Merge branch 'feat/summary-index' into deploy/dev

- Add storageThreshold, storageTooltip, storageTotalDisplay props to UsageInfo
- Implement indeterminate state in ProgressBar for usage below threshold
- Update VectorSpaceInfo to calculate total based on plan type
- Add i18n for storage threshold tooltip (en-US, ja-JP, zh-Hans)

api/.env.example

@@ -716,13 +716,3 @@ SANDBOX_EXPIRED_RECORDS_CLEAN_GRACEFUL_PERIOD=21
 SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_SIZE=1000
 SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS=30
 
-# Sandbox Dify CLI configuration
-# Directory containing dify CLI binaries (dify-cli-<os>-<arch>). Defaults to api/bin when unset.
-SANDBOX_DIFY_CLI_ROOT=
-
-# CLI API URL for sandbox (dify-sandbox or e2b) to call back to Dify API.
-# This URL must be accessible from the sandbox environment.
-# For local development: use http://localhost:5001 or http://127.0.0.1:5001
-# For Docker deployment: use http://api:5001 (internal Docker network)
-# For external sandbox (e.g., e2b): use a publicly accessible URL
-CLI_API_URL=http://localhost:5001

api/app_factory.py

@@ -71,8 +71,6 @@ def create_app() -> DifyApp:
 
 
 def initialize_extensions(app: DifyApp):
-    # Initialize Flask context capture for workflow execution
-    from context.flask_app_context import init_flask_context
     from extensions import (
         ext_app_metrics,
         ext_blueprints,
@@ -102,8 +100,6 @@ def initialize_extensions(app: DifyApp):
         ext_warnings,
     )
 
-    init_flask_context()
-
     extensions = [
         ext_timezone,
         ext_logging,

api/commands.py

@@ -23,7 +23,7 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.index_processor.constant.built_in_field import BuiltInField
 from core.rag.models.document import Document
-from core.tools.utils.system_encryption import encrypt_system_params
+from core.tools.utils.system_oauth_encryption import encrypt_system_oauth_params
 from events.app_event import app_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -862,27 +862,8 @@ def clear_free_plan_tenant_expired_logs(days: int, batch: int, tenant_ids: list[
 
 
 @click.command("clean-workflow-runs", help="Clean expired workflow runs and related data for free tenants.")
-@click.option(
-    "--before-days",
-    "--days",
-    default=30,
-    show_default=True,
-    type=click.IntRange(min=0),
-    help="Delete workflow runs created before N days ago.",
-)
+@click.option("--days", default=30, show_default=True, help="Delete workflow runs created before N days ago.")
 @click.option("--batch-size", default=200, show_default=True, help="Batch size for selecting workflow runs.")
-@click.option(
-    "--from-days-ago",
-    default=None,
-    type=click.IntRange(min=0),
-    help="Lower bound in days ago (older). Must be paired with --to-days-ago.",
-)
-@click.option(
-    "--to-days-ago",
-    default=None,
-    type=click.IntRange(min=0),
-    help="Upper bound in days ago (newer). Must be paired with --from-days-ago.",
-)
 @click.option(
     "--start-from",
     type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
@@ -901,10 +882,8 @@ def clear_free_plan_tenant_expired_logs(days: int, batch: int, tenant_ids: list[
     help="Preview cleanup results without deleting any workflow run data.",
 )
 def clean_workflow_runs(
-    before_days: int,
+    days: int,
     batch_size: int,
-    from_days_ago: int | None,
-    to_days_ago: int | None,
     start_from: datetime.datetime | None,
     end_before: datetime.datetime | None,
     dry_run: bool,
@@ -915,24 +894,11 @@ def clean_workflow_runs(
     if (start_from is None) ^ (end_before is None):
         raise click.UsageError("--start-from and --end-before must be provided together.")
 
-    if (from_days_ago is None) ^ (to_days_ago is None):
-        raise click.UsageError("--from-days-ago and --to-days-ago must be provided together.")
-
-    if from_days_ago is not None and to_days_ago is not None:
-        if start_from or end_before:
-            raise click.UsageError("Choose either day offsets or explicit dates, not both.")
-        if from_days_ago <= to_days_ago:
-            raise click.UsageError("--from-days-ago must be greater than --to-days-ago.")
-        now = datetime.datetime.now()
-        start_from = now - datetime.timedelta(days=from_days_ago)
-        end_before = now - datetime.timedelta(days=to_days_ago)
-        before_days = 0
-
     start_time = datetime.datetime.now(datetime.UTC)
     click.echo(click.style(f"Starting workflow run cleanup at {start_time.isoformat()}.", fg="white"))
 
     WorkflowRunCleanup(
-        days=before_days,
+        days=days,
         batch_size=batch_size,
         start_from=start_from,
         end_before=end_before,
@@ -1245,7 +1211,7 @@ def remove_orphaned_files_on_storage(force: bool):
             click.echo(click.style(f"- Scanning files on storage path {storage_path}", fg="white"))
             files = storage.scan(path=storage_path, files=True, directories=False)
             all_files_on_storage.extend(files)
-        except FileNotFoundError:
+        except FileNotFoundError as e:
             click.echo(click.style(f"  -> Skipping path {storage_path} as it does not exist.", fg="yellow"))
             continue
         except Exception as e:
@@ -1493,60 +1459,6 @@ def file_usage(
             click.echo(click.style(f"Use --offset {offset + limit} to see next page", fg="white"))
 
 
-@click.command("setup-sandbox-system-config", help="Setup system-level sandbox provider configuration.")
-@click.option(
-    "--provider-type", prompt=True, type=click.Choice(["e2b", "docker", "local"]), help="Sandbox provider type"
-)
-@click.option("--config", prompt=True, help='Configuration JSON (e.g., {"api_key": "xxx"} for e2b)')
-def setup_sandbox_system_config(provider_type: str, config: str):
-    """
-    Setup system-level sandbox provider configuration.
-
-    Examples:
-        flask setup-sandbox-system-config --provider-type e2b --config '{"api_key": "e2b_xxx"}'
-        flask setup-sandbox-system-config --provider-type docker --config '{"docker_sock": "unix:///var/run/docker.sock"}'
-        flask setup-sandbox-system-config --provider-type local --config '{}'
-    """
-    from models.sandbox import SandboxProviderSystemConfig
-    from services.sandbox.sandbox_provider_service import PROVIDER_CONFIG_MODELS
-
-    try:
-        click.echo(click.style(f"Validating config: {config}", fg="yellow"))
-        config_dict = TypeAdapter(dict[str, Any]).validate_json(config)
-        click.echo(click.style("Config validated successfully.", fg="green"))
-
-        click.echo(click.style(f"Validating config schema for provider type: {provider_type}", fg="yellow"))
-        model_class = PROVIDER_CONFIG_MODELS.get(provider_type)
-        if model_class:
-            model_class.model_validate(config_dict)
-        click.echo(click.style("Config schema validated successfully.", fg="green"))
-
-        click.echo(click.style("Encrypting config...", fg="yellow"))
-        click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        encrypted_config = encrypt_system_params(config_dict)
-        click.echo(click.style("Config encrypted successfully.", fg="green"))
-    except Exception as e:
-        click.echo(click.style(f"Error validating/encrypting config: {str(e)}", fg="red"))
-        return
-
-    deleted_count = db.session.query(SandboxProviderSystemConfig).filter_by(provider_type=provider_type).delete()
-    if deleted_count > 0:
-        click.echo(
-            click.style(
-                f"Deleted {deleted_count} existing system config for provider type: {provider_type}", fg="yellow"
-            )
-        )
-
-    system_config = SandboxProviderSystemConfig(
-        provider_type=provider_type,
-        encrypted_config=encrypted_config,
-    )
-    db.session.add(system_config)
-    db.session.commit()
-    click.echo(click.style(f"Sandbox system config setup successfully. id: {system_config.id}", fg="green"))
-    click.echo(click.style(f"Provider type: {provider_type}", fg="green"))
-
-
 @click.command("setup-system-tool-oauth-client", help="Setup system tool oauth client.")
 @click.option("--provider", prompt=True, help="Provider name")
 @click.option("--client-params", prompt=True, help="Client Params")
@@ -1566,7 +1478,7 @@ def setup_system_tool_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_params(client_params_dict)
+        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))
@@ -1615,7 +1527,7 @@ def setup_system_trigger_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_params(client_params_dict)
+        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))

api/configs/app_config.py

@@ -2,7 +2,6 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from pydantic import Field
 from pydantic.fields import FieldInfo
 from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict, TomlConfigSettingsSource
 
@@ -83,14 +82,6 @@ class DifyConfig(
         extra="ignore",
     )
 
-    SANDBOX_DIFY_CLI_ROOT: str | None = Field(
-        default=None,
-        description=(
-            "Filesystem directory containing dify CLI binaries named dify-cli-<os>-<arch>. "
-            "Defaults to api/bin when unset."
-        ),
-    )
-
     # Before adding any config,
     # please consider to arrange it in the proper config group of existed or added
     # for better readability and maintainability.

api/configs/feature/__init__.py

@@ -244,17 +244,6 @@ class PluginConfig(BaseSettings):
     )
 
 
-class CliApiConfig(BaseSettings):
-    """
-    Configuration for CLI API (for dify-cli to call back from external sandbox environments)
-    """
-
-    CLI_API_URL: str = Field(
-        description="CLI API URL for external sandbox (e.g., e2b) to call back.",
-        default="http://localhost:5001",
-    )
-
-
 class MarketplaceConfig(BaseSettings):
     """
     Configuration for marketplace
@@ -1320,7 +1309,6 @@ class FeatureConfig(
     TriggerConfig,
     AsyncWorkflowConfig,
     PluginConfig,
-    CliApiConfig,
     MarketplaceConfig,
     DataSetConfig,
     EndpointConfig,

api/context/__init__.py

@@ -1,74 +0,0 @@
-"""
-Core Context - Framework-agnostic context management.
-
-This module provides context management that is independent of any specific
-web framework. Framework-specific implementations register their context
-capture functions at application initialization time.
-
-This ensures the workflow layer remains completely decoupled from Flask
-or any other web framework.
-"""
-
-import contextvars
-from collections.abc import Callable
-
-from core.workflow.context.execution_context import (
-    ExecutionContext,
-    IExecutionContext,
-    NullAppContext,
-)
-
-# Global capturer function - set by framework-specific modules
-_capturer: Callable[[], IExecutionContext] | None = None
-
-
-def register_context_capturer(capturer: Callable[[], IExecutionContext]) -> None:
-    """
-    Register a context capture function.
-
-    This should be called by framework-specific modules (e.g., Flask)
-    during application initialization.
-
-    Args:
-        capturer: Function that captures current context and returns IExecutionContext
-    """
-    global _capturer
-    _capturer = capturer
-
-
-def capture_current_context() -> IExecutionContext:
-    """
-    Capture current execution context.
-
-    This function uses the registered context capturer. If no capturer
-    is registered, it returns a minimal context with only contextvars
-    (suitable for non-framework environments like tests or standalone scripts).
-
-    Returns:
-        IExecutionContext with captured context
-    """
-    if _capturer is None:
-        # No framework registered - return minimal context
-        return ExecutionContext(
-            app_context=NullAppContext(),
-            context_vars=contextvars.copy_context(),
-        )
-
-    return _capturer()
-
-
-def reset_context_provider() -> None:
-    """
-    Reset the context capturer.
-
-    This is primarily useful for testing to ensure a clean state.
-    """
-    global _capturer
-    _capturer = None
-
-
-__all__ = [
-    "capture_current_context",
-    "register_context_capturer",
-    "reset_context_provider",
-]

api/context/flask_app_context.py

@@ -1,198 +0,0 @@
-"""
-Flask App Context - Flask implementation of AppContext interface.
-"""
-
-import contextvars
-from collections.abc import Generator
-from contextlib import contextmanager
-from typing import Any, final
-
-from flask import Flask, current_app, g
-
-from context import register_context_capturer
-from core.workflow.context.execution_context import (
-    AppContext,
-    IExecutionContext,
-)
-
-
-@final
-class FlaskAppContext(AppContext):
-    """
-    Flask implementation of AppContext.
-
-    This adapts Flask's app context to the AppContext interface.
-    """
-
-    def __init__(self, flask_app: Flask) -> None:
-        """
-        Initialize Flask app context.
-
-        Args:
-            flask_app: The Flask application instance
-        """
-        self._flask_app = flask_app
-
-    def get_config(self, key: str, default: Any = None) -> Any:
-        """Get configuration value from Flask app config."""
-        return self._flask_app.config.get(key, default)
-
-    def get_extension(self, name: str) -> Any:
-        """Get Flask extension by name."""
-        return self._flask_app.extensions.get(name)
-
-    @contextmanager
-    def enter(self) -> Generator[None, None, None]:
-        """Enter Flask app context."""
-        with self._flask_app.app_context():
-            yield
-
-    @property
-    def flask_app(self) -> Flask:
-        """Get the underlying Flask app instance."""
-        return self._flask_app
-
-
-def capture_flask_context(user: Any = None) -> IExecutionContext:
-    """
-    Capture current Flask execution context.
-
-    This function captures the Flask app context and contextvars from the
-    current environment. It should be called from within a Flask request or
-    app context.
-
-    Args:
-        user: Optional user object to include in context
-
-    Returns:
-        IExecutionContext with captured Flask context
-
-    Raises:
-        RuntimeError: If called outside Flask context
-    """
-    # Get Flask app instance
-    flask_app = current_app._get_current_object()  # type: ignore
-
-    # Save current user if available
-    saved_user = user
-    if saved_user is None:
-        # Check for user in g (flask-login)
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
-    # Capture contextvars
-    context_vars = contextvars.copy_context()
-
-    return FlaskExecutionContext(
-        flask_app=flask_app,
-        context_vars=context_vars,
-        user=saved_user,
-    )
-
-
-@final
-class FlaskExecutionContext:
-    """
-    Flask-specific execution context.
-
-    This is a specialized version of ExecutionContext that includes Flask app
-    context. It provides the same interface as ExecutionContext but with
-    Flask-specific implementation.
-    """
-
-    def __init__(
-        self,
-        flask_app: Flask,
-        context_vars: contextvars.Context,
-        user: Any = None,
-    ) -> None:
-        """
-        Initialize Flask execution context.
-
-        Args:
-            flask_app: Flask application instance
-            context_vars: Python contextvars
-            user: Optional user object
-        """
-        self._app_context = FlaskAppContext(flask_app)
-        self._context_vars = context_vars
-        self._user = user
-        self._flask_app = flask_app
-
-    @property
-    def app_context(self) -> FlaskAppContext:
-        """Get Flask app context."""
-        return self._app_context
-
-    @property
-    def context_vars(self) -> contextvars.Context:
-        """Get context variables."""
-        return self._context_vars
-
-    @property
-    def user(self) -> Any:
-        """Get user object."""
-        return self._user
-
-    def __enter__(self) -> "FlaskExecutionContext":
-        """Enter the Flask execution context."""
-        # Restore context variables
-        for var, val in self._context_vars.items():
-            var.set(val)
-
-        # Save current user from g if available
-        saved_user = None
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
-        # Enter Flask app context
-        self._cm = self._app_context.enter()
-        self._cm.__enter__()
-
-        # Restore user in new app context
-        if saved_user is not None:
-            g._login_user = saved_user
-
-        return self
-
-    def __exit__(self, *args: Any) -> None:
-        """Exit the Flask execution context."""
-        if hasattr(self, "_cm"):
-            self._cm.__exit__(*args)
-
-    @contextmanager
-    def enter(self) -> Generator[None, None, None]:
-        """Enter Flask execution context as context manager."""
-        # Restore context variables
-        for var, val in self._context_vars.items():
-            var.set(val)
-
-        # Save current user from g if available
-        saved_user = None
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
-        # Enter Flask app context
-        with self._flask_app.app_context():
-            # Restore user in new app context
-            if saved_user is not None:
-                g._login_user = saved_user
-            yield
-
-
-def init_flask_context() -> None:
-    """
-    Initialize Flask context capture by registering the capturer.
-
-    This function should be called during Flask application initialization
-    to register the Flask-specific context capturer with the core context module.
-
-    Example:
-        app = Flask(__name__)
-        init_flask_context()  # Register Flask context capturer
-
-    Note:
-        This function does not need the app instance as it uses Flask's
-        `current_app` to get the app when capturing context.
-    """
-    register_context_capturer(capture_flask_context)

api/controllers/cli_api/__init__.py

@@ -1,27 +0,0 @@
-from flask import Blueprint
-from flask_restx import Namespace
-
-from libs.external_api import ExternalApi
-
-bp = Blueprint("cli_api", __name__, url_prefix="/cli/api")
-
-api = ExternalApi(
-    bp,
-    version="1.0",
-    title="CLI API",
-    description="APIs for Dify CLI to call back from external sandbox environments (e.g., e2b)",
-)
-
-# Create namespace
-cli_api_ns = Namespace("cli_api", description="CLI API operations", path="/")
-
-from .plugin import plugin as _plugin
-
-api.add_namespace(cli_api_ns)
-
-__all__ = [
-    "_plugin",
-    "api",
-    "bp",
-    "cli_api_ns",
-]

api/controllers/cli_api/plugin/plugin.py

@@ -1,137 +0,0 @@
-from flask_restx import Resource
-
-from controllers.cli_api import cli_api_ns
-from controllers.cli_api.plugin.wraps import get_user_tenant, plugin_data
-from controllers.cli_api.wraps import cli_api_only
-from controllers.console.wraps import setup_required
-from core.file.helpers import get_signed_file_url_for_plugin
-from core.plugin.backwards_invocation.app import PluginAppBackwardsInvocation
-from core.plugin.backwards_invocation.base import BaseBackwardsInvocationResponse
-from core.plugin.backwards_invocation.model import PluginModelBackwardsInvocation
-from core.plugin.backwards_invocation.tool import PluginToolBackwardsInvocation
-from core.plugin.entities.request import (
-    RequestInvokeApp,
-    RequestInvokeLLM,
-    RequestInvokeTool,
-    RequestRequestUploadFile,
-)
-from core.tools.entities.tool_entities import ToolProviderType
-from libs.helper import length_prefixed_response
-from models import Account, Tenant
-from models.model import EndUser
-
-
-@cli_api_ns.route("/invoke/llm")
-class CliInvokeLLMApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestInvokeLLM)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeLLM):
-        def generator():
-            response = PluginModelBackwardsInvocation.invoke_llm(user_model.id, tenant_model, payload)
-            return PluginModelBackwardsInvocation.convert_to_event_stream(response)
-
-        return length_prefixed_response(0xF, generator())
-
-
-@cli_api_ns.route("/invoke/tool")
-class CliInvokeToolApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestInvokeTool)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeTool):
-        def generator():
-            return PluginToolBackwardsInvocation.convert_to_event_stream(
-                PluginToolBackwardsInvocation.invoke_tool(
-                    tenant_id=tenant_model.id,
-                    user_id=user_model.id,
-                    tool_type=ToolProviderType.value_of(payload.tool_type),
-                    provider=payload.provider,
-                    tool_name=payload.tool,
-                    tool_parameters=payload.tool_parameters,
-                    credential_id=payload.credential_id,
-                ),
-            )
-
-        return length_prefixed_response(0xF, generator())
-
-
-@cli_api_ns.route("/invoke/app")
-class CliInvokeAppApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestInvokeApp)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeApp):
-        response = PluginAppBackwardsInvocation.invoke_app(
-            app_id=payload.app_id,
-            user_id=user_model.id,
-            tenant_id=tenant_model.id,
-            conversation_id=payload.conversation_id,
-            query=payload.query,
-            stream=payload.response_mode == "streaming",
-            inputs=payload.inputs,
-            files=payload.files,
-        )
-
-        return length_prefixed_response(0xF, PluginAppBackwardsInvocation.convert_to_event_stream(response))
-
-
-@cli_api_ns.route("/upload/file/request")
-class CliUploadFileRequestApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @cli_api_only
-    @plugin_data(payload_type=RequestRequestUploadFile)
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestRequestUploadFile):
-        # generate signed url
-        url = get_signed_file_url_for_plugin(
-            filename=payload.filename,
-            mimetype=payload.mimetype,
-            tenant_id=tenant_model.id,
-            user_id=user_model.id,
-        )
-        return BaseBackwardsInvocationResponse(data={"url": url}).model_dump()
-
-
-@cli_api_ns.route("/fetch/tools/list")
-class CliFetchToolsListApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @cli_api_only
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
-        from sqlalchemy.orm import Session
-
-        from extensions.ext_database import db
-        from services.tools.api_tools_manage_service import ApiToolManageService
-        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
-        from services.tools.mcp_tools_manage_service import MCPToolManageService
-        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
-
-        providers = []
-
-        # Get builtin tools
-        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
-        for provider in builtin_providers:
-            providers.append(provider.to_dict())
-
-        # Get API tools
-        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
-        for provider in api_providers:
-            providers.append(provider.to_dict())
-
-        # Get workflow tools
-        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
-        for provider in workflow_providers:
-            providers.append(provider.to_dict())
-
-        # Get MCP tools
-        with Session(db.engine) as session:
-            mcp_service = MCPToolManageService(session)
-            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
-            for provider in mcp_providers:
-                providers.append(provider.to_dict())
-
-        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/cli_api/plugin/wraps.py

