":
            if not knowledge_config.data_source.info_list.website_info_list:
                raise ValueError("Website source info is required")

    @classmethod
    def process_rule_args_validate(cls, knowledge_config: KnowledgeConfig):
        if not knowledge_config.process_rule:
            raise ValueError("Process rule is required")

        if not knowledge_config.process_rule.mode:
            raise ValueError("Process rule mode is required")

        if knowledge_config.process_rule.mode not in DatasetProcessRule.MODES:
            raise ValueError("Process rule mode is invalid")

        if knowledge_config.process_rule.mode == "automatic":
            knowledge_config.process_rule.rules = None
        else:
            if not knowledge_config.process_rule.rules:
                raise ValueError("Process rule rules is required")

            if knowledge_config.process_rule.rules.pre_processing_rules is None:
                raise ValueError("Process rule pre_processing_rules is required")

            unique_pre_processing_rule_dicts = {}
            for pre_processing_rule in knowledge_config.process_rule.rules.pre_processing_rules:
                if not pre_processing_rule.id:
                    raise ValueError("Process rule pre_processing_rules id is required")

                if not isinstance(pre_processing_rule.enabled, bool):
                    raise ValueError("Process rule pre_processing_rules enabled is invalid")

                unique_pre_processing_rule_dicts[pre_processing_rule.id] = pre_processing_rule

            knowledge_config.process_rule.rules.pre_processing_rules = list(unique_pre_processing_rule_dicts.values())

            if not knowledge_config.process_rule.rules.segmentation:
                raise ValueError("Process rule segmentation is required")

            if not knowledge_config.process_rule.rules.segmentation.separator:
                raise ValueError("Process rule segmentation separator is required")

            if not isinstance(knowledge_config.process_rule.rules.segmentation.separator, str):
                raise ValueError("Process rule segmentation separator is invalid")

            if not (
                knowledge_config.process_rule.mode == "hierarchical"
                and knowledge_config.process_rule.rules.parent_mode == "full-doc"
            ):
                if not knowledge_config.process_rule.rules.segmentation.max_tokens:
                    raise ValueError("Process rule segmentation max_tokens is required")

                if not isinstance(knowledge_config.process_rule.rules.segmentation.max_tokens, int):
                    raise ValueError("Process rule segmentation max_tokens is invalid")

    @classmethod
    def estimate_args_validate(cls, args: dict):
        if "info_list" not in args or not args["info_list"]:
            raise ValueError("Data source info is required")

        if not isinstance(args["info_list"], dict):
            raise ValueError("Data info is invalid")

        if "process_rule" not in args or not args["process_rule"]:
            raise ValueError("Process rule is required")

        if not isinstance(args["process_rule"], dict):
            raise ValueError("Process rule is invalid")

        if "mode" not in args["process_rule"] or not args["process_rule"]["mode"]:
            raise ValueError("Process rule mode is required")

        if args["process_rule"]["mode"] not in DatasetProcessRule.MODES:
            raise ValueError("Process rule mode is invalid")

        if args["process_rule"]["mode"] == "automatic":
            args["process_rule"]["rules"] = {}
        else:
            if "rules" not in args["process_rule"] or not args["process_rule"]["rules"]:
                raise ValueError("Process rule rules is required")

            if not isinstance(args["process_rule"]["rules"], dict):
                raise ValueError("Process rule rules is invalid")

            if (
                "pre_processing_rules" not in args["process_rule"]["rules"]
                or args["process_rule"]["rules"]["pre_processing_rules"] is None
            ):
                raise ValueError("Process rule pre_processing_rules is required")

            if not isinstance(args["process_rule"]["rules"]["pre_processing_rules"], list):
                raise ValueError("Process rule pre_processing_rules is invalid")

            unique_pre_processing_rule_dicts = {}
            for pre_processing_rule in args["process_rule"]["rules"]["pre_processing_rules"]:
                if "id" not in pre_processing_rule or not pre_processing_rule["id"]:
                    raise ValueError("Process rule pre_processing_rules id is required")

                if pre_processing_rule["id"] not in DatasetProcessRule.PRE_PROCESSING_RULES:
                    raise ValueError("Process rule pre_processing_rules id is invalid")

                if "enabled" not in pre_processing_rule or pre_processing_rule["enabled"] is None:
                    raise ValueError("Process rule pre_processing_rules enabled is required")

                if not isinstance(pre_processing_rule["enabled"], bool):
                    raise ValueError("Process rule pre_processing_rules enabled is invalid")

                unique_pre_processing_rule_dicts[pre_processing_rule["id"]] = pre_processing_rule

            args["process_rule"]["rules"]["pre_processing_rules"] = list(unique_pre_processing_rule_dicts.values())

            if (
                "segmentation" not in args["process_rule"]["rules"]
                or args["process_rule"]["rules"]["segmentation"] is None
            ):
                raise ValueError("Process rule segmentation is required")

            if not isinstance(args["process_rule"]["rules"]["segmentation"], dict):
                raise ValueError("Process rule segmentation is invalid")

            if (
                "separator" not in args["process_rule"]["rules"]["segmentation"]
                or not args["process_rule"]["rules"]["segmentation"]["separator"]
            ):
                raise ValueError("Process rule segmentation separator is required")

            if not isinstance(args["process_rule"]["rules"]["segmentation"]["separator"], str):
                raise ValueError("Process rule segmentation separator is invalid")

            if (
                "max_tokens" not in args["process_rule"]["rules"]["segmentation"]
                or not args["process_rule"]["rules"]["segmentation"]["max_tokens"]
            ):
                raise ValueError("Process rule segmentation max_tokens is required")

            if not isinstance(args["process_rule"]["rules"]["segmentation"]["max_tokens"], int):
                raise ValueError("Process rule segmentation max_tokens is invalid")

        # valid summary index setting
        summary_index_setting = args["process_rule"].get("summary_index_setting")
        if summary_index_setting and summary_index_setting.get("enable"):
            if "model_name" not in summary_index_setting or not summary_index_setting["model_name"]:
                raise ValueError("Summary index model name is required")
            if "model_provider_name" not in summary_index_setting or not summary_index_setting["model_provider_name"]:
                raise ValueError("Summary index model provider name is required")

    @staticmethod
    def batch_update_document_status(
        dataset: Dataset, document_ids: list[str], action: Literal["enable", "disable", "archive", "un_archive"], user
    ):
        """
        Batch update document status.

