import json
import logging
from collections.abc import Generator, Mapping, Sequence
from datetime import datetime
from enum import StrEnum
from typing import TYPE_CHECKING, Any, Optional, Union, cast
from uuid import uuid4

import sqlalchemy as sa
from sqlalchemy import (
    DateTime,
    Index,
    PrimaryKeyConstraint,
    Select,
    String,
    UniqueConstraint,
    exists,
    func,
    orm,
    select,
)
from sqlalchemy.orm import Mapped, declared_attr, mapped_column
from typing_extensions import deprecated

from dify_graph.constants import (
    CONVERSATION_VARIABLE_NODE_ID,
    SYSTEM_VARIABLE_NODE_ID,
)
from dify_graph.entities.graph_config import NodeConfigDict, NodeConfigDictAdapter
from dify_graph.entities.pause_reason import HumanInputRequired, PauseReason, PauseReasonType, SchedulingPause
from dify_graph.enums import NodeType, WorkflowExecutionStatus
from dify_graph.file.constants import maybe_file_object
from dify_graph.file.models import File
from dify_graph.variables import utils as variable_utils
from dify_graph.variables.variables import FloatVariable, IntegerVariable, StringVariable
from extensions.ext_storage import Storage
from factories.variable_factory import TypeMismatchError, build_segment_with_type
from libs.datetime_utils import naive_utc_now
from libs.uuid_utils import uuidv7

from ._workflow_exc import NodeNotFoundError, WorkflowDataError

if TYPE_CHECKING:
    from .model import AppMode, UploadFile


from constants import DEFAULT_FILE_NUMBER_LIMITS, HIDDEN_VALUE
from core.helper import encrypter
from dify_graph.variables import SecretVariable, Segment, SegmentType, VariableBase
from factories import variable_factory
from libs import helper

from .account import Account
from .base import Base, DefaultFieldsMixin, TypeBase
from .engine import db
from .enums import CreatorUserRole, DraftVariableType, ExecutionOffLoadType
from .types import EnumText, LongText, StringUUID

logger = logging.getLogger(__name__)


class WorkflowType(StrEnum):
    """
    Workflow Type Enum
    """

    WORKFLOW = "workflow"
    CHAT = "chat"
    RAG_PIPELINE = "rag-pipeline"

    @classmethod
    def value_of(cls, value: str) -> "WorkflowType":
        """
        Get value of given mode.

        :param value: mode value
        :return: mode
        """
        for mode in cls:
            if mode.value == value:
                return mode
        raise ValueError(f"invalid workflow type value {value}")

    @classmethod
    def from_app_mode(cls, app_mode: Union[str, "AppMode"]) -> "WorkflowType":
        """
        Get workflow type from app mode.

        :param app_mode: app mode
        :return: workflow type
        """
        from .model import AppMode

        app_mode = app_mode if isinstance(app_mode, AppMode) else AppMode.value_of(app_mode)
        return cls.WORKFLOW if app_mode == AppMode.WORKFLOW else cls.CHAT


class _InvalidGraphDefinitionError(Exception):
    pass


class Workflow(Base):  # bug
    """
    Workflow, for `Workflow App` and `Chat App workflow mode`.

    Attributes:

    - id (uuid) Workflow ID, pk
    - tenant_id (uuid) Workspace ID
    - app_id (uuid) App ID
    - type (string) Workflow type

        `workflow` for `Workflow App`

        `chat` for `Chat App workflow mode`

    - version (string) Version

        `draft` for draft version (only one for each app), other for version number (redundant)

    - graph (text) Workflow canvas configuration (JSON)

        The entire canvas configuration JSON, including Node, Edge, and other configurations

        - nodes (array[object]) Node list, see Node Schema

        - edges (array[object]) Edge list, see Edge Schema

    - created_by (uuid) Creator ID
    - created_at (timestamp) Creation time
    - updated_by (uuid) `optional` Last updater ID
    - updated_at (timestamp) `optional` Last update time
    """

    __tablename__ = "workflows"
    __table_args__ = (
        sa.PrimaryKeyConstraint("id", name="workflow_pkey"),
        sa.Index("workflow_version_idx", "tenant_id", "app_id", "version"),
    )

    id: Mapped[str] = mapped_column(StringUUID, default=lambda: str(uuid4()))
    tenant_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    app_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    type: Mapped[str] = mapped_column(String(255), nullable=False)
    version: Mapped[str] = mapped_column(String(255), nullable=False)
    marked_name: Mapped[str] = mapped_column(String(255), default="", server_default="")
    marked_comment: Mapped[str] = mapped_column(String(255), default="", server_default="")
    graph: Mapped[str] = mapped_column(LongText)
    _features: Mapped[str] = mapped_column("features", LongText)
    created_by: Mapped[str] = mapped_column(StringUUID, nullable=False)
    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.current_timestamp())
    updated_by: Mapped[str | None] = mapped_column(StringUUID)
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
        nullable=False,
        default=func.current_timestamp(),
        server_default=func.current_timestamp(),
        onupdate=func.current_timestamp(),
    )
    _environment_variables: Mapped[str] = mapped_column("environment_variables", LongText, nullable=False, default="{}")
    _conversation_variables: Mapped[str] = mapped_column(
        "conversation_variables", LongText, nullable=False, default="{}"
    )
    _rag_pipeline_variables: Mapped[str] = mapped_column(
        "rag_pipeline_variables", LongText, nullable=False, default="{}"
    )

    VERSION_DRAFT = "draft"

    @classmethod
    def new(
        cls,
        *,
        tenant_id: str,
        app_id: str,
        type: str,
        version: str,
        graph: str,
        features: str,
        created_by: str,
        environment_variables: Sequence[VariableBase],
        conversation_variables: Sequence[VariableBase],
        rag_pipeline_variables: list[dict],
        marked_name: str = "",
        marked_comment: str = "",
    ) -> "Workflow":
        workflow = Workflow()
        workflow.id = str(uuid4())
        workflow.tenant_id = tenant_id
        workflow.app_id = app_id
        workflow.type = type
        workflow.version = version
        workflow.graph = graph
        workflow.features = features
        workflow.created_by = created_by
        workflow.environment_variables = environment_variables or []
        workflow.conversation_variables = conversation_variables or []
        workflow.rag_pipeline_variables = rag_pipeline_variables or []
        workflow.marked_name = marked_name
        workflow.marked_comment = marked_comment
        workflow.created_at = naive_utc_now()
        workflow.updated_at = workflow.created_at
        return workflow

    @property
    def created_by_account(self):
        return db.session.get(Account, self.created_by)

    @property
    def updated_by_account(self):
        return db.session.get(Account, self.updated_by) if self.updated_by else None

    @property
    def graph_dict(self) -> Mapping[str, Any]:
        # TODO(QuantumGhost): Consider caching `graph_dict` to avoid repeated JSON decoding.
        #
        # Using `functools.cached_property` could help, but some code in the codebase may
        # modify the returned dict, which can cause issues elsewhere.
        #
        # For example, changing this property to a cached property led to errors like the
        # following when single stepping an `Iteration` node:
        #
        #     Root node id 1748401971780start not found in the graph
        #
        # There is currently no standard way to make a dict deeply immutable in Python,
        # and tracking modifications to the returned dict is difficult. For now, we leave
        # the code as-is to avoid these issues.
        #
        # Currently, the following functions / methods would mutate the returned dict:
        #
        # - `_get_graph_and_variable_pool_for_single_node_run`.
        return json.loads(self.graph) if self.graph else {}

    def get_node_config_by_id(self, node_id: str) -> NodeConfigDict:
        """Extract a node configuration from the workflow graph by node ID.
        A node configuration is a dictionary containing the node's properties, including
        the node's id, title, and its data as a dict.
        """
        workflow_graph = self.graph_dict

        if not workflow_graph:
            raise WorkflowDataError(f"workflow graph not found, workflow_id={self.id}")

        nodes = workflow_graph.get("nodes")
        if not nodes:
            raise WorkflowDataError("nodes not found in workflow graph")

        try:
            node_config: dict[str, Any] = next(filter(lambda node: node["id"] == node_id, nodes))
        except StopIteration:
            raise NodeNotFoundError(node_id)
        return NodeConfigDictAdapter.validate_python(node_config)