@@ -1,145 +0,0 @@
-from collections.abc import Callable
-from functools import wraps
-from typing import ParamSpec, TypeVar
-
-from flask import current_app, request
-from flask_login import user_logged_in
-from pydantic import BaseModel
-from sqlalchemy.orm import Session
-
-from core.session.cli_api import CliApiSession, CliApiSessionManager
-from extensions.ext_database import db
-from libs.login import current_user
-from models.account import Tenant
-from models.model import DefaultEndUserSessionID, EndUser
-
-P = ParamSpec("P")
-R = TypeVar("R")
-
-
-class TenantUserPayload(BaseModel):
-    tenant_id: str
-    user_id: str
-
-
-def get_user(tenant_id: str, user_id: str | None) -> EndUser:
-    """
-    Get current user
-
-    NOTE: user_id is not trusted, it could be maliciously set to any value.
-    As a result, it could only be considered as an end user id.
-    """
-    if not user_id:
-        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
-    is_anonymous = user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID
-    try:
-        with Session(db.engine) as session:
-            user_model = None
-
-            if is_anonymous:
-                user_model = (
-                    session.query(EndUser)
-                    .where(
-                        EndUser.session_id == user_id,
-                        EndUser.tenant_id == tenant_id,
-                    )
-                    .first()
-                )
-            else:
-                user_model = (
-                    session.query(EndUser)
-                    .where(
-                        EndUser.id == user_id,
-                        EndUser.tenant_id == tenant_id,
-                    )
-                    .first()
-                )
-
-            if not user_model:
-                user_model = EndUser(
-                    tenant_id=tenant_id,
-                    type="service_api",
-                    is_anonymous=is_anonymous,
-                    session_id=user_id,
-                )
-                session.add(user_model)
-                session.commit()
-                session.refresh(user_model)
-
-    except Exception:
-        raise ValueError("user not found")
-
-    return user_model
-
-
-def get_user_tenant(view_func: Callable[P, R]):
-    @wraps(view_func)
-    def decorated_view(*args: P.args, **kwargs: P.kwargs):
-        session_id = request.headers.get("X-Cli-Api-Session-Id")
-
-        if session_id:
-            session: CliApiSession | None = CliApiSessionManager().get(session_id)
-            if not session:
-                raise ValueError("session not found")
-            user_id = session.user_id
-            tenant_id = session.tenant_id
-        else:
-            payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
-            user_id = payload.user_id
-            tenant_id = payload.tenant_id
-
-        if not tenant_id:
-            raise ValueError("tenant_id is required")
-
-        if not user_id:
-            user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
-
-        try:
-            tenant_model = (
-                db.session.query(Tenant)
-                .where(
-                    Tenant.id == tenant_id,
-                )
-                .first()
-            )
-        except Exception:
-            raise ValueError("tenant not found")
-
-        if not tenant_model:
-            raise ValueError("tenant not found")
-
-        kwargs["tenant_model"] = tenant_model
-
-        user = get_user(tenant_id, user_id)
-        kwargs["user_model"] = user
-
-        current_app.login_manager._update_request_context_with_user(user)  # type: ignore
-        user_logged_in.send(current_app._get_current_object(), user=current_user)  # type: ignore
-
-        return view_func(*args, **kwargs)
-
-    return decorated_view
-
-
-def plugin_data(view: Callable[P, R] | None = None, *, payload_type: type[BaseModel]):
-    def decorator(view_func: Callable[P, R]):
-        def decorated_view(*args: P.args, **kwargs: P.kwargs):
-            try:
-                data = request.get_json()
-            except Exception:
-                raise ValueError("invalid json")
-
-            try:
-                payload = payload_type.model_validate(data)
-            except Exception as e:
-                raise ValueError(f"invalid payload: {str(e)}")
-
-            kwargs["payload"] = payload
-            return view_func(*args, **kwargs)
-
-        return decorated_view
-
-    if view is None:
-        return decorator
-    else:
-        return decorator(view)

api/controllers/cli_api/wraps.py

@@ -1,54 +0,0 @@
-import hashlib
-import hmac
-import time
-from collections.abc import Callable
-from functools import wraps
-from typing import ParamSpec, TypeVar
-
-from flask import abort, request
-
-from core.session.cli_api import CliApiSessionManager
-
-P = ParamSpec("P")
-R = TypeVar("R")
-
-SIGNATURE_TTL_SECONDS = 300
-
-
-def _verify_signature(session_secret: str, timestamp: str, body: bytes, signature: str) -> bool:
-    expected = hmac.new(
-        session_secret.encode(),
-        f"{timestamp}.".encode() + body,
-        hashlib.sha256,
-    ).hexdigest()
-    return hmac.compare_digest(f"sha256={expected}", signature)
-
-
-def cli_api_only(view: Callable[P, R]):
-    @wraps(view)
-    def decorated(*args: P.args, **kwargs: P.kwargs):
-        session_id = request.headers.get("X-Cli-Api-Session-Id")
-        timestamp = request.headers.get("X-Cli-Api-Timestamp")
-        signature = request.headers.get("X-Cli-Api-Signature")
-
-        if not session_id or not timestamp or not signature:
-            abort(401)
-
-        try:
-            ts = int(timestamp)
-            if abs(time.time() - ts) > SIGNATURE_TTL_SECONDS:
-                abort(401)
-        except ValueError:
-            abort(401)
-
-        session = CliApiSessionManager().get(session_id)
-        if not session:
-            abort(401)
-
-        body = request.get_data()
-        if not _verify_signature(session.secret, timestamp, body, signature):
-            abort(401)
-
-        return view(*args, **kwargs)
-
-    return decorated

api/controllers/console/__init__.py

@@ -50,7 +50,6 @@ from .app import (
     agent,
     annotation,
     app,
-    app_asset,
     audio,
     completion,
     conversation,
@@ -127,7 +126,6 @@ from .workspace import (
     model_providers,
     models,
     plugin,
-    sandbox_providers,
     tool_providers,
     trigger_providers,
     workspace,
@@ -146,7 +144,6 @@ __all__ = [
     "api",
     "apikey",
     "app",
-    "app_asset",
     "audio",
     "billing",
     "bp",
@@ -194,7 +191,6 @@ __all__ = [
     "rag_pipeline_import",
     "rag_pipeline_workflow",
     "recommended_app",
-    "sandbox_providers",
     "saved_message",
     "setup",
     "site",

api/controllers/console/app/app.py

@@ -1,3 +1,4 @@
+import re
 import uuid
 from datetime import datetime
 from typing import Any, Literal, TypeAlias
@@ -67,6 +68,48 @@ class AppListQuery(BaseModel):
             raise ValueError("Invalid UUID format in tag_ids.") from exc
 
 
+# XSS prevention: patterns that could lead to XSS attacks
+# Includes: script tags, iframe tags, javascript: protocol, SVG with onload, etc.
+_XSS_PATTERNS = [
+    r"<script[^>]*>.*?</script>",  # Script tags
+    r"<iframe\b[^>]*?(?:/>|>.*?</iframe>)",  # Iframe tags (including self-closing)
+    r"javascript:",  # JavaScript protocol
+    r"<svg[^>]*?\s+onload\s*=[^>]*>",  # SVG with onload handler (attribute-aware, flexible whitespace)
+    r"<.*?on\s*\w+\s*=",  # Event handlers like onclick, onerror, etc.
+    r"<object\b[^>]*(?:\s*/>|>.*?</object\s*>)",  # Object tags (opening tag)
+    r"<embed[^>]*>",  # Embed tags (self-closing)
+    r"<link[^>]*>",  # Link tags with javascript
+]
+
+
+def _validate_xss_safe(value: str | None, field_name: str = "Field") -> str | None:
+    """
+    Validate that a string value doesn't contain potential XSS payloads.
+
+    Args:
+        value: The string value to validate
+        field_name: Name of the field for error messages
+
+    Returns:
+        The original value if safe
+
+    Raises:
+        ValueError: If the value contains XSS patterns
+    """
+    if value is None:
+        return None
+
+    value_lower = value.lower()
+    for pattern in _XSS_PATTERNS:
+        if re.search(pattern, value_lower, re.DOTALL | re.IGNORECASE):
+            raise ValueError(
+                f"{field_name} contains invalid characters or patterns. "
+                "HTML tags, JavaScript, and other potentially dangerous content are not allowed."
+            )
+
+    return value
+
+
 class CreateAppPayload(BaseModel):
     name: str = Field(..., min_length=1, description="App name")
     description: str | None = Field(default=None, description="App description (max 400 chars)", max_length=400)
@@ -75,6 +118,11 @@ class CreateAppPayload(BaseModel):
     icon: str | None = Field(default=None, description="Icon")
     icon_background: str | None = Field(default=None, description="Icon background color")
 
+    @field_validator("name", "description", mode="before")
+    @classmethod
+    def validate_xss_safe(cls, value: str | None, info) -> str | None:
+        return _validate_xss_safe(value, info.field_name)
+
 
 class UpdateAppPayload(BaseModel):
     name: str = Field(..., min_length=1, description="App name")
@@ -85,6 +133,11 @@ class UpdateAppPayload(BaseModel):
     use_icon_as_answer_icon: bool | None = Field(default=None, description="Use icon as answer icon")
     max_active_requests: int | None = Field(default=None, description="Maximum active requests")
 
+    @field_validator("name", "description", mode="before")
+    @classmethod
+    def validate_xss_safe(cls, value: str | None, info) -> str | None:
+        return _validate_xss_safe(value, info.field_name)
+
 
 class CopyAppPayload(BaseModel):
     name: str | None = Field(default=None, description="Name for the copied app")
@@ -93,6 +146,11 @@ class CopyAppPayload(BaseModel):
     icon: str | None = Field(default=None, description="Icon")
     icon_background: str | None = Field(default=None, description="Icon background color")
 
+    @field_validator("name", "description", mode="before")
+    @classmethod
+    def validate_xss_safe(cls, value: str | None, info) -> str | None:
+        return _validate_xss_safe(value, info.field_name)
+
 
 class AppExportQuery(BaseModel):
     include_secret: bool = Field(default=False, description="Include secrets in export")

api/controllers/console/app/app_asset.py

@@ -1,274 +0,0 @@
-from flask import request
-from flask_restx import Resource
-from pydantic import BaseModel, Field, field_validator
-
-from controllers.console import console_ns
-from controllers.console.app.error import (
-    AppAssetFileRequiredError,
-    AppAssetNodeNotFoundError,
-    AppAssetPathConflictError,
-)
-from controllers.console.app.wraps import get_app_model
-from controllers.console.wraps import account_initialization_required, setup_required
-from libs.login import current_account_with_tenant, login_required
-from models import App
-from models.model import AppMode
-from services.app_asset_service import AppAssetService
-from services.errors.app_asset import (
-    AppAssetNodeNotFoundError as ServiceNodeNotFoundError,
-)
-from services.errors.app_asset import (
-    AppAssetParentNotFoundError,
-)
-from services.errors.app_asset import (
-    AppAssetPathConflictError as ServicePathConflictError,
-)
-
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
-
-
-class CreateFolderPayload(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-    parent_id: str | None = None
-
-
-class CreateFilePayload(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-    parent_id: str | None = None
-
-    @field_validator("name", mode="before")
-    @classmethod
-    def strip_name(cls, v: str) -> str:
-        return v.strip() if isinstance(v, str) else v
-
-    @field_validator("parent_id", mode="before")
-    @classmethod
-    def empty_to_none(cls, v: str | None) -> str | None:
-        return v or None
-
-
-class UpdateFileContentPayload(BaseModel):
-    content: str
-
-
-class RenameNodePayload(BaseModel):
-    name: str = Field(..., min_length=1, max_length=255)
-
-
-class MoveNodePayload(BaseModel):
-    parent_id: str | None = None
-
-
-class ReorderNodePayload(BaseModel):
-    after_node_id: str | None = Field(default=None, description="Place after this node, None for first position")
-
-
-def reg(cls: type[BaseModel]) -> None:
-    console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
-
-
-reg(CreateFolderPayload)
-reg(CreateFilePayload)
-reg(UpdateFileContentPayload)
-reg(RenameNodePayload)
-reg(MoveNodePayload)
-reg(ReorderNodePayload)
-
-
-@console_ns.route("/apps/<string:app_id>/assets/tree")
-class AppAssetTreeResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def get(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-        tree = AppAssetService.get_asset_tree(app_model, current_user.id)
-        return {"children": [view.model_dump() for view in tree.transform()]}
-
-
-@console_ns.route("/apps/<string:app_id>/assets/folders")
-class AppAssetFolderResource(Resource):
-    @console_ns.expect(console_ns.models[CreateFolderPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-        payload = CreateFolderPayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.create_folder(app_model, current_user.id, payload.name, payload.parent_id)
-            return node.model_dump(), 201
-        except AppAssetParentNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/files")
-class AppAssetFileResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-
-        file = request.files.get("file")
-        if not file:
-            raise AppAssetFileRequiredError()
-
-        payload = CreateFilePayload.model_validate(request.form.to_dict())
-        content = file.read()
-
-        try:
-            node = AppAssetService.create_file(app_model, current_user.id, payload.name, content, payload.parent_id)
-            return node.model_dump(), 201
-        except AppAssetParentNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>")
-class AppAssetFileDetailResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def get(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        try:
-            content = AppAssetService.get_file_content(app_model, current_user.id, node_id)
-            return {"content": content.decode("utf-8", errors="replace")}
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-    @console_ns.expect(console_ns.models[UpdateFileContentPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def put(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-
-        file = request.files.get("file")
-        if file:
-            content = file.read()
-        else:
-            payload = UpdateFileContentPayload.model_validate(console_ns.payload or {})
-            content = payload.content.encode("utf-8")
-
-        try:
-            node = AppAssetService.update_file_content(app_model, current_user.id, node_id, content)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>")
-class AppAssetNodeResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def delete(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        try:
-            AppAssetService.delete_node(app_model, current_user.id, node_id)
-            return {"result": "success"}, 200
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/rename")
-class AppAssetNodeRenameResource(Resource):
-    @console_ns.expect(console_ns.models[RenameNodePayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        payload = RenameNodePayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.rename_node(app_model, current_user.id, node_id, payload.name)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/move")
-class AppAssetNodeMoveResource(Resource):
-    @console_ns.expect(console_ns.models[MoveNodePayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        payload = MoveNodePayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.move_node(app_model, current_user.id, node_id, payload.parent_id)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except AppAssetParentNotFoundError:
-            raise AppAssetNodeNotFoundError()
-        except ServicePathConflictError:
-            raise AppAssetPathConflictError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/reorder")
-class AppAssetNodeReorderResource(Resource):
-    @console_ns.expect(console_ns.models[ReorderNodePayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        payload = ReorderNodePayload.model_validate(console_ns.payload or {})
-
-        try:
-            node = AppAssetService.reorder_node(app_model, current_user.id, node_id, payload.after_node_id)
-            return node.model_dump()
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()
-
-
-@console_ns.route("/apps/<string:app_id>/assets/publish")
-class AppAssetPublishResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def post(self, app_model: App):
-        current_user, _ = current_account_with_tenant()
-        published = AppAssetService.publish(app_model, current_user.id)
-        return {
-            "id": published.id,
-            "version": published.version,
-            "asset_tree": published.asset_tree.model_dump(),
-        }, 201
-
-
-@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>/download-url")
-class AppAssetFileDownloadUrlResource(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    def get(self, app_model: App, node_id: str):
-        current_user, _ = current_account_with_tenant()
-        try:
-            download_url = AppAssetService.get_file_download_url(app_model, current_user.id, node_id)
-            return {"download_url": download_url}
-        except ServiceNodeNotFoundError:
-            raise AppAssetNodeNotFoundError()

api/controllers/console/app/error.py

@@ -110,24 +110,8 @@ class TracingConfigCheckError(BaseHTTPException):
 
 
 class InvokeRateLimitError(BaseHTTPException):
+    """Raised when the Invoke returns rate limit error."""
+
     error_code = "rate_limit_error"
     description = "Rate Limit Error"
     code = 429
-
-
-class AppAssetNodeNotFoundError(BaseHTTPException):
-    error_code = "app_asset_node_not_found"
-    description = "App asset node not found."
-    code = 404
-
-
-class AppAssetFileRequiredError(BaseHTTPException):
-    error_code = "app_asset_file_required"
-    description = "File is required."
-    code = 400
-
-
-class AppAssetPathConflictError(BaseHTTPException):
-    error_code = "app_asset_path_conflict"
-    description = "Path already exists."
-    code = 409

api/controllers/console/app/message.py

@@ -202,7 +202,6 @@ message_detail_model = console_ns.model(
         "status": fields.String,
         "error": fields.String,
         "parent_message_id": fields.String,
-        "generation_detail": fields.Raw,
     },
 )
 

api/controllers/console/auth/activate.py

@@ -69,13 +69,6 @@ class ActivateCheckApi(Resource):
         if invitation:
             data = invitation.get("data", {})
             tenant = invitation.get("tenant", None)
-
-            # Check workspace permission
-            if tenant:
-                from libs.workspace_permission import check_workspace_member_invite_permission
-
-                check_workspace_member_invite_permission(tenant.id)
-
             workspace_name = tenant.name if tenant else None
             workspace_id = tenant.id if tenant else None
             invitee_email = data.get("email") if data else None

api/controllers/console/datasets/datasets.py

@@ -146,6 +146,7 @@ class DatasetUpdatePayload(BaseModel):
     embedding_model: str | None = None
     embedding_model_provider: str | None = None
     retrieval_model: dict[str, Any] | None = None
+    summary_index_setting: dict[str, Any] | None = None
     partial_member_list: list[dict[str, str]] | None = None
     external_retrieval_model: dict[str, Any] | None = None
     external_knowledge_id: str | None = None

api/controllers/console/datasets/datasets_document.py

@@ -39,9 +39,10 @@ from fields.document_fields import (
 from libs.datetime_utils import naive_utc_now
 from libs.login import current_account_with_tenant, login_required
 from models import DatasetProcessRule, Document, DocumentSegment, UploadFile
-from models.dataset import DocumentPipelineExecutionLog
+from models.dataset import DocumentPipelineExecutionLog, DocumentSegmentSummary
 from services.dataset_service import DatasetService, DocumentService
 from services.entities.knowledge_entities.knowledge_entities import KnowledgeConfig, ProcessRule, RetrievalModel
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 from ..app.error import (
     ProviderModelCurrentlyNotSupportError,
@@ -104,6 +105,10 @@ class DocumentRenamePayload(BaseModel):
     name: str
 
 
+class GenerateSummaryPayload(BaseModel):
+    document_list: list[str]
+
+
 class DocumentDatasetListParam(BaseModel):
     page: int = Field(1, title="Page", description="Page number.")
     limit: int = Field(20, title="Limit", description="Page size.")
@@ -120,6 +125,7 @@ register_schema_models(
     RetrievalModel,
     DocumentRetryPayload,
     DocumentRenamePayload,
+    GenerateSummaryPayload,
 )
 
 
@@ -306,6 +312,94 @@ class DatasetDocumentListApi(Resource):
 
         paginated_documents = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)
         documents = paginated_documents.items
+
+        # Check if dataset has summary index enabled
+        has_summary_index = dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True
+
+        # Filter documents that need summary calculation
+        documents_need_summary = [doc for doc in documents if doc.need_summary is True]
+        document_ids_need_summary = [str(doc.id) for doc in documents_need_summary]
+
+        # Calculate summary_index_status for documents that need summary (only if dataset summary index is enabled)
+        summary_status_map = {}
+        if has_summary_index and document_ids_need_summary:
+            # Get all segments for these documents (excluding qa_model and re_segment)
+            segments = (
+                db.session.query(DocumentSegment.id, DocumentSegment.document_id)
+                .where(
+                    DocumentSegment.document_id.in_(document_ids_need_summary),
+                    DocumentSegment.status != "re_segment",
+                    DocumentSegment.tenant_id == current_tenant_id,
+                )
+                .all()
+            )
+
+            # Group segments by document_id
+            document_segments_map = {}
+            for segment in segments:
+                doc_id = str(segment.document_id)
+                if doc_id not in document_segments_map:
+                    document_segments_map[doc_id] = []
+                document_segments_map[doc_id].append(segment.id)
+
+            # Get all summary records for these segments
+            all_segment_ids = [seg.id for seg in segments]
+            summaries = {}
+            if all_segment_ids:
+                summary_records = (
+                    db.session.query(DocumentSegmentSummary)
+                    .where(
+                        DocumentSegmentSummary.chunk_id.in_(all_segment_ids),
+                        DocumentSegmentSummary.dataset_id == dataset_id,
+                        DocumentSegmentSummary.enabled == True,  # Only count enabled summaries
+                    )
+                    .all()
+                )
+                summaries = {summary.chunk_id: summary.status for summary in summary_records}
+
+            # Calculate summary_index_status for each document
+            for doc_id in document_ids_need_summary:
+                segment_ids = document_segments_map.get(doc_id, [])
+                if not segment_ids:
+                    # No segments, status is "GENERATING" (waiting to generate)
+                    summary_status_map[doc_id] = "GENERATING"
+                    continue
+
+                # Count summary statuses for this document's segments
+                status_counts = {"completed": 0, "generating": 0, "error": 0, "not_started": 0}
+                for segment_id in segment_ids:
+                    status = summaries.get(segment_id, "not_started")
+                    if status in status_counts:
+                        status_counts[status] += 1
+                    else:
+                        status_counts["not_started"] += 1
+
+                total_segments = len(segment_ids)
+                completed_count = status_counts["completed"]
+                generating_count = status_counts["generating"]
+                error_count = status_counts["error"]
+
+                # Determine overall status (only three states: GENERATING, COMPLETED, ERROR)
+                if completed_count == total_segments:
+                    summary_status_map[doc_id] = "COMPLETED"
+                elif error_count > 0:
+                    # Has errors (even if some are completed or generating)
+                    summary_status_map[doc_id] = "ERROR"
+                elif generating_count > 0 or status_counts["not_started"] > 0:
+                    # Still generating or not started
+                    summary_status_map[doc_id] = "GENERATING"
+                else:
+                    # Default to generating
+                    summary_status_map[doc_id] = "GENERATING"
+
+        # Add summary_index_status to each document
+        for document in documents:
+            if has_summary_index and document.need_summary is True:
+                document.summary_index_status = summary_status_map.get(str(document.id), "GENERATING")
+            else:
+                # Return null if summary index is not enabled or document doesn't need summary
+                document.summary_index_status = None
+
         if fetch:
             for document in documents:
                 completed_segments = (
@@ -791,6 +885,7 @@ class DocumentApi(DocumentResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
         else:
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
@@ -826,6 +921,7 @@ class DocumentApi(DocumentResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
 
