import json
import logging
import time
import uuid
from collections.abc import Callable, Generator, Mapping, Sequence
from dataclasses import dataclass
from typing import Any, Literal, cast

from loro import ExportMode
from sqlalchemy import exists, inspect, select
from sqlalchemy.orm import Session, sessionmaker
from sqlalchemy.orm.attributes import flag_modified

from configs import dify_config
from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
from core.app.apps.workflow.app_config_manager import WorkflowAppConfigManager
from core.app.file_access import DatabaseFileAccessController
from core.entities import PluginCredentialType
from core.plugin.impl.model_runtime_factory import create_plugin_model_assembly, create_plugin_provider_manager
from core.repositories import DifyCoreRepositoryFactory
from core.repositories.human_input_repository import FormCreateParams, HumanInputFormRepositoryImpl
from core.trigger.constants import is_trigger_node_type
from core.workflow.human_input_adapter import (
    DeliveryChannelConfig,
    adapt_human_input_node_data_for_graph,
    parse_human_input_delivery_methods,
)
from core.workflow.llm_environment_variable import (
    LLMEnvironmentVariable,
    parse_llm_model_selector,
    resolve_llm_model_config,
    should_resolve_llm_model_selector,
    validate_llm_environment_model_references,
)
from core.workflow.node_factory import (
    LATEST_VERSION,
    get_node_type_classes_mapping,
    is_start_node_type,
)
from core.workflow.node_runtime import (
    apply_dify_debug_email_recipient,
)
from core.workflow.nodes.human_input.callback import (
    DifyHITLCallback,
    render_form_content_before_submission,
    resolve_default_values,
)
from core.workflow.nodes.human_input.entities import FormInputConfig, HumanInputNodeData
from core.workflow.nodes.human_input.enums import HumanInputFormKind
from core.workflow.nodes.human_input.pause_reason import HumanInputRequired
from core.workflow.system_variables import (
    SystemVariableKey,
    build_bootstrap_variables,
    build_system_variables,
    default_system_variables,
    system_variable_selector,
)
from core.workflow.variable_pool_initializer import add_node_inputs_to_pool, add_variables_to_pool
from core.workflow.workflow_entry import WorkflowEntry
from domain.workflow.agent_node import is_dify_agent_node_data
from enterprise.telemetry.draft_trace import enqueue_draft_node_execution_trace
from enums import CloudPlan, DeploymentEdition
from events.app_event import (
    app_draft_loro_epoch_was_replaced,
    app_draft_workflow_was_synced,
    app_published_workflow_was_updated,
)
from extensions.ext_database import db
from extensions.ext_storage import storage
from factories.file_factory import build_from_mapping, build_from_mappings
from graphon.engine_events import NodeEvent, NodeRunFailedEvent, NodeRunStartedEvent, NodeRunSucceededEvent
from graphon.entities import WorkflowNodeExecution
from graphon.entities.graph_config import NodeConfigDict
from graphon.enums import (
    ErrorStrategy,
    NodeType,
    WorkflowNodeExecutionMetadataKey,
    WorkflowNodeExecutionStatus,
)
from graphon.errors import WorkflowNodeRunFailedError
from graphon.file import File
from graphon.node_events import NodeRunResult
from graphon.nodes import BuiltinNodeTypes
from graphon.nodes.base.node import Node
from graphon.nodes.container_effects import ContainerAwaitRequest
from graphon.nodes.http_request import HTTP_REQUEST_CONFIG_FILTER_KEY, build_http_request_config
from graphon.nodes.llm.entities import ModelConfig
from graphon.nodes.start.entities import StartNodeData
from graphon.runtime import VariablePool
from graphon.variable_loader import load_into_variable_pool
from graphon.variables import VariableBase
from graphon.variables.input_entities import VariableEntityType
from graphon.variables.variables import Variable
from libs.datetime_utils import naive_utc_now, to_utc_timestamp
from models import Account
from models.agent import WorkflowAgentBindingType, WorkflowAgentNodeBinding
from models.human_input import HumanInputFormRecipient, RecipientType
from models.model import App, AppMode
from models.tools import WorkflowToolProvider
from models.workflow import Workflow, WorkflowNodeExecutionModel, WorkflowNodeExecutionTriggeredFrom, WorkflowType
from repositories.factory import DifyAPIRepositoryFactory
from services.agent.retirement_service import WorkflowAgentRetirementService
from services.billing_service import BillingService
from services.enterprise.enterprise_service import EnterpriseService
from services.errors.app import (
    IsDraftWorkflowError,
    TriggerNodeLimitExceededError,
    WorkflowHashNotEqualError,
    WorkflowNotFoundError,
)
from services.human_input_service import HumanInputService
from services.system_feature_service import SystemFeatureService
from services.workflow.workflow_converter import WorkflowConverter
from services.workflow_loro_document import (
    LoroDraftEmptyGraphError,
    LoroDraftEpochChangedError,
    LoroDraftSyncResult,
    export_loro_delta,
    load_loro_draft,
    merge_loro_update,
    project_loro_graph,
)
from services.workflow_node_variables import get_human_input_form_variable_mapping
from services.workflow_ref_service import WorkflowRef
from services.workflow_run_agg import WorkflowRunAgg
from services.workflow_version_number_service import allocate_version_number

from .errors.workflow_service import DraftWorkflowDeletionError, WorkflowInUseError
from .human_input_delivery_test_service import (
    DeliveryTestContext,
    DeliveryTestEmailRecipient,
    DeliveryTestError,
    DeliveryTestUnsupportedError,
    HumanInputDeliveryTestService,
)
from .workflow_draft_variable_service import DraftVariableSaver, DraftVarLoader, WorkflowDraftVariableService
from .workflow_restore import apply_published_workflow_snapshot_to_draft


@dataclass(frozen=True)
class _DebugHumanInputNode:
    node_id: str
    title: str
    node_data: HumanInputNodeData
    variable_pool: VariablePool

    def render_form_content_before_submission(self) -> str:
        return render_form_content_before_submission(
            self.node_data,
            variable_pool=self.variable_pool,
        )

    def resolve_default_values(self) -> Mapping[str, Any]:
        return resolve_default_values(
            self.node_data,
            variable_pool=self.variable_pool,
        )

    def render_form_content_with_outputs(
        self,
        form_content: str,
        outputs: Mapping[str, Any],
        field_names: Sequence[str],
        form_inputs: Sequence[FormInputConfig] | None = None,
    ) -> str:
        return DifyHITLCallback.render_form_content_with_outputs(
            form_content=form_content,
            outputs=outputs,  # type: ignore[arg-type]
            field_names=field_names,
            form_inputs=form_inputs,
        )

    @property
    def workflow_execution_id(self) -> str:
        return "debug-human-input"

    @property
    def node_title(self) -> str:
        return self.title


HumanInputNode = _DebugHumanInputNode

_file_access_controller = DatabaseFileAccessController()


def _merge_environment_variable_patch(
    current_variables: Sequence[VariableBase],
    environment_variable_upserts: Sequence[VariableBase],
    deleted_environment_variable_ids: Sequence[str],
) -> list[VariableBase]:
    """Merge a per-ID environment-variable patch while preserving untouched server values."""
    upserts_by_id: dict[str, VariableBase] = {}
    for variable in environment_variable_upserts:
        if not variable.id:
            raise ValueError("Patched environment variables require an id.")
        if variable.id in upserts_by_id:
            raise ValueError(f"Duplicate patched environment variable id: {variable.id}")
        upserts_by_id[variable.id] = variable

    deleted_ids = set(deleted_environment_variable_ids)
    if len(deleted_ids) != len(deleted_environment_variable_ids):
        raise ValueError("Deleted environment variable ids must be unique.")
    if "" in deleted_ids:
        raise ValueError("Deleted environment variable ids must not be empty.")
    if conflicting_ids := deleted_ids.intersection(upserts_by_id):
        conflicting_id = min(conflicting_ids)
        raise ValueError(f"Environment variable cannot be upserted and deleted in the same patch: {conflicting_id}")

    existing_ids: set[str] = set()
    merged_variables: list[VariableBase] = []
    for variable in current_variables:
        variable_id = variable.id
        if variable_id:
            existing_ids.add(variable_id)
        if variable_id in deleted_ids:
            continue
        merged_variables.append(upserts_by_id.get(variable_id, variable))

    merged_variables.extend(
        variable for variable_id, variable in upserts_by_id.items() if variable_id not in existing_ids
    )
    names = [variable.name for variable in merged_variables]
    if len(set(names)) != len(names):
        raise ValueError("Environment variable names must be unique.")
    return merged_variables


class WorkflowService:
    """
    Workflow Service
    """

    def __init__(self, session_maker: sessionmaker | None = None):
        """Initialize WorkflowService with repository dependencies."""
        if session_maker is None:
            session_maker = sessionmaker(bind=db.engine, expire_on_commit=False)
        self._node_execution_service_repo = DifyAPIRepositoryFactory.create_api_workflow_node_execution_repository(
            session_maker
        )

    def get_node_last_run(self, app_model: App, workflow: Workflow, node_id: str) -> WorkflowNodeExecutionModel | None:
        """
        Get the most recent execution for a specific node.

        Args:
            app_model: The application model
            workflow: The workflow model
            node_id: The node identifier

        Returns:
            The most recent WorkflowNodeExecutionModel for the node, or None if not found
        """
        return self._node_execution_service_repo.get_node_last_execution(
            tenant_id=app_model.tenant_id,
            app_id=app_model.id,
            workflow_id=workflow.id,
            node_id=node_id,
        )

    def is_workflow_exist(self, app_model: App, *, session: Session) -> bool:
        stmt = select(
            exists().where(
                Workflow.tenant_id == app_model.tenant_id,
                Workflow.app_id == app_model.id,
                Workflow.version == Workflow.VERSION_DRAFT,
            )
        )
        return session.execute(stmt).scalar_one()

    @staticmethod
    def get_draft_workflow(app_model: App, *, session: Session) -> Workflow | None:
        """
        Return the app's draft workflow, or None if no draft exists.