    @staticmethod
    def get_node_type_from_node_config(node_config: Mapping[str, Any]) -> NodeType:
        """Extract type of a node from the node configuration returned by `get_node_config_by_id`."""
        node_config_data = node_config.get("data", {})
        # Get node class
        node_type = NodeType(node_config_data.get("type"))
        return node_type

    @staticmethod
    def get_enclosing_node_type_and_id(
        node_config: Mapping[str, Any],
    ) -> tuple[NodeType, str] | None:
        in_loop = node_config.get("isInLoop", False)
        in_iteration = node_config.get("isInIteration", False)
        if in_loop:
            loop_id = node_config.get("loop_id")
            if loop_id is None:
                raise _InvalidGraphDefinitionError("invalid graph")
            return NodeType.LOOP, loop_id
        elif in_iteration:
            iteration_id = node_config.get("iteration_id")
            if iteration_id is None:
                raise _InvalidGraphDefinitionError("invalid graph")
            return NodeType.ITERATION, iteration_id
        else:
            return None

    @property
    def features(self) -> str:
        """
        Convert old features structure to new features structure.
        """
        if not self._features:
            return self._features

        features = json.loads(self._features)
        if features.get("file_upload", {}).get("image", {}).get("enabled", False):
            image_enabled = True
            image_number_limits = int(features["file_upload"]["image"].get("number_limits", DEFAULT_FILE_NUMBER_LIMITS))
            image_transfer_methods = features["file_upload"]["image"].get(
                "transfer_methods", ["remote_url", "local_file"]
            )
            features["file_upload"]["enabled"] = image_enabled
            features["file_upload"]["number_limits"] = image_number_limits
            features["file_upload"]["allowed_file_upload_methods"] = image_transfer_methods
            features["file_upload"]["allowed_file_types"] = features["file_upload"].get("allowed_file_types", ["image"])
            features["file_upload"]["allowed_file_extensions"] = features["file_upload"].get(
                "allowed_file_extensions", []
            )
            del features["file_upload"]["image"]
            self._features = json.dumps(features)
        return self._features

    @features.setter
    def features(self, value: str):
        self._features = value

    @property
    def features_dict(self) -> dict[str, Any]:
        return json.loads(self.features) if self.features else {}

    def walk_nodes(
        self, specific_node_type: NodeType | None = None
    ) -> Generator[tuple[str, Mapping[str, Any]], None, None]:
        """
        Walk through the workflow nodes, yield each node configuration.

        Each node configuration is a tuple containing the node's id and the node's properties.

        Node properties example:
        {
            "type": "llm",
            "title": "LLM",
            "desc": "",
            "variables": [],
            "model":
              {
                "provider": "langgenius/openai/openai",
                "name": "gpt-4",
                "mode": "chat",
                "completion_params": { "temperature": 0.7 },
              },
            "prompt_template": [{ "role": "system", "text": "" }],
            "context": { "enabled": false, "variable_selector": [] },
            "vision": { "enabled": false },
            "memory":
              {
                "window": { "enabled": false, "size": 10 },
                "query_prompt_template": "{{#sys.query#}}\n\n{{#sys.files#}}",
                "role_prefix": { "user": "", "assistant": "" },
              },
            "selected": false,
        }

        For specific node type, refer to `dify_graph.nodes`
        """
        graph_dict = self.graph_dict
        if "nodes" not in graph_dict:
            raise WorkflowDataError("nodes not found in workflow graph")

        if specific_node_type:
            yield from (
                (node["id"], node["data"])
                for node in graph_dict["nodes"]
                if node["data"]["type"] == specific_node_type.value
            )
        else:
            yield from ((node["id"], node["data"]) for node in graph_dict["nodes"])

    def user_input_form(self, to_old_structure: bool = False) -> list[Any]:
        # get start node from graph
        if not self.graph:
            return []

        graph_dict = self.graph_dict
        if "nodes" not in graph_dict:
            return []

        start_node = next(
            (node for node in graph_dict["nodes"] if node["data"]["type"] == "start"),
            None,
        )
        if not start_node:
            return []

        # get user_input_form from start node
        variables: list[Any] = start_node.get("data", {}).get("variables", [])

        if to_old_structure:
            old_structure_variables: list[dict[str, Any]] = []
            for variable in variables:
                old_structure_variables.append({variable["type"]: variable})

            return old_structure_variables

        return variables

    def rag_pipeline_user_input_form(self) -> list:
        # get user_input_form from start node
        variables: list[Any] = self.rag_pipeline_variables

        return variables

    @property
    def unique_hash(self) -> str:
        """
        Get hash of workflow.

        :return: hash
        """
        entity = {"graph": self.graph_dict, "features": self.features_dict}

        return helper.generate_text_hash(json.dumps(entity, sort_keys=True))

    @property
    @deprecated(
        "This property is not accurate for determining if a workflow is published as a tool."
        "It only checks if there's a WorkflowToolProvider for the app, "
        "not if this specific workflow version is the one being used by the tool."
    )
    def tool_published(self) -> bool:
        """
        DEPRECATED: This property is not accurate for determining if a workflow is published as a tool.
        It only checks if there's a WorkflowToolProvider for the app, not if this specific workflow version
        is the one being used by the tool.

        For accurate checking, use a direct query with tenant_id, app_id, and version.
        """
        from .tools import WorkflowToolProvider

        stmt = select(
            exists().where(
                WorkflowToolProvider.tenant_id == self.tenant_id,
                WorkflowToolProvider.app_id == self.app_id,
            )
        )
        return db.session.execute(stmt).scalar_one()

    @property
    def environment_variables(
        self,
    ) -> Sequence[StringVariable | IntegerVariable | FloatVariable | SecretVariable]:
        # TODO: find some way to init `self._environment_variables` when instance created.
        if self._environment_variables is None:
            self._environment_variables = "{}"

        # Use workflow.tenant_id to avoid relying on request user in background threads
        tenant_id = self.tenant_id

        if not tenant_id:
            return []

        environment_variables_dict: dict[str, Any] = json.loads(self._environment_variables or "{}")
        results = [
            variable_factory.build_environment_variable_from_mapping(v) for v in environment_variables_dict.values()
        ]

        # decrypt secret variables value
        def decrypt_func(
            var: VariableBase,
        ) -> StringVariable | IntegerVariable | FloatVariable | SecretVariable:
            if isinstance(var, SecretVariable):
                return var.model_copy(update={"value": encrypter.decrypt_token(tenant_id=tenant_id, token=var.value)})
            elif isinstance(var, (StringVariable, IntegerVariable, FloatVariable)):
                return var
            else:
                # Other variable types are not supported for environment variables
                raise AssertionError(f"Unexpected variable type for environment variable: {type(var)}")

        decrypted_results: list[SecretVariable | StringVariable | IntegerVariable | FloatVariable] = [
            decrypt_func(var) for var in results
        ]
        return decrypted_results

    @environment_variables.setter
    def environment_variables(self, value: Sequence[VariableBase]):
        if not value:
            self._environment_variables = "{}"
            return

        # Use workflow.tenant_id to avoid relying on request user in background threads
        tenant_id = self.tenant_id

        if not tenant_id:
            self._environment_variables = "{}"
            return

        value = list(value)
        if any(var for var in value if not var.id):
            raise ValueError("environment variable require a unique id")

        # Compare inputs and origin variables,
        # if the value is HIDDEN_VALUE, use the origin variable value (only update `name`).
        origin_variables_dictionary = {var.id: var for var in self.environment_variables}
        for i, variable in enumerate(value):
            if variable.id in origin_variables_dictionary and variable.value == HIDDEN_VALUE:
                value[i] = origin_variables_dictionary[variable.id].model_copy(update={"name": variable.name})