         return response, 200
@@ -1193,3 +1289,216 @@ class DocumentPipelineExecutionLogApi(DocumentResource):
             "input_data": log.input_data,
             "datasource_node_id": log.datasource_node_id,
         }, 200
+
+
+@console_ns.route("/datasets/<uuid:dataset_id>/documents/generate-summary")
+class DocumentGenerateSummaryApi(Resource):
+    @console_ns.doc("generate_summary_for_documents")
+    @console_ns.doc(description="Generate summary index for documents")
+    @console_ns.doc(params={"dataset_id": "Dataset ID"})
+    @console_ns.expect(console_ns.models[GenerateSummaryPayload.__name__])
+    @console_ns.response(200, "Summary generation started successfully")
+    @console_ns.response(400, "Invalid request or dataset configuration")
+    @console_ns.response(403, "Permission denied")
+    @console_ns.response(404, "Dataset not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @cloud_edition_billing_rate_limit_check("knowledge")
+    def post(self, dataset_id):
+        """
+        Generate summary index for specified documents.
+
+        This endpoint checks if the dataset configuration supports summary generation
+        (indexing_technique must be 'high_quality' and summary_index_setting.enable must be true),
+        then asynchronously generates summary indexes for the provided documents.
+        """
+        current_user, _ = current_account_with_tenant()
+        dataset_id = str(dataset_id)
+
+        # Get dataset
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound("Dataset not found.")
+
+        # Check permissions
+        if not current_user.is_dataset_editor:
+            raise Forbidden()
+
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        # Validate request payload
+        payload = GenerateSummaryPayload.model_validate(console_ns.payload or {})
+        document_list = payload.document_list
+
+        if not document_list:
+            raise ValueError("document_list cannot be empty.")
+
+        # Check if dataset configuration supports summary generation
+        if dataset.indexing_technique != "high_quality":
+            raise ValueError(
+                f"Summary generation is only available for 'high_quality' indexing technique. "
+                f"Current indexing technique: {dataset.indexing_technique}"
+            )
+
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            raise ValueError("Summary index is not enabled for this dataset. Please enable it in the dataset settings.")
+
+        # Verify all documents exist and belong to the dataset
+        documents = (
+            db.session.query(Document)
+            .filter(
+                Document.id.in_(document_list),
+                Document.dataset_id == dataset_id,
+            )
+            .all()
+        )
+
+        if len(documents) != len(document_list):
+            found_ids = {doc.id for doc in documents}
+            missing_ids = set(document_list) - found_ids
+            raise NotFound(f"Some documents not found: {list(missing_ids)}")
+
+        # Dispatch async tasks for each document
+        for document in documents:
+            # Skip qa_model documents as they don't generate summaries
+            if document.doc_form == "qa_model":
+                logger.info("Skipping summary generation for qa_model document %s", document.id)
+                continue
+
+            # Dispatch async task
+            generate_summary_index_task(dataset_id, document.id)
+            logger.info(
+                "Dispatched summary generation task for document %s in dataset %s",
+                document.id,
+                dataset_id,
+            )
+
+        return {"result": "success"}, 200
+
+
+@console_ns.route("/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/summary-status")
+class DocumentSummaryStatusApi(DocumentResource):
+    @console_ns.doc("get_document_summary_status")
+    @console_ns.doc(description="Get summary index generation status for a document")
+    @console_ns.doc(params={"dataset_id": "Dataset ID", "document_id": "Document ID"})
+    @console_ns.response(200, "Summary status retrieved successfully")
+    @console_ns.response(404, "Document not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, document_id):
+        """
+        Get summary index generation status for a document.
+
+        Returns:
+        - total_segments: Total number of segments in the document
+        - summary_status: Dictionary with status counts
+          - completed: Number of summaries completed
+          - generating: Number of summaries being generated
+          - error: Number of summaries with errors
+          - not_started: Number of segments without summary records
+        - summaries: List of summary records with status and content preview
+        """
+        current_user, _ = current_account_with_tenant()
+        dataset_id = str(dataset_id)
+        document_id = str(document_id)
+
+        # Get document
+        document = self.get_document(dataset_id, document_id)
+
+        # Get dataset
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound("Dataset not found.")
+
+        # Check permissions
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        # Get all segments for this document
+        segments = (
+            db.session.query(DocumentSegment)
+            .filter(
+                DocumentSegment.document_id == document_id,
+                DocumentSegment.dataset_id == dataset_id,
+                DocumentSegment.status == "completed",
+                DocumentSegment.enabled == True,
+            )
+            .all()
+        )
+
+        total_segments = len(segments)
+
+        # Get all summary records for these segments
+        segment_ids = [segment.id for segment in segments]
+        summaries = []
+        if segment_ids:
+            summaries = (
+                db.session.query(DocumentSegmentSummary)
+                .filter(
+                    DocumentSegmentSummary.document_id == document_id,
+                    DocumentSegmentSummary.dataset_id == dataset_id,
+                    DocumentSegmentSummary.chunk_id.in_(segment_ids),
+                    DocumentSegmentSummary.enabled == True,  # Only return enabled summaries
+                )
+                .all()
+            )
+
+        # Create a mapping of chunk_id to summary
+        summary_map = {summary.chunk_id: summary for summary in summaries}
+
+        # Count statuses
+        status_counts = {
+            "completed": 0,
+            "generating": 0,
+            "error": 0,
+            "not_started": 0,
+        }
+
+        summary_list = []
+        for segment in segments:
+            summary = summary_map.get(segment.id)
+            if summary:
+                status = summary.status
+                status_counts[status] = status_counts.get(status, 0) + 1
+                summary_list.append(
+                    {
+                        "segment_id": segment.id,
+                        "segment_position": segment.position,
+                        "status": summary.status,
+                        "summary_preview": (
+                            summary.summary_content[:100] + "..."
+                            if summary.summary_content and len(summary.summary_content) > 100
+                            else summary.summary_content
+                        ),
+                        "error": summary.error,
+                        "created_at": int(summary.created_at.timestamp()) if summary.created_at else None,
+                        "updated_at": int(summary.updated_at.timestamp()) if summary.updated_at else None,
+                    }
+                )
+            else:
+                status_counts["not_started"] += 1
+                summary_list.append(
+                    {
+                        "segment_id": segment.id,
+                        "segment_position": segment.position,
+                        "status": "not_started",
+                        "summary_preview": None,
+                        "error": None,
+                        "created_at": None,
+                        "updated_at": None,
+                    }
+                )
+
+        return {
+            "total_segments": total_segments,
+            "summary_status": status_counts,
+            "summaries": summary_list,
+        }, 200

api/controllers/console/datasets/datasets_segments.py

@@ -32,7 +32,7 @@ from extensions.ext_redis import redis_client
 from fields.segment_fields import child_chunk_fields, segment_fields
 from libs.helper import escape_like_pattern
 from libs.login import current_account_with_tenant, login_required
-from models.dataset import ChildChunk, DocumentSegment
+from models.dataset import ChildChunk, DocumentSegment, DocumentSegmentSummary
 from models.model import UploadFile
 from services.dataset_service import DatasetService, DocumentService, SegmentService
 from services.entities.knowledge_entities.knowledge_entities import ChildChunkUpdateArgs, SegmentUpdateArgs
@@ -41,6 +41,23 @@ from services.errors.chunk import ChildChunkIndexingError as ChildChunkIndexingS
 from tasks.batch_create_segment_to_index_task import batch_create_segment_to_index_task
 
 
+def _get_segment_with_summary(segment, dataset_id):
+    """Helper function to marshal segment and add summary information."""
+    segment_dict = marshal(segment, segment_fields)
+    # Query summary for this segment (only enabled summaries)
+    summary = (
+        db.session.query(DocumentSegmentSummary)
+        .where(
+            DocumentSegmentSummary.chunk_id == segment.id,
+            DocumentSegmentSummary.dataset_id == dataset_id,
+            DocumentSegmentSummary.enabled == True,  # Only return enabled summaries
+        )
+        .first()
+    )
+    segment_dict["summary"] = summary.summary_content if summary else None
+    return segment_dict
+
+
 class SegmentListQuery(BaseModel):
     limit: int = Field(default=20, ge=1, le=100)
     status: list[str] = Field(default_factory=list)
@@ -63,6 +80,7 @@ class SegmentUpdatePayload(BaseModel):
     keywords: list[str] | None = None
     regenerate_child_chunks: bool = False
     attachment_ids: list[str] | None = None
+    summary: str | None = None  # Summary content for summary index
 
 
 class BatchImportPayload(BaseModel):
@@ -180,8 +198,32 @@ class DatasetDocumentSegmentListApi(Resource):
 
         segments = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)
 
+        # Query summaries for all segments in this page (batch query for efficiency)
+        segment_ids = [segment.id for segment in segments.items]
+        summaries = {}
+        if segment_ids:
+            summary_records = (
+                db.session.query(DocumentSegmentSummary)
+                .where(
+                    DocumentSegmentSummary.chunk_id.in_(segment_ids),
+                    DocumentSegmentSummary.dataset_id == dataset_id,
+                )
+                .all()
+            )
+            # Only include enabled summaries
+            summaries = {
+                summary.chunk_id: summary.summary_content for summary in summary_records if summary.enabled is True
+            }
+
+        # Add summary to each segment
+        segments_with_summary = []
+        for segment in segments.items:
+            segment_dict = marshal(segment, segment_fields)
+            segment_dict["summary"] = summaries.get(segment.id)
+            segments_with_summary.append(segment_dict)
+
         response = {
-            "data": marshal(segments.items, segment_fields),
+            "data": segments_with_summary,
             "limit": limit,
             "total": segments.total,
             "total_pages": segments.pages,
@@ -327,7 +369,7 @@ class DatasetDocumentSegmentAddApi(Resource):
         payload_dict = payload.model_dump(exclude_none=True)
         SegmentService.segment_create_args_validate(payload_dict, document)
         segment = SegmentService.create_segment(payload_dict, document, dataset)
-        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+        return {"data": _get_segment_with_summary(segment, dataset_id), "doc_form": document.doc_form}, 200
 
 
 @console_ns.route("/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>")
@@ -389,10 +431,12 @@ class DatasetDocumentSegmentUpdateApi(Resource):
         payload = SegmentUpdatePayload.model_validate(console_ns.payload or {})
         payload_dict = payload.model_dump(exclude_none=True)
         SegmentService.segment_create_args_validate(payload_dict, document)
+
+        # Update segment (summary update with change detection is handled in SegmentService.update_segment)
         segment = SegmentService.update_segment(
             SegmentUpdateArgs.model_validate(payload.model_dump(exclude_none=True)), segment, document, dataset
         )
-        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+        return {"data": _get_segment_with_summary(segment, dataset_id), "doc_form": document.doc_form}, 200
 
     @setup_required
     @login_required

api/controllers/console/datasets/hit_testing.py

@@ -1,6 +1,13 @@
-from flask_restx import Resource
+from flask_restx import Resource, fields
 
 from controllers.common.schema import register_schema_model
+from fields.hit_testing_fields import (
+    child_chunk_fields,
+    document_fields,
+    files_fields,
+    hit_testing_record_fields,
+    segment_fields,
+)
 from libs.login import login_required
 
 from .. import console_ns
@@ -14,13 +21,45 @@ from ..wraps import (
 register_schema_model(console_ns, HitTestingPayload)
 
 
+def _get_or_create_model(model_name: str, field_def):
+    """Get or create a flask_restx model to avoid dict type issues in Swagger."""
+    existing = console_ns.models.get(model_name)
+    if existing is None:
+        existing = console_ns.model(model_name, field_def)
+    return existing
+
+
+# Register models for flask_restx to avoid dict type issues in Swagger
+document_model = _get_or_create_model("HitTestingDocument", document_fields)
+
+segment_fields_copy = segment_fields.copy()
+segment_fields_copy["document"] = fields.Nested(document_model)
+segment_model = _get_or_create_model("HitTestingSegment", segment_fields_copy)
+
+child_chunk_model = _get_or_create_model("HitTestingChildChunk", child_chunk_fields)
+files_model = _get_or_create_model("HitTestingFile", files_fields)
+
+hit_testing_record_fields_copy = hit_testing_record_fields.copy()
+hit_testing_record_fields_copy["segment"] = fields.Nested(segment_model)
+hit_testing_record_fields_copy["child_chunks"] = fields.List(fields.Nested(child_chunk_model))
+hit_testing_record_fields_copy["files"] = fields.List(fields.Nested(files_model))
+hit_testing_record_model = _get_or_create_model("HitTestingRecord", hit_testing_record_fields_copy)
+
+# Response model for hit testing API
+hit_testing_response_fields = {
+    "query": fields.String,
+    "records": fields.List(fields.Nested(hit_testing_record_model)),
+}
+hit_testing_response_model = _get_or_create_model("HitTestingResponse", hit_testing_response_fields)
+
+
 @console_ns.route("/datasets/<uuid:dataset_id>/hit-testing")
 class HitTestingApi(Resource, DatasetsHitTestingBase):
     @console_ns.doc("test_dataset_retrieval")
     @console_ns.doc(description="Test dataset knowledge retrieval")
     @console_ns.doc(params={"dataset_id": "Dataset ID"})
     @console_ns.expect(console_ns.models[HitTestingPayload.__name__])
-    @console_ns.response(200, "Hit testing completed successfully")
+    @console_ns.response(200, "Hit testing completed successfully", model=hit_testing_response_model)
     @console_ns.response(404, "Dataset not found")
     @console_ns.response(400, "Invalid parameters")
     @setup_required

api/controllers/console/workspace/dsl.py

@@ -1,65 +0,0 @@
-import json
-
-import httpx
-import yaml
-from flask_restx import Resource, reqparse
-from sqlalchemy.orm import Session
-from werkzeug.exceptions import Forbidden
-
-from controllers.console import console_ns
-from controllers.console.wraps import account_initialization_required, setup_required
-from core.plugin.impl.exc import PluginPermissionDeniedError
-from extensions.ext_database import db
-from libs.login import current_account_with_tenant, login_required
-from models.model import App
-from models.workflow import Workflow
-from services.app_dsl_service import AppDslService
-
-
-@console_ns.route("/workspaces/current/dsl/predict")
-class DSLPredictApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        user, _ = current_account_with_tenant()
-        if not user.is_admin_or_owner:
-            raise Forbidden()
-
-        parser = (
-            reqparse.RequestParser()
-            .add_argument("app_id", type=str, required=True, location="json")
-            .add_argument("current_node_id", type=str, required=True, location="json")
-        )
-        args = parser.parse_args()
-
-        app_id: str = args["app_id"]
-        current_node_id: str = args["current_node_id"]
-
-        with Session(db.engine) as session:
-            app = session.query(App).filter_by(id=app_id).first()
-            workflow = session.query(Workflow).filter_by(app_id=app_id, version=Workflow.VERSION_DRAFT).first()
-
-        if not app:
-            raise ValueError("App not found")
-        if not workflow:
-            raise ValueError("Workflow not found")
-
-        try:
-            i = 0
-            for node_id, _ in workflow.walk_nodes():
-                if node_id == current_node_id:
-                    break
-                i += 1
-
-            dsl = yaml.safe_load(AppDslService.export_dsl(app_model=app))
-
-            response = httpx.post(
-                "http://spark-832c:8000/predict",
-                json={"graph_data": dsl, "source_node_index": i},
-            )
-            return {
-                "nodes": json.loads(response.json()),
-            }
-        except PluginPermissionDeniedError as e:
-            raise ValueError(e.description) from e

api/controllers/console/workspace/members.py

@@ -107,12 +107,6 @@ class MemberInviteEmailApi(Resource):
         inviter = current_user
         if not inviter.current_tenant:
             raise ValueError("No current tenant")
-
-        # Check workspace permission for member invitations
-        from libs.workspace_permission import check_workspace_member_invite_permission
-
-        check_workspace_member_invite_permission(inviter.current_tenant.id)
-
         invitation_results = []
         console_web_url = dify_config.CONSOLE_WEB_URL
 

api/controllers/console/workspace/sandbox_providers.py

@@ -1,95 +0,0 @@
-import logging
-
-from flask_restx import Resource, fields, reqparse
-
-from controllers.console import console_ns
-from controllers.console.wraps import account_initialization_required, setup_required
-from core.model_runtime.utils.encoders import jsonable_encoder
-from libs.login import current_account_with_tenant, login_required
-from services.sandbox.sandbox_provider_service import SandboxProviderService
-
-logger = logging.getLogger(__name__)
-
-
-@console_ns.route("/workspaces/current/sandbox-providers")
-class SandboxProviderListApi(Resource):
-    @console_ns.doc("list_sandbox_providers")
-    @console_ns.doc(description="Get list of available sandbox providers with configuration status")
-    @console_ns.response(200, "Success", fields.List(fields.Raw(description="Sandbox provider information")))
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def get(self):
-        _, current_tenant_id = current_account_with_tenant()
-        providers = SandboxProviderService.list_providers(current_tenant_id)
-        return jsonable_encoder([p.model_dump() for p in providers])
-
-
-config_parser = reqparse.RequestParser()
-config_parser.add_argument("config", type=dict, required=True, location="json")
-
-
-@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/config")
-class SandboxProviderConfigApi(Resource):
-    @console_ns.doc("save_sandbox_provider_config")
-    @console_ns.doc(description="Save or update configuration for a sandbox provider")
-    @console_ns.expect(config_parser)
-    @console_ns.response(200, "Success")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self, provider_type: str):
-        _, current_tenant_id = current_account_with_tenant()
-        args = config_parser.parse_args()
-
-        try:
-            result = SandboxProviderService.save_config(
-                tenant_id=current_tenant_id,
-                provider_type=provider_type,
-                config=args["config"],
-            )
-            return result
-        except ValueError as e:
-            return {"message": str(e)}, 400
-
-    @console_ns.doc("delete_sandbox_provider_config")
-    @console_ns.doc(description="Delete configuration for a sandbox provider")
-    @console_ns.response(200, "Success")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def delete(self, provider_type: str):
-        _, current_tenant_id = current_account_with_tenant()
-
-        try:
-            result = SandboxProviderService.delete_config(
-                tenant_id=current_tenant_id,
-                provider_type=provider_type,
-            )
-            return result
-        except ValueError as e:
-            return {"message": str(e)}, 400
-
-
-@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/activate")
-class SandboxProviderActivateApi(Resource):
-    """Activate a sandbox provider."""
-
-    @console_ns.doc("activate_sandbox_provider")
-    @console_ns.doc(description="Activate a sandbox provider for the current workspace")
-    @console_ns.response(200, "Success")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self, provider_type: str):
-        """Activate a sandbox provider."""
-        _, current_tenant_id = current_account_with_tenant()
-
-        try:
-            result = SandboxProviderService.activate_provider(
-                tenant_id=current_tenant_id,
-                provider_type=provider_type,
-            )
-            return result
-        except ValueError as e:
-            return {"message": str(e)}, 400

api/controllers/console/workspace/workspace.py

@@ -20,7 +20,6 @@ from controllers.console.error import AccountNotLinkTenantError
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
-    only_edition_enterprise,
     setup_required,
 )
 from enums.cloud_plan import CloudPlan
@@ -29,7 +28,6 @@ from libs.helper import TimestampField
 from libs.login import current_account_with_tenant, login_required
 from models.account import Tenant, TenantStatus
 from services.account_service import TenantService
-from services.enterprise.enterprise_service import EnterpriseService
 from services.feature_service import FeatureService
 from services.file_service import FileService
 from services.workspace_service import WorkspaceService
@@ -290,31 +288,3 @@ class WorkspaceInfoApi(Resource):
         db.session.commit()
 
         return {"result": "success", "tenant": marshal(WorkspaceService.get_tenant_info(tenant), tenant_fields)}
-
-
-@console_ns.route("/workspaces/current/permission")
-class WorkspacePermissionApi(Resource):
-    """Get workspace permissions for the current workspace."""
-
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @only_edition_enterprise
-    def get(self):
-        """
-        Get workspace permission settings.
-        Returns permission flags that control workspace features like member invitations and owner transfer.
-        """
-        _, current_tenant_id = current_account_with_tenant()
-
-        if not current_tenant_id:
-            raise ValueError("No current tenant")
-
-        # Get workspace permissions from enterprise service
-        permission = EnterpriseService.WorkspacePermissionService.get_permission(current_tenant_id)
-
-        return {
-            "workspace_id": permission.workspace_id,
-            "allow_member_invite": permission.allow_member_invite,
-            "allow_owner_transfer": permission.allow_owner_transfer,
-        }, 200

api/controllers/console/wraps.py

@@ -286,12 +286,13 @@ def enable_change_email(view: Callable[P, R]):
 def is_allow_transfer_owner(view: Callable[P, R]):
     @wraps(view)
     def decorated(*args: P.args, **kwargs: P.kwargs):
-        from libs.workspace_permission import check_workspace_owner_transfer_permission
-
         _, current_tenant_id = current_account_with_tenant()
-        # Check both billing/plan level and workspace policy level permissions
-        check_workspace_owner_transfer_permission(current_tenant_id)
-        return view(*args, **kwargs)
+        features = FeatureService.get_features(current_tenant_id)
+        if features.is_allow_transfer_workspace:
+            return view(*args, **kwargs)
+
+        # otherwise, return 403
+        abort(403)
 
     return decorated
 

api/controllers/files/__init__.py

@@ -14,7 +14,7 @@ api = ExternalApi(
 
 files_ns = Namespace("files", description="File operations", path="/")
 
-from . import image_preview, storage_download, tool_files, upload
+from . import image_preview, tool_files, upload
 
 api.add_namespace(files_ns)
 