        Reuses the caller's active session so workflow reads stay in the same
        transaction as the surrounding request or task. No service construction
        or Flask database binding is needed for this read.
        """
        return session.scalar(
            select(Workflow)
            .where(
                Workflow.tenant_id == app_model.tenant_id,
                Workflow.app_id == app_model.id,
                Workflow.version == Workflow.VERSION_DRAFT,
            )
            .limit(1)
        )

    def get_draft_workflow_for_update(self, app_model: App, *, session: Session) -> Workflow | None:
        """Return the app draft while holding its row lock for the caller's transaction."""
        return session.scalar(
            select(Workflow)
            .where(
                Workflow.tenant_id == app_model.tenant_id,
                Workflow.app_id == app_model.id,
                Workflow.version == Workflow.VERSION_DRAFT,
            )
            .limit(1)
            .with_for_update()
        )

    @staticmethod
    def get_published_workflow_by_id(
        app_model: App,
        workflow_id: str,
        *,
        session: Session,
        for_update: bool = False,
    ) -> Workflow | None:
        """Fetch a published workflow by ID in the caller's transaction.

        With ``for_update=True``, the source version stays locked until that
        transaction ends. Restore uses the lock while copying Agent bindings so
        a concurrent delete cannot release the same owner.
        """
        stmt = (
            select(Workflow)
            .where(
                Workflow.tenant_id == app_model.tenant_id,
                Workflow.app_id == app_model.id,
                Workflow.id == workflow_id,
            )
            .limit(1)
        )
        if for_update:
            stmt = stmt.with_for_update()
        workflow = session.scalar(stmt)
        if not workflow:
            return None
        if workflow.version == Workflow.VERSION_DRAFT:
            raise IsDraftWorkflowError(
                f"Cannot use draft workflow version. Workflow ID: {workflow_id}. "
                f"Please use a published workflow version or leave workflow_id empty."
            )
        return workflow

    def get_published_workflow(self, app_model: App, *, session: Session) -> Workflow | None:
        """
        Get published workflow

        Reuses the caller's active session so workflow reads stay in the same
        transaction as the surrounding request or task.
        """

        if not app_model.workflow_id:
            return None

        workflow = session.scalar(
            select(Workflow)
            .where(
                Workflow.tenant_id == app_model.tenant_id,
                Workflow.app_id == app_model.id,
                Workflow.id == app_model.workflow_id,
            )
            .limit(1)
        )

        return workflow

    def get_tenant_app_maintainers(
        self, app_ids: Sequence[str], tenant_id: str, *, session: Session
    ) -> dict[str, str | None]:
        """Return requested normal apps and their maintainers within a tenant."""
        if not app_ids:
            return {}

        stmt = select(App.id, App.maintainer).where(
            App.id.in_(app_ids), App.tenant_id == tenant_id, App.status == "normal"
        )
        return {str(app_id): maintainer for app_id, maintainer in session.execute(stmt)}

    def get_all_published_workflow(
        self,
        *,
        session: Session,
        app_model: App,
        page: int,
        limit: int,
        user_id: str | None,
        named_only: bool = False,
        include_draft: bool = True,
    ) -> tuple[Sequence[Workflow], bool]:
        """
        Get published workflow with pagination

        The console lists the draft alongside published versions (``include_draft=True``,
        the default); callers that only want published versions, where the draft would
        break restore or ordering, pass ``include_draft=False``.
        """
        if not app_model.workflow_id:
            return [], False

        stmt = (
            select(Workflow)
            .where(Workflow.app_id == app_model.id)
            # The draft leads the list; its `created_at` is the app's creation time, so it would
            # otherwise sort last. Published versions then order by publish time: `version` is a
            # stringified timestamp whose microseconds are omitted when zero, so ordering by it
            # misplaces versions across second boundaries, and `version_number` is NULL for
            # versions published before numbering was introduced.
            .order_by(
                (Workflow.version == Workflow.VERSION_DRAFT).desc(),
                Workflow.created_at.desc(),
                Workflow.id.desc(),
            )
            .limit(limit + 1)
            .offset((page - 1) * limit)
        )

        if user_id:
            stmt = stmt.where(Workflow.created_by == user_id)

        if named_only:
            stmt = stmt.where(Workflow.marked_name != "")

        if not include_draft:
            stmt = stmt.where(Workflow.version != Workflow.VERSION_DRAFT)

        workflows = session.scalars(stmt).all()

        has_more = len(workflows) > limit
        if has_more:
            workflows = workflows[:-1]

        return workflows, has_more

    def sync_draft_workflow(
        self,
        *,
        app_model: App,
        graph: dict[str, Any],
        features: dict[str, Any],
        unique_hash: str | None,
        account: Account,
        environment_variables: Sequence[VariableBase],
        conversation_variables: Sequence[VariableBase],
        session: Session,
        environment_variable_upserts: Sequence[VariableBase] | None = None,
        deleted_environment_variable_ids: Sequence[str] = (),
        preserve_environment_variables: bool = False,
        commit: bool = True,
        sync_agent_bindings: bool = True,
        graph_only: bool = False,
        graph_writer: Literal["server", "http", "initialize"] = "server",
    ) -> Workflow:
        """
        Sync draft workflow.

        DSL import disables the intermediate commit and Agent binding sync so
        portable package references can be materialized atomically after the
        draft workflow has received its target-workspace id.

        Existing drafts are row-locked before the hash check. Collaborative
        graph-only saves preserve independently persisted draft fields, while
        an explicit per-ID environment patch is merged with the graph.
        In Loro mode the HTTP endpoint can initialize a missing draft only.

        :raises WorkflowHashNotEqualError
        """
        if environment_variable_upserts is None and deleted_environment_variable_ids:
            raise ValueError("Deleted environment variable ids require an environment variable patch.")

        # fetch draft workflow by app_model
        workflow = self.get_draft_workflow_for_update(app_model=app_model, session=session)

        if workflow and (graph_writer == "initialize" or workflow.unique_hash != unique_hash):
            raise WorkflowHashNotEqualError()

        # Collaboration persists features and variables through dedicated endpoints. A graph save
        # must not overwrite those newer database values with another collaborator's stale cache.
        if not graph_only or not workflow:
            self.validate_features_structure(app_model=app_model, features=features)

        # validate graph structure
        self.validate_graph_structure(graph=graph)

        # create draft workflow if not found
        if not workflow:
            initial_environment_variables = (
                _merge_environment_variable_patch(
                    environment_variables,
                    environment_variable_upserts,
                    deleted_environment_variable_ids,
                )
                if environment_variable_upserts is not None
                else list(environment_variables)
            )
            workflow = Workflow(
                tenant_id=app_model.tenant_id,
                app_id=app_model.id,
                type=WorkflowType.from_app_mode(app_model.mode),
                version=Workflow.VERSION_DRAFT,
                graph=json.dumps(graph),
                _features=json.dumps(features),
                created_by=account.id,
                _environment_variables=initial_environment_variables,
                _conversation_variables=conversation_variables,
            )
            session.add(workflow)
        # update draft workflow if found
        else:
            workflow.graph = json.dumps(graph)
            workflow.updated_by = account.id
            workflow.updated_at = naive_utc_now()
            if not graph_only:
                workflow.features = json.dumps(features)
                workflow.conversation_variables = conversation_variables
            if environment_variable_upserts is not None:
                workflow.environment_variables = _merge_environment_variable_patch(
                    workflow.environment_variables,
                    environment_variable_upserts,
                    deleted_environment_variable_ids,
                )
            elif not graph_only and not preserve_environment_variables:
                workflow.environment_variables = environment_variables

        from services.agent.workflow_publish_service import WorkflowAgentPublishService

        session.flush()
        retirement_candidates: set[str] = set()
        if sync_agent_bindings:
            retirement_candidates = WorkflowAgentPublishService.sync_agent_bindings_for_draft(
                session=session,
                draft_workflow=workflow,
                account_id=account.id,
            )
            WorkflowAgentPublishService.validate_agent_nodes_for_draft_sync(
                session=session,
                draft_workflow=workflow,
            )

        if commit:
            replacement_epoch = self.align_loro_draft_after_graph_write(
                workflow, replace=graph_writer == "server", session=session
            )
            session.commit()
            if replacement_epoch is not None:
                app_draft_loro_epoch_was_replaced.send(app_model.id, epoch=replacement_epoch)
            WorkflowAgentRetirementService.retire_unowned(
                tenant_id=app_model.tenant_id,
                agent_ids=retirement_candidates,
                account_id=account.id,
            )
            app_draft_workflow_was_synced.send(app_model, synced_draft_workflow=workflow)

        # return draft workflow
        return workflow

    @staticmethod
    def align_loro_draft_after_graph_write(workflow: Workflow, *, replace: bool, session: Session) -> str | None:
        """Prepare a fresh collaborative history for a replacement while the draft is locked.

        A replacement and its epoch must commit together so edits based on the old graph
        cannot merge into the new graph. Return the new epoch for notification after commit.
        An HTTP write retires existing Loro history without starting a collaborative session.
        """
        if replace and dify_config.ENABLE_COLLABORATION_MODE:
            from services.agent.workflow_publish_service import WorkflowAgentPublishService

            projected = WorkflowAgentPublishService.project_draft_bindings_to_graph(
                session=session, draft_workflow=workflow
            )
            doc = load_loro_draft(None, projected)
            workflow.loro_draft_snapshot = doc.export(ExportMode.Snapshot())
            workflow.loro_draft_epoch = str(uuid.uuid4())
            return workflow.loro_draft_epoch

        if workflow.loro_draft_snapshot is not None:
            workflow.loro_draft_epoch = str(uuid.uuid4())
            workflow.loro_draft_snapshot = None
        return None

    def sync_loro_draft(
        self,
        *,
        app_model: App,
        account_id: str,
        epoch: str,
        version: bytes | None,
        update: bytes | None,
        allow_empty: bool,
        session: Session,
    ) -> LoroDraftSyncResult:
        """Merge updates for an admitted editor, committing history and projection before confirming.