        # encrypt secret variables value
        def encrypt_func(var: VariableBase) -> VariableBase:
            if isinstance(var, SecretVariable):
                return var.model_copy(update={"value": encrypter.encrypt_token(tenant_id=tenant_id, token=var.value)})
            else:
                return var

        encrypted_vars = list(map(encrypt_func, value))
        environment_variables_json = json.dumps(
            {var.name: var.model_dump() for var in encrypted_vars},
            ensure_ascii=False,
        )
        self._environment_variables = environment_variables_json

    def to_dict(self, *, include_secret: bool = False) -> Mapping[str, Any]:
        environment_variables = list(self.environment_variables)
        environment_variables = [
            v if not isinstance(v, SecretVariable) or include_secret else v.model_copy(update={"value": ""})
            for v in environment_variables
        ]

        result = {
            "graph": self.graph_dict,
            "features": self.features_dict,
            "environment_variables": [var.model_dump(mode="json") for var in environment_variables],
            "conversation_variables": [var.model_dump(mode="json") for var in self.conversation_variables],
            "rag_pipeline_variables": self.rag_pipeline_variables,
        }
        return result

    @property
    def conversation_variables(self) -> Sequence[VariableBase]:
        # TODO: find some way to init `self._conversation_variables` when instance created.
        if self._conversation_variables is None:
            self._conversation_variables = "{}"

        variables_dict: dict[str, Any] = json.loads(self._conversation_variables)
        results = [variable_factory.build_conversation_variable_from_mapping(v) for v in variables_dict.values()]
        return results

    @conversation_variables.setter
    def conversation_variables(self, value: Sequence[VariableBase]):
        self._conversation_variables = json.dumps(
            {var.name: var.model_dump() for var in value},
            ensure_ascii=False,
        )

    @property
    def rag_pipeline_variables(self) -> list[dict]:
        # TODO: find some way to init `self._conversation_variables` when instance created.
        if self._rag_pipeline_variables is None:
            self._rag_pipeline_variables = "{}"

        variables_dict: dict[str, Any] = json.loads(self._rag_pipeline_variables)
        results = list(variables_dict.values())
        return results

    @rag_pipeline_variables.setter
    def rag_pipeline_variables(self, values: list[dict]) -> None:
        self._rag_pipeline_variables = json.dumps(
            {item["variable"]: item for item in values},
            ensure_ascii=False,
        )

    @staticmethod
    def version_from_datetime(d: datetime) -> str:
        return str(d)


class WorkflowRun(Base):
    """
    Workflow Run

    Attributes:

    - id (uuid) Run ID
    - tenant_id (uuid) Workspace ID
    - app_id (uuid) App ID

    - workflow_id (uuid) Workflow ID
    - type (string) Workflow type
    - triggered_from (string) Trigger source

        `debugging` for canvas debugging

        `app-run` for (published) app execution

    - version (string) Version
    - graph (text) Workflow canvas configuration (JSON)
    - inputs (text) Input parameters
    - status (string) Execution status, `running` / `succeeded` / `failed` / `stopped`
    - outputs (text) `optional` Output content
    - error (string) `optional` Error reason
    - elapsed_time (float) `optional` Time consumption (s)
    - total_tokens (int) `optional` Total tokens used
    - total_steps (int) Total steps (redundant), default 0
    - created_by_role (string) Creator role

        - `account` Console account

        - `end_user` End user

    - created_by (uuid) Runner ID
    - created_at (timestamp) Run time
    - finished_at (timestamp) End time
    """

    __tablename__ = "workflow_runs"
    __table_args__ = (
        sa.PrimaryKeyConstraint("id", name="workflow_run_pkey"),
        sa.Index("workflow_run_triggerd_from_idx", "tenant_id", "app_id", "triggered_from"),
        sa.Index("workflow_run_created_at_id_idx", "created_at", "id"),
    )

    id: Mapped[str] = mapped_column(StringUUID, default=lambda: str(uuid4()))
    tenant_id: Mapped[str] = mapped_column(StringUUID)
    app_id: Mapped[str] = mapped_column(StringUUID)

    workflow_id: Mapped[str] = mapped_column(StringUUID)
    type: Mapped[str] = mapped_column(String(255))
    triggered_from: Mapped[str] = mapped_column(String(255))
    version: Mapped[str] = mapped_column(String(255))
    graph: Mapped[str | None] = mapped_column(LongText)
    inputs: Mapped[str | None] = mapped_column(LongText)
    status: Mapped[WorkflowExecutionStatus] = mapped_column(
        EnumText(WorkflowExecutionStatus, length=255),
        nullable=False,
    )
    outputs: Mapped[str | None] = mapped_column(LongText, default="{}")
    error: Mapped[str | None] = mapped_column(LongText)
    elapsed_time: Mapped[float] = mapped_column(sa.Float, nullable=False, server_default=sa.text("0"))
    total_tokens: Mapped[int] = mapped_column(sa.BigInteger, server_default=sa.text("0"))
    total_steps: Mapped[int] = mapped_column(sa.Integer, server_default=sa.text("0"), nullable=True)
    created_by_role: Mapped[CreatorUserRole] = mapped_column(EnumText(CreatorUserRole, length=255))  # account, end_user
    created_by: Mapped[str] = mapped_column(StringUUID, nullable=False)
    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.current_timestamp())
    finished_at: Mapped[datetime | None] = mapped_column(DateTime)
    exceptions_count: Mapped[int] = mapped_column(sa.Integer, server_default=sa.text("0"), nullable=True)

    pause: Mapped[Optional["WorkflowPause"]] = orm.relationship(
        "WorkflowPause",
        primaryjoin="WorkflowRun.id == foreign(WorkflowPause.workflow_run_id)",
        uselist=False,
        # require explicit preloading.
        lazy="raise",
        back_populates="workflow_run",
    )

    @property
    @deprecated("This method is retained for historical reasons; avoid using it if possible.")
    def created_by_account(self):
        created_by_role = CreatorUserRole(self.created_by_role)
        return db.session.get(Account, self.created_by) if created_by_role == CreatorUserRole.ACCOUNT else None

    @property
    @deprecated("This method is retained for historical reasons; avoid using it if possible.")
    def created_by_end_user(self):
        from .model import EndUser

        created_by_role = CreatorUserRole(self.created_by_role)
        return db.session.get(EndUser, self.created_by) if created_by_role == CreatorUserRole.END_USER else None

    @property
    def graph_dict(self) -> Mapping[str, Any]:
        return json.loads(self.graph) if self.graph else {}

    @property
    def inputs_dict(self) -> Mapping[str, Any]:
        return json.loads(self.inputs) if self.inputs else {}

    @property
    def outputs_dict(self) -> Mapping[str, Any]:
        return json.loads(self.outputs) if self.outputs else {}

    @property
    @deprecated("This method is retained for historical reasons; avoid using it if possible.")
    def message(self):
        from .model import Message

        return (
            db.session.query(Message).where(Message.app_id == self.app_id, Message.workflow_run_id == self.id).first()
        )

    @property
    @deprecated("This method is retained for historical reasons; avoid using it if possible.")
    def workflow(self):
        return db.session.query(Workflow).where(Workflow.id == self.workflow_id).first()

    def to_dict(self):
        return {
            "id": self.id,
            "tenant_id": self.tenant_id,
            "app_id": self.app_id,
            "workflow_id": self.workflow_id,
            "type": self.type,
            "triggered_from": self.triggered_from,
            "version": self.version,
            "graph": self.graph_dict,
            "inputs": self.inputs_dict,
            "status": self.status,
            "outputs": self.outputs_dict,
            "error": self.error,
            "elapsed_time": self.elapsed_time,
            "total_tokens": self.total_tokens,
            "total_steps": self.total_steps,
            "created_by_role": self.created_by_role,
            "created_by": self.created_by,
            "created_at": self.created_at,
            "finished_at": self.finished_at,
            "exceptions_count": self.exceptions_count,
        }

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "WorkflowRun":
        return cls(
            id=data.get("id"),
            tenant_id=data.get("tenant_id"),
            app_id=data.get("app_id"),
            workflow_id=data.get("workflow_id"),
            type=data.get("type"),
            triggered_from=data.get("triggered_from"),
            version=data.get("version"),
            graph=json.dumps(data.get("graph")),
            inputs=json.dumps(data.get("inputs")),
            status=data.get("status"),
            outputs=json.dumps(data.get("outputs")),
            error=data.get("error"),
            elapsed_time=data.get("elapsed_time"),
            total_tokens=data.get("total_tokens"),
            total_steps=data.get("total_steps"),
            created_by_role=data.get("created_by_role"),
            created_by=data.get("created_by"),
            created_at=data.get("created_at"),
            finished_at=data.get("finished_at"),
            exceptions_count=data.get("exceptions_count"),
        )


class WorkflowNodeExecutionTriggeredFrom(StrEnum):
    """
    Workflow Node Execution Triggered From Enum
    """

    SINGLE_STEP = "single-step"
    WORKFLOW_RUN = "workflow-run"
    RAG_PIPELINE_RUN = "rag-pipeline-run"


class WorkflowNodeExecutionModel(Base):  # This model is expected to have `offload_data` preloaded in most cases.
    """
    Workflow Node Execution

    - id (uuid) Execution ID
    - tenant_id (uuid) Workspace ID
    - app_id (uuid) App ID
    - workflow_id (uuid) Workflow ID
    - triggered_from (string) Trigger source

        `single-step` for single-step debugging

        `workflow-run` for workflow execution (debugging / user execution)

    - workflow_run_id (uuid) `optional` Workflow run ID

        Null for single-step debugging.