@@ -23,7 +23,6 @@ __all__ = [
     "bp",
     "files_ns",
     "image_preview",
-    "storage_download",
     "tool_files",
     "upload",
 ]

api/controllers/files/storage_download.py

@@ -1,56 +0,0 @@
-from urllib.parse import quote, unquote
-
-from flask import Response, request
-from flask_restx import Resource
-from pydantic import BaseModel, Field
-from werkzeug.exceptions import Forbidden, NotFound
-
-from controllers.files import files_ns
-from extensions.ext_storage import storage
-from extensions.storage.file_presign_storage import FilePresignStorage
-
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
-
-
-class StorageDownloadQuery(BaseModel):
-    timestamp: str = Field(..., description="Unix timestamp used in the signature")
-    nonce: str = Field(..., description="Random string for signature")
-    sign: str = Field(..., description="HMAC signature")
-
-
-files_ns.schema_model(
-    StorageDownloadQuery.__name__,
-    StorageDownloadQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
-)
-
-
-@files_ns.route("/storage/<path:filename>/download")
-class StorageFileDownloadApi(Resource):
-    def get(self, filename: str):
-        filename = unquote(filename)
-
-        args = StorageDownloadQuery.model_validate(request.args.to_dict(flat=True))
-
-        if not FilePresignStorage.verify_signature(
-            filename=filename,
-            timestamp=args.timestamp,
-            nonce=args.nonce,
-            sign=args.sign,
-        ):
-            raise Forbidden("Invalid or expired download link")
-
-        try:
-            generator = storage.load_stream(filename)
-        except FileNotFoundError:
-            raise NotFound("File not found")
-
-        encoded_filename = quote(filename.split("/")[-1])
-
-        return Response(
-            generator,
-            mimetype="application/octet-stream",
-            direct_passthrough=True,
-            headers={
-                "Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}",
-            },
-        )

api/controllers/inner_api/plugin/plugin.py

@@ -448,53 +448,3 @@ class PluginFetchAppInfoApi(Resource):
         return BaseBackwardsInvocationResponse(
             data=PluginAppBackwardsInvocation.fetch_app_info(payload.app_id, tenant_model.id)
         ).model_dump()
-
-
-@inner_api_ns.route("/fetch/tools/list")
-class PluginFetchToolsListApi(Resource):
-    @get_user_tenant
-    @setup_required
-    @plugin_inner_api_only
-    @inner_api_ns.doc("plugin_fetch_tools_list")
-    @inner_api_ns.doc(description="Fetch all available tools through plugin interface")
-    @inner_api_ns.doc(
-        responses={
-            200: "Tools list retrieved successfully",
-            401: "Unauthorized - invalid API key",
-            404: "Service not available",
-        }
-    )
-    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
-        from sqlalchemy.orm import Session
-
-        from extensions.ext_database import db
-        from services.tools.api_tools_manage_service import ApiToolManageService
-        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
-        from services.tools.mcp_tools_manage_service import MCPToolManageService
-        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
-
-        providers = []
-
-        # Get builtin tools
-        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
-        for provider in builtin_providers:
-            providers.append(provider.to_dict())
-
-        # Get API tools
-        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
-        for provider in api_providers:
-            providers.append(provider.to_dict())
-
-        # Get workflow tools
-        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
-        for provider in workflow_providers:
-            providers.append(provider.to_dict())
-
-        # Get MCP tools
-        with Session(db.engine) as session:
-            mcp_service = MCPToolManageService(session)
-            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
-            for provider in mcp_providers:
-                providers.append(provider.to_dict())
-
-        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/inner_api/plugin/wraps.py

@@ -75,6 +75,7 @@ def get_user_tenant(view_func: Callable[P, R]):
     @wraps(view_func)
     def decorated_view(*args: P.args, **kwargs: P.kwargs):
         payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
+
         user_id = payload.user_id
         tenant_id = payload.tenant_id
 

api/controllers/inner_api/wraps.py

@@ -5,15 +5,14 @@ from hashlib import sha1
 from hmac import new as hmac_new
 from typing import ParamSpec, TypeVar
 
+P = ParamSpec("P")
+R = TypeVar("R")
 from flask import abort, request
 
 from configs import dify_config
 from extensions.ext_database import db
 from models.model import EndUser
 
-P = ParamSpec("P")
-R = TypeVar("R")
-
 
 def billing_inner_api_only(view: Callable[P, R]):
     @wraps(view)
@@ -89,11 +88,11 @@ def plugin_inner_api_only(view: Callable[P, R]):
         if not dify_config.PLUGIN_DAEMON_KEY:
             abort(404)
 
-        # validate using inner api key
+        # get header 'X-Inner-Api-Key'
         inner_api_key = request.headers.get("X-Inner-Api-Key")
-        if inner_api_key and inner_api_key == dify_config.INNER_API_KEY_FOR_PLUGIN:
-            return view(*args, **kwargs)
+        if not inner_api_key or inner_api_key != dify_config.INNER_API_KEY_FOR_PLUGIN:
+            abort(404)
 
-        abort(401)
+        return view(*args, **kwargs)
 
     return decorated

api/core/agent/agent_app_runner.py

@@ -1,380 +0,0 @@
-import logging
-from collections.abc import Generator
-from copy import deepcopy
-from typing import Any
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.agent.entities import AgentEntity, AgentLog, AgentResult
-from core.agent.patterns.strategy_factory import StrategyFactory
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMUsage,
-    PromptMessage,
-    PromptMessageContentType,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.__base.tool import Tool
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class AgentAppRunner(BaseAgentRunner):
-    def _create_tool_invoke_hook(self, message: Message):
-        """
-        Create a tool invoke hook that uses ToolEngine.agent_invoke.
-        This hook handles file creation and returns proper meta information.
-        """
-        # Get trace manager from app generate entity
-        trace_manager = self.application_generate_entity.trace_manager
-
-        def tool_invoke_hook(
-            tool: Tool, tool_args: dict[str, Any], tool_name: str
-        ) -> tuple[str, list[str], ToolInvokeMeta]:
-            """Hook that uses agent_invoke for proper file and meta handling."""
-            tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-                tool=tool,
-                tool_parameters=tool_args,
-                user_id=self.user_id,
-                tenant_id=self.tenant_id,
-                message=message,
-                invoke_from=self.application_generate_entity.invoke_from,
-                agent_tool_callback=self.agent_callback,
-                trace_manager=trace_manager,
-                app_id=self.application_generate_entity.app_config.app_id,
-                message_id=message.id,
-                conversation_id=self.conversation.id,
-            )
-
-            # Publish files and track IDs
-            for message_file_id in message_files:
-                self.queue_manager.publish(
-                    QueueMessageFileEvent(message_file_id=message_file_id),
-                    PublishFrom.APPLICATION_MANAGER,
-                )
-                self._current_message_file_ids.append(message_file_id)
-
-            return tool_invoke_response, message_files, tool_invoke_meta
-
-        return tool_invoke_hook
-
-    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
-        """
-        Run Agent application
-        """
-        self.query = query
-        app_generate_entity = self.application_generate_entity
-
-        app_config = self.app_config
-        assert app_config is not None, "app_config is required"
-        assert app_config.agent is not None, "app_config.agent is required"
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, _ = self._init_prompt_tools()
-
-        assert app_config.agent
-
-        # Create tool invoke hook for agent_invoke
-        tool_invoke_hook = self._create_tool_invoke_hook(message)
-
-        # Get instruction for ReAct strategy
-        instruction = self.app_config.prompt_template.simple_prompt_template or ""
-
-        # Use factory to create appropriate strategy
-        strategy = StrategyFactory.create_strategy(
-            model_features=self.model_features,
-            model_instance=self.model_instance,
-            tools=list(tool_instances.values()),
-            files=list(self.files),
-            max_iterations=app_config.agent.max_iteration,
-            context=self.build_execution_context(),
-            agent_strategy=self.config.strategy,
-            tool_invoke_hook=tool_invoke_hook,
-            instruction=instruction,
-        )
-
-        # Initialize state variables
-        current_agent_thought_id = None
-        has_published_thought = False
-        current_tool_name: str | None = None
-        self._current_message_file_ids: list[str] = []
-
-        # organize prompt messages
-        prompt_messages = self._organize_prompt_messages()
-
-        # Run strategy
-        generator = strategy.run(
-            prompt_messages=prompt_messages,
-            model_parameters=app_generate_entity.model_conf.parameters,
-            stop=app_generate_entity.model_conf.stop,
-            stream=True,
-        )
-
-        # Consume generator and collect result
-        result: AgentResult | None = None
-        try:
-            while True:
-                try:
-                    output = next(generator)
-                except StopIteration as e:
-                    # Generator finished, get the return value
-                    result = e.value
-                    break
-
-                if isinstance(output, LLMResultChunk):
-                    # Handle LLM chunk
-                    if current_agent_thought_id and not has_published_thought:
-                        self.queue_manager.publish(
-                            QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
-                            PublishFrom.APPLICATION_MANAGER,
-                        )
-                        has_published_thought = True
-
-                    yield output
-
-                elif isinstance(output, AgentLog):
-                    # Handle Agent Log using log_type for type-safe dispatch
-                    if output.status == AgentLog.LogStatus.START:
-                        if output.log_type == AgentLog.LogType.ROUND:
-                            # Start of a new round
-                            message_file_ids: list[str] = []
-                            current_agent_thought_id = self.create_agent_thought(
-                                message_id=message.id,
-                                message="",
-                                tool_name="",
-                                tool_input="",
-                                messages_ids=message_file_ids,
-                            )
-                            has_published_thought = False
-
-                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
-                            if current_agent_thought_id is None:
-                                continue
-
-                            # Tool call start - extract data from structured fields
-                            current_tool_name = output.data.get("tool_name", "")
-                            tool_input = output.data.get("tool_args", {})
-
-                            self.save_agent_thought(
-                                agent_thought_id=current_agent_thought_id,
-                                tool_name=current_tool_name,
-                                tool_input=tool_input,
-                                thought=None,
-                                observation=None,
-                                tool_invoke_meta=None,
-                                answer=None,
-                                messages_ids=[],
-                            )
-                            self.queue_manager.publish(
-                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
-                                PublishFrom.APPLICATION_MANAGER,
-                            )
-
-                    elif output.status == AgentLog.LogStatus.SUCCESS:
-                        if output.log_type == AgentLog.LogType.THOUGHT:
-                            if current_agent_thought_id is None:
-                                continue
-
-                            thought_text = output.data.get("thought")
-                            self.save_agent_thought(
-                                agent_thought_id=current_agent_thought_id,
-                                tool_name=None,
-                                tool_input=None,
-                                thought=thought_text,
-                                observation=None,
-                                tool_invoke_meta=None,
-                                answer=None,
-                                messages_ids=[],
-                            )
-                            self.queue_manager.publish(
-                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
-                                PublishFrom.APPLICATION_MANAGER,
-                            )
-
-                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
-                            if current_agent_thought_id is None:
-                                continue
-
-                            # Tool call finished
-                            tool_output = output.data.get("output")
-                            # Get meta from strategy output (now properly populated)
-                            tool_meta = output.data.get("meta")
-
-                            # Wrap tool_meta with tool_name as key (required by agent_service)
-                            if tool_meta and current_tool_name:
-                                tool_meta = {current_tool_name: tool_meta}
-
-                            self.save_agent_thought(
-                                agent_thought_id=current_agent_thought_id,
-                                tool_name=None,
-                                tool_input=None,
-                                thought=None,
-                                observation=tool_output,
-                                tool_invoke_meta=tool_meta,
-                                answer=None,
-                                messages_ids=self._current_message_file_ids,
-                            )
-                            # Clear message file ids after saving
-                            self._current_message_file_ids = []
-                            current_tool_name = None
-
-                            self.queue_manager.publish(
-                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
-                                PublishFrom.APPLICATION_MANAGER,
-                            )
-
-                        elif output.log_type == AgentLog.LogType.ROUND:
-                            if current_agent_thought_id is None:
-                                continue
-
-                            # Round finished - save LLM usage and answer
-                            llm_usage = output.metadata.get(AgentLog.LogMetadata.LLM_USAGE)
-                            llm_result = output.data.get("llm_result")
-                            final_answer = output.data.get("final_answer")
-
-                            self.save_agent_thought(
-                                agent_thought_id=current_agent_thought_id,
-                                tool_name=None,
-                                tool_input=None,
-                                thought=llm_result,
-                                observation=None,
-                                tool_invoke_meta=None,
-                                answer=final_answer,
-                                messages_ids=[],
-                                llm_usage=llm_usage,
-                            )
-                            self.queue_manager.publish(
-                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
-                                PublishFrom.APPLICATION_MANAGER,
-                            )
-
-        except Exception:
-            # Re-raise any other exceptions
-            raise
-
-        # Process final result
-        if isinstance(result, AgentResult):
-            final_answer = result.text
-            usage = result.usage or LLMUsage.empty_usage()
-
-            # Publish end event
-            self.queue_manager.publish(
-                QueueMessageEndEvent(
-                    llm_result=LLMResult(
-                        model=self.model_instance.model,
-                        prompt_messages=prompt_messages,
-                        message=AssistantPromptMessage(content=final_answer),
-                        usage=usage,
-                        system_fingerprint="",
-                    )
-                ),
-                PublishFrom.APPLICATION_MANAGER,
-            )
-
-    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Initialize system message
-        """
-        if not prompt_template:
-            return prompt_messages or []
-
-        prompt_messages = prompt_messages or []
-
-        if prompt_messages and isinstance(prompt_messages[0], SystemPromptMessage):
-            prompt_messages[0] = SystemPromptMessage(content=prompt_template)
-            return prompt_messages
-
-        if not prompt_messages:
-            return [SystemPromptMessage(content=prompt_template)]
-
-        prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
-        return prompt_messages
-
-    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        As for now, gpt supports both fc and vision at the first iteration.
-        We need to remove the image messages from the prompt messages at the first iteration.
-        """
-        prompt_messages = deepcopy(prompt_messages)
-
-        for prompt_message in prompt_messages:
-            if isinstance(prompt_message, UserPromptMessage):
-                if isinstance(prompt_message.content, list):
-                    prompt_message.content = "\n".join(
-                        [
-                            content.data
-                            if content.type == PromptMessageContentType.TEXT
-                            else "[image]"
-                            if content.type == PromptMessageContentType.IMAGE
-                            else "[file]"
-                            for content in prompt_message.content
-                        ]
-                    )
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self):
-        # For ReAct strategy, use the agent prompt template
-        if self.config.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT and self.config.prompt:
-            prompt_template = self.config.prompt.first_prompt
-        else:
-            prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
-
-        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
-        query_prompt_messages = self._organize_user_query(self.query or "", [])
-
-        self.history_prompt_messages = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
-            history_messages=self.history_prompt_messages,
-            memory=self.memory,
-        ).get_prompt()
-
-        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
-        if len(self._current_thoughts) != 0:
-            # clear messages after the first iteration
-            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
-        return prompt_messages

api/core/agent/base_agent_runner.py

@@ -6,7 +6,7 @@ from typing import Union, cast
 
 from sqlalchemy import select
 
-from core.agent.entities import AgentEntity, AgentToolEntity, ExecutionContext
+from core.agent.entities import AgentEntity, AgentToolEntity
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager
@@ -116,20 +116,9 @@ class BaseAgentRunner(AppRunner):
         features = model_schema.features if model_schema and model_schema.features else []
         self.stream_tool_call = ModelFeature.STREAM_TOOL_CALL in features
         self.files = application_generate_entity.files if ModelFeature.VISION in features else []
-        self.model_features = features
         self.query: str | None = ""
         self._current_thoughts: list[PromptMessage] = []
 
-    def build_execution_context(self) -> ExecutionContext:
-        """Build execution context."""
-        return ExecutionContext(
-            user_id=self.user_id,
-            app_id=self.app_config.app_id,
-            conversation_id=self.conversation.id,
-            message_id=self.message.id,
-            tenant_id=self.tenant_id,
-        )
-
     def _repack_app_generate_entity(
         self, app_generate_entity: AgentChatAppGenerateEntity
     ) -> AgentChatAppGenerateEntity:

api/core/agent/cot_agent_runner.py

@@ -0,0 +1,437 @@
+import json
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Generator, Mapping, Sequence
+from typing import Any
+
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.entities import AgentScratchpadUnit
+from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.ops.ops_trace_manager import TraceQueueManager
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
+from core.tools.__base.tool import Tool
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
+from core.workflow.nodes.agent.exc import AgentMaxIterationError
+from models.model import Message
+
+logger = logging.getLogger(__name__)
+
+
+class CotAgentRunner(BaseAgentRunner, ABC):
+    _is_first_iteration = True
+    _ignore_observation_providers = ["wenxin"]
+    _historic_prompt_messages: list[PromptMessage]
+    _agent_scratchpad: list[AgentScratchpadUnit]
+    _instruction: str
+    _query: str
+    _prompt_messages_tools: Sequence[PromptMessageTool]
+
+    def run(
+        self,
+        message: Message,
+        query: str,
+        inputs: Mapping[str, str],
+    ) -> Generator:
+        """
+        Run Cot agent application
+        """
+
+        app_generate_entity = self.application_generate_entity
+        self._repack_app_generate_entity(app_generate_entity)
+        self._init_react_state(query)
+
+        trace_manager = app_generate_entity.trace_manager
+
+        # check model mode
+        if "Observation" not in app_generate_entity.model_conf.stop:
+            if app_generate_entity.model_conf.provider not in self._ignore_observation_providers:
+                app_generate_entity.model_conf.stop.append("Observation")
+
+        app_config = self.app_config
+        assert app_config.agent
+
+        # init instruction
+        inputs = inputs or {}
+        instruction = app_config.prompt_template.simple_prompt_template or ""
+        self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
+
+        iteration_step = 1
+        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
+
+        # convert tools into ModelRuntime Tool format
+        tool_instances, prompt_messages_tools = self._init_prompt_tools()
+        self._prompt_messages_tools = prompt_messages_tools
+
+        function_call_state = True
+        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
+        final_answer = ""
+        prompt_messages: list = []  # Initialize prompt_messages
+        agent_thought_id = ""  # Initialize agent_thought_id
+
+        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
+            if not final_llm_usage_dict["usage"]:
+                final_llm_usage_dict["usage"] = usage
+            else:
+                llm_usage = final_llm_usage_dict["usage"]
+                llm_usage.prompt_tokens += usage.prompt_tokens
+                llm_usage.completion_tokens += usage.completion_tokens
+                llm_usage.total_tokens += usage.total_tokens
+                llm_usage.prompt_price += usage.prompt_price
+                llm_usage.completion_price += usage.completion_price
+                llm_usage.total_price += usage.total_price
+
+        model_instance = self.model_instance
+
+        while function_call_state and iteration_step <= max_iteration_steps:
+            # continue to run until there is not any tool call
+            function_call_state = False
+
+            if iteration_step == max_iteration_steps:
+                # the last iteration, remove all tools
+                self._prompt_messages_tools = []
+
+            message_file_ids: list[str] = []
+
+            agent_thought_id = self.create_agent_thought(
+                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
+            )
+
+            if iteration_step > 1:
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                )
+
+            # recalc llm max tokens
+            prompt_messages = self._organize_prompt_messages()
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
+            # invoke model
+            chunks = model_instance.invoke_llm(
+                prompt_messages=prompt_messages,
+                model_parameters=app_generate_entity.model_conf.parameters,
+                tools=[],
+                stop=app_generate_entity.model_conf.stop,
+                stream=True,
+                user=self.user_id,
+                callbacks=[],
+            )
+
+            usage_dict: dict[str, LLMUsage | None] = {}
+            react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
+            scratchpad = AgentScratchpadUnit(
+                agent_response="",
+                thought="",
+                action_str="",
+                observation="",
+                action=None,
+            )
+
+            # publish agent thought if it's first iteration
+            if iteration_step == 1:
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                )
+
+            for chunk in react_chunks:
+                if isinstance(chunk, AgentScratchpadUnit.Action):
+                    action = chunk
+                    # detect action
+                    assert scratchpad.agent_response is not None
+                    scratchpad.agent_response += json.dumps(chunk.model_dump())
+                    scratchpad.action_str = json.dumps(chunk.model_dump())
+                    scratchpad.action = action
+                else:
+                    assert scratchpad.agent_response is not None
+                    scratchpad.agent_response += chunk
+                    assert scratchpad.thought is not None
+                    scratchpad.thought += chunk
+                    yield LLMResultChunk(
+                        model=self.model_config.model,
+                        prompt_messages=prompt_messages,
+                        system_fingerprint="",
+                        delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
+                    )
+
+            assert scratchpad.thought is not None
+            scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
+            self._agent_scratchpad.append(scratchpad)
+
+            # Check if max iteration is reached and model still wants to call tools
+            if iteration_step == max_iteration_steps and scratchpad.action:
+                if scratchpad.action.action_name.lower() != "final answer":
+                    raise AgentMaxIterationError(app_config.agent.max_iteration)
+
+            # get llm usage
+            if "usage" in usage_dict:
+                if usage_dict["usage"] is not None:
+                    increase_usage(llm_usage, usage_dict["usage"])
+            else:
+                usage_dict["usage"] = LLMUsage.empty_usage()
+
+            self.save_agent_thought(
+                agent_thought_id=agent_thought_id,
+                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
+                tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
+                tool_invoke_meta={},
+                thought=scratchpad.thought or "",
+                observation="",
+                answer=scratchpad.agent_response or "",
+                messages_ids=[],
+                llm_usage=usage_dict["usage"],
+            )
+
+            if not scratchpad.is_final():
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                )
+
+            if not scratchpad.action:
+                # failed to extract action, return final answer directly
+                final_answer = ""
+            else:
+                if scratchpad.action.action_name.lower() == "final answer":
+                    # action is final answer, return final answer directly
+                    try:
+                        if isinstance(scratchpad.action.action_input, dict):
+                            final_answer = json.dumps(scratchpad.action.action_input, ensure_ascii=False)
+                        elif isinstance(scratchpad.action.action_input, str):
+                            final_answer = scratchpad.action.action_input
+                        else:
+                            final_answer = f"{scratchpad.action.action_input}"
+                    except TypeError:
+                        final_answer = f"{scratchpad.action.action_input}"
+                else:
+                    function_call_state = True
+                    # action is tool call, invoke tool
+                    tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
+                        action=scratchpad.action,
+                        tool_instances=tool_instances,
+                        message_file_ids=message_file_ids,
+                        trace_manager=trace_manager,
+                    )
+                    scratchpad.observation = tool_invoke_response
+                    scratchpad.agent_response = tool_invoke_response
+
+                    self.save_agent_thought(
+                        agent_thought_id=agent_thought_id,
+                        tool_name=scratchpad.action.action_name,
+                        tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
+                        thought=scratchpad.thought or "",
+                        observation={scratchpad.action.action_name: tool_invoke_response},
+                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
+                        answer=scratchpad.agent_response,
+                        messages_ids=message_file_ids,
+                        llm_usage=usage_dict["usage"],
+                    )
+
+                    self.queue_manager.publish(
+                        QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                    )
+
+                # update prompt tool message
+                for prompt_tool in self._prompt_messages_tools:
+                    self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
+
+            iteration_step += 1
+
+        yield LLMResultChunk(
+            model=model_instance.model,
+            prompt_messages=prompt_messages,
+            delta=LLMResultChunkDelta(
+                index=0, message=AssistantPromptMessage(content=final_answer), usage=llm_usage["usage"]
+            ),
+            system_fingerprint="",
+        )
+
+        # save agent thought
+        self.save_agent_thought(
+            agent_thought_id=agent_thought_id,
+            tool_name="",
+            tool_input={},
+            tool_invoke_meta={},
+            thought=final_answer,
+            observation={},
+            answer=final_answer,
+            messages_ids=[],
+        )
+        # publish end event
+        self.queue_manager.publish(
+            QueueMessageEndEvent(
+                llm_result=LLMResult(
+                    model=model_instance.model,
+                    prompt_messages=prompt_messages,
+                    message=AssistantPromptMessage(content=final_answer),
+                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
+                    system_fingerprint="",
+                )
+            ),
+            PublishFrom.APPLICATION_MANAGER,
+        )
+
+    def _handle_invoke_action(
+        self,
+        action: AgentScratchpadUnit.Action,
+        tool_instances: Mapping[str, Tool],
+        message_file_ids: list[str],
+        trace_manager: TraceQueueManager | None = None,
+    ) -> tuple[str, ToolInvokeMeta]:
+        """
+        handle invoke action
+        :param action: action
+        :param tool_instances: tool instances
+        :param message_file_ids: message file ids
+        :param trace_manager: trace manager
+        :return: observation, meta
+        """
+        # action is tool call, invoke tool
+        tool_call_name = action.action_name
+        tool_call_args = action.action_input
+        tool_instance = tool_instances.get(tool_call_name)
+
+        if not tool_instance:
+            answer = f"there is not a tool named {tool_call_name}"
+            return answer, ToolInvokeMeta.error_instance(answer)
+
+        if isinstance(tool_call_args, str):
+            try:
+                tool_call_args = json.loads(tool_call_args)
+            except json.JSONDecodeError:
+                pass
+
+        # invoke tool
+        tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+            tool=tool_instance,
+            tool_parameters=tool_call_args,
+            user_id=self.user_id,
+            tenant_id=self.tenant_id,
+            message=self.message,
+            invoke_from=self.application_generate_entity.invoke_from,
+            agent_tool_callback=self.agent_callback,
+            trace_manager=trace_manager,
+        )
+
+        # publish files
+        for message_file_id in message_files:
+            # publish message file
+            self.queue_manager.publish(
+                QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
+            )
+            # add message file ids
+            message_file_ids.append(message_file_id)
+
+        return tool_invoke_response, tool_invoke_meta
+
+    def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
+        """
+        convert dict to action
+        """
+        return AgentScratchpadUnit.Action(action_name=action["action"], action_input=action["action_input"])
+
+    def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: Mapping[str, Any]) -> str:
+        """
+        fill in inputs from external data tools
+        """
+        for key, value in inputs.items():
+            try:
+                instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
+            except Exception:
+                continue
+
+        return instruction
+
+    def _init_react_state(self, query):
+        """
+        init agent scratchpad
+        """
+        self._query = query
+        self._agent_scratchpad = []
+        self._historic_prompt_messages = self._organize_historic_prompt_messages()
+
+    @abstractmethod
+    def _organize_prompt_messages(self) -> list[PromptMessage]:
+        """
+        organize prompt messages
+        """
+
+    def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
+        """
+        format assistant message
+        """
+        message = ""
+        for scratchpad in agent_scratchpad:
+            if scratchpad.is_final():
+                message += f"Final Answer: {scratchpad.agent_response}"
+            else:
+                message += f"Thought: {scratchpad.thought}\n\n"
+                if scratchpad.action_str:
+                    message += f"Action: {scratchpad.action_str}\n\n"
+                if scratchpad.observation:
+                    message += f"Observation: {scratchpad.observation}\n\n"
+
+        return message
+
+    def _organize_historic_prompt_messages(
+        self, current_session_messages: list[PromptMessage] | None = None
+    ) -> list[PromptMessage]:
+        """
+        organize historic prompt messages
+        """
+        result: list[PromptMessage] = []
+        scratchpads: list[AgentScratchpadUnit] = []
+        current_scratchpad: AgentScratchpadUnit | None = None
+
+        for message in self.history_prompt_messages:
+            if isinstance(message, AssistantPromptMessage):
+                if not current_scratchpad:
+                    assert isinstance(message.content, str)
+                    current_scratchpad = AgentScratchpadUnit(
+                        agent_response=message.content,
+                        thought=message.content or "I am thinking about how to help you",
+                        action_str="",
+                        action=None,
+                        observation=None,
+                    )
+                    scratchpads.append(current_scratchpad)
+                if message.tool_calls:
+                    try:
+                        current_scratchpad.action = AgentScratchpadUnit.Action(
+                            action_name=message.tool_calls[0].function.name,
+                            action_input=json.loads(message.tool_calls[0].function.arguments),
+                        )
+                        current_scratchpad.action_str = json.dumps(current_scratchpad.action.to_dict())
+                    except Exception:
+                        logger.exception("Failed to parse tool call from assistant message")
+            elif isinstance(message, ToolPromptMessage):
+                if current_scratchpad:
+                    assert isinstance(message.content, str)
+                    current_scratchpad.observation = message.content
+                else:
+                    raise NotImplementedError("expected str type")
+            elif isinstance(message, UserPromptMessage):
+                if scratchpads:
+                    result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
+                    scratchpads = []
+                    current_scratchpad = None
+
+                result.append(message)
+
+        if scratchpads:
+            result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
+
+        historic_prompts = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=current_session_messages or [],
+            history_messages=result,
+            memory=self.memory,
+        ).get_prompt()
+        return historic_prompts

api/core/agent/cot_chat_agent_runner.py

@@ -0,0 +1,118 @@
+import json
+
+from core.agent.cot_agent_runner import CotAgentRunner
+from core.file import file_manager
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
+from core.model_runtime.utils.encoders import jsonable_encoder
+
+
+class CotChatAgentRunner(CotAgentRunner):
+    def _organize_system_prompt(self) -> SystemPromptMessage:
+        """
+        Organize system prompt
+        """
+        assert self.app_config.agent
+        assert self.app_config.agent.prompt
+
+        prompt_entity = self.app_config.agent.prompt
+        if not prompt_entity:
+            raise ValueError("Agent prompt configuration is not set")
+        first_prompt = prompt_entity.first_prompt
+
+        system_prompt = (
+            first_prompt.replace("{{instruction}}", self._instruction)
+            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
+            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
+        )
+
+        return SystemPromptMessage(content=system_prompt)
+
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Organize user query
+        """
+        if self.files:
+            # get image detail config
+            image_detail_config = (
+                self.application_generate_entity.file_upload_config.image_config.detail
+                if (
+                    self.application_generate_entity.file_upload_config
+                    and self.application_generate_entity.file_upload_config.image_config
+                )
+                else None
+            )
+            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+            for file in self.files:
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(
+                        file,
+                        image_detail_config=image_detail_config,
+                    )
+                )
+            prompt_message_contents.append(TextPromptMessageContent(data=query))
+
+            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+        else:
+            prompt_messages.append(UserPromptMessage(content=query))
+
+        return prompt_messages
+
+    def _organize_prompt_messages(self) -> list[PromptMessage]:
+        """
+        Organize
+        """
+        # organize system prompt
+        system_message = self._organize_system_prompt()
+
+        # organize current assistant messages
+        agent_scratchpad = self._agent_scratchpad
+        if not agent_scratchpad:
+            assistant_messages = []
+        else:
+            assistant_message = AssistantPromptMessage(content="")
+            assistant_message.content = ""  # FIXME: type check tell mypy that assistant_message.content is str
+            for unit in agent_scratchpad:
+                if unit.is_final():
+                    assert isinstance(assistant_message.content, str)
+                    assistant_message.content += f"Final Answer: {unit.agent_response}"
+                else:
+                    assert isinstance(assistant_message.content, str)
+                    assistant_message.content += f"Thought: {unit.thought}\n\n"
+                    if unit.action_str:
+                        assistant_message.content += f"Action: {unit.action_str}\n\n"
+                    if unit.observation:
+                        assistant_message.content += f"Observation: {unit.observation}\n\n"
+
+            assistant_messages = [assistant_message]
+
+        # query messages
+        query_messages = self._organize_user_query(self._query, [])
+
+        if assistant_messages:
+            # organize historic prompt messages
+            historic_messages = self._organize_historic_prompt_messages(
+                [system_message, *query_messages, *assistant_messages, UserPromptMessage(content="continue")]
+            )
+            messages = [
+                system_message,
+                *historic_messages,
+                *query_messages,
+                *assistant_messages,
+                UserPromptMessage(content="continue"),
+            ]
+        else:
+            # organize historic prompt messages
+            historic_messages = self._organize_historic_prompt_messages([system_message, *query_messages])
+            messages = [system_message, *historic_messages, *query_messages]
+
+        # join all messages
+        return messages

api/core/agent/cot_completion_agent_runner.py

@@ -0,0 +1,87 @@
+import json
+
+from core.agent.cot_agent_runner import CotAgentRunner
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.utils.encoders import jsonable_encoder
+
+
+class CotCompletionAgentRunner(CotAgentRunner):
+    def _organize_instruction_prompt(self) -> str:
+        """
+        Organize instruction prompt
+        """
+        if self.app_config.agent is None:
+            raise ValueError("Agent configuration is not set")
+        prompt_entity = self.app_config.agent.prompt
+        if prompt_entity is None:
+            raise ValueError("prompt entity is not set")
+        first_prompt = prompt_entity.first_prompt
+
+        system_prompt = (
+            first_prompt.replace("{{instruction}}", self._instruction)
+            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
+            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
+        )
+
+        return system_prompt
+
+    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] | None = None) -> str:
+        """
+        Organize historic prompt
+        """
+        historic_prompt_messages = self._organize_historic_prompt_messages(current_session_messages)
+        historic_prompt = ""
+
+        for message in historic_prompt_messages:
+            if isinstance(message, UserPromptMessage):
+                historic_prompt += f"Question: {message.content}\n\n"
+            elif isinstance(message, AssistantPromptMessage):
+                if isinstance(message.content, str):
+                    historic_prompt += message.content + "\n\n"
+                elif isinstance(message.content, list):
+                    for content in message.content:
+                        if not isinstance(content, TextPromptMessageContent):
+                            continue
+                        historic_prompt += content.data
+
+        return historic_prompt
+
+    def _organize_prompt_messages(self) -> list[PromptMessage]:
+        """
+        Organize prompt messages
+        """
+        # organize system prompt
+        system_prompt = self._organize_instruction_prompt()
+
+        # organize historic prompt messages
+        historic_prompt = self._organize_historic_prompt()
+
+        # organize current assistant messages
+        agent_scratchpad = self._agent_scratchpad
+        assistant_prompt = ""
+        for unit in agent_scratchpad or []:
+            if unit.is_final():
+                assistant_prompt += f"Final Answer: {unit.agent_response}"
+            else:
+                assistant_prompt += f"Thought: {unit.thought}\n\n"
+                if unit.action_str:
+                    assistant_prompt += f"Action: {unit.action_str}\n\n"
+                if unit.observation:
+                    assistant_prompt += f"Observation: {unit.observation}\n\n"
+
+        # query messages
+        query_prompt = f"Question: {self._query}"
+
+        # join all messages
+        prompt = (
+            system_prompt.replace("{{historic_messages}}", historic_prompt)
+            .replace("{{agent_scratchpad}}", assistant_prompt)
+            .replace("{{query}}", query_prompt)
+        )
+
+        return [UserPromptMessage(content=prompt)]

api/core/agent/entities.py

@@ -1,5 +1,3 @@
-import uuid
-from collections.abc import Mapping
 from enum import StrEnum
 from typing import Any, Union
 
@@ -94,96 +92,3 @@ class AgentInvokeMessage(ToolInvokeMessage):
     """
 
     pass
-
-
-class ExecutionContext(BaseModel):
-    """Execution context containing trace and audit information.
-
-    This context carries all the IDs and metadata that are not part of
-    the core business logic but needed for tracing, auditing, and
-    correlation purposes.
-    """
-
-    user_id: str | None = None
-    app_id: str | None = None
-    conversation_id: str | None = None
-    message_id: str | None = None
-    tenant_id: str | None = None
-
-    @classmethod
-    def create_minimal(cls, user_id: str | None = None) -> "ExecutionContext":
-        """Create a minimal context with only essential fields."""
-        return cls(user_id=user_id)
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dictionary for passing to legacy code."""
-        return {
-            "user_id": self.user_id,
-            "app_id": self.app_id,
-            "conversation_id": self.conversation_id,
-            "message_id": self.message_id,
-            "tenant_id": self.tenant_id,
-        }
-
-    def with_updates(self, **kwargs) -> "ExecutionContext":
-        """Create a new context with updated fields."""
-        data = self.to_dict()
-        data.update(kwargs)
-
-        return ExecutionContext(
-            user_id=data.get("user_id"),
-            app_id=data.get("app_id"),
-            conversation_id=data.get("conversation_id"),
-            message_id=data.get("message_id"),
-            tenant_id=data.get("tenant_id"),
-        )
-
-
-class AgentLog(BaseModel):
-    """
-    Agent Log.
-    """
-
-    class LogType(StrEnum):
-        """Type of agent log entry."""
-
-        ROUND = "round"  # A complete iteration round
-        THOUGHT = "thought"  # LLM thinking/reasoning
-        TOOL_CALL = "tool_call"  # Tool invocation
-
-    class LogMetadata(StrEnum):
-        STARTED_AT = "started_at"
-        FINISHED_AT = "finished_at"
-        ELAPSED_TIME = "elapsed_time"
-        TOTAL_PRICE = "total_price"
-        TOTAL_TOKENS = "total_tokens"
-        PROVIDER = "provider"
-        CURRENCY = "currency"
-        LLM_USAGE = "llm_usage"
-        ICON = "icon"
-        ICON_DARK = "icon_dark"
-
-    class LogStatus(StrEnum):
-        START = "start"
-        ERROR = "error"
-        SUCCESS = "success"
-
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="The id of the log")
-    label: str = Field(..., description="The label of the log")
-    log_type: LogType = Field(..., description="The type of the log")
-    parent_id: str | None = Field(default=None, description="Leave empty for root log")
-    error: str | None = Field(default=None, description="The error message")
-    status: LogStatus = Field(..., description="The status of the log")
-    data: Mapping[str, Any] = Field(..., description="Detailed log data")
-    metadata: Mapping[LogMetadata, Any] = Field(default={}, description="The metadata of the log")
-
-
-class AgentResult(BaseModel):
-    """
-    Agent execution result.
-    """
-
-    text: str = Field(default="", description="The generated text")
-    files: list[Any] = Field(default_factory=list, description="Files produced during execution")
-    usage: Any | None = Field(default=None, description="LLM usage statistics")
-    finish_reason: str | None = Field(default=None, description="Reason for completion")