        The draft row lock serializes seed and merges across workers. A rejected or duplicate
        update neither changes the saved graph nor repeats its Agent binding side effects.
        """
        workflow = self.get_draft_workflow_for_update(app_model, session=session)
        if workflow is None:
            raise WorkflowNotFoundError("Draft workflow not found.")
        current_epoch = workflow.loro_draft_epoch or workflow.id
        if epoch != current_epoch:
            raise LoroDraftEpochChangedError("Draft epoch changed.")

        graph = workflow.graph_dict
        from services.agent.workflow_publish_service import WorkflowAgentPublishService

        snapshot = workflow.loro_draft_snapshot
        seed = (
            WorkflowAgentPublishService.project_draft_bindings_to_graph(session=session, draft_workflow=workflow)
            if snapshot is None
            else graph
        )
        doc = load_loro_draft(snapshot, seed)
        before = doc.state_vv
        if update is not None:
            merge_loro_update(doc, update)
        changed = not before.includes_vv(doc.state_vv)
        needs_commit = changed or snapshot is None
        if needs_commit:
            workflow.loro_draft_epoch = current_epoch
            workflow.loro_draft_snapshot = doc.export(ExportMode.Snapshot())
        graph_changed = False
        retirement_candidates: set[str] = set()
        if changed:
            projected = project_loro_graph(doc)
            if not projected["nodes"] and not projected["edges"] and (graph.get("nodes") or graph.get("edges")):
                if not allow_empty:
                    raise LoroDraftEmptyGraphError("Clearing the draft requires confirmation.")
            graph_changed = graph != projected
            if graph_changed:
                self.validate_graph_structure(projected)
                workflow.graph = json.dumps(projected, allow_nan=False)
                workflow.updated_at = naive_utc_now()
                workflow.updated_by = account_id

                session.flush()
                retirement_candidates = WorkflowAgentPublishService.sync_agent_bindings_for_draft(
                    session=session, draft_workflow=workflow, account_id=account_id
                )
                WorkflowAgentPublishService.validate_agent_nodes_for_draft_sync(
                    session=session, draft_workflow=workflow
                )

        if needs_commit and not graph_changed:
            flag_modified(workflow, "updated_at")
        delta = export_loro_delta(doc, version)
        result = LoroDraftSyncResult(
            epoch=current_epoch,
            version=doc.state_vv.encode(),
            update=delta,
            hash=workflow.unique_hash,
            updated_at=to_utc_timestamp(workflow.updated_at or workflow.created_at),
            changed=changed,
        )
        if needs_commit:
            session.commit()
        if graph_changed:
            try:
                WorkflowAgentRetirementService.retire_unowned(
                    tenant_id=app_model.tenant_id, agent_ids=retirement_candidates, account_id=account_id
                )
                app_draft_workflow_was_synced.send(app_model, synced_draft_workflow=workflow)
            except Exception:
                logging.getLogger(__name__).exception("Post-commit effects failed for Loro draft %s", current_epoch)
        return result

    def update_draft_workflow_environment_variables(
        self,
        *,
        app_model: App,
        environment_variables: Sequence[VariableBase],
        account: Account,
        session: Session,
    ) -> None:
        """Replace every environment variable on a draft workflow and commit the transaction."""
        # fetch draft workflow by app_model
        workflow = self.get_draft_workflow(app_model=app_model, session=session)

        if not workflow:
            raise ValueError("No draft workflow found.")

        workflow.environment_variables = environment_variables
        workflow.updated_by = account.id
        workflow.updated_at = naive_utc_now()

        # commit db session changes
        session.commit()

    def patch_draft_workflow_environment_variables(
        self,
        *,
        app_model: App,
        environment_variables: Sequence[VariableBase],
        deleted_environment_variable_ids: Sequence[str],
        account: Account,
        session: Session,
    ) -> None:
        """Atomically merge per-ID environment-variable upserts and deletions into a draft workflow.

        The draft row is locked before reading its current variables so concurrent patches preserve
        variables they do not touch. Existing variables keep their order and new variables are appended.
        The transaction is committed before this method returns.
        """
        workflow = self.get_draft_workflow_for_update(app_model=app_model, session=session)
        if not workflow:
            raise ValueError("No draft workflow found.")

        workflow.environment_variables = _merge_environment_variable_patch(
            workflow.environment_variables,
            environment_variables,
            deleted_environment_variable_ids,
        )
        workflow.updated_by = account.id
        workflow.updated_at = naive_utc_now()
        session.commit()

    def update_draft_workflow_conversation_variables(
        self,
        *,
        app_model: App,
        conversation_variables: Sequence[VariableBase],
        account: Account,
        session: Session,
    ):
        """
        Update draft workflow conversation variables
        """
        # fetch draft workflow by app_model
        workflow = self.get_draft_workflow(app_model=app_model, session=session)

        if not workflow:
            raise ValueError("No draft workflow found.")

        workflow.conversation_variables = conversation_variables
        workflow.updated_by = account.id
        workflow.updated_at = naive_utc_now()

        # commit db session changes
        session.commit()

    def update_draft_workflow_features(
        self,
        *,
        app_model: App,
        features: dict,
        account: Account,
        session: Session,
    ):
        """
        Update draft workflow features
        """
        # fetch draft workflow by app_model
        workflow = self.get_draft_workflow(app_model=app_model, session=session)

        if not workflow:
            raise ValueError("No draft workflow found.")

        # validate features structure
        self.validate_features_structure(app_model=app_model, features=features)

        workflow.features = json.dumps(features)
        workflow.updated_by = account.id
        workflow.updated_at = naive_utc_now()

        # commit db session changes
        session.commit()

    def restore_published_workflow_to_draft(
        self,
        *,
        app_model: App,
        workflow_id: str,
        account: Account,
        session: Session,
    ) -> Workflow:
        """Restore a published workflow snapshot into the draft workflow.

        Secret environment variables are copied server-side from the selected
        published workflow so the normal draft sync flow stays stateless.
        """
        source_workflow = self.get_published_workflow_by_id(
            app_model=app_model,
            workflow_id=workflow_id,
            session=session,
            for_update=True,
        )
        if not source_workflow:
            raise WorkflowNotFoundError("Workflow not found.")

        self.validate_features_structure(app_model=app_model, features=source_workflow.normalized_features_dict)
        self.validate_graph_structure(graph=source_workflow.graph_dict)

        draft_workflow = self.get_draft_workflow_for_update(app_model=app_model, session=session)
        draft_workflow, is_new_draft = apply_published_workflow_snapshot_to_draft(
            tenant_id=app_model.tenant_id,
            app_id=app_model.id,
            source_workflow=source_workflow,
            draft_workflow=draft_workflow,
            account=account,
            updated_at_factory=naive_utc_now,
        )

        if is_new_draft:
            session.add(draft_workflow)

        from services.agent.workflow_publish_service import WorkflowAgentPublishService

        session.flush()
        retirement_candidates = WorkflowAgentPublishService.restore_agent_node_bindings_to_draft(
            session=session,
            source_workflow=source_workflow,
            draft_workflow=draft_workflow,
            account_id=account.id,
        )

        replacement_epoch = self.align_loro_draft_after_graph_write(draft_workflow, replace=True, session=session)
        session.commit()
        if replacement_epoch is not None:
            app_draft_loro_epoch_was_replaced.send(app_model.id, epoch=replacement_epoch)
        WorkflowAgentRetirementService.retire_unowned(
            tenant_id=app_model.tenant_id,
            agent_ids=retirement_candidates,
            account_id=account.id,
        )
        app_draft_workflow_was_synced.send(app_model, synced_draft_workflow=draft_workflow)

        return draft_workflow

    def publish_workflow(
        self,
        *,
        session: Session,
        app_model: App,
        account: Account,
        marked_name: str = "",
        marked_comment: str = "",
    ) -> Workflow:
        draft_workflow_stmt = select(Workflow).where(
            Workflow.tenant_id == app_model.tenant_id,
            Workflow.app_id == app_model.id,
            Workflow.version == Workflow.VERSION_DRAFT,
        )
        draft_workflow = session.scalar(draft_workflow_stmt)
        if not draft_workflow:
            raise ValueError("No valid workflow found.")

        validate_llm_environment_model_references(
            graph=draft_workflow.graph_dict,
            environment_variables=draft_workflow.environment_variables,
        )

        # Validate credentials before publishing, for credential policy check
        if SystemFeatureService.is_plugin_manager_enabled():
            self._validate_workflow_credentials(draft_workflow, session=session)

        # validate graph structure
        self.validate_graph_structure(graph=draft_workflow.graph_dict)
        from services.knowledge_fs.knowledge_retrieval_v2.validation import (
            validate_control_space_references,
        )

        validate_control_space_references(
            session=session,
            tenant_id=app_model.tenant_id,
            graph=draft_workflow.graph_dict,
        )

        from services.agent.workflow_publish_service import WorkflowAgentPublishService

        WorkflowAgentPublishService.validate_agent_nodes_for_publish(
            session=session,
            draft_workflow=draft_workflow,
        )