    - index (int) Execution sequence number, used for displaying Tracing Node order
    - predecessor_node_id (string) `optional` Predecessor node ID, used for displaying execution path
    - node_id (string) Node ID
    - node_type (string) Node type, such as `start`
    - title (string) Node title
    - inputs (json) All predecessor node variable content used in the node
    - process_data (json) Node process data
    - outputs (json) `optional` Node output variables
    - status (string) Execution status, `running` / `succeeded` / `failed`
    - error (string) `optional` Error reason
    - elapsed_time (float) `optional` Time consumption (s)
    - execution_metadata (text) Metadata

        - total_tokens (int) `optional` Total tokens used

        - total_price (decimal) `optional` Total cost

        - currency (string) `optional` Currency, such as USD / RMB

    - created_at (timestamp) Run time
    - created_by_role (string) Creator role

        - `account` Console account

        - `end_user` End user

    - created_by (uuid) Runner ID
    - finished_at (timestamp) End time
    """

    __tablename__ = "workflow_node_executions"

    @declared_attr.directive
    @classmethod
    def __table_args__(cls) -> Any:
        return (
            PrimaryKeyConstraint("id", name="workflow_node_execution_pkey"),
            Index(
                "workflow_node_execution_workflow_run_id_idx",
                "workflow_run_id",
            ),
            Index(
                "workflow_node_execution_node_run_idx",
                "tenant_id",
                "app_id",
                "workflow_id",
                "triggered_from",
                "node_id",
            ),
            Index(
                "workflow_node_execution_id_idx",
                "tenant_id",
                "app_id",
                "workflow_id",
                "triggered_from",
                "node_execution_id",
            ),
            Index(
                # The first argument is the index name,
                # which we leave as `None`` to allow auto-generation by the ORM.
                None,
                cls.tenant_id,
                cls.workflow_id,
                cls.node_id,
                # MyPy may flag the following line because it doesn't recognize that
                # the `declared_attr` decorator passes the receiving class as the first
                # argument to this method, allowing us to reference class attributes.
                cls.created_at.desc(),
            ),
        )

    id: Mapped[str] = mapped_column(StringUUID, default=lambda: str(uuid4()))
    tenant_id: Mapped[str] = mapped_column(StringUUID)
    app_id: Mapped[str] = mapped_column(StringUUID)
    workflow_id: Mapped[str] = mapped_column(StringUUID)
    triggered_from: Mapped[str] = mapped_column(String(255))
    workflow_run_id: Mapped[str | None] = mapped_column(StringUUID)
    index: Mapped[int] = mapped_column(sa.Integer)
    predecessor_node_id: Mapped[str | None] = mapped_column(String(255))
    node_execution_id: Mapped[str | None] = mapped_column(String(255))
    node_id: Mapped[str] = mapped_column(String(255))
    node_type: Mapped[str] = mapped_column(String(255))
    title: Mapped[str] = mapped_column(String(255))
    inputs: Mapped[str | None] = mapped_column(LongText)
    process_data: Mapped[str | None] = mapped_column(LongText)
    outputs: Mapped[str | None] = mapped_column(LongText)
    status: Mapped[str] = mapped_column(String(255))
    error: Mapped[str | None] = mapped_column(LongText)
    elapsed_time: Mapped[float] = mapped_column(sa.Float, server_default=sa.text("0"))
    execution_metadata: Mapped[str | None] = mapped_column(LongText)
    created_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.current_timestamp())
    created_by_role: Mapped[str] = mapped_column(String(255))
    created_by: Mapped[str] = mapped_column(StringUUID)
    finished_at: Mapped[datetime | None] = mapped_column(DateTime)

    offload_data: Mapped[list["WorkflowNodeExecutionOffload"]] = orm.relationship(
        "WorkflowNodeExecutionOffload",
        primaryjoin="WorkflowNodeExecutionModel.id == foreign(WorkflowNodeExecutionOffload.node_execution_id)",
        uselist=True,
        lazy="raise",
        back_populates="execution",
    )

    @staticmethod
    def preload_offload_data(
        query: Select[tuple["WorkflowNodeExecutionModel"]] | orm.Query["WorkflowNodeExecutionModel"],
    ):
        return query.options(orm.selectinload(WorkflowNodeExecutionModel.offload_data))

    @staticmethod
    def preload_offload_data_and_files(
        query: Select[tuple["WorkflowNodeExecutionModel"]] | orm.Query["WorkflowNodeExecutionModel"],
    ):
        return query.options(
            orm.selectinload(WorkflowNodeExecutionModel.offload_data).options(
                # Using `joinedload` instead of `selectinload` to minimize database roundtrips,
                # as `selectinload` would require separate queries for `inputs_file` and `outputs_file`.
                orm.selectinload(WorkflowNodeExecutionOffload.file),
            )
        )

    @property
    def created_by_account(self):
        created_by_role = CreatorUserRole(self.created_by_role)
        if created_by_role == CreatorUserRole.ACCOUNT:
            stmt = select(Account).where(Account.id == self.created_by)
            return db.session.scalar(stmt)
        return None

    @property
    def created_by_end_user(self):
        from .model import EndUser

        created_by_role = CreatorUserRole(self.created_by_role)
        if created_by_role == CreatorUserRole.END_USER:
            stmt = select(EndUser).where(EndUser.id == self.created_by)
            return db.session.scalar(stmt)
        return None

    @property
    def inputs_dict(self):
        return json.loads(self.inputs) if self.inputs else None

    @property
    def outputs_dict(self) -> dict[str, Any] | None:
        return json.loads(self.outputs) if self.outputs else None

    @property
    def process_data_dict(self):
        return json.loads(self.process_data) if self.process_data else None

    @property
    def execution_metadata_dict(self) -> dict[str, Any]:
        # When the metadata is unset, we return an empty dictionary instead of `None`.
        # This approach streamlines the logic for the caller, making it easier to handle
        # cases where metadata is absent.
        return json.loads(self.execution_metadata) if self.execution_metadata else {}

    @property
    def extras(self) -> dict[str, Any]:
        from core.tools.tool_manager import ToolManager
        from core.trigger.trigger_manager import TriggerManager

        extras: dict[str, Any] = {}
        execution_metadata = self.execution_metadata_dict
        if execution_metadata:
            if self.node_type == NodeType.TOOL and "tool_info" in execution_metadata:
                tool_info: dict[str, Any] = execution_metadata["tool_info"]
                extras["icon"] = ToolManager.get_tool_icon(
                    tenant_id=self.tenant_id,
                    provider_type=tool_info["provider_type"],
                    provider_id=tool_info["provider_id"],
                )
            elif self.node_type == NodeType.DATASOURCE and "datasource_info" in execution_metadata:
                datasource_info = execution_metadata["datasource_info"]
                extras["icon"] = datasource_info.get("icon")
            elif self.node_type == NodeType.TRIGGER_PLUGIN and "trigger_info" in execution_metadata:
                trigger_info = execution_metadata["trigger_info"] or {}
                provider_id = trigger_info.get("provider_id")
                if provider_id:
                    extras["icon"] = TriggerManager.get_trigger_plugin_icon(
                        tenant_id=self.tenant_id,
                        provider_id=provider_id,
                    )
        return extras

    def _get_offload_by_type(self, type_: ExecutionOffLoadType) -> Optional["WorkflowNodeExecutionOffload"]:
        return next(iter([i for i in self.offload_data if i.type_ == type_]), None)