api/core/agent/fc_agent_runner.py

@@ -0,0 +1,468 @@
+import json
+import logging
+from collections.abc import Generator
+from copy import deepcopy
+from typing import Any, Union
+
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
+from core.file import file_manager
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageContentType,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
+from core.workflow.nodes.agent.exc import AgentMaxIterationError
+from models.model import Message
+
+logger = logging.getLogger(__name__)
+
+
+class FunctionCallAgentRunner(BaseAgentRunner):
+    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
+        """
+        Run FunctionCall agent application
+        """
+        self.query = query
+        app_generate_entity = self.application_generate_entity
+
+        app_config = self.app_config
+        assert app_config is not None, "app_config is required"
+        assert app_config.agent is not None, "app_config.agent is required"
+
+        # convert tools into ModelRuntime Tool format
+        tool_instances, prompt_messages_tools = self._init_prompt_tools()
+
+        assert app_config.agent
+
+        iteration_step = 1
+        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
+
+        # continue to run until there is not any tool call
+        function_call_state = True
+        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
+        final_answer = ""
+        prompt_messages: list = []  # Initialize prompt_messages
+
+        # get tracing instance
+        trace_manager = app_generate_entity.trace_manager
+
+        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
+            if not final_llm_usage_dict["usage"]:
+                final_llm_usage_dict["usage"] = usage
+            else:
+                llm_usage = final_llm_usage_dict["usage"]
+                llm_usage.prompt_tokens += usage.prompt_tokens
+                llm_usage.completion_tokens += usage.completion_tokens
+                llm_usage.total_tokens += usage.total_tokens
+                llm_usage.prompt_price += usage.prompt_price
+                llm_usage.completion_price += usage.completion_price
+                llm_usage.total_price += usage.total_price
+
+        model_instance = self.model_instance
+
+        while function_call_state and iteration_step <= max_iteration_steps:
+            function_call_state = False
+
+            if iteration_step == max_iteration_steps:
+                # the last iteration, remove all tools
+                prompt_messages_tools = []
+
+            message_file_ids: list[str] = []
+            agent_thought_id = self.create_agent_thought(
+                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
+            )
+
+            # recalc llm max tokens
+            prompt_messages = self._organize_prompt_messages()
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
+            # invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
+                prompt_messages=prompt_messages,
+                model_parameters=app_generate_entity.model_conf.parameters,
+                tools=prompt_messages_tools,
+                stop=app_generate_entity.model_conf.stop,
+                stream=self.stream_tool_call,
+                user=self.user_id,
+                callbacks=[],
+            )
+
+            tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+
+            # save full response
+            response = ""
+
+            # save tool call names and inputs
+            tool_call_names = ""
+            tool_call_inputs = ""
+
+            current_llm_usage = None
+
+            if isinstance(chunks, Generator):
+                is_first_chunk = True
+                for chunk in chunks:
+                    if is_first_chunk:
+                        self.queue_manager.publish(
+                            QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                        )
+                        is_first_chunk = False
+                    # check if there is any tool call
+                    if self.check_tool_calls(chunk):
+                        function_call_state = True
+                        tool_calls.extend(self.extract_tool_calls(chunk) or [])
+                        tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
+                        try:
+                            tool_call_inputs = json.dumps(
+                                {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
+                            )
+                        except TypeError:
+                            # fallback: force ASCII to handle non-serializable objects
+                            tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
+
+                    if chunk.delta.message and chunk.delta.message.content:
+                        if isinstance(chunk.delta.message.content, list):
+                            for content in chunk.delta.message.content:
+                                response += content.data
+                        else:
+                            response += str(chunk.delta.message.content)
+
+                    if chunk.delta.usage:
+                        increase_usage(llm_usage, chunk.delta.usage)
+                        current_llm_usage = chunk.delta.usage
+
+                    yield chunk
+            else:
+                result = chunks
+                # check if there is any tool call
+                if self.check_blocking_tool_calls(result):
+                    function_call_state = True
+                    tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
+                    tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
+                    try:
+                        tool_call_inputs = json.dumps(
+                            {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
+                        )
+                    except TypeError:
+                        # fallback: force ASCII to handle non-serializable objects
+                        tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
+
+                if result.usage:
+                    increase_usage(llm_usage, result.usage)
+                    current_llm_usage = result.usage
+
+                if result.message and result.message.content:
+                    if isinstance(result.message.content, list):
+                        for content in result.message.content:
+                            response += content.data
+                    else:
+                        response += str(result.message.content)
+
+                if not result.message.content:
+                    result.message.content = ""
+
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                )
+
+                yield LLMResultChunk(
+                    model=model_instance.model,
+                    prompt_messages=result.prompt_messages,
+                    system_fingerprint=result.system_fingerprint,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=result.message,
+                        usage=result.usage,
+                    ),
+                )
+
+            assistant_message = AssistantPromptMessage(content=response, tool_calls=[])
+            if tool_calls:
+                assistant_message.tool_calls = [
+                    AssistantPromptMessage.ToolCall(
+                        id=tool_call[0],
+                        type="function",
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                            name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
+                        ),
+                    )
+                    for tool_call in tool_calls
+                ]
+
+            self._current_thoughts.append(assistant_message)
+
+            # save thought
+            self.save_agent_thought(
+                agent_thought_id=agent_thought_id,
+                tool_name=tool_call_names,
+                tool_input=tool_call_inputs,
+                thought=response,
+                tool_invoke_meta=None,
+                observation=None,
+                answer=response,
+                messages_ids=[],
+                llm_usage=current_llm_usage,
+            )
+            self.queue_manager.publish(
+                QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+            )
+
+            final_answer += response + "\n"
+
+            # Check if max iteration is reached and model still wants to call tools
+            if iteration_step == max_iteration_steps and tool_calls:
+                raise AgentMaxIterationError(app_config.agent.max_iteration)
+
+            # call tools
+            tool_responses = []
+            for tool_call_id, tool_call_name, tool_call_args in tool_calls:
+                tool_instance = tool_instances.get(tool_call_name)
+                if not tool_instance:
+                    tool_response = {
+                        "tool_call_id": tool_call_id,
+                        "tool_call_name": tool_call_name,
+                        "tool_response": f"there is not a tool named {tool_call_name}",
+                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
+                    }
+                else:
+                    # invoke tool
+                    tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                        tool=tool_instance,
+                        tool_parameters=tool_call_args,
+                        user_id=self.user_id,
+                        tenant_id=self.tenant_id,
+                        message=self.message,
+                        invoke_from=self.application_generate_entity.invoke_from,
+                        agent_tool_callback=self.agent_callback,
+                        trace_manager=trace_manager,
+                        app_id=self.application_generate_entity.app_config.app_id,
+                        message_id=self.message.id,
+                        conversation_id=self.conversation.id,
+                    )
+                    # publish files
+                    for message_file_id in message_files:
+                        # publish message file
+                        self.queue_manager.publish(
+                            QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
+                        )
+                        # add message file ids
+                        message_file_ids.append(message_file_id)
+
+                    tool_response = {
+                        "tool_call_id": tool_call_id,
+                        "tool_call_name": tool_call_name,
+                        "tool_response": tool_invoke_response,
+                        "meta": tool_invoke_meta.to_dict(),
+                    }
+
+                tool_responses.append(tool_response)
+                if tool_response["tool_response"] is not None:
+                    self._current_thoughts.append(
+                        ToolPromptMessage(
+                            content=str(tool_response["tool_response"]),
+                            tool_call_id=tool_call_id,
+                            name=tool_call_name,
+                        )
+                    )
+
+            if len(tool_responses) > 0:
+                # save agent thought
+                self.save_agent_thought(
+                    agent_thought_id=agent_thought_id,
+                    tool_name="",
+                    tool_input="",
+                    thought="",
+                    tool_invoke_meta={
+                        tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
+                    },
+                    observation={
+                        tool_response["tool_call_name"]: tool_response["tool_response"]
+                        for tool_response in tool_responses
+                    },
+                    answer="",
+                    messages_ids=message_file_ids,
+                )
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
+                )
+
+            # update prompt tool
+            for prompt_tool in prompt_messages_tools:
+                self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
+
+            iteration_step += 1
+
+        # publish end event
+        self.queue_manager.publish(
+            QueueMessageEndEvent(
+                llm_result=LLMResult(
+                    model=model_instance.model,
+                    prompt_messages=prompt_messages,
+                    message=AssistantPromptMessage(content=final_answer),
+                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
+                    system_fingerprint="",
+                )
+            ),
+            PublishFrom.APPLICATION_MANAGER,
+        )
+
+    def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
+        """
+        Check if there is any tool call in llm result chunk
+        """
+        if llm_result_chunk.delta.message.tool_calls:
+            return True
+        return False
+
+    def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
+        """
+        Check if there is any blocking tool call in llm result
+        """
+        if llm_result.message.tool_calls:
+            return True
+        return False
+
+    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
+        """
+        Extract tool calls from llm result chunk
+
+        Returns:
+            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
+        """
+        tool_calls = []
+        for prompt_message in llm_result_chunk.delta.message.tool_calls:
+            args = {}
+            if prompt_message.function.arguments != "":
+                args = json.loads(prompt_message.function.arguments)
+
+            tool_calls.append(
+                (
+                    prompt_message.id,
+                    prompt_message.function.name,
+                    args,
+                )
+            )
+
+        return tool_calls
+
+    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
+        """
+        Extract blocking tool calls from llm result
+
+        Returns:
+            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
+        """
+        tool_calls = []
+        for prompt_message in llm_result.message.tool_calls:
+            args = {}
+            if prompt_message.function.arguments != "":
+                args = json.loads(prompt_message.function.arguments)
+
+            tool_calls.append(
+                (
+                    prompt_message.id,
+                    prompt_message.function.name,
+                    args,
+                )
+            )
+
+        return tool_calls
+
+    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Initialize system message
+        """
+        if not prompt_messages and prompt_template:
+            return [
+                SystemPromptMessage(content=prompt_template),
+            ]
+
+        if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
+            prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
+
+        return prompt_messages or []
+
+    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Organize user query
+        """
+        if self.files:
+            # get image detail config
+            image_detail_config = (
+                self.application_generate_entity.file_upload_config.image_config.detail
+                if (
+                    self.application_generate_entity.file_upload_config
+                    and self.application_generate_entity.file_upload_config.image_config
+                )
+                else None
+            )
+            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+            for file in self.files:
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(
+                        file,
+                        image_detail_config=image_detail_config,
+                    )
+                )
+            prompt_message_contents.append(TextPromptMessageContent(data=query))
+
+            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+        else:
+            prompt_messages.append(UserPromptMessage(content=query))
+
+        return prompt_messages
+
+    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        As for now, gpt supports both fc and vision at the first iteration.
+        We need to remove the image messages from the prompt messages at the first iteration.
+        """
+        prompt_messages = deepcopy(prompt_messages)
+
+        for prompt_message in prompt_messages:
+            if isinstance(prompt_message, UserPromptMessage):
+                if isinstance(prompt_message.content, list):
+                    prompt_message.content = "\n".join(
+                        [
+                            content.data
+                            if content.type == PromptMessageContentType.TEXT
+                            else "[image]"
+                            if content.type == PromptMessageContentType.IMAGE
+                            else "[file]"
+                            for content in prompt_message.content
+                        ]
+                    )
+
+        return prompt_messages
+
+    def _organize_prompt_messages(self):
+        prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
+        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
+        query_prompt_messages = self._organize_user_query(self.query or "", [])
+
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory,
+        ).get_prompt()
+
+        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
+        if len(self._current_thoughts) != 0:
+            # clear messages after the first iteration
+            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
+        return prompt_messages

api/core/agent/patterns/README.md

@@ -1,55 +0,0 @@
-# Agent Patterns
-
-A unified agent pattern module that powers both Agent V2 workflow nodes and agent applications. Strategies share a common execution contract while adapting to model capabilities and tool availability.
-
-## Overview
-
-The module applies a strategy pattern around LLM/tool orchestration. `StrategyFactory` auto-selects the best implementation based on model features or an explicit agent strategy, and each strategy streams logs and usage consistently.
-
-## Key Features
-
-- **Dual strategies**
-  - `FunctionCallStrategy`: uses native LLM function/tool calling when the model exposes `TOOL_CALL`, `MULTI_TOOL_CALL`, or `STREAM_TOOL_CALL`.
-  - `ReActStrategy`: ReAct (reasoning + acting) flow driven by `CotAgentOutputParser`, used when function calling is unavailable or explicitly requested.
-- **Explicit or auto selection**
-  - `StrategyFactory.create_strategy` prefers an explicit `AgentEntity.Strategy` (FUNCTION_CALLING or CHAIN_OF_THOUGHT).
-  - Otherwise it falls back to function calling when tool-call features exist, or ReAct when they do not.
-- **Unified execution contract**
-  - `AgentPattern.run` yields streaming `AgentLog` entries and `LLMResultChunk` data, returning an `AgentResult` with text, files, usage, and `finish_reason`.
-  - Iterations are configurable and hard-capped at 99 rounds; the last round forces a final answer by withholding tools.
-- **Tool handling and hooks**
-  - Tools convert to `PromptMessageTool` objects before invocation.
-  - Optional `tool_invoke_hook` lets callers override tool execution (e.g., agent apps) while workflow runs use `ToolEngine.generic_invoke`.
-  - Tool outputs support text, links, JSON, variables, blobs, retriever resources, and file attachments; `target=="self"` files are reloaded into model context, others are returned as outputs.
-- **File-aware arguments**
-  - Tool args accept `[File: <id>]` or `[Files: <id1, id2>]` placeholders that resolve to `File` objects before invocation, enabling models to reference uploaded files safely.
-- **ReAct prompt shaping**
-  - System prompts replace `{{instruction}}`, `{{tools}}`, and `{{tool_names}}` placeholders.
-  - Adds `Observation` to stop sequences and appends scratchpad text so the model sees prior Thought/Action/Observation history.
-- **Observability and accounting**
-  - Standardized `AgentLog` entries for rounds, model thoughts, and tool calls, including usage aggregation (`LLMUsage`) across streaming and non-streaming paths.
-
-## Architecture
-
-```
-agent/patterns/
-├── base.py              # Shared utilities: logging, usage, tool invocation, file handling
-├── function_call.py     # Native function-calling loop with tool execution
-├── react.py             # ReAct loop with CoT parsing and scratchpad wiring
-└── strategy_factory.py  # Strategy selection by model features or explicit override
-```
-
-## Usage
-
-- For auto-selection:
-  - Call `StrategyFactory.create_strategy(model_features, model_instance, context, tools, files, ...)` and run the returned strategy with prompt messages and model params.
-- For explicit behavior:
-  - Pass `agent_strategy=AgentEntity.Strategy.FUNCTION_CALLING` to force native calls (falls back to ReAct if unsupported), or `CHAIN_OF_THOUGHT` to force ReAct.
-- Both strategies stream chunks and logs; collect the generator output until it returns an `AgentResult`.
-
-## Integration Points
-
-- **Model runtime**: delegates to `ModelInstance.invoke_llm` for both streaming and non-streaming calls.
-- **Tool system**: defaults to `ToolEngine.generic_invoke`, with `tool_invoke_hook` for custom callers.
-- **Files**: flows through `File` objects for tool inputs/outputs and model-context attachments.
-- **Execution context**: `ExecutionContext` fields (user/app/conversation/message) propagate to tool invocations and logging.

api/core/agent/patterns/__init__.py

@@ -1,19 +0,0 @@
-"""Agent patterns module.
-
-This module provides different strategies for agent execution:
-- FunctionCallStrategy: Uses native function/tool calling
-- ReActStrategy: Uses ReAct (Reasoning + Acting) approach
-- StrategyFactory: Factory for creating strategies based on model features
-"""
-
-from .base import AgentPattern
-from .function_call import FunctionCallStrategy
-from .react import ReActStrategy
-from .strategy_factory import StrategyFactory
-
-__all__ = [
-    "AgentPattern",
-    "FunctionCallStrategy",
-    "ReActStrategy",
-    "StrategyFactory",
-]

api/core/agent/patterns/base.py

@@ -1,474 +0,0 @@
-"""Base class for agent strategies."""
-
-from __future__ import annotations
-
-import json
-import re
-import time
-from abc import ABC, abstractmethod
-from collections.abc import Callable, Generator
-from typing import TYPE_CHECKING, Any
-
-from core.agent.entities import AgentLog, AgentResult, ExecutionContext
-from core.file import File
-from core.model_manager import ModelInstance
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMResultChunkDelta,
-    PromptMessage,
-    PromptMessageTool,
-)
-from core.model_runtime.entities.llm_entities import LLMUsage
-from core.model_runtime.entities.message_entities import TextPromptMessageContent
-from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMeta
-
-if TYPE_CHECKING:
-    from core.tools.__base.tool import Tool
-
-# Type alias for tool invoke hook
-# Returns: (response_content, message_file_ids, tool_invoke_meta)
-ToolInvokeHook = Callable[["Tool", dict[str, Any], str], tuple[str, list[str], ToolInvokeMeta]]
-
-
-class AgentPattern(ABC):
-    """Base class for agent execution strategies."""
-
-    def __init__(
-        self,
-        model_instance: ModelInstance,
-        tools: list[Tool],
-        context: ExecutionContext,
-        max_iterations: int = 10,
-        workflow_call_depth: int = 0,
-        files: list[File] = [],
-        tool_invoke_hook: ToolInvokeHook | None = None,
-    ):
-        """Initialize the agent strategy."""
-        self.model_instance = model_instance
-        self.tools = tools
-        self.context = context
-        self.max_iterations = min(max_iterations, 99)  # Cap at 99 iterations
-        self.workflow_call_depth = workflow_call_depth
-        self.files: list[File] = files
-        self.tool_invoke_hook = tool_invoke_hook
-
-    @abstractmethod
-    def run(
-        self,
-        prompt_messages: list[PromptMessage],
-        model_parameters: dict[str, Any],
-        stop: list[str] = [],
-        stream: bool = True,
-    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
-        """Execute the agent strategy."""
-        pass
-
-    def _accumulate_usage(self, total_usage: dict[str, Any], delta_usage: LLMUsage) -> None:
-        """Accumulate LLM usage statistics."""
-        if not total_usage.get("usage"):
-            # Create a copy to avoid modifying the original
-            total_usage["usage"] = LLMUsage(
-                prompt_tokens=delta_usage.prompt_tokens,
-                prompt_unit_price=delta_usage.prompt_unit_price,
-                prompt_price_unit=delta_usage.prompt_price_unit,
-                prompt_price=delta_usage.prompt_price,
-                completion_tokens=delta_usage.completion_tokens,
-                completion_unit_price=delta_usage.completion_unit_price,
-                completion_price_unit=delta_usage.completion_price_unit,
-                completion_price=delta_usage.completion_price,
-                total_tokens=delta_usage.total_tokens,
-                total_price=delta_usage.total_price,
-                currency=delta_usage.currency,
-                latency=delta_usage.latency,
-            )
-        else:
-            current: LLMUsage = total_usage["usage"]
-            current.prompt_tokens += delta_usage.prompt_tokens
-            current.completion_tokens += delta_usage.completion_tokens
-            current.total_tokens += delta_usage.total_tokens
-            current.prompt_price += delta_usage.prompt_price
-            current.completion_price += delta_usage.completion_price
-            current.total_price += delta_usage.total_price
-
-    def _extract_content(self, content: Any) -> str:
-        """Extract text content from message content."""
-        if isinstance(content, list):
-            # Content items are PromptMessageContentUnionTypes
-            text_parts = []
-            for c in content:
-                # Check if it's a TextPromptMessageContent (which has data attribute)
-                if isinstance(c, TextPromptMessageContent):
-                    text_parts.append(c.data)
-            return "".join(text_parts)
-        return str(content)
-
-    def _has_tool_calls(self, chunk: LLMResultChunk) -> bool:
-        """Check if chunk contains tool calls."""
-        # LLMResultChunk always has delta attribute
-        return bool(chunk.delta.message and chunk.delta.message.tool_calls)
-
-    def _has_tool_calls_result(self, result: LLMResult) -> bool:
-        """Check if result contains tool calls (non-streaming)."""
-        # LLMResult always has message attribute
-        return bool(result.message and result.message.tool_calls)
-
-    def _extract_tool_calls(self, chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
-        """Extract tool calls from streaming chunk."""
-        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
-        if chunk.delta.message and chunk.delta.message.tool_calls:
-            for tool_call in chunk.delta.message.tool_calls:
-                if tool_call.function:
-                    try:
-                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
-                    except json.JSONDecodeError:
-                        args = {}
-                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
-        return tool_calls
-
-    def _extract_tool_calls_result(self, result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
-        """Extract tool calls from non-streaming result."""
-        tool_calls = []
-        if result.message and result.message.tool_calls:
-            for tool_call in result.message.tool_calls:
-                if tool_call.function:
-                    try:
-                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
-                    except json.JSONDecodeError:
-                        args = {}
-                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
-        return tool_calls
-
-    def _extract_text_from_message(self, message: PromptMessage) -> str:
-        """Extract text content from a prompt message."""
-        # PromptMessage always has content attribute
-        content = message.content
-        if isinstance(content, str):
-            return content
-        elif isinstance(content, list):
-            # Extract text from content list
-            text_parts = []
-            for item in content:
-                if isinstance(item, TextPromptMessageContent):
-                    text_parts.append(item.data)
-            return " ".join(text_parts)
-        return ""
-
-    def _get_tool_metadata(self, tool_instance: Tool) -> dict[AgentLog.LogMetadata, Any]:
-        """Get metadata for a tool including provider and icon info."""
-        from core.tools.tool_manager import ToolManager
-
-        metadata: dict[AgentLog.LogMetadata, Any] = {}
-        if tool_instance.entity and tool_instance.entity.identity:
-            identity = tool_instance.entity.identity
-            if identity.provider:
-                metadata[AgentLog.LogMetadata.PROVIDER] = identity.provider
-
-            # Get icon using ToolManager for proper URL generation
-            tenant_id = self.context.tenant_id
-            if tenant_id and identity.provider:
-                try:
-                    provider_type = tool_instance.tool_provider_type()
-                    icon = ToolManager.get_tool_icon(tenant_id, provider_type, identity.provider)
-                    if isinstance(icon, str):
-                        metadata[AgentLog.LogMetadata.ICON] = icon
-                    elif isinstance(icon, dict):
-                        # Handle icon dict with background/content or light/dark variants
-                        metadata[AgentLog.LogMetadata.ICON] = icon
-                except Exception:
-                    # Fallback to identity.icon if ToolManager fails
-                    if identity.icon:
-                        metadata[AgentLog.LogMetadata.ICON] = identity.icon
-            elif identity.icon:
-                metadata[AgentLog.LogMetadata.ICON] = identity.icon
-        return metadata
-
-    def _create_log(
-        self,
-        label: str,
-        log_type: AgentLog.LogType,
-        status: AgentLog.LogStatus,
-        data: dict[str, Any] | None = None,
-        parent_id: str | None = None,
-        extra_metadata: dict[AgentLog.LogMetadata, Any] | None = None,
-    ) -> AgentLog:
-        """Create a new AgentLog with standard metadata."""
-        metadata: dict[AgentLog.LogMetadata, Any] = {
-            AgentLog.LogMetadata.STARTED_AT: time.perf_counter(),
-        }
-        if extra_metadata:
-            metadata.update(extra_metadata)
-
-        return AgentLog(
-            label=label,
-            log_type=log_type,
-            status=status,
-            data=data or {},
-            parent_id=parent_id,
-            metadata=metadata,
-        )
-
-    def _finish_log(
-        self,
-        log: AgentLog,
-        data: dict[str, Any] | None = None,
-        usage: LLMUsage | None = None,
-    ) -> AgentLog:
-        """Finish an AgentLog by updating its status and metadata."""
-        log.status = AgentLog.LogStatus.SUCCESS
-
-        if data is not None:
-            log.data = data
-
-        # Calculate elapsed time
-        started_at = log.metadata.get(AgentLog.LogMetadata.STARTED_AT, time.perf_counter())
-        finished_at = time.perf_counter()
-
-        # Update metadata
-        log.metadata = {
-            **log.metadata,
-            AgentLog.LogMetadata.FINISHED_AT: finished_at,
-            # Calculate elapsed time in seconds
-            AgentLog.LogMetadata.ELAPSED_TIME: round(finished_at - started_at, 4),
-        }
-
-        # Add usage information if provided
-        if usage:
-            log.metadata.update(
-                {
-                    AgentLog.LogMetadata.TOTAL_PRICE: usage.total_price,
-                    AgentLog.LogMetadata.CURRENCY: usage.currency,
-                    AgentLog.LogMetadata.TOTAL_TOKENS: usage.total_tokens,
-                    AgentLog.LogMetadata.LLM_USAGE: usage,
-                }
-            )
-
-        return log
-
-    def _replace_file_references(self, tool_args: dict[str, Any]) -> dict[str, Any]:
-        """
-        Replace file references in tool arguments with actual File objects.
-
-        Args:
-            tool_args: Dictionary of tool arguments
-
-        Returns:
-            Updated tool arguments with file references replaced
-        """
-        # Process each argument in the dictionary
-        processed_args: dict[str, Any] = {}
-        for key, value in tool_args.items():
-            processed_args[key] = self._process_file_reference(value)
-        return processed_args
-
-    def _process_file_reference(self, data: Any) -> Any:
-        """
-        Recursively process data to replace file references.
-        Supports both single file [File: file_id] and multiple files [Files: file_id1, file_id2, ...].
-
-        Args:
-            data: The data to process (can be dict, list, str, or other types)
-
-        Returns:
-            Processed data with file references replaced
-        """
-        single_file_pattern = re.compile(r"^\[File:\s*([^\]]+)\]$")
-        multiple_files_pattern = re.compile(r"^\[Files:\s*([^\]]+)\]$")
-
-        if isinstance(data, dict):
-            # Process dictionary recursively
-            return {key: self._process_file_reference(value) for key, value in data.items()}
-        elif isinstance(data, list):
-            # Process list recursively
-            return [self._process_file_reference(item) for item in data]
-        elif isinstance(data, str):
-            # Check for single file pattern [File: file_id]
-            single_match = single_file_pattern.match(data.strip())
-            if single_match:
-                file_id = single_match.group(1).strip()
-                # Find the file in self.files
-                for file in self.files:
-                    if file.id and str(file.id) == file_id:
-                        return file
-                # If file not found, return original value
-                return data
-
-            # Check for multiple files pattern [Files: file_id1, file_id2, ...]
-            multiple_match = multiple_files_pattern.match(data.strip())
-            if multiple_match:
-                file_ids_str = multiple_match.group(1).strip()
-                # Split by comma and strip whitespace
-                file_ids = [fid.strip() for fid in file_ids_str.split(",")]
-
-                # Find all matching files
-                matched_files: list[File] = []
-                for file_id in file_ids:
-                    for file in self.files:
-                        if file.id and str(file.id) == file_id:
-                            matched_files.append(file)
-                            break
-
-                # Return list of files if any were found, otherwise return original
-                return matched_files or data
-
-            return data
-        else:
-            # Return other types as-is
-            return data
-
-    def _create_text_chunk(self, text: str, prompt_messages: list[PromptMessage]) -> LLMResultChunk:
-        """Create a text chunk for streaming."""
-        return LLMResultChunk(
-            model=self.model_instance.model,
-            prompt_messages=prompt_messages,
-            delta=LLMResultChunkDelta(
-                index=0,
-                message=AssistantPromptMessage(content=text),
-                usage=None,
-            ),
-            system_fingerprint="",
-        )
-
-    def _invoke_tool(
-        self,
-        tool_instance: Tool,
-        tool_args: dict[str, Any],
-        tool_name: str,
-    ) -> tuple[str, list[File], ToolInvokeMeta | None]:
-        """
-        Invoke a tool and collect its response.
-
-        Args:
-            tool_instance: The tool instance to invoke
-            tool_args: Tool arguments
-            tool_name: Name of the tool
-
-        Returns:
-            Tuple of (response_content, tool_files, tool_invoke_meta)
-        """
-        # Process tool_args to replace file references with actual File objects
-        tool_args = self._replace_file_references(tool_args)
-
-        # If a tool invoke hook is set, use it instead of generic_invoke
-        if self.tool_invoke_hook:
-            response_content, _, tool_invoke_meta = self.tool_invoke_hook(tool_instance, tool_args, tool_name)
-            # Note: message_file_ids are stored in DB, we don't convert them to File objects here
-            # The caller (AgentAppRunner) handles file publishing
-            return response_content, [], tool_invoke_meta
-
-        # Default: use generic_invoke for workflow scenarios
-        # Import here to avoid circular import
-        from core.tools.tool_engine import DifyWorkflowCallbackHandler, ToolEngine
-
-        tool_response = ToolEngine().generic_invoke(
-            tool=tool_instance,
-            tool_parameters=tool_args,
-            user_id=self.context.user_id or "",
-            workflow_tool_callback=DifyWorkflowCallbackHandler(),
-            workflow_call_depth=self.workflow_call_depth,
-            app_id=self.context.app_id,
-            conversation_id=self.context.conversation_id,
-            message_id=self.context.message_id,
-        )
-
-        # Collect response and files
-        response_content = ""
-        tool_files: list[File] = []
-
-        for response in tool_response:
-            if response.type == ToolInvokeMessage.MessageType.TEXT:
-                assert isinstance(response.message, ToolInvokeMessage.TextMessage)
-                response_content += response.message.text
-
-            elif response.type == ToolInvokeMessage.MessageType.LINK:
-                # Handle link messages
-                if isinstance(response.message, ToolInvokeMessage.TextMessage):
-                    response_content += f"[Link: {response.message.text}]"
-
-            elif response.type == ToolInvokeMessage.MessageType.IMAGE:
-                # Handle image URL messages
-                if isinstance(response.message, ToolInvokeMessage.TextMessage):
-                    response_content += f"[Image: {response.message.text}]"
-
-            elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK:
-                # Handle image link messages
-                if isinstance(response.message, ToolInvokeMessage.TextMessage):
-                    response_content += f"[Image: {response.message.text}]"
-
-            elif response.type == ToolInvokeMessage.MessageType.BINARY_LINK:
-                # Handle binary file link messages
-                if isinstance(response.message, ToolInvokeMessage.TextMessage):
-                    filename = response.meta.get("filename", "file") if response.meta else "file"
-                    response_content += f"[File: {filename} - {response.message.text}]"
-
-            elif response.type == ToolInvokeMessage.MessageType.JSON:
-                # Handle JSON messages
-                if isinstance(response.message, ToolInvokeMessage.JsonMessage):
-                    response_content += json.dumps(response.message.json_object, ensure_ascii=False, indent=2)
-
-            elif response.type == ToolInvokeMessage.MessageType.BLOB:
-                # Handle blob messages - convert to text representation
-                if isinstance(response.message, ToolInvokeMessage.BlobMessage):
-                    mime_type = (
-                        response.meta.get("mime_type", "application/octet-stream")
-                        if response.meta
-                        else "application/octet-stream"
-                    )
-                    size = len(response.message.blob)
-                    response_content += f"[Binary data: {mime_type}, size: {size} bytes]"
-
-            elif response.type == ToolInvokeMessage.MessageType.VARIABLE:
-                # Handle variable messages
-                if isinstance(response.message, ToolInvokeMessage.VariableMessage):
-                    var_name = response.message.variable_name
-                    var_value = response.message.variable_value
-                    if isinstance(var_value, str):
-                        response_content += var_value
-                    else:
-                        response_content += f"[Variable {var_name}: {json.dumps(var_value, ensure_ascii=False)}]"
-
-            elif response.type == ToolInvokeMessage.MessageType.BLOB_CHUNK:
-                # Handle blob chunk messages - these are parts of a larger blob
-                if isinstance(response.message, ToolInvokeMessage.BlobChunkMessage):
-                    response_content += f"[Blob chunk {response.message.sequence}: {len(response.message.blob)} bytes]"
-
-            elif response.type == ToolInvokeMessage.MessageType.RETRIEVER_RESOURCES:
-                # Handle retriever resources messages
-                if isinstance(response.message, ToolInvokeMessage.RetrieverResourceMessage):
-                    response_content += response.message.context
-
-            elif response.type == ToolInvokeMessage.MessageType.FILE:
-                # Extract file from meta
-                if response.meta and "file" in response.meta:
-                    file = response.meta["file"]
-                    if isinstance(file, File):
-                        # Check if file is for model or tool output
-                        if response.meta.get("target") == "self":
-                            # File is for model - add to files for next prompt
-                            self.files.append(file)
-                            response_content += f"File '{file.filename}' has been loaded into your context."
-                        else:
-                            # File is tool output
-                            tool_files.append(file)
-
-        return response_content, tool_files, None
-
-    def _find_tool_by_name(self, tool_name: str) -> Tool | None:
-        """Find a tool instance by its name."""
-        for tool in self.tools:
-            if tool.entity.identity.name == tool_name:
-                return tool
-        return None
-
-    def _convert_tools_to_prompt_format(self) -> list[PromptMessageTool]:
-        """Convert tools to prompt message format."""
-        prompt_tools: list[PromptMessageTool] = []
-        for tool in self.tools:
-            prompt_tools.append(tool.to_prompt_message_tool())
-        return prompt_tools
-
-    def _update_usage_with_empty(self, llm_usage: dict[str, Any]) -> None:
-        """Initialize usage tracking with empty usage if not set."""
-        if "usage" not in llm_usage or llm_usage["usage"] is None:
-            llm_usage["usage"] = LLMUsage.empty_usage()