        # billing check
        if dify_config.DEPLOYMENT_EDITION == DeploymentEdition.CLOUD:
            limit_info = BillingService.get_info(app_model.tenant_id)
            if limit_info.subscription.plan == CloudPlan.SANDBOX:
                # Check trigger node count limit for SANDBOX plan
                trigger_node_count = sum(
                    1
                    for _, node_data in draft_workflow.walk_nodes()
                    if (node_type_str := node_data.get("type"))
                    and isinstance(node_type_str, str)
                    and is_trigger_node_type(node_type_str)
                )
                if trigger_node_count > 2:
                    raise TriggerNodeLimitExceededError(count=trigger_node_count, limit=2)

        # create new workflow
        workflow = Workflow.new(
            tenant_id=app_model.tenant_id,
            app_id=app_model.id,
            type=draft_workflow.type,
            version=Workflow.version_from_datetime(naive_utc_now()),
            version_number=allocate_version_number(session=session, app_id=app_model.id),
            graph=draft_workflow.graph,
            created_by=account.id,
            environment_variables=draft_workflow.environment_variables,
            conversation_variables=draft_workflow.conversation_variables,
            marked_name=marked_name,
            marked_comment=marked_comment,
            rag_pipeline_variables=draft_workflow.rag_pipeline_variables,
            features=draft_workflow.features,
        )

        # commit db session changes
        session.add(workflow)
        WorkflowAgentPublishService.copy_agent_node_bindings_to_published(
            session=session,
            draft_workflow=draft_workflow,
            published_workflow=workflow,
        )

        # trigger app workflow events
        app_published_workflow_was_updated.send(
            app_model,
            published_workflow=workflow,
            session=session,
        )

        # return new workflow
        return workflow

    def _validate_workflow_credentials(self, workflow: Workflow, *, session: Session) -> None:
        """
        Validate all credentials in workflow nodes before publishing.

        :param workflow: The workflow to validate
        :raises ValueError: If any credentials violate policy compliance
        """
        graph_dict = workflow.graph_dict
        nodes = graph_dict.get("nodes", [])
        has_llm_model_reference = any(
            node.get("data", {}).get("type") == "llm"
            and should_resolve_llm_model_selector(node.get("data", {}).get("model_selector"))
            for node in nodes
        )
        environment_variables = (
            {variable.name: variable for variable in workflow.environment_variables} if has_llm_model_reference else {}
        )

        for node in nodes:
            self.validate_node_credentials(workflow.tenant_id, node, environment_variables, session=session)

    def validate_node_credentials(
        self,
        tenant_id: str,
        node: Mapping[str, Any],
        environment_variables: Mapping[str, VariableBase],
        *,
        session: Session,
    ) -> None:
        """Publish's credential check for one node.

        :raises ValueError: naming the node when a credential it needs violates policy or is unusable
        """
        node_data = node.get("data", {})
        node_type = node_data.get("type")
        node_id = node.get("id", "unknown")

        try:
            # Extract and validate credentials based on node type
            if node_type == "tool":
                credential_id = node_data.get("credential_id")
                provider = node_data.get("provider_id")
                if provider:
                    if credential_id:
                        # Check specific credential
                        from core.helper.credential_utils import check_credential_policy_compliance

                        check_credential_policy_compliance(
                            credential_id=credential_id,
                            provider=provider,
                            credential_type=PluginCredentialType.TOOL,
                        )
                    else:
                        # Check default workspace credential for this provider
                        self._check_default_tool_credential(tenant_id, provider, session=session)

            elif node_type == "agent":
                agent_params = node_data.get("agent_parameters", {})

                model_config = agent_params.get("model", {}).get("value", {})
                if model_config.get("provider") and model_config.get("model"):
                    self._validate_llm_model_config(tenant_id, model_config["provider"], model_config["model"])

                    # Validate load balancing credentials for agent model if load balancing is enabled
                    agent_model_node_data = {"model": model_config}
                    self._validate_load_balancing_credentials(
                        tenant_id, agent_model_node_data, node_id, session=session
                    )

                # Validate agent tools
                tools = agent_params.get("tools", {}).get("value", [])
                for tool in tools:
                    # Agent tools store provider in provider_name field
                    provider = tool.get("provider_name")
                    credential_id = tool.get("credential_id")
                    if provider:
                        if credential_id:
                            from core.helper.credential_utils import check_credential_policy_compliance

                            check_credential_policy_compliance(credential_id, provider, PluginCredentialType.TOOL)
                        else:
                            self._check_default_tool_credential(tenant_id, provider, session=session)

            elif node_type in ["llm", "knowledge_retrieval", "parameter_extractor", "question_classifier"]:
                validation_node_data = node_data
                model_config = node_data.get("model", {})
                if node_type == "llm" and should_resolve_llm_model_selector(node_data.get("model_selector")):
                    selector = parse_llm_model_selector(node_data["model_selector"])
                    variable = environment_variables.get(selector[1])
                    if not isinstance(variable, LLMEnvironmentVariable):
                        raise ValueError(
                            f"LLM environment variable '{selector[1]}' was not found or is not an LLM variable"
                        )
                    resolved_model = resolve_llm_model_config(
                        node_model=ModelConfig.model_validate(model_config),
                        variable_name=selector[1],
                        variable_value=variable.value,
                    )
                    model_config = resolved_model.model_dump(mode="json")
                    validation_node_data = {**node_data, "model": model_config}
                provider = model_config.get("provider")
                model_name = model_config.get("name")

                if provider and model_name:
                    # Validate that the provider+model combination can fetch valid credentials
                    self._validate_llm_model_config(tenant_id, provider, model_name)
                    # Validate load balancing credentials if load balancing is enabled
                    self._validate_load_balancing_credentials(tenant_id, validation_node_data, node_id, session=session)
                else:
                    raise ValueError(f"Node {node_id} ({node_type}): Missing provider or model configuration")

        except Exception as e:
            if isinstance(e, ValueError):
                raise e
            else:
                raise ValueError(f"Node {node_id} ({node_type}): {str(e)}") from e

    def _validate_llm_model_config(self, tenant_id: str, provider: str, model_name: str) -> None:
        """
        Validate that an LLM model configuration can fetch valid credentials and has active status.

        This method attempts to get the model instance and validates that:
        1. The provider exists and is configured
        2. The model exists in the provider
        3. Credentials can be fetched for the model
        4. The credentials pass policy compliance checks
        5. The model status is ACTIVE (not NO_CONFIGURE, DISABLED, etc.)

        :param tenant_id: The tenant ID
        :param provider: The provider name
        :param model_name: The model name
        :raises ValueError: If the model configuration is invalid or credentials fail policy checks
        """
        try:
            from graphon.model_runtime.entities.model_entities import ModelType

            # Model instance resolution and provider status lookup must reuse the
            # same request-scoped runtime so validation does not silently split
            # provider discovery and credential reads across different caches.
            assembly = create_plugin_model_assembly(tenant_id=tenant_id)

            # Get model instance to validate provider+model combination
            assembly.model_manager.get_model_instance(
                tenant_id=tenant_id, provider=provider, model_type=ModelType.LLM, model=model_name
            )

            # The ModelInstance constructor will automatically check credential policy compliance
            # via ProviderConfiguration.get_current_credentials() -> _check_credential_policy_compliance()
            # If it fails, an exception will be raised

            # Additionally, check the model status to ensure it's ACTIVE
            provider_configurations = assembly.provider_manager.get_configurations(tenant_id)
            models = provider_configurations.get_models(provider=provider, model_type=ModelType.LLM)

            target_model = None
            for model in models:
                if model.model == model_name and model.provider.provider == provider:
                    target_model = model
                    break

            if target_model:
                target_model.raise_for_status()
            else:
                raise ValueError(f"Model {model_name} not found for provider {provider}")

        except Exception as e:
            raise ValueError(
                f"Failed to validate LLM model configuration (provider: {provider}, model: {model_name}): {str(e)}"
            ) from e

    def _check_default_tool_credential(self, tenant_id: str, provider: str, *, session: Session) -> None:
        """
        Check credential policy compliance for the default workspace credential of a tool provider.

        This method finds the default credential for the given provider and validates it.
        Uses the same fallback logic as runtime to handle deauthorized credentials.

        :param tenant_id: The tenant ID
        :param provider: The tool provider name
        :raises ValueError: If no default credential exists or if it fails policy compliance
        """
        try:
            from models.tools import BuiltinToolProvider

            # Use the same fallback logic as runtime: get the first available credential
            # ordered by is_default DESC, created_at ASC (same as tool_manager.py)
            default_provider = session.scalar(
                select(BuiltinToolProvider)
                .where(
                    BuiltinToolProvider.tenant_id == tenant_id,
                    BuiltinToolProvider.provider == provider,
                )
                .order_by(BuiltinToolProvider.is_default.desc(), BuiltinToolProvider.created_at.asc())
                .limit(1)
            )

            if not default_provider:
                # plugin does not require credentials, skip
                return

            # Check credential policy compliance using the default credential ID
            from core.helper.credential_utils import check_credential_policy_compliance

            check_credential_policy_compliance(
                credential_id=default_provider.id,
                provider=provider,
                credential_type=PluginCredentialType.TOOL,
                check_existence=False,
            )

        except Exception as e:
            raise ValueError(f"Failed to validate default credential for tool provider {provider}: {str(e)}") from e

    def _validate_load_balancing_credentials(
        self, tenant_id: str, node_data: Mapping[str, Any], node_id: str, *, session: Session
    ) -> None:
        """
        Validate load balancing credentials for a workflow node.