    @property
    def inputs_truncated(self) -> bool:
        """Check if inputs were truncated (offloaded to external storage)."""
        return self._get_offload_by_type(ExecutionOffLoadType.INPUTS) is not None

    @property
    def outputs_truncated(self) -> bool:
        """Check if outputs were truncated (offloaded to external storage)."""
        return self._get_offload_by_type(ExecutionOffLoadType.OUTPUTS) is not None

    @property
    def process_data_truncated(self) -> bool:
        """Check if process_data were truncated (offloaded to external storage)."""
        return self._get_offload_by_type(ExecutionOffLoadType.PROCESS_DATA) is not None

    @staticmethod
    def _load_full_content(session: orm.Session, file_id: str, storage: Storage):
        from .model import UploadFile

        stmt = sa.select(UploadFile).where(UploadFile.id == file_id)
        file = session.scalars(stmt).first()
        assert file is not None, f"UploadFile with id {file_id} should exist but not"
        content = storage.load(file.key)
        return json.loads(content)

    def load_full_inputs(self, session: orm.Session, storage: Storage) -> Mapping[str, Any] | None:
        offload = self._get_offload_by_type(ExecutionOffLoadType.INPUTS)
        if offload is None:
            return self.inputs_dict

        return self._load_full_content(session, offload.file_id, storage)

    def load_full_outputs(self, session: orm.Session, storage: Storage) -> Mapping[str, Any] | None:
        offload: WorkflowNodeExecutionOffload | None = self._get_offload_by_type(ExecutionOffLoadType.OUTPUTS)
        if offload is None:
            return self.outputs_dict

        return self._load_full_content(session, offload.file_id, storage)

    def load_full_process_data(self, session: orm.Session, storage: Storage) -> Mapping[str, Any] | None:
        offload: WorkflowNodeExecutionOffload | None = self._get_offload_by_type(ExecutionOffLoadType.PROCESS_DATA)
        if offload is None:
            return self.process_data_dict

        return self._load_full_content(session, offload.file_id, storage)


class WorkflowNodeExecutionOffload(Base):
    __tablename__ = "workflow_node_execution_offload"
    __table_args__ = (
        # PostgreSQL 14 treats NULL values as distinct in unique constraints by default,
        # allowing multiple records with NULL values for the same column combination.
        #
        # This behavior allows us to have multiple records with NULL node_execution_id,
        # simplifying garbage collection process.
        UniqueConstraint(
            "node_execution_id",
            "type",
            # Note: PostgreSQL 15+ supports explicit `nulls distinct` behavior through
            # `postgresql_nulls_not_distinct=False`, which would make our intention clearer.
            # We rely on PostgreSQL's default behavior of treating NULLs as distinct values.
            # postgresql_nulls_not_distinct=False,
        ),
    )
    _HASH_COL_SIZE = 64

    id: Mapped[str] = mapped_column(
        StringUUID,
        primary_key=True,
        default=lambda: str(uuid4()),
    )

    created_at: Mapped[datetime] = mapped_column(
        DateTime, default=naive_utc_now, server_default=func.current_timestamp()
    )

    tenant_id: Mapped[str] = mapped_column(StringUUID)
    app_id: Mapped[str] = mapped_column(StringUUID)

    # `node_execution_id` indicates the `WorkflowNodeExecutionModel` associated with this offload record.
    # A value of `None` signifies that this offload record is not linked to any execution record
    # and should be considered for garbage collection.
    node_execution_id: Mapped[str | None] = mapped_column(StringUUID, nullable=True)
    type_: Mapped[ExecutionOffLoadType] = mapped_column(EnumText(ExecutionOffLoadType), name="type", nullable=False)

    # Design Decision: Combining inputs and outputs into a single object was considered to reduce I/O
    # operations. However, due to the current design of `WorkflowNodeExecutionRepository`,
    # the `save` method is called at two distinct times:
    #
    # - When the node starts execution: the `inputs` field exists, but the `outputs` field is absent
    # - When the node completes execution (either succeeded or failed): the `outputs` field becomes available
    #
    # It's difficult to correlate these two successive calls to `save` for combined storage.
    # Converting the `WorkflowNodeExecutionRepository` to buffer the first `save` call and flush
    # when execution completes was also considered, but this would make the execution state unobservable
    # until completion, significantly damaging the observability of workflow execution.
    #
    # Given these constraints, `inputs` and `outputs` are stored separately to maintain real-time
    # observability and system reliability.

    # `file_id` references to the offloaded storage object containing the data.
    file_id: Mapped[str] = mapped_column(StringUUID, nullable=False)

    execution: Mapped[WorkflowNodeExecutionModel] = orm.relationship(
        foreign_keys=[node_execution_id],
        lazy="raise",
        uselist=False,
        primaryjoin="WorkflowNodeExecutionOffload.node_execution_id == WorkflowNodeExecutionModel.id",
        back_populates="offload_data",
    )

    file: Mapped[Optional["UploadFile"]] = orm.relationship(
        foreign_keys=[file_id],
        lazy="raise",
        uselist=False,
        primaryjoin="WorkflowNodeExecutionOffload.file_id == UploadFile.id",
    )


class WorkflowAppLogCreatedFrom(StrEnum):
    """
    Workflow App Log Created From Enum
    """

    SERVICE_API = "service-api"
    WEB_APP = "web-app"
    INSTALLED_APP = "installed-app"

    @classmethod
    def value_of(cls, value: str) -> "WorkflowAppLogCreatedFrom":
        """
        Get value of given mode.

        :param value: mode value
        :return: mode
        """
        for mode in cls:
            if mode.value == value:
                return mode
        raise ValueError(f"invalid workflow app log created from value {value}")


class WorkflowAppLog(TypeBase):
    """
    Workflow App execution log, excluding workflow debugging records.

    Attributes:

    - id (uuid) run ID
    - tenant_id (uuid) Workspace ID
    - app_id (uuid) App ID
    - workflow_id (uuid) Associated Workflow ID
    - workflow_run_id (uuid) Associated Workflow Run ID
    - created_from (string) Creation source

        `service-api` App Execution OpenAPI

        `web-app` WebApp

        `installed-app` Installed App

    - created_by_role (string) Creator role

        - `account` Console account

        - `end_user` End user

    - created_by (uuid) Creator ID, depends on the user table according to created_by_role
    - created_at (timestamp) Creation time
    """

    __tablename__ = "workflow_app_logs"
    __table_args__ = (
        sa.PrimaryKeyConstraint("id", name="workflow_app_log_pkey"),
        sa.Index("workflow_app_log_app_idx", "tenant_id", "app_id"),
        sa.Index("workflow_app_log_workflow_run_id_idx", "workflow_run_id"),
    )

    id: Mapped[str] = mapped_column(
        StringUUID, insert_default=lambda: str(uuid4()), default_factory=lambda: str(uuid4()), init=False
    )
    tenant_id: Mapped[str] = mapped_column(StringUUID)
    app_id: Mapped[str] = mapped_column(StringUUID)
    workflow_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    workflow_run_id: Mapped[str] = mapped_column(StringUUID)
    created_from: Mapped[str] = mapped_column(String(255), nullable=False)
    created_by_role: Mapped[str] = mapped_column(String(255), nullable=False)
    created_by: Mapped[str] = mapped_column(StringUUID, nullable=False)
    created_at: Mapped[datetime] = mapped_column(
        DateTime, nullable=False, server_default=func.current_timestamp(), init=False
    )

    @property
    def workflow_run(self):
        if self.workflow_run_id:
            from sqlalchemy.orm import sessionmaker

            from repositories.factory import DifyAPIRepositoryFactory

            session_maker = sessionmaker(bind=db.engine, expire_on_commit=False)
            repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
            return repo.get_workflow_run_by_id_without_tenant(run_id=self.workflow_run_id)

        return None

    @property
    def created_by_account(self):
        created_by_role = CreatorUserRole(self.created_by_role)
        return db.session.get(Account, self.created_by) if created_by_role == CreatorUserRole.ACCOUNT else None

    @property
    def created_by_end_user(self):
        from .model import EndUser

        created_by_role = CreatorUserRole(self.created_by_role)
        return db.session.get(EndUser, self.created_by) if created_by_role == CreatorUserRole.END_USER else None

    def to_dict(self):
        return {
            "id": self.id,
            "tenant_id": self.tenant_id,
            "app_id": self.app_id,
            "workflow_id": self.workflow_id,
            "workflow_run_id": self.workflow_run_id,
            "created_from": self.created_from,
            "created_by_role": self.created_by_role,
            "created_by": self.created_by,
            "created_at": self.created_at,
        }


class WorkflowArchiveLog(TypeBase):
    """
    Workflow archive log.