api/core/agent/patterns/function_call.py

@@ -1,299 +0,0 @@
-"""Function Call strategy implementation."""
-
-import json
-from collections.abc import Generator
-from typing import Any, Union
-
-from core.agent.entities import AgentLog, AgentResult
-from core.file import File
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMResultChunkDelta,
-    LLMUsage,
-    PromptMessage,
-    PromptMessageTool,
-    ToolPromptMessage,
-)
-from core.tools.entities.tool_entities import ToolInvokeMeta
-
-from .base import AgentPattern
-
-
-class FunctionCallStrategy(AgentPattern):
-    """Function Call strategy using model's native tool calling capability."""
-
-    def run(
-        self,
-        prompt_messages: list[PromptMessage],
-        model_parameters: dict[str, Any],
-        stop: list[str] = [],
-        stream: bool = True,
-    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
-        """Execute the function call agent strategy."""
-        # Convert tools to prompt format
-        prompt_tools: list[PromptMessageTool] = self._convert_tools_to_prompt_format()
-
-        # Initialize tracking
-        iteration_step: int = 1
-        max_iterations: int = self.max_iterations + 1
-        function_call_state: bool = True
-        total_usage: dict[str, LLMUsage | None] = {"usage": None}
-        messages: list[PromptMessage] = list(prompt_messages)  # Create mutable copy
-        final_text: str = ""
-        finish_reason: str | None = None
-        output_files: list[File] = []  # Track files produced by tools
-
-        while function_call_state and iteration_step <= max_iterations:
-            function_call_state = False
-            round_log = self._create_log(
-                label=f"ROUND {iteration_step}",
-                log_type=AgentLog.LogType.ROUND,
-                status=AgentLog.LogStatus.START,
-                data={},
-            )
-            yield round_log
-            # On last iteration, remove tools to force final answer
-            current_tools: list[PromptMessageTool] = [] if iteration_step == max_iterations else prompt_tools
-            model_log = self._create_log(
-                label=f"{self.model_instance.model} Thought",
-                log_type=AgentLog.LogType.THOUGHT,
-                status=AgentLog.LogStatus.START,
-                data={},
-                parent_id=round_log.id,
-                extra_metadata={
-                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
-                },
-            )
-            yield model_log
-
-            # Track usage for this round only
-            round_usage: dict[str, LLMUsage | None] = {"usage": None}
-
-            # Invoke model
-            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
-                prompt_messages=messages,
-                model_parameters=model_parameters,
-                tools=current_tools,
-                stop=stop,
-                stream=stream,
-                user=self.context.user_id,
-                callbacks=[],
-            )
-
-            # Process response
-            tool_calls, response_content, chunk_finish_reason = yield from self._handle_chunks(
-                chunks, round_usage, model_log
-            )
-            messages.append(self._create_assistant_message(response_content, tool_calls))
-
-            # Accumulate to total usage
-            round_usage_value = round_usage.get("usage")
-            if round_usage_value:
-                self._accumulate_usage(total_usage, round_usage_value)
-
-            # Update final text if no tool calls (this is likely the final answer)
-            if not tool_calls:
-                final_text = response_content
-
-            # Update finish reason
-            if chunk_finish_reason:
-                finish_reason = chunk_finish_reason
-
-            # Process tool calls
-            tool_outputs: dict[str, str] = {}
-            if tool_calls:
-                function_call_state = True
-                # Execute tools
-                for tool_call_id, tool_name, tool_args in tool_calls:
-                    tool_response, tool_files, _ = yield from self._handle_tool_call(
-                        tool_name, tool_args, tool_call_id, messages, round_log
-                    )
-                    tool_outputs[tool_name] = tool_response
-                    # Track files produced by tools
-                    output_files.extend(tool_files)
-            yield self._finish_log(
-                round_log,
-                data={
-                    "llm_result": response_content,
-                    "tool_calls": [
-                        {"name": tc[1], "args": tc[2], "output": tool_outputs.get(tc[1], "")} for tc in tool_calls
-                    ]
-                    if tool_calls
-                    else [],
-                    "final_answer": final_text if not function_call_state else None,
-                },
-                usage=round_usage.get("usage"),
-            )
-            iteration_step += 1
-
-        # Return final result
-        from core.agent.entities import AgentResult
-
-        return AgentResult(
-            text=final_text,
-            files=output_files,
-            usage=total_usage.get("usage") or LLMUsage.empty_usage(),
-            finish_reason=finish_reason,
-        )
-
-    def _handle_chunks(
-        self,
-        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
-        llm_usage: dict[str, LLMUsage | None],
-        start_log: AgentLog,
-    ) -> Generator[
-        LLMResultChunk | AgentLog,
-        None,
-        tuple[list[tuple[str, str, dict[str, Any]]], str, str | None],
-    ]:
-        """Handle LLM response chunks and extract tool calls and content.
-
-        Returns a tuple of (tool_calls, response_content, finish_reason).
-        """
-        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
-        response_content: str = ""
-        finish_reason: str | None = None
-        if isinstance(chunks, Generator):
-            # Streaming response
-            for chunk in chunks:
-                # Extract tool calls
-                if self._has_tool_calls(chunk):
-                    tool_calls.extend(self._extract_tool_calls(chunk))
-
-                # Extract content
-                if chunk.delta.message and chunk.delta.message.content:
-                    response_content += self._extract_content(chunk.delta.message.content)
-
-                # Track usage
-                if chunk.delta.usage:
-                    self._accumulate_usage(llm_usage, chunk.delta.usage)
-
-                # Capture finish reason
-                if chunk.delta.finish_reason:
-                    finish_reason = chunk.delta.finish_reason
-
-                yield chunk
-        else:
-            # Non-streaming response
-            result: LLMResult = chunks
-
-            if self._has_tool_calls_result(result):
-                tool_calls.extend(self._extract_tool_calls_result(result))
-
-            if result.message and result.message.content:
-                response_content += self._extract_content(result.message.content)
-
-            if result.usage:
-                self._accumulate_usage(llm_usage, result.usage)
-
-            # Convert to streaming format
-            yield LLMResultChunk(
-                model=result.model,
-                prompt_messages=result.prompt_messages,
-                delta=LLMResultChunkDelta(index=0, message=result.message, usage=result.usage),
-            )
-        yield self._finish_log(
-            start_log,
-            data={
-                "result": response_content,
-            },
-            usage=llm_usage.get("usage"),
-        )
-        return tool_calls, response_content, finish_reason
-
-    def _create_assistant_message(
-        self, content: str, tool_calls: list[tuple[str, str, dict[str, Any]]] | None = None
-    ) -> AssistantPromptMessage:
-        """Create assistant message with tool calls."""
-        if tool_calls is None:
-            return AssistantPromptMessage(content=content)
-        return AssistantPromptMessage(
-            content=content or "",
-            tool_calls=[
-                AssistantPromptMessage.ToolCall(
-                    id=tc[0],
-                    type="function",
-                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tc[1], arguments=json.dumps(tc[2])),
-                )
-                for tc in tool_calls
-            ],
-        )
-
-    def _handle_tool_call(
-        self,
-        tool_name: str,
-        tool_args: dict[str, Any],
-        tool_call_id: str,
-        messages: list[PromptMessage],
-        round_log: AgentLog,
-    ) -> Generator[AgentLog, None, tuple[str, list[File], ToolInvokeMeta | None]]:
-        """Handle a single tool call and return response with files and meta."""
-        # Find tool
-        tool_instance = self._find_tool_by_name(tool_name)
-        if not tool_instance:
-            raise ValueError(f"Tool {tool_name} not found")
-
-        # Get tool metadata (provider, icon, etc.)
-        tool_metadata = self._get_tool_metadata(tool_instance)
-
-        # Create tool call log
-        tool_call_log = self._create_log(
-            label=f"CALL {tool_name}",
-            log_type=AgentLog.LogType.TOOL_CALL,
-            status=AgentLog.LogStatus.START,
-            data={
-                "tool_call_id": tool_call_id,
-                "tool_name": tool_name,
-                "tool_args": tool_args,
-            },
-            parent_id=round_log.id,
-            extra_metadata=tool_metadata,
-        )
-        yield tool_call_log
-
-        # Invoke tool using base class method with error handling
-        try:
-            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args, tool_name)
-
-            yield self._finish_log(
-                tool_call_log,
-                data={
-                    **tool_call_log.data,
-                    "output": response_content,
-                    "files": len(tool_files),
-                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
-                },
-            )
-            final_content = response_content or "Tool executed successfully"
-            # Add tool response to messages
-            messages.append(
-                ToolPromptMessage(
-                    content=final_content,
-                    tool_call_id=tool_call_id,
-                    name=tool_name,
-                )
-            )
-            return response_content, tool_files, tool_invoke_meta
-        except Exception as e:
-            # Tool invocation failed, yield error log
-            error_message = str(e)
-            tool_call_log.status = AgentLog.LogStatus.ERROR
-            tool_call_log.error = error_message
-            tool_call_log.data = {
-                **tool_call_log.data,
-                "error": error_message,
-            }
-            yield tool_call_log
-
-            # Add error message to conversation
-            error_content = f"Tool execution failed: {error_message}"
-            messages.append(
-                ToolPromptMessage(
-                    content=error_content,
-                    tool_call_id=tool_call_id,
-                    name=tool_name,
-                )
-            )
-            return error_content, [], None

api/core/agent/patterns/react.py

@@ -1,418 +0,0 @@
-"""ReAct strategy implementation."""
-
-from __future__ import annotations
-
-import json
-from collections.abc import Generator
-from typing import TYPE_CHECKING, Any, Union
-
-from core.agent.entities import AgentLog, AgentResult, AgentScratchpadUnit, ExecutionContext
-from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
-from core.file import File
-from core.model_manager import ModelInstance
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMResultChunkDelta,
-    PromptMessage,
-    SystemPromptMessage,
-)
-
-from .base import AgentPattern, ToolInvokeHook
-
-if TYPE_CHECKING:
-    from core.tools.__base.tool import Tool
-
-
-class ReActStrategy(AgentPattern):
-    """ReAct strategy using reasoning and acting approach."""
-
-    def __init__(
-        self,
-        model_instance: ModelInstance,
-        tools: list[Tool],
-        context: ExecutionContext,
-        max_iterations: int = 10,
-        workflow_call_depth: int = 0,
-        files: list[File] = [],
-        tool_invoke_hook: ToolInvokeHook | None = None,
-        instruction: str = "",
-    ):
-        """Initialize the ReAct strategy with instruction support."""
-        super().__init__(
-            model_instance=model_instance,
-            tools=tools,
-            context=context,
-            max_iterations=max_iterations,
-            workflow_call_depth=workflow_call_depth,
-            files=files,
-            tool_invoke_hook=tool_invoke_hook,
-        )
-        self.instruction = instruction
-
-    def run(
-        self,
-        prompt_messages: list[PromptMessage],
-        model_parameters: dict[str, Any],
-        stop: list[str] = [],
-        stream: bool = True,
-    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
-        """Execute the ReAct agent strategy."""
-        # Initialize tracking
-        agent_scratchpad: list[AgentScratchpadUnit] = []
-        iteration_step: int = 1
-        max_iterations: int = self.max_iterations + 1
-        react_state: bool = True
-        total_usage: dict[str, Any] = {"usage": None}
-        output_files: list[File] = []  # Track files produced by tools
-        final_text: str = ""
-        finish_reason: str | None = None
-
-        # Add "Observation" to stop sequences
-        if "Observation" not in stop:
-            stop = stop.copy()
-            stop.append("Observation")
-
-        while react_state and iteration_step <= max_iterations:
-            react_state = False
-            round_log = self._create_log(
-                label=f"ROUND {iteration_step}",
-                log_type=AgentLog.LogType.ROUND,
-                status=AgentLog.LogStatus.START,
-                data={},
-            )
-            yield round_log
-
-            # Build prompt with/without tools based on iteration
-            include_tools = iteration_step < max_iterations
-            current_messages = self._build_prompt_with_react_format(
-                prompt_messages, agent_scratchpad, include_tools, self.instruction
-            )
-
-            model_log = self._create_log(
-                label=f"{self.model_instance.model} Thought",
-                log_type=AgentLog.LogType.THOUGHT,
-                status=AgentLog.LogStatus.START,
-                data={},
-                parent_id=round_log.id,
-                extra_metadata={
-                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
-                },
-            )
-            yield model_log
-
-            # Track usage for this round only
-            round_usage: dict[str, Any] = {"usage": None}
-
-            # Use current messages directly (files are handled by base class if needed)
-            messages_to_use = current_messages
-
-            # Invoke model
-            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
-                prompt_messages=messages_to_use,
-                model_parameters=model_parameters,
-                stop=stop,
-                stream=stream,
-                user=self.context.user_id or "",
-                callbacks=[],
-            )
-
-            # Process response
-            scratchpad, chunk_finish_reason = yield from self._handle_chunks(
-                chunks, round_usage, model_log, current_messages
-            )
-            agent_scratchpad.append(scratchpad)
-
-            # Accumulate to total usage
-            round_usage_value = round_usage.get("usage")
-            if round_usage_value:
-                self._accumulate_usage(total_usage, round_usage_value)
-
-            # Update finish reason
-            if chunk_finish_reason:
-                finish_reason = chunk_finish_reason
-
-            # Check if we have an action to execute
-            if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
-                react_state = True
-                # Execute tool
-                observation, tool_files = yield from self._handle_tool_call(
-                    scratchpad.action, current_messages, round_log
-                )
-                scratchpad.observation = observation
-                # Track files produced by tools
-                output_files.extend(tool_files)
-
-                # Add observation to scratchpad for display
-                yield self._create_text_chunk(f"\nObservation: {observation}\n", current_messages)
-            else:
-                # Extract final answer
-                if scratchpad.action and scratchpad.action.action_input:
-                    final_answer = scratchpad.action.action_input
-                    if isinstance(final_answer, dict):
-                        final_answer = json.dumps(final_answer, ensure_ascii=False)
-                    final_text = str(final_answer)
-                elif scratchpad.thought:
-                    # If no action but we have thought, use thought as final answer
-                    final_text = scratchpad.thought
-
-            yield self._finish_log(
-                round_log,
-                data={
-                    "thought": scratchpad.thought,
-                    "action": scratchpad.action_str if scratchpad.action else None,
-                    "observation": scratchpad.observation or None,
-                    "final_answer": final_text if not react_state else None,
-                },
-                usage=round_usage.get("usage"),
-            )
-            iteration_step += 1
-
-        # Return final result
-
-        from core.agent.entities import AgentResult
-
-        return AgentResult(
-            text=final_text, files=output_files, usage=total_usage.get("usage"), finish_reason=finish_reason
-        )
-
-    def _build_prompt_with_react_format(
-        self,
-        original_messages: list[PromptMessage],
-        agent_scratchpad: list[AgentScratchpadUnit],
-        include_tools: bool = True,
-        instruction: str = "",
-    ) -> list[PromptMessage]:
-        """Build prompt messages with ReAct format."""
-        # Copy messages to avoid modifying original
-        messages = list(original_messages)
-
-        # Find and update the system prompt that should already exist
-        system_prompt_found = False
-        for i, msg in enumerate(messages):
-            if isinstance(msg, SystemPromptMessage):
-                system_prompt_found = True
-                # The system prompt from frontend already has the template, just replace placeholders
-
-                # Format tools
-                tools_str = ""
-                tool_names = []
-                if include_tools and self.tools:
-                    # Convert tools to prompt message tools format
-                    prompt_tools = [tool.to_prompt_message_tool() for tool in self.tools]
-                    tool_names = [tool.name for tool in prompt_tools]
-
-                    # Format tools as JSON for comprehensive information
-                    from core.model_runtime.utils.encoders import jsonable_encoder
-
-                    tools_str = json.dumps(jsonable_encoder(prompt_tools), indent=2)
-                    tool_names_str = ", ".join(f'"{name}"' for name in tool_names)
-                else:
-                    tools_str = "No tools available"
-                    tool_names_str = ""
-
-                # Replace placeholders in the existing system prompt
-                updated_content = msg.content
-                assert isinstance(updated_content, str)
-                updated_content = updated_content.replace("{{instruction}}", instruction)
-                updated_content = updated_content.replace("{{tools}}", tools_str)
-                updated_content = updated_content.replace("{{tool_names}}", tool_names_str)
-
-                # Create new SystemPromptMessage with updated content
-                messages[i] = SystemPromptMessage(content=updated_content)
-                break
-
-        # If no system prompt found, that's unexpected but add scratchpad anyway
-        if not system_prompt_found:
-            # This shouldn't happen if frontend is working correctly
-            pass
-
-        # Format agent scratchpad
-        scratchpad_str = ""
-        if agent_scratchpad:
-            scratchpad_parts: list[str] = []
-            for unit in agent_scratchpad:
-                if unit.thought:
-                    scratchpad_parts.append(f"Thought: {unit.thought}")
-                if unit.action_str:
-                    scratchpad_parts.append(f"Action:\n```\n{unit.action_str}\n```")
-                if unit.observation:
-                    scratchpad_parts.append(f"Observation: {unit.observation}")
-            scratchpad_str = "\n".join(scratchpad_parts)
-
-        # If there's a scratchpad, append it to the last message
-        if scratchpad_str:
-            messages.append(AssistantPromptMessage(content=scratchpad_str))
-
-        return messages
-
-    def _handle_chunks(
-        self,
-        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
-        llm_usage: dict[str, Any],
-        model_log: AgentLog,
-        current_messages: list[PromptMessage],
-    ) -> Generator[
-        LLMResultChunk | AgentLog,
-        None,
-        tuple[AgentScratchpadUnit, str | None],
-    ]:
-        """Handle LLM response chunks and extract action/thought.
-
-        Returns a tuple of (scratchpad_unit, finish_reason).
-        """
-        usage_dict: dict[str, Any] = {}
-
-        # Convert non-streaming to streaming format if needed
-        if isinstance(chunks, LLMResult):
-            # Create a generator from the LLMResult
-            def result_to_chunks() -> Generator[LLMResultChunk, None, None]:
-                yield LLMResultChunk(
-                    model=chunks.model,
-                    prompt_messages=chunks.prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=0,
-                        message=chunks.message,
-                        usage=chunks.usage,
-                        finish_reason=None,  # LLMResult doesn't have finish_reason, only streaming chunks do
-                    ),
-                    system_fingerprint=chunks.system_fingerprint or "",
-                )
-
-            streaming_chunks = result_to_chunks()
-        else:
-            streaming_chunks = chunks
-
-        react_chunks = CotAgentOutputParser.handle_react_stream_output(streaming_chunks, usage_dict)
-
-        # Initialize scratchpad unit
-        scratchpad = AgentScratchpadUnit(
-            agent_response="",
-            thought="",
-            action_str="",
-            observation="",
-            action=None,
-        )
-
-        finish_reason: str | None = None
-
-        # Process chunks
-        for chunk in react_chunks:
-            if isinstance(chunk, AgentScratchpadUnit.Action):
-                # Action detected
-                action_str = json.dumps(chunk.model_dump())
-                scratchpad.agent_response = (scratchpad.agent_response or "") + action_str
-                scratchpad.action_str = action_str
-                scratchpad.action = chunk
-
-                yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
-            else:
-                # Text chunk
-                chunk_text = str(chunk)
-                scratchpad.agent_response = (scratchpad.agent_response or "") + chunk_text
-                scratchpad.thought = (scratchpad.thought or "") + chunk_text
-
-                yield self._create_text_chunk(chunk_text, current_messages)
-
-        # Update usage
-        if usage_dict.get("usage"):
-            if llm_usage.get("usage"):
-                self._accumulate_usage(llm_usage, usage_dict["usage"])
-            else:
-                llm_usage["usage"] = usage_dict["usage"]
-
-        # Clean up thought
-        scratchpad.thought = (scratchpad.thought or "").strip() or "I am thinking about how to help you"
-
-        # Finish model log
-        yield self._finish_log(
-            model_log,
-            data={
-                "thought": scratchpad.thought,
-                "action": scratchpad.action_str if scratchpad.action else None,
-            },
-            usage=llm_usage.get("usage"),
-        )
-
-        return scratchpad, finish_reason
-
-    def _handle_tool_call(
-        self,
-        action: AgentScratchpadUnit.Action,
-        prompt_messages: list[PromptMessage],
-        round_log: AgentLog,
-    ) -> Generator[AgentLog, None, tuple[str, list[File]]]:
-        """Handle tool call and return observation with files."""
-        tool_name = action.action_name
-        tool_args: dict[str, Any] | str = action.action_input
-
-        # Find tool instance first to get metadata
-        tool_instance = self._find_tool_by_name(tool_name)
-        tool_metadata = self._get_tool_metadata(tool_instance) if tool_instance else {}
-
-        # Start tool log with tool metadata
-        tool_log = self._create_log(
-            label=f"CALL {tool_name}",
-            log_type=AgentLog.LogType.TOOL_CALL,
-            status=AgentLog.LogStatus.START,
-            data={
-                "tool_name": tool_name,
-                "tool_args": tool_args,
-            },
-            parent_id=round_log.id,
-            extra_metadata=tool_metadata,
-        )
-        yield tool_log
-
-        if not tool_instance:
-            # Finish tool log with error
-            yield self._finish_log(
-                tool_log,
-                data={
-                    **tool_log.data,
-                    "error": f"Tool {tool_name} not found",
-                },
-            )
-            return f"Tool {tool_name} not found", []
-
-        # Ensure tool_args is a dict
-        tool_args_dict: dict[str, Any]
-        if isinstance(tool_args, str):
-            try:
-                tool_args_dict = json.loads(tool_args)
-            except json.JSONDecodeError:
-                tool_args_dict = {"input": tool_args}
-        elif not isinstance(tool_args, dict):
-            tool_args_dict = {"input": str(tool_args)}
-        else:
-            tool_args_dict = tool_args
-
-        # Invoke tool using base class method with error handling
-        try:
-            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args_dict, tool_name)
-
-            # Finish tool log
-            yield self._finish_log(
-                tool_log,
-                data={
-                    **tool_log.data,
-                    "output": response_content,
-                    "files": len(tool_files),
-                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
-                },
-            )
-
-            return response_content or "Tool executed successfully", tool_files
-        except Exception as e:
-            # Tool invocation failed, yield error log
-            error_message = str(e)
-            tool_log.status = AgentLog.LogStatus.ERROR
-            tool_log.error = error_message
-            tool_log.data = {
-                **tool_log.data,
-                "error": error_message,
-            }
-            yield tool_log
-
-            return f"Tool execution failed: {error_message}", []