        Args:
            dataset (Dataset): The dataset object
            document_ids (list[str]): List of document IDs to update
            action (Literal["enable", "disable", "archive", "un_archive"]): Action to perform
            user: Current user performing the action

        Raises:
            DocumentIndexingError: If document is being indexed or not in correct state
            ValueError: If action is invalid
        """
        if not document_ids:
            return

        # Early validation of action parameter
        valid_actions = ["enable", "disable", "archive", "un_archive"]
        if action not in valid_actions:
            raise ValueError(f"Invalid action: {action}. Must be one of {valid_actions}")

        documents_to_update = []

        # First pass: validate all documents and prepare updates
        for document_id in document_ids:
            document = DocumentService.get_document(dataset.id, document_id)
            if not document:
                continue

            # Check if document is being indexed
            indexing_cache_key = f"document_{document.id}_indexing"
            cache_result = redis_client.get(indexing_cache_key)
            if cache_result is not None:
                raise DocumentIndexingError(f"Document:{document.name} is being indexed, please try again later")

            # Prepare update based on action
            update_info = DocumentService._prepare_document_status_update(document, action, user)
            if update_info:
                documents_to_update.append(update_info)

        # Second pass: apply all updates in a single transaction
        if documents_to_update:
            try:
                for update_info in documents_to_update:
                    document = update_info["document"]
                    updates = update_info["updates"]

                    # Apply updates to the document
                    for field, value in updates.items():
                        setattr(document, field, value)

                    db.session.add(document)

                # Batch commit all changes
                db.session.commit()
            except Exception as e:
                # Rollback on any error
                db.session.rollback()
                raise e
            # Execute async tasks and set Redis cache after successful commit
            # propagation_error is used to capture any errors for submitting async task execution
            propagation_error = None
            for update_info in documents_to_update:
                try:
                    # Execute async tasks after successful commit
                    if update_info["async_task"]:
                        task_info = update_info["async_task"]
                        task_func = task_info["function"]
                        task_args = task_info["args"]
                        task_func.delay(*task_args)
                except Exception as e:
                    # Log the error but do not rollback the transaction
                    logger.exception("Error executing async task for document %s", update_info["document"].id)
                    # don't raise the error immediately, but capture it for later
                    propagation_error = e
                try:
                    # Set Redis cache if needed after successful commit
                    if update_info["set_cache"]:
                        document = update_info["document"]
                        indexing_cache_key = f"document_{document.id}_indexing"
                        redis_client.setex(indexing_cache_key, 600, 1)
                except Exception as e:
                    # Log the error but do not rollback the transaction
                    logger.exception("Error setting cache for document %s", update_info["document"].id)
            # Raise any propagation error after all updates
            if propagation_error:
                raise propagation_error

    @staticmethod
    def _prepare_document_status_update(
        document: Document, action: Literal["enable", "disable", "archive", "un_archive"], user
    ):
        """Prepare document status update information.

        Args:
            document: Document object to update
            action: Action to perform
            user: Current user

        Returns:
            dict: Update information or None if no update needed
        """
        now = naive_utc_now()

        match action:
            case "enable":
                return DocumentService._prepare_enable_update(document, now)
            case "disable":
                return DocumentService._prepare_disable_update(document, user, now)
            case "archive":
                return DocumentService._prepare_archive_update(document, user, now)
            case "un_archive":
                return DocumentService._prepare_unarchive_update(document, now)

        return None

    @staticmethod
    def _prepare_enable_update(document, now):
        """Prepare updates for enabling a document."""
        if document.enabled:
            return None

        return {
            "document": document,
            "updates": {"enabled": True, "disabled_at": None, "disabled_by": None, "updated_at": now},
            "async_task": {"function": add_document_to_index_task, "args": [document.id]},
            "set_cache": True,
        }

    @staticmethod
    def _prepare_disable_update(document, user, now):
        """Prepare updates for disabling a document."""
        if not document.completed_at or document.indexing_status != "completed":
            raise DocumentIndexingError(f"Document: {document.name} is not completed.")

        if not document.enabled:
            return None

        return {
            "document": document,
            "updates": {"enabled": False, "disabled_at": now, "disabled_by": user.id, "updated_at": now},
            "async_task": {"function": remove_document_from_index_task, "args": [document.id]},
            "set_cache": True,
        }

    @staticmethod
    def _prepare_archive_update(document, user, now):
        """Prepare updates for archiving a document."""
        if document.archived:
            return None

        update_info = {
            "document": document,
            "updates": {"archived": True, "archived_at": now, "archived_by": user.id, "updated_at": now},
            "async_task": None,
            "set_cache": False,
        }