    Stores essential workflow run snapshot data for archived app logs.

    Field sources:
    - Shared fields (tenant/app/workflow/run ids, created_by*): from WorkflowRun for consistency.
    - log_* fields: from WorkflowAppLog when present; null if the run has no app log.
    - run_* fields: workflow run snapshot fields from WorkflowRun.
    - trigger_metadata: snapshot from WorkflowTriggerLog when present.
    """

    __tablename__ = "workflow_archive_logs"
    __table_args__ = (
        sa.PrimaryKeyConstraint("id", name="workflow_archive_log_pkey"),
        sa.Index("workflow_archive_log_app_idx", "tenant_id", "app_id"),
        sa.Index("workflow_archive_log_workflow_run_id_idx", "workflow_run_id"),
        sa.Index("workflow_archive_log_run_created_at_idx", "run_created_at"),
    )

    id: Mapped[str] = mapped_column(
        StringUUID, insert_default=lambda: str(uuidv7()), default_factory=lambda: str(uuidv7()), init=False
    )

    tenant_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    app_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    workflow_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    workflow_run_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
    created_by_role: Mapped[str] = mapped_column(String(255), nullable=False)
    created_by: Mapped[str] = mapped_column(StringUUID, nullable=False)

    log_id: Mapped[str | None] = mapped_column(StringUUID, nullable=True)
    log_created_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
    log_created_from: Mapped[str | None] = mapped_column(String(255), nullable=True)

    run_version: Mapped[str] = mapped_column(String(255), nullable=False)
    run_status: Mapped[str] = mapped_column(String(255), nullable=False)
    run_triggered_from: Mapped[str] = mapped_column(String(255), nullable=False)
    run_error: Mapped[str | None] = mapped_column(LongText, nullable=True)
    run_elapsed_time: Mapped[float] = mapped_column(sa.Float, nullable=False, server_default=sa.text("0"))
    run_total_tokens: Mapped[int] = mapped_column(sa.BigInteger, server_default=sa.text("0"))
    run_total_steps: Mapped[int] = mapped_column(sa.Integer, server_default=sa.text("0"), nullable=True)
    run_created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False)
    run_finished_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
    run_exceptions_count: Mapped[int] = mapped_column(sa.Integer, server_default=sa.text("0"), nullable=True)

    trigger_metadata: Mapped[str | None] = mapped_column(LongText, nullable=True)
    archived_at: Mapped[datetime] = mapped_column(
        DateTime, nullable=False, server_default=func.current_timestamp(), init=False
    )

    @property
    def workflow_run_summary(self) -> dict[str, Any]:
        return {
            "id": self.workflow_run_id,
            "status": self.run_status,
            "triggered_from": self.run_triggered_from,
            "elapsed_time": self.run_elapsed_time,
            "total_tokens": self.run_total_tokens,
        }


class ConversationVariable(TypeBase):
    __tablename__ = "workflow_conversation_variables"

    id: Mapped[str] = mapped_column(StringUUID, primary_key=True)
    conversation_id: Mapped[str] = mapped_column(StringUUID, nullable=False, primary_key=True, index=True)
    app_id: Mapped[str] = mapped_column(StringUUID, nullable=False, index=True)
    data: Mapped[str] = mapped_column(LongText, nullable=False)
    created_at: Mapped[datetime] = mapped_column(
        DateTime, nullable=False, server_default=func.current_timestamp(), index=True, init=False
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime, nullable=False, server_default=func.current_timestamp(), onupdate=func.current_timestamp(), init=False
    )

    @classmethod
    def from_variable(cls, *, app_id: str, conversation_id: str, variable: VariableBase) -> "ConversationVariable":
        obj = cls(
            id=variable.id,
            app_id=app_id,
            conversation_id=conversation_id,
            data=variable.model_dump_json(),
        )
        return obj

    def to_variable(self) -> VariableBase:
        mapping = json.loads(self.data)
        return variable_factory.build_conversation_variable_from_mapping(mapping)


# Only `sys.query` and `sys.files` could be modified.
_EDITABLE_SYSTEM_VARIABLE = frozenset(["query", "files"])


class WorkflowDraftVariable(Base):
    """`WorkflowDraftVariable` record variables and outputs generated during
    debugging workflow or chatflow.

    IMPORTANT: This model maintains multiple invariant rules that must be preserved.
    Do not instantiate this class directly with the constructor.

    Instead, use the factory methods (`new_conversation_variable`, `new_sys_variable`,
    `new_node_variable`) defined below to ensure all invariants are properly maintained.
    """

    @staticmethod
    def unique_app_id_node_id_name() -> list[str]:
        return [
            "app_id",
            "node_id",
            "name",
        ]

    __tablename__ = "workflow_draft_variables"
    __table_args__ = (
        UniqueConstraint(*unique_app_id_node_id_name()),
        Index("workflow_draft_variable_file_id_idx", "file_id"),
    )
    # Required for instance variable annotation.
    __allow_unmapped__ = True

    # id is the unique identifier of a draft variable.
    id: Mapped[str] = mapped_column(StringUUID, primary_key=True, default=lambda: str(uuid4()))

    created_at: Mapped[datetime] = mapped_column(
        DateTime,
        nullable=False,
        default=naive_utc_now,
        server_default=func.current_timestamp(),
    )

    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
        nullable=False,
        default=naive_utc_now,
        server_default=func.current_timestamp(),
        onupdate=func.current_timestamp(),
    )

    # "`app_id` maps to the `id` field in the `model.App` model."
    app_id: Mapped[str] = mapped_column(StringUUID, nullable=False)

    # `last_edited_at` records when the value of a given draft variable
    # is edited.
    #
    # If it's not edited after creation, its value is `None`.
    last_edited_at: Mapped[datetime | None] = mapped_column(
        DateTime,
        nullable=True,
        default=None,
    )

    # The `node_id` field is special.
    #
    # If the variable is a conversation variable or a system variable, then the value of `node_id`
    # is `conversation` or `sys`, respective.
    #
    # Otherwise, if the variable is a variable belonging to a specific node, the value of `_node_id` is
    # the identity of correspond node in graph definition. An example of node id is `"1745769620734"`.
    #
    # However, there's one caveat. The id of the first "Answer" node in chatflow is "answer". (Other
    # "Answer" node conform the rules above.)
    node_id: Mapped[str] = mapped_column(sa.String(255), nullable=False, name="node_id")

    # From `VARIABLE_PATTERN`, we may conclude that the length of a top level variable is less than
    # 80 chars.
    #
    # ref: api/dify_graph/entities/variable_pool.py:18
    name: Mapped[str] = mapped_column(sa.String(255), nullable=False)
    description: Mapped[str] = mapped_column(
        sa.String(255),
        default="",
        nullable=False,
    )

    selector: Mapped[str] = mapped_column(sa.String(255), nullable=False, name="selector")

    # The data type of this variable's value
    #
    # If the variable is offloaded, `value_type` represents the type of the truncated value,
    # which may differ from the original value's type. Typically, they are the same,
    # but in cases where the structurally truncated  value still exceeds the size limit,
    # text slicing is applied, and the `value_type` is converted to `STRING`.
    value_type: Mapped[SegmentType] = mapped_column(EnumText(SegmentType, length=20))

    # The variable's value serialized as a JSON string
    #
    # If the variable is offloaded, `value` contains a truncated version, not the full original value.
    value: Mapped[str] = mapped_column(LongText, nullable=False, name="value")

    # Controls whether the variable should be displayed in the variable inspection panel
    visible: Mapped[bool] = mapped_column(sa.Boolean, nullable=False, default=True)