        :param tenant_id: The workspace that owns the workflow
        :param node_data: The node data containing model configuration
        :param node_id: The node ID for error reporting
        :raises ValueError: If load balancing credentials violate policy compliance
        """
        # Extract model configuration
        model_config = node_data.get("model", {})
        provider = model_config.get("provider")
        model_name = model_config.get("name")

        if not provider or not model_name:
            return  # No model config to validate

        # Check if this model has load balancing enabled
        if self._is_load_balancing_enabled(tenant_id, provider, model_name):
            # Get all load balancing configurations for this model
            load_balancing_configs = self._get_load_balancing_configs(tenant_id, provider, model_name, session=session)
            # Validate each load balancing configuration
            try:
                for config in load_balancing_configs:
                    if config.get("credential_id"):
                        from core.helper.credential_utils import check_credential_policy_compliance

                        check_credential_policy_compliance(
                            config["credential_id"], provider, PluginCredentialType.MODEL
                        )
            except Exception as e:
                raise ValueError(f"Invalid load balancing credentials for {provider}/{model_name}: {str(e)}") from e

    def _is_load_balancing_enabled(self, tenant_id: str, provider: str, model_name: str) -> bool:
        """
        Check if load balancing is enabled for a specific model.

        :param tenant_id: The tenant ID
        :param provider: The provider name
        :param model_name: The model name
        :return: True if load balancing is enabled, False otherwise
        """
        try:
            from graphon.model_runtime.entities.model_entities import ModelType

            # Get provider configurations
            provider_manager = create_plugin_provider_manager(tenant_id=tenant_id)
            provider_configurations = provider_manager.get_configurations(tenant_id)
            provider_configuration = provider_configurations.get(provider)

            if not provider_configuration:
                return False

            # Get provider model setting
            provider_model_setting = provider_configuration.get_provider_model_setting(
                model_type=ModelType.LLM,
                model=model_name,
            )
            return provider_model_setting is not None and provider_model_setting.load_balancing_enabled

        except Exception:
            # If we can't determine the status, assume load balancing is not enabled
            return False

    def _get_load_balancing_configs(
        self, tenant_id: str, provider: str, model_name: str, *, session: Session
    ) -> list[dict[str, Any]]:
        """
        Get all load balancing configurations for a model.

        :param tenant_id: The tenant ID
        :param provider: The provider name
        :param model_name: The model name
        :return: List of load balancing configuration dictionaries
        """
        try:
            from services.model_load_balancing_service import ModelLoadBalancingService

            model_load_balancing_service = ModelLoadBalancingService()
            _, configs = model_load_balancing_service.get_load_balancing_configs(
                tenant_id=tenant_id,
                provider=provider,
                model=model_name,
                model_type="llm",  # Load balancing is primarily used for LLM models
                session=session,
                config_from="predefined-model",  # Check both predefined and custom models
            )

            _, custom_configs = model_load_balancing_service.get_load_balancing_configs(
                tenant_id=tenant_id,
                provider=provider,
                model=model_name,
                model_type="llm",
                session=session,
                config_from="custom-model",
            )
            all_configs = cast(list[dict[str, Any]], configs) + cast(list[dict[str, Any]], custom_configs)

            return [config for config in all_configs if config.get("credential_id")]

        except Exception:
            # If we can't get the configurations, return empty list
            # This will prevent validation errors from breaking the workflow
            return []

    def get_default_block_configs(self) -> Sequence[Mapping[str, object]]:
        """
        Get default block configs
        """
        # return default block config
        default_block_configs: list[Mapping[str, object]] = []
        for node_type, node_class_mapping in get_node_type_classes_mapping().items():
            node_class = node_class_mapping[LATEST_VERSION]
            filters = None
            if node_type == BuiltinNodeTypes.HTTP_REQUEST:
                filters = {
                    HTTP_REQUEST_CONFIG_FILTER_KEY: build_http_request_config(
                        max_connect_timeout=dify_config.HTTP_REQUEST_MAX_CONNECT_TIMEOUT,
                        max_read_timeout=dify_config.HTTP_REQUEST_MAX_READ_TIMEOUT,
                        max_write_timeout=dify_config.HTTP_REQUEST_MAX_WRITE_TIMEOUT,
                        max_binary_size=dify_config.HTTP_REQUEST_NODE_MAX_BINARY_SIZE,
                        max_text_size=dify_config.HTTP_REQUEST_NODE_MAX_TEXT_SIZE,
                        ssl_verify=dify_config.HTTP_REQUEST_NODE_SSL_VERIFY,
                        ssrf_default_max_retries=dify_config.SSRF_DEFAULT_MAX_RETRIES,
                    )
                }
            default_config = node_class.get_default_config(filters=filters)
            if default_config:
                default_block_configs.append(default_config)

        return default_block_configs

    def get_default_block_config(
        self, node_type: str, filters: Mapping[str, object] | None = None
    ) -> Mapping[str, object]:
        """
        Get default config of node.
        :param node_type: node type
        :param filters: filter by node config parameters.
        :return:
        """
        node_type_enum: NodeType = node_type
        node_mapping = get_node_type_classes_mapping()

        # return default block config
        if node_type_enum not in node_mapping:
            return {}

        node_class = node_mapping[node_type_enum][LATEST_VERSION]
        resolved_filters = dict(filters) if filters else {}
        if node_type_enum == BuiltinNodeTypes.HTTP_REQUEST and HTTP_REQUEST_CONFIG_FILTER_KEY not in resolved_filters:
            resolved_filters[HTTP_REQUEST_CONFIG_FILTER_KEY] = build_http_request_config(
                max_connect_timeout=dify_config.HTTP_REQUEST_MAX_CONNECT_TIMEOUT,
                max_read_timeout=dify_config.HTTP_REQUEST_MAX_READ_TIMEOUT,
                max_write_timeout=dify_config.HTTP_REQUEST_MAX_WRITE_TIMEOUT,
                max_binary_size=dify_config.HTTP_REQUEST_NODE_MAX_BINARY_SIZE,
                max_text_size=dify_config.HTTP_REQUEST_NODE_MAX_TEXT_SIZE,
                ssl_verify=dify_config.HTTP_REQUEST_NODE_SSL_VERIFY,
                ssrf_default_max_retries=dify_config.SSRF_DEFAULT_MAX_RETRIES,
            )
        default_config = node_class.get_default_config(filters=resolved_filters or None)
        if not default_config:
            return {}

        return default_config

    def run_draft_workflow_node(
        self,
        app_model: App,
        draft_workflow: Workflow,
        node_id: str,
        user_inputs: Mapping[str, Any],
        account: Account,
        query: str = "",
        files: Sequence[File] | None = None,
    ) -> WorkflowNodeExecutionModel:
        """
        Run draft workflow node
        """
        files = files or []
        node_config = draft_workflow.get_node_config_by_id(node_id)
        node_type = Workflow.get_node_type_from_node_config(node_config)
        node_data = node_config["data"]
        is_agent_preview = is_dify_agent_node_data(node_data)
        if is_agent_preview and (
            draft_workflow.tenant_id != app_model.tenant_id or draft_workflow.app_id != app_model.id
        ):
            raise ValueError("Workflow does not belong to the application")

        with Session(bind=db.engine, expire_on_commit=False) as session, session.begin():
            draft_var_srv = WorkflowDraftVariableService(session)
            draft_var_srv.prefill_conversation_variable_default_values(draft_workflow, user_id=account.id)

        if is_start_node_type(node_type):
            with Session(bind=db.engine) as session, session.begin():
                draft_var_srv = WorkflowDraftVariableService(session)
                conversation_id = draft_var_srv.get_or_create_conversation(
                    account_id=account.id,
                    app=app_model,
                    workflow=draft_workflow,
                )
                if node_type == BuiltinNodeTypes.START:
                    start_data = StartNodeData.model_validate(node_data, from_attributes=True)
                    user_inputs = _rebuild_file_for_user_inputs_in_start_node(
                        tenant_id=draft_workflow.tenant_id, start_node_data=start_data, user_inputs=user_inputs
                    )
                # init variable pool
                variable_pool = _setup_variable_pool(
                    query=query,
                    files=files or [],
                    user_id=account.id,
                    user_inputs=user_inputs,
                    workflow=draft_workflow,
                    node_id=node_id,
                    # NOTE(QuantumGhost): We rely on `DraftVarLoader` to load conversation variables.
                    conversation_variables=[],
                    node_type=node_type,
                    conversation_id=conversation_id,
                )

        else:
            variable_pool = VariablePool()
            add_variables_to_pool(
                variable_pool,
                build_bootstrap_variables(
                    system_variables=build_system_variables(),
                    environment_variables=draft_workflow.environment_variables,
                ),
            )

        variable_loader = DraftVarLoader(
            engine=db.engine,
            app_id=app_model.id,
            tenant_id=app_model.tenant_id,
            user_id=account.id,
        )

        enclosing_node_type_and_id = draft_workflow.get_enclosing_node_type_and_id(node_config)
        if enclosing_node_type_and_id:
            _, enclosing_node_id = enclosing_node_type_and_id
        else:
            enclosing_node_id = None

        run = WorkflowEntry.single_step_run(
            execution_driver=WorkflowRunAgg.run,
            workflow=draft_workflow,
            node_id=node_id,
            user_inputs=user_inputs,
            user_id=account.id,
            variable_pool=variable_pool,
            variable_loader=variable_loader,
        )

        repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
            session_factory=db.engine,
            tenant_id=app_model.tenant_id,
            user=account,
            app_id=app_model.id,
            triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP,
        )
        node, node_events = run
        if is_agent_preview:
            # Standalone previews have no WorkflowRun. The general debug bootstrap
            # inserts a synthetic run ID; do not use it as this caller's owner.
            variable_pool.remove(system_variable_selector(SystemVariableKey.WORKFLOW_EXECUTION_ID))

        started_event: NodeRunStartedEvent | None = None
        started_execution: WorkflowNodeExecution | None = None