        # Only set async task and cache if document is currently enabled
        if document.enabled:
            update_info["async_task"] = {"function": remove_document_from_index_task, "args": [document.id]}
            update_info["set_cache"] = True

        return update_info

    @staticmethod
    def _prepare_unarchive_update(document, now):
        """Prepare updates for unarchiving a document."""
        if not document.archived:
            return None

        update_info = {
            "document": document,
            "updates": {"archived": False, "archived_at": None, "archived_by": None, "updated_at": now},
            "async_task": None,
            "set_cache": False,
        }

        # Only re-index if the document is currently enabled
        if document.enabled:
            update_info["async_task"] = {"function": add_document_to_index_task, "args": [document.id]}
            update_info["set_cache"] = True

        return update_info


class SegmentService:
    @classmethod
    def segment_create_args_validate(cls, args: dict, document: Document):
        if document.doc_form == "qa_model":
            if "answer" not in args or not args["answer"]:
                raise ValueError("Answer is required")
            if not args["answer"].strip():
                raise ValueError("Answer is empty")
        if "content" not in args or not args["content"] or not args["content"].strip():
            raise ValueError("Content is empty")

        if args.get("attachment_ids"):
            if not isinstance(args["attachment_ids"], list):
                raise ValueError("Attachment IDs is invalid")
            single_chunk_attachment_limit = dify_config.SINGLE_CHUNK_ATTACHMENT_LIMIT
            if len(args["attachment_ids"]) > single_chunk_attachment_limit:
                raise ValueError(f"Exceeded maximum attachment limit of {single_chunk_attachment_limit}")

    @classmethod
    def create_segment(cls, args: dict, document: Document, dataset: Dataset):
        assert isinstance(current_user, Account)
        assert current_user.current_tenant_id is not None

        content = args["content"]
        doc_id = str(uuid.uuid4())
        segment_hash = helper.generate_text_hash(content)
        tokens = 0
        if dataset.indexing_technique == "high_quality":
            model_manager = ModelManager()
            embedding_model = 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,
            )
            # calc embedding use tokens
            tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
        lock_name = f"add_segment_lock_document_id_{document.id}"
        try:
            with redis_client.lock(lock_name, timeout=600):
                max_position = (
                    db.session.query(func.max(DocumentSegment.position))
                    .where(DocumentSegment.document_id == document.id)
                    .scalar()
                )
                segment_document = DocumentSegment(
                    tenant_id=current_user.current_tenant_id,
                    dataset_id=document.dataset_id,
                    document_id=document.id,
                    index_node_id=doc_id,
                    index_node_hash=segment_hash,
                    position=max_position + 1 if max_position else 1,
                    content=content,
                    word_count=len(content),
                    tokens=tokens,
                    status="completed",
                    indexing_at=naive_utc_now(),
                    completed_at=naive_utc_now(),
                    created_by=current_user.id,
                )
                if document.doc_form == "qa_model":
                    segment_document.word_count += len(args["answer"])
                    segment_document.answer = args["answer"]

            db.session.add(segment_document)
            # update document word count
            assert document.word_count is not None
            document.word_count += segment_document.word_count
            db.session.add(document)
            db.session.commit()

            if args["attachment_ids"]:
                for attachment_id in args["attachment_ids"]:
                    binding = SegmentAttachmentBinding(
                        tenant_id=current_user.current_tenant_id,
                        dataset_id=document.dataset_id,
                        document_id=document.id,
                        segment_id=segment_document.id,
                        attachment_id=attachment_id,
                    )
                    db.session.add(binding)
                db.session.commit()

            # save vector index
            try:
                keywords = args.get("keywords")
                keywords_list = [keywords] if keywords is not None else None
                VectorService.create_segments_vector(keywords_list, [segment_document], dataset, document.doc_form)
            except Exception as e:
                logger.exception("create segment index failed")
                segment_document.enabled = False
                segment_document.disabled_at = naive_utc_now()
                segment_document.status = "error"
                segment_document.error = str(e)
                db.session.commit()
            segment = db.session.query(DocumentSegment).where(DocumentSegment.id == segment_document.id).first()
            return segment
        except LockNotOwnedError:
            pass

    @classmethod
    def multi_create_segment(cls, segments: list, document: Document, dataset: Dataset):
        assert isinstance(current_user, Account)
        assert current_user.current_tenant_id is not None

        lock_name = f"multi_add_segment_lock_document_id_{document.id}"
        increment_word_count = 0
        try:
            with redis_client.lock(lock_name, timeout=600):
                embedding_model = None
                if dataset.indexing_technique == "high_quality":
                    model_manager = ModelManager()
                    embedding_model = 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,
                    )
                max_position = (
                    db.session.query(func.max(DocumentSegment.position))
                    .where(DocumentSegment.document_id == document.id)
                    .scalar()
                )
                pre_segment_data_list = []
                segment_data_list = []
                keywords_list = []
                position = max_position + 1 if max_position else 1
                for segment_item in segments:
                    content = segment_item["content"]
                    doc_id = str(uuid.uuid4())
                    segment_hash = helper.generate_text_hash(content)
                    tokens = 0
                    if dataset.indexing_technique == "high_quality" and embedding_model:
                        # calc embedding use tokens
                        if document.doc_form == "qa_model":
                            tokens = embedding_model.get_text_embedding_num_tokens(
                                texts=[content + segment_item["answer"]]
                            )[0]
                        else:
                            tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]

                    segment_document = DocumentSegment(
                        tenant_id=current_user.current_tenant_id,
                        dataset_id=document.dataset_id,
                        document_id=document.id,
                        index_node_id=doc_id,
                        index_node_hash=segment_hash,
                        position=position,
                        content=content,
                        word_count=len(content),
                        tokens=tokens,
                        keywords=segment_item.get("keywords", []),
                        status="completed",
                        indexing_at=naive_utc_now(),
                        completed_at=naive_utc_now(),
                        created_by=current_user.id,
                    )
                    if document.doc_form == "qa_model":
                        segment_document.answer = segment_item["answer"]
                        segment_document.word_count += len(segment_item["answer"])
                    increment_word_count += segment_document.word_count
                    db.session.add(segment_document)
                    segment_data_list.append(segment_document)
                    position += 1