    # Determines whether this variable can be modified by users
    editable: Mapped[bool] = mapped_column(sa.Boolean, nullable=False, default=False)

    # The `node_execution_id` field identifies the workflow node execution that created this variable.
    # It corresponds to the `id` field in the `WorkflowNodeExecutionModel` model.
    #
    # This field is not `None` for system variables and node variables, and is  `None`
    # for conversation variables.
    node_execution_id: Mapped[str | None] = mapped_column(
        StringUUID,
        nullable=True,
        default=None,
    )

    # Reference to WorkflowDraftVariableFile for offloaded large variables
    #
    # Indicates whether the current draft variable is offloaded.
    # If not offloaded, this field will be None.
    file_id: Mapped[str | None] = mapped_column(
        StringUUID,
        nullable=True,
        default=None,
        comment="Reference to WorkflowDraftVariableFile if variable is offloaded to external storage",
    )

    is_default_value: Mapped[bool] = mapped_column(
        sa.Boolean,
        nullable=False,
        default=False,
        comment=(
            "Indicates whether the current value is the default for a conversation variable. "
            "Always `FALSE` for other types of variables."
        ),
    )

    # Relationship to WorkflowDraftVariableFile
    variable_file: Mapped[Optional["WorkflowDraftVariableFile"]] = orm.relationship(
        foreign_keys=[file_id],
        lazy="raise",
        uselist=False,
        primaryjoin="WorkflowDraftVariableFile.id == WorkflowDraftVariable.file_id",
    )

    # Cache for deserialized value
    #
    # NOTE(QuantumGhost): This field serves two purposes:
    #
    # 1. Caches deserialized values to reduce repeated parsing costs
    # 2. Allows modification of the deserialized value after retrieval,
    #    particularly important for `File`` variables which require database
    #    lookups to obtain storage_key and other metadata
    #
    # Use double underscore prefix for better encapsulation,
    # making this attribute harder to access from outside the class.
    __value: Segment | None

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        """
        The constructor of `WorkflowDraftVariable` is not intended for
        direct use outside this file. Its solo purpose is setup private state
        used by the model instance.

        Please use the factory methods
        (`new_conversation_variable`, `new_sys_variable`, `new_node_variable`)
        defined below to create instances of this class.
        """
        super().__init__(*args, **kwargs)
        self.__value = None

    @orm.reconstructor
    def _init_on_load(self):
        self.__value = None

    def get_selector(self) -> list[str]:
        selector: Any = json.loads(self.selector)
        if not isinstance(selector, list):
            logger.error(
                "invalid selector loaded from database, type=%s, value=%s",
                type(selector).__name__,
                self.selector,
            )
            raise ValueError("invalid selector.")
        return cast(list[str], selector)

    def _set_selector(self, value: list[str]):
        self.selector = json.dumps(value)

    def _loads_value(self) -> Segment:
        value = json.loads(self.value)
        return self.build_segment_with_type(self.value_type, value)

    @staticmethod
    def rebuild_file_types(value: Any):
        # NOTE(QuantumGhost): Temporary workaround for structured data handling.
        # By this point, `output` has been converted to dict by
        # `WorkflowEntry.handle_special_values`, so we need to
        # reconstruct File objects from their serialized form
        # to maintain proper variable saving behavior.
        #
        # Ideally, we should work with structured data objects directly
        # rather than their serialized forms.
        # However, multiple components in the codebase depend on
        # `WorkflowEntry.handle_special_values`, making a comprehensive migration challenging.
        if isinstance(value, dict):
            if not maybe_file_object(value):
                return cast(Any, value)
            return File.model_validate(value)
        elif isinstance(value, list) and value:
            value_list = cast(list[Any], value)
            first: Any = value_list[0]
            if not maybe_file_object(first):
                return cast(Any, value)
            file_list: list[File] = [File.model_validate(cast(dict[str, Any], i)) for i in value_list]
            return cast(Any, file_list)
        else:
            return cast(Any, value)

    @classmethod
    def build_segment_with_type(cls, segment_type: SegmentType, value: Any) -> Segment:
        # Extends `variable_factory.build_segment_with_type` functionality by
        # reconstructing `FileSegment`` or `ArrayFileSegment`` objects from
        # their serialized dictionary or list representations, respectively.
        if segment_type == SegmentType.FILE:
            if isinstance(value, File):
                return build_segment_with_type(segment_type, value)
            elif isinstance(value, dict):
                file = cls.rebuild_file_types(value)
                return build_segment_with_type(segment_type, file)
            else:
                raise TypeMismatchError(f"expected dict or File for FileSegment, got {type(value)}")
        if segment_type == SegmentType.ARRAY_FILE:
            if not isinstance(value, list):
                raise TypeMismatchError(f"expected list for ArrayFileSegment, got {type(value)}")
            file_list = cls.rebuild_file_types(value)
            return build_segment_with_type(segment_type=segment_type, value=file_list)

        return build_segment_with_type(segment_type=segment_type, value=value)

    def get_value(self) -> Segment:
        """Decode the serialized value into its corresponding `Segment` object.

        This method caches the result, so repeated calls will return the same
        object instance without re-parsing the serialized data.

        If you need to modify the returned `Segment`, use `value.model_copy()`
        to create a copy first to avoid affecting the cached instance.

        For more information about the caching mechanism, see the documentation
        of the `__value` field.

        Returns:
            Segment: The deserialized value as a Segment object.
        """

        if self.__value is not None:
            return self.__value
        value = self._loads_value()
        self.__value = value
        return value

    def set_name(self, name: str):
        self.name = name
        self._set_selector([self.node_id, name])

    def set_value(self, value: Segment):
        """Updates the `value` and corresponding `value_type` fields in the database model.

        This method also stores the provided Segment object in the deserialized cache
        without creating a copy, allowing for efficient value access.

        Args:
            value: The Segment object to store as the variable's value.
        """
        self.__value = value
        self.value = variable_utils.dumps_with_segments(value)
        self.value_type = value.value_type

    def get_node_id(self) -> str | None:
        if self.get_variable_type() == DraftVariableType.NODE:
            return self.node_id
        else:
            return None

    def get_variable_type(self) -> DraftVariableType:
        match self.node_id:
            case DraftVariableType.CONVERSATION:
                return DraftVariableType.CONVERSATION
            case DraftVariableType.SYS:
                return DraftVariableType.SYS
            case _:
                return DraftVariableType.NODE

    def is_truncated(self) -> bool:
        return self.file_id is not None

    @classmethod
    def _new(
        cls,
        *,
        app_id: str,
        node_id: str,
        name: str,
        value: Segment,
        node_execution_id: str | None,
        description: str = "",
        file_id: str | None = None,
    ) -> "WorkflowDraftVariable":
        variable = WorkflowDraftVariable()
        variable.id = str(uuid4())
        variable.created_at = naive_utc_now()
        variable.updated_at = naive_utc_now()
        variable.description = description
        variable.app_id = app_id
        variable.node_id = node_id
        variable.name = name
        variable.set_value(value)
        variable.file_id = file_id
        variable._set_selector(list(variable_utils.to_selector(node_id, name)))
        variable.node_execution_id = node_execution_id
        return variable

    @classmethod
    def new_conversation_variable(
        cls,
        *,
        app_id: str,
        name: str,
        value: Segment,
        description: str = "",
    ) -> "WorkflowDraftVariable":
        variable = cls._new(
            app_id=app_id,
            node_id=CONVERSATION_VARIABLE_NODE_ID,
            name=name,
            value=value,
            description=description,
            node_execution_id=None,
        )
        variable.editable = True
        return variable

    @classmethod
    def new_sys_variable(
        cls,
        *,
        app_id: str,
        name: str,
        value: Segment,
        node_execution_id: str,
        editable: bool = False,
    ) -> "WorkflowDraftVariable":
        variable = cls._new(
            app_id=app_id,
            node_id=SYSTEM_VARIABLE_NODE_ID,
            name=name,
            node_execution_id=node_execution_id,
            value=value,
        )
        variable.editable = editable
        return variable

    @classmethod
    def new_node_variable(
        cls,
        *,
        app_id: str,
        node_id: str,
        name: str,
        value: Segment,
        node_execution_id: str,
        visible: bool = True,
        editable: bool = True,
        file_id: str | None = None,
    ) -> "WorkflowDraftVariable":
        variable = cls._new(
            app_id=app_id,
            node_id=node_id,
            name=name,
            node_execution_id=node_execution_id,
            value=value,
            file_id=file_id,
        )
        variable.visible = visible
        variable.editable = editable
        return variable

    @property
    def edited(self):
        return self.last_edited_at is not None


class WorkflowDraftVariableFile(Base):
    """Stores metadata about files associated with large workflow draft variables.