        def persist_node_start() -> Generator[NodeEvent | ContainerAwaitRequest]:
            nonlocal started_event, started_execution
            for event in node_events:
                if isinstance(event, NodeRunStartedEvent) and event.node_id == node_id:
                    started_event = event
                    # Agent workspace allocation requires this caller row before the node body runs.
                    started_execution = WorkflowNodeExecution(
                        id=event.id,
                        node_execution_id=event.id,
                        workflow_id=draft_workflow.id,
                        index=1,
                        predecessor_node_id=event.predecessor_node_id,
                        node_id=event.node_id,
                        node_type=event.node_type,
                        title=event.node_title,
                        process_data=event.node_run_result.process_data or None,
                        metadata=event.node_run_result.metadata,
                        created_at=event.start_at,
                    )
                    repository.save_synchronously(started_execution)
                yield event

        start_at = time.perf_counter()
        try:
            node_execution = self._handle_single_step_result(
                invoke_node_fn=lambda: (node, persist_node_start()),
                start_at=start_at,
                node_id=node_id,
            )
        except BaseException:
            if is_agent_preview and started_execution is not None:
                started_execution.status = WorkflowNodeExecutionStatus.FAILED
                started_execution.error = "Agent preview execution interrupted"
                started_execution.elapsed_time = time.perf_counter() - start_at
                started_execution.finished_at = naive_utc_now()
                try:
                    repository.save_synchronously(started_execution)
                    # Keep the terminal append for LogStore and other async repositories.
                    repository.save(started_execution)
                except Exception as persistence_error:
                    logging.getLogger(__name__).warning(
                        "Agent preview terminal persistence failed (%s)", type(persistence_error).__name__
                    )
            raise

        node_execution.workflow_id = draft_workflow.id
        if started_event is not None:
            node_execution.id = started_event.id
            node_execution.node_execution_id = started_event.id
            node_execution.created_at = started_event.start_at
            node_execution.predecessor_node_id = started_event.predecessor_node_id
        repository.save_synchronously(node_execution)
        # LogStore needs its terminal append in addition to the synchronous SQL caller row.
        repository.save(node_execution)

        workflow_node_execution = self._node_execution_service_repo.get_execution_by_id(node_execution.id)
        if workflow_node_execution is None:
            raise ValueError(f"WorkflowNodeExecution with id {node_execution.id} not found after saving")

        with sessionmaker(db.engine).begin() as session:
            outputs = workflow_node_execution.load_full_outputs(session, storage)

        with sessionmaker(bind=db.engine).begin() as session:
            draft_var_saver = DraftVariableSaver(
                session=session,
                tenant_id=app_model.tenant_id,
                app_id=app_model.id,
                node_id=workflow_node_execution.node_id,
                node_type=workflow_node_execution.node_type,
                enclosing_node_id=enclosing_node_id,
                node_execution_id=node_execution.id,
                user=account,
            )
            draft_var_saver.save(process_data=node_execution.process_data, outputs=outputs)

        enqueue_draft_node_execution_trace(
            execution=workflow_node_execution,
            outputs=outputs,
            workflow_execution_id=None,
            user_id=account.id,
        )

        return workflow_node_execution

    def get_human_input_form_preview(
        self,
        *,
        app_model: App,
        account: Account,
        node_id: str,
        inputs: Mapping[str, Any] | None = None,
        session: Session,
    ) -> Mapping[str, Any]:
        """
        Build a human input form preview for a draft workflow.

        Args:
            app_model: Target application model.
            account: Current account.
            node_id: Human input node ID.
            inputs: Values used to fill missing upstream variables referenced in form_content.
        """
        draft_workflow = self.get_draft_workflow(app_model=app_model, session=session)
        if not draft_workflow:
            raise ValueError("Workflow not initialized")

        node_config = draft_workflow.get_node_config_by_id(node_id)
        node_type = Workflow.get_node_type_from_node_config(node_config)
        if node_type != BuiltinNodeTypes.HUMAN_INPUT:
            raise ValueError("Node type must be human-input.")

        # inputs: values used to fill missing upstream variables referenced in form_content.
        variable_pool = self._build_human_input_variable_pool(
            app_model=app_model,
            workflow=draft_workflow,
            node_config=node_config,
            manual_inputs=inputs or {},
            user_id=account.id,
        )
        node = self._build_human_input_node_for_debugging(
            workflow=draft_workflow,
            account=account,
            node_config=node_config,
            variable_pool=variable_pool,
        )

        rendered_content = node.render_form_content_before_submission()
        resolved_default_values = node.resolve_default_values()
        node_data = node.node_data
        human_input_required = HumanInputRequired(
            form_id=node_id,
            form_content=rendered_content,
            inputs=node_data.inputs,
            actions=node_data.user_actions,
            node_id=node_id,
            node_title=node.title,
            resolved_default_values=resolved_default_values,
        )
        return human_input_required.model_dump(mode="json")

    def submit_human_input_form_preview(
        self,
        *,
        app_model: App,
        account: Account,
        node_id: str,
        form_inputs: Mapping[str, Any],
        inputs: Mapping[str, Any] | None = None,
        action: str,
        session: Session,
    ) -> Mapping[str, Any]:
        """
        Submit a human input form preview for a draft workflow.

        Args:
            app_model: Target application model.
            account: Current account.
            node_id: Human input node ID.
            form_inputs: Values the user provides for the form's own fields.
            inputs: Values used to fill missing upstream variables referenced in form_content.
            action: Selected action ID.
        """
        draft_workflow = self.get_draft_workflow(app_model=app_model, session=session)
        if not draft_workflow:
            raise ValueError("Workflow not initialized")

        node_config = draft_workflow.get_node_config_by_id(node_id)
        node_type = Workflow.get_node_type_from_node_config(node_config)
        if node_type != BuiltinNodeTypes.HUMAN_INPUT:
            raise ValueError("Node type must be human-input.")

        # inputs: values used to fill missing upstream variables referenced in form_content.
        # form_inputs: values the user provides for the form's own fields.
        variable_pool = self._build_human_input_variable_pool(
            app_model=app_model,
            workflow=draft_workflow,
            node_config=node_config,
            manual_inputs=inputs or {},
            user_id=account.id,
        )
        node = self._build_human_input_node_for_debugging(
            workflow=draft_workflow,
            account=account,
            node_config=node_config,
            variable_pool=variable_pool,
        )
        node_data = node.node_data

        human_input_service = HumanInputService(session_factory=sessionmaker(db.engine))
        normalized_form_inputs = human_input_service.validate_and_normalize_submission(
            tenant_id=app_model.tenant_id,
            form_definition=node_data,
            selected_action_id=action,
            form_data=form_inputs,
        )

        rendered_content = node.render_form_content_before_submission()
        selected_action = next(
            (user_action for user_action in node_data.user_actions if user_action.id == action),
            None,
        )
        outputs: dict[str, Any] = dict(normalized_form_inputs)
        outputs["__action_id"] = action
        outputs["__action_value"] = selected_action.title if selected_action else ""
        outputs["__rendered_content"] = node.render_form_content_with_outputs(
            rendered_content,
            outputs,
            node_data.outputs_field_names(),
            node_data.inputs,
        )

        enclosing_node_type_and_id = draft_workflow.get_enclosing_node_type_and_id(node_config)
        enclosing_node_id = enclosing_node_type_and_id[1] if enclosing_node_type_and_id else None
        with sessionmaker(bind=db.engine).begin() as session:
            draft_var_saver = DraftVariableSaver(
                session=session,
                tenant_id=app_model.tenant_id,
                app_id=app_model.id,
                node_id=node_id,
                node_type=BuiltinNodeTypes.HUMAN_INPUT,
                node_execution_id=str(uuid.uuid4()),
                user=account,
                enclosing_node_id=enclosing_node_id,
            )
            draft_var_saver.save(outputs=outputs, process_data={})

        return outputs

    def test_human_input_delivery(
        self,
        *,
        app_model: App,
        account: Account,
        node_id: str,
        delivery_method_id: str,
        inputs: Mapping[str, Any] | None = None,
        session: Session,
    ) -> None:
        draft_workflow = self.get_draft_workflow(app_model=app_model, session=session)
        if not draft_workflow:
            raise ValueError("Workflow not initialized")

        node_config = draft_workflow.get_node_config_by_id(node_id)
        node_type = Workflow.get_node_type_from_node_config(node_config)
        if node_type != BuiltinNodeTypes.HUMAN_INPUT:
            raise ValueError("Node type must be human-input.")

        node_data = HumanInputNodeData.model_validate(
            adapt_human_input_node_data_for_graph(node_config["data"]),
            from_attributes=True,
        )
        delivery_method = self._resolve_human_input_delivery_method(
            node_data=node_data,
            delivery_method_id=delivery_method_id,
        )
        if delivery_method is None:
            raise ValueError("Delivery method not found.")
        delivery_method = apply_dify_debug_email_recipient(
            delivery_method,
            enabled=True,
            actor_id=account.id,
        )

        variable_pool = self._build_human_input_variable_pool(
            app_model=app_model,
            workflow=draft_workflow,
            node_config=node_config,
            manual_inputs=inputs or {},
            user_id=account.id,
        )
        node = self._build_human_input_node_for_debugging(
            workflow=draft_workflow,
            account=account,
            node_config=node_config,
            variable_pool=variable_pool,
        )
        rendered_content = node.render_form_content_before_submission()
        resolved_default_values = node.resolve_default_values()
        form_id, recipients = self._create_human_input_delivery_test_form(
            app_model=app_model,
            node_id=node_id,
            node_data=node_data,
            delivery_method=delivery_method,
            rendered_content=rendered_content,
            resolved_default_values=resolved_default_values,
        )
        test_service = HumanInputDeliveryTestService()
        context = DeliveryTestContext(
            tenant_id=app_model.tenant_id,
            app_id=app_model.id,
            node_id=node_id,
            node_title=node_data.title,
            rendered_content=rendered_content,
            template_vars={"form_id": form_id},
            recipients=recipients,
            variable_pool=variable_pool,
        )
        try:
            test_service.send_test(context=context, method=delivery_method)
        except DeliveryTestUnsupportedError as exc:
            raise ValueError("Delivery method does not support test send.") from exc
        except DeliveryTestError as exc:
            raise ValueError(str(exc)) from exc