                    pre_segment_data_list.append(segment_document)
                    if "keywords" in segment_item:
                        keywords_list.append(segment_item["keywords"])
                    else:
                        keywords_list.append(None)
                # update document word count
                assert document.word_count is not None
                document.word_count += increment_word_count
                db.session.add(document)
                try:
                    # save vector index
                    VectorService.create_segments_vector(
                        keywords_list, pre_segment_data_list, dataset, document.doc_form
                    )
                except Exception as e:
                    logger.exception("create segment index failed")
                    for segment_document in segment_data_list:
                        segment_document.enabled = False
                        segment_document.disabled_at = naive_utc_now()
                        segment_document.status = "error"
                        segment_document.error = str(e)
                db.session.commit()
                return segment_data_list
        except LockNotOwnedError:
            pass

    @classmethod
    def update_segment(cls, args: SegmentUpdateArgs, segment: DocumentSegment, document: Document, dataset: Dataset):
        assert isinstance(current_user, Account)
        assert current_user.current_tenant_id is not None

        indexing_cache_key = f"segment_{segment.id}_indexing"
        cache_result = redis_client.get(indexing_cache_key)
        if cache_result is not None:
            raise ValueError("Segment is indexing, please try again later")
        if args.enabled is not None:
            action = args.enabled
            if segment.enabled != action:
                if not action:
                    segment.enabled = action
                    segment.disabled_at = naive_utc_now()
                    segment.disabled_by = current_user.id
                    db.session.add(segment)
                    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 segment
        if not segment.enabled:
            if args.enabled is not None:
                if not args.enabled:
                    raise ValueError("Can't update disabled segment")
            else:
                raise ValueError("Can't update disabled segment")
        try:
            word_count_change = segment.word_count
            content = args.content or segment.content
            if segment.content == content:
                segment.word_count = len(content)
                if document.doc_form == "qa_model":
                    segment.answer = args.answer
                    segment.word_count += len(args.answer) if args.answer else 0
                word_count_change = segment.word_count - word_count_change
                keyword_changed = False
                if args.keywords:
                    if Counter(segment.keywords) != Counter(args.keywords):
                        segment.keywords = args.keywords
                        keyword_changed = True
                segment.enabled = True
                segment.disabled_at = None
                segment.disabled_by = None
                db.session.add(segment)
                db.session.commit()
                # update document word count
                if word_count_change != 0:
                    assert document.word_count is not None
                    document.word_count = max(0, document.word_count + word_count_change)
                    db.session.add(document)
                # update segment index task
                if document.doc_form == IndexStructureType.PARENT_CHILD_INDEX and args.regenerate_child_chunks:
                    # regenerate child chunks
                    # get embedding model instance
                    if dataset.indexing_technique == "high_quality":
                        # check embedding model setting
                        model_manager = ModelManager()

                        if dataset.embedding_model_provider:
                            embedding_model_instance = model_manager.get_model_instance(
                                tenant_id=dataset.tenant_id,
                                provider=dataset.embedding_model_provider,
                                model_type=ModelType.TEXT_EMBEDDING,
                                model=dataset.embedding_model,
                            )
                        else:
                            embedding_model_instance = model_manager.get_default_model_instance(
                                tenant_id=dataset.tenant_id,
                                model_type=ModelType.TEXT_EMBEDDING,
                            )
                    else:
                        raise ValueError("The knowledge base index technique is not high quality!")
                    # get the process rule
                    processing_rule = (
                        db.session.query(DatasetProcessRule)
                        .where(DatasetProcessRule.id == document.dataset_process_rule_id)
                        .first()
                    )
                    if processing_rule:
                        VectorService.generate_child_chunks(
                            segment, document, dataset, embedding_model_instance, processing_rule, True
                        )
                elif document.doc_form in (IndexStructureType.PARAGRAPH_INDEX, IndexStructureType.QA_INDEX):
                    if args.enabled or keyword_changed:
                        # update segment vector index
                        VectorService.update_segment_vector(args.keywords, segment, dataset)
                # update summary index if summary is provided and has changed
                if args.summary is not None:
                    # When user manually provides summary, allow saving even if summary_index_setting doesn't exist
                    # summary_index_setting is only needed for LLM generation, not for manual summary vectorization
                    # Vectorization uses dataset.embedding_model, which doesn't require summary_index_setting
                    if dataset.indexing_technique == "high_quality":
                        # Query existing summary from database
                        from models.dataset import DocumentSegmentSummary

                        existing_summary = (
                            db.session.query(DocumentSegmentSummary)
                            .where(
                                DocumentSegmentSummary.chunk_id == segment.id,
                                DocumentSegmentSummary.dataset_id == dataset.id,
                            )
                            .first()
                        )

                        # Check if summary has changed
                        existing_summary_content = existing_summary.summary_content if existing_summary else None
                        if existing_summary_content != args.summary:
                            # Summary has changed, update it
                            from services.summary_index_service import SummaryIndexService

                            try:
                                SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
                            except Exception:
                                logger.exception("Failed to update summary for segment %s", segment.id)
                                # Don't fail the entire update if summary update fails
            else:
                segment_hash = helper.generate_text_hash(content)
                tokens = 0
                if dataset.indexing_technique == "high_quality":
                    model_manager = ModelManager()
                    embedding_model = 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,
                    )