    This model acts as an intermediary between WorkflowDraftVariable and UploadFile,
    allowing for proper cleanup of orphaned files when variables are updated or deleted.

    The MIME type of the stored content is recorded in `UploadFile.mime_type`.
    Possible values are 'application/json' for JSON types other than plain text,
    and 'text/plain' for JSON strings.
    """

    __tablename__ = "workflow_draft_variable_files"

    # Primary key
    id: Mapped[str] = mapped_column(
        StringUUID,
        primary_key=True,
        default=lambda: str(uuidv7()),
    )

    created_at: Mapped[datetime] = mapped_column(
        DateTime,
        nullable=False,
        default=naive_utc_now,
        server_default=func.current_timestamp(),
    )

    tenant_id: Mapped[str] = mapped_column(
        StringUUID,
        nullable=False,
        comment="The tenant to which the WorkflowDraftVariableFile belongs, referencing Tenant.id",
    )

    app_id: Mapped[str] = mapped_column(
        StringUUID,
        nullable=False,
        comment="The application to which the WorkflowDraftVariableFile belongs, referencing App.id",
    )

    user_id: Mapped[str] = mapped_column(
        StringUUID,
        nullable=False,
        comment="The owner to of the WorkflowDraftVariableFile, referencing Account.id",
    )

    # Reference to the `UploadFile.id` field
    upload_file_id: Mapped[str] = mapped_column(
        StringUUID,
        nullable=False,
        comment="Reference to UploadFile containing the large variable data",
    )

    # -------------- metadata about the variable content --------------

    # The `size` is already recorded in UploadFiles. It is duplicated here to avoid an additional database lookup.
    size: Mapped[int | None] = mapped_column(
        sa.BigInteger,
        nullable=False,
        comment="Size of the original variable content in bytes",
    )

    length: Mapped[int | None] = mapped_column(
        sa.Integer,
        nullable=True,
        comment=(
            "Length of the original variable content. For array and array-like types, "
            "this represents the number of elements. For object types, it indicates the number of keys. "
            "For other types, the value is NULL."
        ),
    )

    # The `value_type` field records the type of the original value.
    value_type: Mapped[SegmentType] = mapped_column(
        EnumText(SegmentType, length=20),
        nullable=False,
    )

    # Relationship to UploadFile
    upload_file: Mapped["UploadFile"] = orm.relationship(
        foreign_keys=[upload_file_id],
        lazy="raise",
        uselist=False,
        primaryjoin="WorkflowDraftVariableFile.upload_file_id == UploadFile.id",
    )


def is_system_variable_editable(name: str) -> bool:
    return name in _EDITABLE_SYSTEM_VARIABLE


class WorkflowPause(DefaultFieldsMixin, Base):
    """
    WorkflowPause records the paused state and related metadata for a specific workflow run.

    Each `WorkflowRun` can have zero or one associated `WorkflowPause`, depending on its execution status.
    If a `WorkflowRun` is in the `PAUSED` state, there must be a corresponding `WorkflowPause`
    that has not yet been resumed.
    Otherwise, there should be no active (non-resumed) `WorkflowPause` linked to that run.

    This model captures the execution context required to resume workflow processing at a later time.
    """

    __tablename__ = "workflow_pauses"
    __table_args__ = (
        # Design Note:
        # Instead of adding a `pause_id` field to the `WorkflowRun` model—which would require a migration
        # on a potentially large table—we reference `WorkflowRun` from `WorkflowPause` and enforce a unique
        # constraint on `workflow_run_id` to guarantee a one-to-one relationship.
        UniqueConstraint("workflow_run_id"),
    )

    # `workflow_id` represents the unique identifier of the workflow associated with this pause.
    # It corresponds to the `id` field in the `Workflow` model.
    #
    # Since an application can have multiple versions of a workflow, each with its own unique ID,
    # the `app_id` alone is insufficient to determine which workflow version should be loaded
    # when resuming a suspended workflow.
    workflow_id: Mapped[str] = mapped_column(
        StringUUID,
        nullable=False,
    )

    # `workflow_run_id` represents the identifier of the execution of workflow,
    # correspond to the `id` field of `WorkflowRun`.
    workflow_run_id: Mapped[str] = mapped_column(
        StringUUID,
        nullable=False,
    )

    # `resumed_at` records the timestamp when the suspended workflow was resumed.
    # It is set to `NULL` if the workflow has not been resumed.
    #
    # NOTE: Resuming a suspended WorkflowPause does not delete the record immediately.
    # It only set `resumed_at` to a non-null value.
    resumed_at: Mapped[datetime | None] = mapped_column(
        sa.DateTime,
        nullable=True,
    )

    # state_object_key stores the object key referencing the serialized runtime state
    # of the `GraphEngine`. This object captures the complete execution context of the
    # workflow at the moment it was paused, enabling accurate resumption.
    state_object_key: Mapped[str] = mapped_column(String(length=255), nullable=False)

    # Relationship to WorkflowRun
    workflow_run: Mapped["WorkflowRun"] = orm.relationship(
        foreign_keys=[workflow_run_id],
        # require explicit preloading.
        lazy="raise",
        uselist=False,
        primaryjoin="WorkflowPause.workflow_run_id == WorkflowRun.id",
        back_populates="pause",
    )


class WorkflowPauseReason(DefaultFieldsMixin, Base):
    __tablename__ = "workflow_pause_reasons"

    # `pause_id` represents the identifier of the pause,
    # correspond to the `id` field of `WorkflowPause`.
    pause_id: Mapped[str] = mapped_column(StringUUID, nullable=False, index=True)

    type_: Mapped[PauseReasonType] = mapped_column(EnumText(PauseReasonType), nullable=False)

    # form_id is not empty if and if only type_ == PauseReasonType.HUMAN_INPUT_REQUIRED
    #
    form_id: Mapped[str] = mapped_column(
        String(36),
        nullable=False,
        default="",
    )

    # message records the text description of this pause reason. For example,
    # "The workflow has been paused due to scheduling."
    #
    # Empty message means that this pause reason is not speified.
    message: Mapped[str] = mapped_column(
        String(255),
        nullable=False,
        default="",
    )

    # `node_id` is the identifier of node causing the pasue, correspond to
    # `Node.id`. Empty `node_id` means that this pause reason is not caused by any specific node
    # (E.G. time slicing pauses.)
    node_id: Mapped[str] = mapped_column(
        String(255),
        nullable=False,
        default="",
    )

    # Relationship to WorkflowPause
    pause: Mapped[WorkflowPause] = orm.relationship(
        foreign_keys=[pause_id],
        # require explicit preloading.
        lazy="raise",
        uselist=False,
        primaryjoin="WorkflowPauseReason.pause_id == WorkflowPause.id",
    )

    @classmethod
    def from_entity(cls, pause_reason: PauseReason) -> "WorkflowPauseReason":
        if isinstance(pause_reason, HumanInputRequired):
            return cls(
                type_=PauseReasonType.HUMAN_INPUT_REQUIRED, form_id=pause_reason.form_id, node_id=pause_reason.node_id
            )
        elif isinstance(pause_reason, SchedulingPause):
            return cls(type_=PauseReasonType.SCHEDULED_PAUSE, message=pause_reason.message, node_id="")
        else:
            raise AssertionError(f"Unknown pause reason type: {pause_reason}")

    def to_entity(self) -> PauseReason:
        if self.type_ == PauseReasonType.HUMAN_INPUT_REQUIRED:
            return HumanInputRequired(
                form_id=self.form_id,
                form_content="",
                node_id=self.node_id,
                node_title="",
            )
        elif self.type_ == PauseReasonType.SCHEDULED_PAUSE:
            return SchedulingPause(message=self.message)
        else:
            raise AssertionError(f"Unknown pause reason type: {self.type_}")