    @staticmethod
    def _resolve_human_input_delivery_method(
        *,
        node_data: HumanInputNodeData,
        delivery_method_id: str,
    ) -> DeliveryChannelConfig | None:
        for method in parse_human_input_delivery_methods(node_data):
            if str(method.id) == delivery_method_id:
                return method
        return None

    def _create_human_input_delivery_test_form(
        self,
        *,
        app_model: App,
        node_id: str,
        node_data: HumanInputNodeData,
        delivery_method: DeliveryChannelConfig,
        rendered_content: str,
        resolved_default_values: Mapping[str, Any],
    ) -> tuple[str, list[DeliveryTestEmailRecipient]]:
        repo = HumanInputFormRepositoryImpl(tenant_id=app_model.tenant_id, app_id=app_model.id)
        params = FormCreateParams(
            workflow_execution_id=None,
            node_id=node_id,
            form_config=node_data,
            rendered_content=rendered_content,
            delivery_methods=[delivery_method],
            display_in_ui=False,
            resolved_default_values=resolved_default_values,
            form_kind=HumanInputFormKind.DELIVERY_TEST,
        )
        form_entity = repo.create_form(params)
        return form_entity.id, self._load_email_recipients(form_entity.id)

    @staticmethod
    def _load_email_recipients(form_id: str) -> list[DeliveryTestEmailRecipient]:
        logger = logging.getLogger(__name__)

        with Session(bind=db.engine) as session:
            recipients = session.scalars(
                select(HumanInputFormRecipient).where(HumanInputFormRecipient.form_id == form_id)
            ).all()
        recipients_data: list[DeliveryTestEmailRecipient] = []
        for recipient in recipients:
            if recipient.recipient_type not in {RecipientType.EMAIL_MEMBER, RecipientType.EMAIL_EXTERNAL}:
                continue
            if not recipient.access_token:
                continue
            try:
                payload = json.loads(recipient.recipient_payload)
            except (json.JSONDecodeError, ValueError):
                logger.exception("Failed to parse human input recipient payload for delivery test.")
                continue
            email = payload.get("email")
            if email:
                recipients_data.append(DeliveryTestEmailRecipient(email=email, form_token=recipient.access_token))
        return recipients_data

    def _build_human_input_node_for_debugging(
        self,
        *,
        workflow: Workflow,
        account: Account,
        node_config: NodeConfigDict,
        variable_pool: VariablePool,
    ) -> _DebugHumanInputNode:
        _ = workflow, account
        node_data = HumanInputNodeData.model_validate(adapt_human_input_node_data_for_graph(node_config["data"]))
        return HumanInputNode(
            node_id=node_config["id"],
            title=node_data.title,
            node_data=node_data,
            variable_pool=variable_pool,
        )

    def _build_human_input_variable_pool(
        self,
        *,
        app_model: App,
        workflow: Workflow,
        node_config: NodeConfigDict,
        manual_inputs: Mapping[str, Any],
        user_id: str,
    ) -> VariablePool:
        with Session(bind=db.engine, expire_on_commit=False) as session, session.begin():
            draft_var_srv = WorkflowDraftVariableService(session)
            draft_var_srv.prefill_conversation_variable_default_values(workflow, user_id=user_id)

        variable_pool = VariablePool()
        add_variables_to_pool(
            variable_pool,
            build_bootstrap_variables(
                system_variables=default_system_variables(),
                environment_variables=workflow.environment_variables,
            ),
        )

        variable_loader = DraftVarLoader(
            engine=db.engine,
            app_id=app_model.id,
            tenant_id=app_model.tenant_id,
            user_id=user_id,
        )
        human_input_node_data = HumanInputNodeData.model_validate(
            adapt_human_input_node_data_for_graph(node_config["data"])
        )
        variable_mapping = get_human_input_form_variable_mapping(node_config["id"], human_input_node_data)
        normalized_user_inputs: dict[str, Any] = dict(manual_inputs)

        load_into_variable_pool(
            variable_loader=variable_loader,
            variable_pool=variable_pool,
            variable_mapping=variable_mapping,
            user_inputs=normalized_user_inputs,
        )
        WorkflowEntry.mapping_user_inputs_to_variable_pool(
            variable_mapping=variable_mapping,
            user_inputs=normalized_user_inputs,
            variable_pool=variable_pool,
            tenant_id=app_model.tenant_id,
        )

        return variable_pool

    def run_free_workflow_node(
        self, node_data: dict[str, Any], tenant_id: str, user_id: str, node_id: str, user_inputs: dict[str, Any]
    ) -> WorkflowNodeExecution:
        """
        Run free workflow node
        """
        # run free workflow node
        start_at = time.perf_counter()

        node_execution = self._handle_single_step_result(
            invoke_node_fn=lambda: WorkflowEntry.run_free_node(
                node_id=node_id,
                node_data=node_data,
                tenant_id=tenant_id,
                user_id=user_id,
                user_inputs=user_inputs,
            ),
            start_at=start_at,
            node_id=node_id,
        )

        return node_execution

    def _handle_single_step_result(
        self,
        invoke_node_fn: Callable[
            [],
            tuple[Node, Generator[NodeEvent | ContainerAwaitRequest]],
        ],
        start_at: float,
        node_id: str,
    ) -> WorkflowNodeExecution:
        """
        Handle single step execution and return WorkflowNodeExecution.

        Args:
            invoke_node_fn: Function to invoke node execution
            start_at: Execution start time
            node_id: ID of the node being executed

        Returns:
            WorkflowNodeExecution: The execution result
        """
        node, node_run_result, run_succeeded, error = self._execute_node_safely(invoke_node_fn)

        # Create base node execution
        node_execution = WorkflowNodeExecution(
            id=str(uuid.uuid4()),
            workflow_id="",  # Single-step execution has no workflow ID
            index=1,
            node_id=node_id,
            node_type=node.node_type,
            title=node.title,
            elapsed_time=time.perf_counter() - start_at,
            created_at=naive_utc_now(),
            finished_at=naive_utc_now(),
        )
        # Populate execution result data
        self._populate_execution_result(node_execution, node_run_result, run_succeeded, error)

        return node_execution

    def _execute_node_safely(
        self,
        invoke_node_fn: Callable[
            [],
            tuple[Node, Generator[NodeEvent | ContainerAwaitRequest]],
        ],
    ) -> tuple[Node, NodeRunResult | None, bool, str | None]:
        """
        Execute node safely and handle errors according to error strategy.

        Returns:
            Tuple of (node, node_run_result, run_succeeded, error)
        """
        try:
            node, node_events = invoke_node_fn()
            node_run_result = next(
                (
                    event.node_run_result
                    for event in node_events
                    if isinstance(event, (NodeRunSucceededEvent, NodeRunFailedEvent))
                ),
                None,
            )

            if not node_run_result:
                raise ValueError("Node execution failed - no result returned")

            # Apply error strategy if node failed
            if node_run_result.status == WorkflowNodeExecutionStatus.FAILED and node.error_strategy:
                node_run_result = self._apply_error_strategy(node, node_run_result)

            run_succeeded = node_run_result.status in (
                WorkflowNodeExecutionStatus.SUCCEEDED,
                WorkflowNodeExecutionStatus.EXCEPTION,
            )
            error = node_run_result.error if not run_succeeded else None
            return node, node_run_result, run_succeeded, error
        except WorkflowNodeRunFailedError as e:
            node = e.node
            run_succeeded = False
            node_run_result = None
            error = e.error
            return node, node_run_result, run_succeeded, error

    def _apply_error_strategy(self, node: Node, node_run_result: NodeRunResult) -> NodeRunResult:
        """Apply error strategy when node execution fails."""
        # TODO(Novice): Maybe we should apply error strategy to node level?
        error_outputs = {
            "error_message": node_run_result.error,
            "error_type": node_run_result.error_type,
        }

        # Add default values if strategy is DEFAULT_VALUE
        if node.error_strategy is ErrorStrategy.DEFAULT_VALUE:
            error_outputs.update(node.default_value_dict)

        return NodeRunResult(
            status=WorkflowNodeExecutionStatus.EXCEPTION,
            error=node_run_result.error,
            inputs=node_run_result.inputs,
            metadata={WorkflowNodeExecutionMetadataKey.ERROR_STRATEGY: node.error_strategy},
            outputs=error_outputs,
        )

    def _populate_execution_result(
        self,
        node_execution: WorkflowNodeExecution,
        node_run_result: NodeRunResult | None,
        run_succeeded: bool,
        error: str | None,
    ) -> None:
        """Populate node execution with result data."""
        if run_succeeded and node_run_result:
            node_execution.inputs = (
                WorkflowEntry.handle_special_values(node_run_result.inputs) if node_run_result.inputs else None
            )
            node_execution.process_data = (
                WorkflowEntry.handle_special_values(node_run_result.process_data)
                if node_run_result.process_data
                else None
            )
            node_execution.outputs = node_run_result.outputs
            node_execution.metadata = node_run_result.metadata

            # Set status and error based on result
            node_execution.status = node_run_result.status
            if node_run_result.status == WorkflowNodeExecutionStatus.EXCEPTION:
                node_execution.error = node_run_result.error
        else:
            node_execution.status = WorkflowNodeExecutionStatus.FAILED
            node_execution.error = error

    def convert_to_workflow(self, app_model: App, account: Account, args: dict[str, Any], *, session: Session) -> App:
        """
        Basic mode of chatbot app(expert mode) to workflow
        Completion App to Workflow App

        :param app_model: App instance
        :param account: Account instance
        :param args: dict
        :return:
        """
        # chatbot convert to workflow mode
        workflow_converter = WorkflowConverter()

        if app_model.mode not in {AppMode.CHAT, AppMode.COMPLETION}:
            raise ValueError(f"Current App mode: {app_model.mode} is not supported convert to workflow.")

        source_app_id = app_model.id
        # convert to workflow
        new_app: App = workflow_converter.convert_to_workflow(
            app_model=app_model,
            account=account,
            name=args.get("name", "Default Name"),
            icon_type=args.get("icon_type", "emoji"),
            icon=args.get("icon", "🤖"),
            icon_background=args.get("icon_background", "#FFEAD5"),
            session=session,
        )

        if SystemFeatureService.is_webapp_auth_enabled():
            # The converter committed and expired the App. Read its identity without
            # opening another database transaction before calling Enterprise.
            identity = inspect(new_app).identity
            assert identity is not None
            EnterpriseService.WebAppAuth.inherit_app_access_mode(source_app_id, str(identity[0]))

        return new_app

    def validate_graph_structure(self, graph: Mapping[str, Any]):
        """
        Validate workflow graph structure.