                    # calc embedding use tokens
                    if document.doc_form == "qa_model":
                        segment.answer = args.answer
                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content + segment.answer])[0]  # type: ignore
                    else:
                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
                segment.content = content
                segment.index_node_hash = segment_hash
                segment.word_count = len(content)
                segment.tokens = tokens
                segment.status = "completed"
                segment.indexing_at = naive_utc_now()
                segment.completed_at = naive_utc_now()
                segment.updated_by = current_user.id
                segment.updated_at = naive_utc_now()
                segment.enabled = True
                segment.disabled_at = None
                segment.disabled_by = None
                if document.doc_form == "qa_model":
                    segment.answer = args.answer
                    segment.word_count += len(args.answer) if args.answer else 0
                word_count_change = segment.word_count - word_count_change
                # update document word count
                if word_count_change != 0:
                    assert document.word_count is not None
                    document.word_count = max(0, document.word_count + word_count_change)
                    db.session.add(document)
                db.session.add(segment)
                db.session.commit()
                if document.doc_form == IndexStructureType.PARENT_CHILD_INDEX and args.regenerate_child_chunks:
                    # get embedding model instance
                    if dataset.indexing_technique == "high_quality":
                        # check embedding model setting
                        model_manager = ModelManager()

                        if dataset.embedding_model_provider:
                            embedding_model_instance = model_manager.get_model_instance(
                                tenant_id=dataset.tenant_id,
                                provider=dataset.embedding_model_provider,
                                model_type=ModelType.TEXT_EMBEDDING,
                                model=dataset.embedding_model,
                            )
                        else:
                            embedding_model_instance = model_manager.get_default_model_instance(
                                tenant_id=dataset.tenant_id,
                                model_type=ModelType.TEXT_EMBEDDING,
                            )
                    else:
                        raise ValueError("The knowledge base index technique is not high quality!")
                    # get the process rule
                    processing_rule = (
                        db.session.query(DatasetProcessRule)
                        .where(DatasetProcessRule.id == document.dataset_process_rule_id)
                        .first()
                    )
                    if processing_rule:
                        VectorService.generate_child_chunks(
                            segment, document, dataset, embedding_model_instance, processing_rule, True
                        )
                elif document.doc_form in (IndexStructureType.PARAGRAPH_INDEX, IndexStructureType.QA_INDEX):
                    # update segment vector index
                    VectorService.update_segment_vector(args.keywords, segment, dataset)
                # Handle summary index when content changed
                if dataset.indexing_technique == "high_quality":
                    from models.dataset import DocumentSegmentSummary

                    existing_summary = (
                        db.session.query(DocumentSegmentSummary)
                        .where(
                            DocumentSegmentSummary.chunk_id == segment.id,
                            DocumentSegmentSummary.dataset_id == dataset.id,
                        )
                        .first()
                    )

                    if args.summary is None:
                        # User didn't provide summary, auto-regenerate if segment previously had summary
                        # Auto-regeneration only happens if summary_index_setting exists and enable is True
                        if (
                            existing_summary
                            and dataset.summary_index_setting
                            and dataset.summary_index_setting.get("enable") is True
                        ):
                            # Segment previously had summary, regenerate it with new content
                            from services.summary_index_service import SummaryIndexService

                            try:
                                SummaryIndexService.generate_and_vectorize_summary(
                                    segment, dataset, dataset.summary_index_setting
                                )
                                logger.info("Auto-regenerated summary for segment %s after content change", segment.id)
                            except Exception:
                                logger.exception("Failed to auto-regenerate summary for segment %s", segment.id)
                                # Don't fail the entire update if summary regeneration fails
                    else:
                        # User provided summary, check if it has changed
                        # Manual summary updates are allowed even if summary_index_setting doesn't exist
                        existing_summary_content = existing_summary.summary_content if existing_summary else None
                        if existing_summary_content != args.summary:
                            # Summary has changed, use user-provided summary
                            from services.summary_index_service import SummaryIndexService

                            try:
                                SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
                                logger.info("Updated summary for segment %s with user-provided content", segment.id)
                            except Exception:
                                logger.exception("Failed to update summary for segment %s", segment.id)
                                # Don't fail the entire update if summary update fails
                        else:
                            # Summary hasn't changed, regenerate based on new content
                            # Auto-regeneration only happens if summary_index_setting exists and enable is True
                            if (
                                existing_summary
                                and dataset.summary_index_setting
                                and dataset.summary_index_setting.get("enable") is True
                            ):
                                from services.summary_index_service import SummaryIndexService

                                try:
                                    SummaryIndexService.generate_and_vectorize_summary(
                                        segment, dataset, dataset.summary_index_setting
                                    )
                                    logger.info(
                                        "Regenerated summary for segment %s after content change (summary unchanged)",
                                        segment.id,
                                    )
                                except Exception:
                                    logger.exception("Failed to regenerate summary for segment %s", segment.id)
                                    # Don't fail the entire update if summary regeneration fails
            # update multimodel vector index
            VectorService.update_multimodel_vector(segment, args.attachment_ids or [], dataset)
        except Exception as e:
            logger.exception("update segment index failed")
            segment.enabled = False
            segment.disabled_at = naive_utc_now()
            segment.status = "error"
            segment.error = str(e)
            db.session.commit()
        new_segment = db.session.query(DocumentSegment).where(DocumentSegment.id == segment.id).first()
        if not new_segment:
            raise ValueError("new_segment is not found")
        return new_segment

    @classmethod
    def delete_segment(cls, segment: DocumentSegment, document: Document, dataset: Dataset):
        indexing_cache_key = f"segment_{segment.id}_delete_indexing"
        cache_result = redis_client.get(indexing_cache_key)
        if cache_result is not None:
            raise ValueError("Segment is deleting.")

        # enabled segment need to delete index
        if segment.enabled:
            # send delete segment index task
            redis_client.setex(indexing_cache_key, 600, 1)

            # Get child chunk IDs before parent segment is deleted
            child_node_ids = []
            if segment.index_node_id:
                child_chunks = (
                    db.session.query(ChildChunk.index_node_id)
                    .where(
                        ChildChunk.segment_id == segment.id,
                        ChildChunk.dataset_id == dataset.id,
                    )
                    .all()
                )
                child_node_ids = [chunk[0] for chunk in child_chunks if chunk[0]]

            delete_segment_from_index_task.delay(
                [segment.index_node_id], dataset.id, document.id, [segment.id], child_node_ids
            )

        db.session.delete(segment)
        # update document word count
        assert document.word_count is not None
        document.word_count -= segment.word_count
        db.session.add(document)
        db.session.commit()

    @classmethod
    def delete_segments(cls, segment_ids: list, document: Document, dataset: Dataset):
        assert current_user is not None
        # Check if segment_ids is not empty to avoid WHERE false condition
        if not segment_ids or len(segment_ids) == 0:
            return
        segments_info = (
            db.session.query(DocumentSegment)
            .with_entities(DocumentSegment.index_node_id, DocumentSegment.id, DocumentSegment.word_count)
            .where(
                DocumentSegment.id.in_(segment_ids),
                DocumentSegment.dataset_id == dataset.id,
                DocumentSegment.document_id == document.id,
                DocumentSegment.tenant_id == current_user.current_tenant_id,
            )
            .all()
        )

        if not segments_info:
            return

        index_node_ids = [info[0] for info in segments_info]
        segment_db_ids = [info[1] for info in segments_info]
        total_words = sum(info[2] for info in segments_info if info[2] is not None)

        # Get child chunk IDs before parent segments are deleted
        child_node_ids = []
        if index_node_ids:
            child_chunks = (
                db.session.query(ChildChunk.index_node_id)
                .where(
                    ChildChunk.segment_id.in_(segment_db_ids),
                    ChildChunk.dataset_id == dataset.id,
                )
                .all()
            )
            child_node_ids = [chunk[0] for chunk in child_chunks if chunk[0]]

        # Start async cleanup with both parent and child node IDs
        if index_node_ids or child_node_ids:
            delete_segment_from_index_task.delay(
                index_node_ids, dataset.id, document.id, segment_db_ids, child_node_ids
            )

        if document.word_count is None:
            document.word_count = 0
        else:
            document.word_count = max(0, document.word_count - total_words)

        db.session.add(document)

        # Delete database records
        db.session.query(DocumentSegment).where(DocumentSegment.id.in_(segment_ids)).delete()
        db.session.commit()

    @classmethod
    def update_segments_status(
        cls, segment_ids: list, action: Literal["enable", "disable"], dataset: Dataset, document: Document
    ):
        assert current_user is not None

        # Check if segment_ids is not empty to avoid WHERE false condition
        if not segment_ids or len(segment_ids) == 0:
            return
        match action:
            case "enable":
                segments = db.session.scalars(
                    select(DocumentSegment).where(
                        DocumentSegment.id.in_(segment_ids),
                        DocumentSegment.dataset_id == dataset.id,
                        DocumentSegment.document_id == document.id,
                        DocumentSegment.enabled == False,
                    )
                ).all()
                if not segments:
                    return
                real_deal_segment_ids = []
                for segment in segments:
                    indexing_cache_key = f"segment_{segment.id}_indexing"
                    cache_result = redis_client.get(indexing_cache_key)
                    if cache_result is not None:
                        continue
                    segment.enabled = True
                    segment.disabled_at = None
                    segment.disabled_by = None
                    db.session.add(segment)
                    real_deal_segment_ids.append(segment.id)
                db.session.commit()

                enable_segments_to_index_task.delay(real_deal_segment_ids, dataset.id, document.id)
            case "disable":
                segments = db.session.scalars(
                    select(DocumentSegment).where(
                        DocumentSegment.id.in_(segment_ids),
                        DocumentSegment.dataset_id == dataset.id,
                        DocumentSegment.document_id == document.id,
                        DocumentSegment.enabled == True,
                    )
                ).all()
                if not segments:
                    return
                real_deal_segment_ids = []
                for segment in segments:
                    indexing_cache_key = f"segment_{segment.id}_indexing"
                    cache_result = redis_client.get(indexing_cache_key)
                    if cache_result is not None:
                        continue
                    segment.enabled = False
                    segment.disabled_at = naive_utc_now()
                    segment.disabled_by = current_user.id
                    db.session.add(segment)
                    real_deal_segment_ids.append(segment.id)
                db.session.commit()

                disable_segments_from_index_task.delay(real_deal_segment_ids, dataset.id, document.id)