        This performs a lightweight validation on the graph, checking for structural
        inconsistencies such as the coexistence of start and trigger nodes.
        """
        node_configs = graph.get("nodes", [])
        node_configs = cast(list[dict[str, Any]], node_configs)

        # is empty graph
        if not node_configs:
            return

        node_types: set[NodeType] = set()
        for node in node_configs:
            node_type = node.get("data", {}).get("type")
            if node_type:
                node_types.add(node_type)

        # start node and trigger node cannot coexist
        if BuiltinNodeTypes.START in node_types:
            if any(is_trigger_node_type(nt) for nt in node_types):
                raise ValueError("Start node and trigger nodes cannot coexist in the same workflow")

        for node in node_configs:
            node_data = node.get("data", {})
            node_type = node_data.get("type")

            if node_type == BuiltinNodeTypes.HUMAN_INPUT:
                self._validate_human_input_node_data(node_data)

    def validate_features_structure(self, app_model: App, features: dict[str, Any]):
        match app_model.mode:
            case AppMode.ADVANCED_CHAT:
                return AdvancedChatAppConfigManager.config_validate(
                    tenant_id=app_model.tenant_id, config=features, only_structure_validate=True
                )
            case AppMode.WORKFLOW:
                return WorkflowAppConfigManager.config_validate(
                    tenant_id=app_model.tenant_id, config=features, only_structure_validate=True
                )
            case _:
                raise ValueError(f"Invalid app mode: {app_model.mode}")

    def _validate_human_input_node_data(self, node_data: dict[str, Any]) -> None:
        """
        Validate HumanInput node data format.

        Args:
            node_data: The node data dictionary

        Raises:
            ValueError: If the node data format is invalid
        """
        from core.workflow.nodes.human_input.entities import HumanInputNodeData

        try:
            HumanInputNodeData.model_validate(adapt_human_input_node_data_for_graph(node_data))
        except Exception as e:
            raise ValueError(f"Invalid HumanInput node data: {str(e)}") from e

    def update_workflow(
        self,
        *,
        session: Session,
        account_id: str,
        data: dict[str, Any],
        workflow_ref: WorkflowRef,
    ) -> Workflow | None:
        """
        Update workflow attributes

        :param session: SQLAlchemy database session
        :param account_id: Account ID (for permission check)
        :param data: Dictionary containing fields to update
        :param workflow_ref: Owner-bound workflow reference
        :return: Updated workflow or None if not found
        """
        stmt = select(Workflow).where(
            Workflow.id == workflow_ref.workflow_id,
            Workflow.tenant_id == workflow_ref.tenant_id,
            Workflow.app_id == workflow_ref.owner_id,
        )
        workflow = session.scalar(stmt)

        if not workflow:
            return None

        allowed_fields = ["marked_name", "marked_comment"]

        for field, value in data.items():
            if field in allowed_fields:
                setattr(workflow, field, value)

        workflow.updated_by = account_id
        workflow.updated_at = naive_utc_now()

        return workflow

    def delete_workflow(self, *, session: Session, workflow_ref: WorkflowRef) -> list[str]:
        """Stage a published Workflow and its binding owners for deletion.

        The exact owner key is tenant, App, Workflow, and Workflow version. The
        Workflow row lock serializes source-version reads and restoration with
        deletion. The caller must commit successfully before retiring the
        returned, sorted and deduplicated inline Agent candidates.

        :param session: SQLAlchemy database session
        :param workflow_ref: Owner-bound workflow reference
        :return: Inline Agent IDs whose owner binding is staged for deletion
        :raises: ValueError if workflow not found
        :raises: WorkflowInUseError if workflow is in use
        :raises: DraftWorkflowDeletionError if workflow is a draft version
        """
        stmt = (
            select(Workflow)
            .where(
                Workflow.id == workflow_ref.workflow_id,
                Workflow.tenant_id == workflow_ref.tenant_id,
                Workflow.app_id == workflow_ref.owner_id,
            )
            .with_for_update()
        )
        workflow = session.scalar(stmt)

        if not workflow:
            raise ValueError(f"Workflow with ID {workflow_ref.workflow_id} not found")

        # Check if workflow is a draft version
        if workflow.version == Workflow.VERSION_DRAFT:
            raise DraftWorkflowDeletionError("Cannot delete draft workflow versions")

        # Check if this workflow is currently referenced by an app
        app_stmt = select(App).where(App.workflow_id == workflow_ref.workflow_id)
        app = session.scalar(app_stmt)
        if app:
            # Cannot delete a workflow that's currently in use by an app
            raise WorkflowInUseError(f"Cannot delete workflow that is currently in use by app '{app.id}'")

        # Don't use workflow.tool_published as it's not accurate for specific workflow versions
        # Check if there's a tool provider using this specific workflow version
        tool_provider = session.scalar(
            select(WorkflowToolProvider).where(
                WorkflowToolProvider.tenant_id == workflow.tenant_id,
                WorkflowToolProvider.app_id == workflow.app_id,
                WorkflowToolProvider.version == workflow.version,
            )
        )

        if tool_provider:
            # Cannot delete a workflow that's published as a tool
            raise WorkflowInUseError("Cannot delete workflow that is published as a tool")

        bindings = session.scalars(
            select(WorkflowAgentNodeBinding).where(
                WorkflowAgentNodeBinding.tenant_id == workflow.tenant_id,
                WorkflowAgentNodeBinding.app_id == workflow.app_id,
                WorkflowAgentNodeBinding.workflow_id == workflow.id,
                WorkflowAgentNodeBinding.workflow_version == workflow.version,
            )
        ).all()
        retirement_candidates = sorted(
            {
                binding.agent_id
                for binding in bindings
                if binding.binding_type == WorkflowAgentBindingType.INLINE_AGENT and binding.agent_id
            }
        )
        for binding in bindings:
            session.delete(binding)
        session.delete(workflow)
        return retirement_candidates


def _setup_variable_pool(
    query: str,
    files: Sequence[File],
    user_id: str,
    user_inputs: Mapping[str, Any],
    workflow: Workflow,
    node_id: str,
    node_type: NodeType,
    conversation_id: str,
    conversation_variables: list[VariableBase],
):
    # Only inject system variables for START node type.
    if is_start_node_type(node_type):
        system_variable_values: dict[str, Any] = {
            "user_id": user_id,
            "app_id": workflow.app_id,
            "timestamp": to_utc_timestamp(naive_utc_now()),
            "workflow_id": workflow.id,
            "files": files or [],
            "workflow_execution_id": str(uuid.uuid4()),
        }

        # Only add chatflow-specific variables for non-workflow types.
        if workflow.type != WorkflowType.WORKFLOW:
            system_variable_values.update(
                {
                    "query": query,
                    "conversation_id": conversation_id,
                    "dialogue_count": 1,
                }
            )

        system_variable = build_system_variables(system_variable_values)
    else:
        system_variable = default_system_variables()

    # init variable pool
    variable_pool = VariablePool()
    add_variables_to_pool(
        variable_pool,
        build_bootstrap_variables(
            system_variables=system_variable,
            environment_variables=workflow.environment_variables,
            conversation_variables=cast(list[Variable], conversation_variables),
        ),
    )
    if is_start_node_type(node_type):
        add_node_inputs_to_pool(variable_pool, node_id=node_id, inputs=user_inputs)

    return variable_pool


def _rebuild_file_for_user_inputs_in_start_node(
    tenant_id: str, start_node_data: StartNodeData, user_inputs: Mapping[str, Any]
) -> Mapping[str, Any]:
    inputs_copy = dict(user_inputs)

    for variable in start_node_data.variables:
        if variable.type not in (VariableEntityType.FILE, VariableEntityType.FILE_LIST):
            continue
        if variable.variable not in user_inputs:
            continue
        value = user_inputs[variable.variable]
        file = _rebuild_single_file(tenant_id=tenant_id, value=value, variable_entity_type=variable.type)
        inputs_copy[variable.variable] = file
    return inputs_copy


def _rebuild_single_file(tenant_id: str, value: Any, variable_entity_type: VariableEntityType) -> File | Sequence[File]:
    if variable_entity_type == VariableEntityType.FILE:
        if not isinstance(value, dict):
            raise ValueError(f"expected dict for file object, got {type(value)}")
        return build_from_mapping(mapping=value, tenant_id=tenant_id, access_controller=_file_access_controller)
    elif variable_entity_type == VariableEntityType.FILE_LIST:
        if not isinstance(value, list):
            raise ValueError(f"expected list for file list object, got {type(value)}")
        if len(value) == 0:
            return []
        if not isinstance(value[0], dict):
            raise ValueError(f"expected dict for first element in the file list, got {type(value)}")
        return build_from_mappings(mappings=value, tenant_id=tenant_id, access_controller=_file_access_controller)
    else:
        raise Exception("unreachable")