    @classmethod
    def create_child_chunk(
        cls, content: str, segment: DocumentSegment, document: Document, dataset: Dataset
    ) -> ChildChunk:
        assert isinstance(current_user, Account)

        lock_name = f"add_child_lock_{segment.id}"
        with redis_client.lock(lock_name, timeout=20):
            index_node_id = str(uuid.uuid4())
            index_node_hash = helper.generate_text_hash(content)
            max_position = (
                db.session.query(func.max(ChildChunk.position))
                .where(
                    ChildChunk.tenant_id == current_user.current_tenant_id,
                    ChildChunk.dataset_id == dataset.id,
                    ChildChunk.document_id == document.id,
                    ChildChunk.segment_id == segment.id,
                )
                .scalar()
            )
            child_chunk = ChildChunk(
                tenant_id=current_user.current_tenant_id,
                dataset_id=dataset.id,
                document_id=document.id,
                segment_id=segment.id,
                position=max_position + 1 if max_position else 1,
                index_node_id=index_node_id,
                index_node_hash=index_node_hash,
                content=content,
                word_count=len(content),
                type="customized",
                created_by=current_user.id,
            )
            db.session.add(child_chunk)
            # save vector index
            try:
                VectorService.create_child_chunk_vector(child_chunk, dataset)
            except Exception as e:
                logger.exception("create child chunk index failed")
                db.session.rollback()
                raise ChildChunkIndexingError(str(e))
            db.session.commit()

            return child_chunk

    @classmethod
    def update_child_chunks(
        cls,
        child_chunks_update_args: list[ChildChunkUpdateArgs],
        segment: DocumentSegment,
        document: Document,
        dataset: Dataset,
    ) -> list[ChildChunk]:
        assert isinstance(current_user, Account)
        child_chunks = db.session.scalars(
            select(ChildChunk).where(
                ChildChunk.dataset_id == dataset.id,
                ChildChunk.document_id == document.id,
                ChildChunk.segment_id == segment.id,
            )
        ).all()
        child_chunks_map = {chunk.id: chunk for chunk in child_chunks}

        new_child_chunks, update_child_chunks, delete_child_chunks, new_child_chunks_args = [], [], [], []

        for child_chunk_update_args in child_chunks_update_args:
            if child_chunk_update_args.id:
                child_chunk = child_chunks_map.pop(child_chunk_update_args.id, None)
                if child_chunk:
                    if child_chunk.content != child_chunk_update_args.content:
                        child_chunk.content = child_chunk_update_args.content
                        child_chunk.word_count = len(child_chunk.content)
                        child_chunk.updated_by = current_user.id
                        child_chunk.updated_at = naive_utc_now()
                        child_chunk.type = "customized"
                        update_child_chunks.append(child_chunk)
            else:
                new_child_chunks_args.append(child_chunk_update_args)
        if child_chunks_map:
            delete_child_chunks = list(child_chunks_map.values())
        try:
            if update_child_chunks:
                db.session.bulk_save_objects(update_child_chunks)

            if delete_child_chunks:
                for child_chunk in delete_child_chunks:
                    db.session.delete(child_chunk)
            if new_child_chunks_args:
                child_chunk_count = len(child_chunks)
                for position, args in enumerate(new_child_chunks_args, start=child_chunk_count + 1):
                    index_node_id = str(uuid.uuid4())
                    index_node_hash = helper.generate_text_hash(args.content)
                    child_chunk = ChildChunk(
                        tenant_id=current_user.current_tenant_id,
                        dataset_id=dataset.id,
                        document_id=document.id,
                        segment_id=segment.id,
                        position=position,
                        index_node_id=index_node_id,
                        index_node_hash=index_node_hash,
                        content=args.content,
                        word_count=len(args.content),
                        type="customized",
                        created_by=current_user.id,
                    )

                    db.session.add(child_chunk)
                    db.session.flush()
                    new_child_chunks.append(child_chunk)
            VectorService.update_child_chunk_vector(new_child_chunks, update_child_chunks, delete_child_chunks, dataset)
            db.session.commit()
        except Exception as e:
            logger.exception("update child chunk index failed")
            db.session.rollback()
            raise ChildChunkIndexingError(str(e))
        return sorted(new_child_chunks + update_child_chunks, key=lambda x: x.position)

    @classmethod
    def update_child_chunk(
        cls,
        content: str,
        child_chunk: ChildChunk,
        segment: DocumentSegment,
        document: Document,
        dataset: Dataset,
    ) -> ChildChunk:
        assert current_user is not None

        try:
            child_chunk.content = content
            child_chunk.word_count = len(content)
            child_chunk.updated_by = current_user.id
            child_chunk.updated_at = naive_utc_now()
            child_chunk.type = "customized"
            db.session.add(child_chunk)
            VectorService.update_child_chunk_vector([], [child_chunk], [], dataset)
            db.session.commit()
        except Exception as e:
            logger.exception("update child chunk index failed")
            db.session.rollback()
            raise ChildChunkIndexingError(str(e))
        return child_chunk

    @classmethod
    def delete_child_chunk(cls, child_chunk: ChildChunk, dataset: Dataset):
        db.session.delete(child_chunk)
        try:
            VectorService.delete_child_chunk_vector(child_chunk, dataset)
        except Exception as e:
            logger.exception("delete child chunk index failed")
            db.session.rollback()
            raise ChildChunkDeleteIndexError(str(e))
        db.session.commit()

    @classmethod
    def get_child_chunks(
        cls, segment_id: str, document_id: str, dataset_id: str, page: int, limit: int, keyword: str | None = None
    ):
        assert isinstance(current_user, Account)

        query = (
            select(ChildChunk)
            .filter_by(
                tenant_id=current_user.current_tenant_id,
                dataset_id=dataset_id,
                document_id=document_id,
                segment_id=segment_id,
            )
            .order_by(ChildChunk.position.asc())
        )
        if keyword:
            escaped_keyword = helper.escape_like_pattern(keyword)
            query = query.where(ChildChunk.content.ilike(f"%{escaped_keyword}%", escape="\\"))
        return db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)

    @classmethod
    def get_child_chunk_by_id(cls, child_chunk_id: str, tenant_id: str) -> ChildChunk | None:
        """Get a child chunk by its ID."""
        result = (
            db.session.query(ChildChunk)
            .where(ChildChunk.id == child_chunk_id, ChildChunk.tenant_id == tenant_id)
            .first()
        )
        return result if isinstance(result, ChildChunk) else None

    @classmethod
    def get_segments(
        cls,
        document_id: str,
        tenant_id: str,
        status_list: list[str] | None = None,
        keyword: str | None = None,
        page: int = 1,
        limit: int = 20,
    ):
        """Get segments for a document with optional filtering."""
        query = select(DocumentSegment).where(
            DocumentSegment.document_id == document_id, DocumentSegment.tenant_id == tenant_id
        )

        # Check if status_list is not empty to avoid WHERE false condition
        if status_list and len(status_list) > 0:
            query = query.where(DocumentSegment.status.in_(status_list))

        if keyword:
            escaped_keyword = helper.escape_like_pattern(keyword)
            query = query.where(DocumentSegment.content.ilike(f"%{escaped_keyword}%", escape="\\"))

        query = query.order_by(DocumentSegment.position.asc(), DocumentSegment.id.asc())
        paginated_segments = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)

        return paginated_segments.items, paginated_segments.total

    @classmethod
    def get_segment_by_id(cls, segment_id: str, tenant_id: str) -> DocumentSegment | None:
        """Get a segment by its ID."""
        result = (
            db.session.query(DocumentSegment)
            .where(DocumentSegment.id == segment_id, DocumentSegment.tenant_id == tenant_id)
            .first()
        )
        return result if isinstance(result, DocumentSegment) else None

    @classmethod
    def get_segments_by_document_and_dataset(
        cls,
        document_id: str,
        dataset_id: str,
        status: str | None = None,
        enabled: bool | None = None,
    ) -> Sequence[DocumentSegment]:
        """
        Get segments for a document in a dataset with optional filtering.

        Args:
            document_id: Document ID
            dataset_id: Dataset ID
            status: Optional status filter (e.g., "completed")
            enabled: Optional enabled filter (True/False)

        Returns:
            Sequence of DocumentSegment instances
        """
        query = select(DocumentSegment).where(
            DocumentSegment.document_id == document_id,
            DocumentSegment.dataset_id == dataset_id,
        )

        if status is not None:
            query = query.where(DocumentSegment.status == status)

        if enabled is not None:
            query = query.where(DocumentSegment.enabled == enabled)

        return db.session.scalars(query).all()


class DatasetCollectionBindingService:
    @classmethod
    def get_dataset_collection_binding(
        cls, provider_name: str, model_name: str, collection_type: str = "dataset"
    ) -> DatasetCollectionBinding:
        dataset_collection_binding = (
            db.session.query(DatasetCollectionBinding)
            .where(
                DatasetCollectionBinding.provider_name == provider_name,
                DatasetCollectionBinding.model_name == model_name,
                DatasetCollectionBinding.type == collection_type,
            )
            .order_by(DatasetCollectionBinding.created_at)
            .first()
        )

        if not dataset_collection_binding:
            dataset_collection_binding = DatasetCollectionBinding(
                provider_name=provider_name,
                model_name=model_name,
                collection_name=Dataset.gen_collection_name_by_id(str(uuid.uuid4())),
                type=collection_type,
            )
            db.session.add(dataset_collection_binding)
            db.session.commit()
        return dataset_collection_binding

    @classmethod
    def get_dataset_collection_binding_by_id_and_type(
        cls, collection_binding_id: str, collection_type: str = "dataset"
    ) -> DatasetCollectionBinding:
        dataset_collection_binding = (
            db.session.query(DatasetCollectionBinding)
            .where(
                DatasetCollectionBinding.id == collection_binding_id, DatasetCollectionBinding.type == collection_type
            )
            .order_by(DatasetCollectionBinding.created_at)
            .first()
        )
        if not dataset_collection_binding:
            raise ValueError("Dataset collection binding not found")

        return dataset_collection_binding


class DatasetPermissionService:
    @classmethod
    def get_dataset_partial_member_list(cls, dataset_id):
        user_list_query = db.session.scalars(
            select(
                DatasetPermission.account_id,
            ).where(DatasetPermission.dataset_id == dataset_id)
        ).all()

        return user_list_query

    @classmethod
    def update_partial_member_list(cls, tenant_id, dataset_id, user_list):
        try:
            db.session.query(DatasetPermission).where(DatasetPermission.dataset_id == dataset_id).delete()
            permissions = []
            for user in user_list:
                permission = DatasetPermission(
                    tenant_id=tenant_id,
                    dataset_id=dataset_id,
                    account_id=user["user_id"],
                )
                permissions.append(permission)

            db.session.add_all(permissions)
            db.session.commit()
        except Exception as e:
            db.session.rollback()
            raise e

    @classmethod
    def check_permission(cls, user, dataset, requested_permission, requested_partial_member_list):
        if not user.is_dataset_editor:
            raise NoPermissionError("User does not have permission to edit this dataset.")

        if user.is_dataset_operator and dataset.permission != requested_permission:
            raise NoPermissionError("Dataset operators cannot change the dataset permissions.")

        if user.is_dataset_operator and requested_permission == "partial_members":
            if not requested_partial_member_list:
                raise ValueError("Partial member list is required when setting to partial members.")

            local_member_list = cls.get_dataset_partial_member_list(dataset.id)
            request_member_list = [user["user_id"] for user in requested_partial_member_list]
            if set(local_member_list) != set(request_member_list):
                raise ValueError("Dataset operators cannot change the dataset permissions.")

    @classmethod
    def clear_partial_member_list(cls, dataset_id):
        try:
            db.session.query(DatasetPermission).where(DatasetPermission.dataset_id == dataset_id).delete()
            db.session.commit()
        except Exception as e:
            db.session.rollback()
            raise e