api/core/agent/patterns/strategy_factory.py

@@ -1,107 +0,0 @@
-"""Strategy factory for creating agent strategies."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from core.agent.entities import AgentEntity, ExecutionContext
-from core.file.models import File
-from core.model_manager import ModelInstance
-from core.model_runtime.entities.model_entities import ModelFeature
-
-from .base import AgentPattern, ToolInvokeHook
-from .function_call import FunctionCallStrategy
-from .react import ReActStrategy
-
-if TYPE_CHECKING:
-    from core.tools.__base.tool import Tool
-
-
-class StrategyFactory:
-    """Factory for creating agent strategies based on model features."""
-
-    # Tool calling related features
-    TOOL_CALL_FEATURES = {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL}
-
-    @staticmethod
-    def create_strategy(
-        model_features: list[ModelFeature],
-        model_instance: ModelInstance,
-        context: ExecutionContext,
-        tools: list[Tool],
-        files: list[File],
-        max_iterations: int = 10,
-        workflow_call_depth: int = 0,
-        agent_strategy: AgentEntity.Strategy | None = None,
-        tool_invoke_hook: ToolInvokeHook | None = None,
-        instruction: str = "",
-    ) -> AgentPattern:
-        """
-        Create an appropriate strategy based on model features.
-
-        Args:
-            model_features: List of model features/capabilities
-            model_instance: Model instance to use
-            context: Execution context containing trace/audit information
-            tools: Available tools
-            files: Available files
-            max_iterations: Maximum iterations for the strategy
-            workflow_call_depth: Depth of workflow calls
-            agent_strategy: Optional explicit strategy override
-            tool_invoke_hook: Optional hook for custom tool invocation (e.g., agent_invoke)
-            instruction: Optional instruction for ReAct strategy
-
-        Returns:
-            AgentStrategy instance
-        """
-        # If explicit strategy is provided and it's Function Calling, try to use it if supported
-        if agent_strategy == AgentEntity.Strategy.FUNCTION_CALLING:
-            if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
-                return FunctionCallStrategy(
-                    model_instance=model_instance,
-                    context=context,
-                    tools=tools,
-                    files=files,
-                    max_iterations=max_iterations,
-                    workflow_call_depth=workflow_call_depth,
-                    tool_invoke_hook=tool_invoke_hook,
-                )
-            # Fallback to ReAct if FC is requested but not supported
-
-        # If explicit strategy is Chain of Thought (ReAct)
-        if agent_strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
-            return ReActStrategy(
-                model_instance=model_instance,
-                context=context,
-                tools=tools,
-                files=files,
-                max_iterations=max_iterations,
-                workflow_call_depth=workflow_call_depth,
-                tool_invoke_hook=tool_invoke_hook,
-                instruction=instruction,
-            )
-
-        # Default auto-selection logic
-        if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
-            # Model supports native function calling
-            return FunctionCallStrategy(
-                model_instance=model_instance,
-                context=context,
-                tools=tools,
-                files=files,
-                max_iterations=max_iterations,
-                workflow_call_depth=workflow_call_depth,
-                tool_invoke_hook=tool_invoke_hook,
-            )
-        else:
-            # Use ReAct strategy for models without function calling
-            return ReActStrategy(
-                model_instance=model_instance,
-                context=context,
-                tools=tools,
-                files=files,
-                max_iterations=max_iterations,
-                workflow_call_depth=workflow_call_depth,
-                tool_invoke_hook=tool_invoke_hook,
-                instruction=instruction,
-            )

api/core/app/apps/advanced_chat/app_generator.py

@@ -24,13 +24,11 @@ from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
-from core.app.layers.sandbox_layer import SandboxLayer
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from core.repositories import DifyCoreRepositoryFactory
-from core.sandbox.storage.archive_storage import ArchiveSandboxStorage
 from core.workflow.repositories.draft_variable_repository import (
     DraftVariableSaverFactory,
 )
@@ -42,7 +40,6 @@ from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
-from models.workflow_features import WorkflowFeatures
 from services.conversation_service import ConversationService
 from services.workflow_draft_variable_service import (
     DraftVarLoader,
@@ -515,23 +512,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 if workflow is None:
                     raise ValueError("Workflow not found")
 
-                graph_engine_layers: tuple = ()
-                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
-                    if application_generate_entity.workflow_run_id is None:
-                        raise ValueError("workflow_run_id is required when sandbox is enabled")
-                    graph_engine_layers = (
-                        SandboxLayer(
-                            tenant_id=application_generate_entity.app_config.tenant_id,
-                            app_id=application_generate_entity.app_config.app_id,
-                            workflow_version=workflow.version,
-                            sandbox_id=application_generate_entity.workflow_run_id,
-                            sandbox_storage=ArchiveSandboxStorage(
-                                tenant_id=application_generate_entity.app_config.tenant_id,
-                                sandbox_id=application_generate_entity.workflow_run_id,
-                            ),
-                        ),
-                    )
-
                 # Determine system_user_id based on invocation source
                 is_external_api_call = application_generate_entity.invoke_from in {
                     InvokeFrom.WEB_APP,
@@ -562,7 +542,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 app=app,
                 workflow_execution_repository=workflow_execution_repository,
                 workflow_node_execution_repository=workflow_node_execution_repository,
-                graph_engine_layers=graph_engine_layers,
             )
 
             try:

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -4,7 +4,6 @@ import re
 import time
 from collections.abc import Callable, Generator, Mapping
 from contextlib import contextmanager
-from dataclasses import dataclass, field
 from threading import Thread
 from typing import Any, Union
 
@@ -20,7 +19,6 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
-    ChunkType,
     MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
@@ -72,122 +70,13 @@ from core.workflow.runtime import GraphRuntimeState
 from core.workflow.system_variable import SystemVariable
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models import Account, Conversation, EndUser, LLMGenerationDetail, Message, MessageFile
+from models import Account, Conversation, EndUser, Message, MessageFile
 from models.enums import CreatorUserRole
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class StreamEventBuffer:
-    """
-    Buffer for recording stream events in order to reconstruct the generation sequence.
-    Records the exact order of text chunks, thoughts, and tool calls as they stream.
-    """
-
-    # Accumulated reasoning content (each thought block is a separate element)
-    reasoning_content: list[str] = field(default_factory=list)
-    # Current reasoning buffer (accumulates until we see a different event type)
-    _current_reasoning: str = ""
-    # Tool calls with their details
-    tool_calls: list[dict] = field(default_factory=list)
-    # Tool call ID to index mapping for updating results
-    _tool_call_id_map: dict[str, int] = field(default_factory=dict)
-    # Sequence of events in stream order
-    sequence: list[dict] = field(default_factory=list)
-    # Current position in answer text
-    _content_position: int = 0
-    # Track last event type to detect transitions
-    _last_event_type: str | None = None
-
-    def _flush_current_reasoning(self) -> None:
-        """Flush accumulated reasoning to the list and add to sequence."""
-        if self._current_reasoning.strip():
-            self.reasoning_content.append(self._current_reasoning.strip())
-            self.sequence.append({"type": "reasoning", "index": len(self.reasoning_content) - 1})
-            self._current_reasoning = ""
-
-    def record_text_chunk(self, text: str) -> None:
-        """Record a text chunk event."""
-        if not text:
-            return
-
-        # Flush any pending reasoning first
-        if self._last_event_type == "thought":
-            self._flush_current_reasoning()
-
-        text_len = len(text)
-        start_pos = self._content_position
-
-        # If last event was also content, extend it; otherwise create new
-        if self.sequence and self.sequence[-1].get("type") == "content":
-            self.sequence[-1]["end"] = start_pos + text_len
-        else:
-            self.sequence.append({"type": "content", "start": start_pos, "end": start_pos + text_len})
-
-        self._content_position += text_len
-        self._last_event_type = "content"
-
-    def record_thought_chunk(self, text: str) -> None:
-        """Record a thought/reasoning chunk event."""
-        if not text:
-            return
-
-        # Accumulate thought content
-        self._current_reasoning += text
-        self._last_event_type = "thought"
-
-    def record_tool_call(self, tool_call_id: str, tool_name: str, tool_arguments: str) -> None:
-        """Record a tool call event."""
-        if not tool_call_id:
-            return
-
-        # Flush any pending reasoning first
-        if self._last_event_type == "thought":
-            self._flush_current_reasoning()
-
-        # Check if this tool call already exists (we might get multiple chunks)
-        if tool_call_id in self._tool_call_id_map:
-            idx = self._tool_call_id_map[tool_call_id]
-            # Update arguments if provided
-            if tool_arguments:
-                self.tool_calls[idx]["arguments"] = tool_arguments
-        else:
-            # New tool call
-            tool_call = {
-                "id": tool_call_id or "",
-                "name": tool_name or "",
-                "arguments": tool_arguments or "",
-                "result": "",
-                "elapsed_time": None,
-            }
-            self.tool_calls.append(tool_call)
-            idx = len(self.tool_calls) - 1
-            self._tool_call_id_map[tool_call_id] = idx
-            self.sequence.append({"type": "tool_call", "index": idx})
-
-        self._last_event_type = "tool_call"
-
-    def record_tool_result(self, tool_call_id: str, result: str, tool_elapsed_time: float | None = None) -> None:
-        """Record a tool result event (update existing tool call)."""
-        if not tool_call_id:
-            return
-        if tool_call_id in self._tool_call_id_map:
-            idx = self._tool_call_id_map[tool_call_id]
-            self.tool_calls[idx]["result"] = result
-            self.tool_calls[idx]["elapsed_time"] = tool_elapsed_time
-
-    def finalize(self) -> None:
-        """Finalize the buffer, flushing any pending data."""
-        if self._last_event_type == "thought":
-            self._flush_current_reasoning()
-
-    def has_data(self) -> bool:
-        """Check if there's any meaningful data recorded."""
-        return bool(self.reasoning_content or self.tool_calls or self.sequence)
-
-
 class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
@@ -255,8 +144,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._workflow_run_id: str = ""
         self._draft_var_saver_factory = draft_var_saver_factory
         self._graph_runtime_state: GraphRuntimeState | None = None
-        # Stream event buffer for recording generation sequence
-        self._stream_buffer = StreamEventBuffer()
         self._seed_graph_runtime_state_from_queue_manager()
 
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
@@ -496,7 +383,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         queue_message: Union[WorkflowQueueMessage, MessageQueueMessage] | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
-        """Handle text chunk events and record to stream buffer for sequence reconstruction."""
+        """Handle text chunk events."""
         delta_text = event.text
         if delta_text is None:
             return
@@ -518,52 +405,9 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        tool_call = event.tool_call
-        tool_result = event.tool_result
-        tool_payload = tool_call or tool_result
-        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else ""
-        tool_name = tool_payload.name if tool_payload and tool_payload.name else ""
-        tool_arguments = tool_call.arguments if tool_call and tool_call.arguments else ""
-        tool_files = tool_result.files if tool_result else []
-        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
-        tool_icon = tool_payload.icon if tool_payload else None
-        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
-        # Record stream event based on chunk type
-        chunk_type = event.chunk_type or ChunkType.TEXT
-        match chunk_type:
-            case ChunkType.TEXT:
-                self._stream_buffer.record_text_chunk(delta_text)
-                self._task_state.answer += delta_text
-            case ChunkType.THOUGHT:
-                # Reasoning should not be part of final answer text
-                self._stream_buffer.record_thought_chunk(delta_text)
-            case ChunkType.TOOL_CALL:
-                self._stream_buffer.record_tool_call(
-                    tool_call_id=tool_call_id,
-                    tool_name=tool_name,
-                    tool_arguments=tool_arguments,
-                )
-            case ChunkType.TOOL_RESULT:
-                self._stream_buffer.record_tool_result(
-                    tool_call_id=tool_call_id,
-                    result=delta_text,
-                    tool_elapsed_time=tool_elapsed_time,
-                )
-                self._task_state.answer += delta_text
-            case _:
-                pass
+        self._task_state.answer += delta_text
         yield self._message_cycle_manager.message_to_stream_response(
-            answer=delta_text,
-            message_id=self._message_id,
-            from_variable_selector=event.from_variable_selector,
-            chunk_type=event.chunk_type.value if event.chunk_type else None,
-            tool_call_id=tool_call_id or None,
-            tool_name=tool_name or None,
-            tool_arguments=tool_arguments or None,
-            tool_files=tool_files,
-            tool_elapsed_time=tool_elapsed_time,
-            tool_icon=tool_icon,
-            tool_icon_dark=tool_icon_dark,
+            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
         )
 
     def _handle_iteration_start_event(
@@ -931,7 +775,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
 
         # If there are assistant files, remove markdown image links from answer
         answer_text = self._task_state.answer
-        answer_text = self._strip_think_blocks(answer_text)
         if self._recorded_files:
             # Remove markdown image links since we're storing files separately
             answer_text = re.sub(r"!\[.*?\]\(.*?\)", "", answer_text).strip()
@@ -983,54 +826,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
-        # Save generation detail (reasoning/tool calls/sequence) from stream buffer
-        self._save_generation_detail(session=session, message=message)
-
-    @staticmethod
-    def _strip_think_blocks(text: str) -> str:
-        """Remove <think>...</think> blocks (including their content) from text."""
-        if not text or "<think" not in text.lower():
-            return text
-
-        clean_text = re.sub(r"<think[^>]*>.*?</think>", "", text, flags=re.IGNORECASE | re.DOTALL)
-        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
-        return clean_text
-
-    def _save_generation_detail(self, *, session: Session, message: Message) -> None:
-        """
-        Save LLM generation detail for Chatflow using stream event buffer.
-        The buffer records the exact order of events as they streamed,
-        allowing accurate reconstruction of the generation sequence.
-        """
-        # Finalize the stream buffer to flush any pending data
-        self._stream_buffer.finalize()
-
-        # Only save if there's meaningful data
-        if not self._stream_buffer.has_data():
-            return
-
-        reasoning_content = self._stream_buffer.reasoning_content
-        tool_calls = self._stream_buffer.tool_calls
-        sequence = self._stream_buffer.sequence
-
-        # Check if generation detail already exists for this message
-        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
-
-        if existing:
-            existing.reasoning_content = json.dumps(reasoning_content) if reasoning_content else None
-            existing.tool_calls = json.dumps(tool_calls) if tool_calls else None
-            existing.sequence = json.dumps(sequence) if sequence else None
-        else:
-            generation_detail = LLMGenerationDetail(
-                tenant_id=self._application_generate_entity.app_config.tenant_id,
-                app_id=self._application_generate_entity.app_config.app_id,
-                message_id=message.id,
-                reasoning_content=json.dumps(reasoning_content) if reasoning_content else None,
-                tool_calls=json.dumps(tool_calls) if tool_calls else None,
-                sequence=json.dumps(sequence) if sequence else None,
-            )
-            session.add(generation_detail)
-
     def _seed_graph_runtime_state_from_queue_manager(self) -> None:
         """Bootstrap the cached runtime state from the queue manager when present."""
         candidate = self._base_task_pipeline.queue_manager.graph_runtime_state

api/core/app/apps/agent_chat/app_runner.py

@@ -3,8 +3,10 @@ from typing import cast
 
 from sqlalchemy import select
 
-from core.agent.agent_app_runner import AgentAppRunner
+from core.agent.cot_chat_agent_runner import CotChatAgentRunner
+from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
 from core.agent.entities import AgentEntity
+from core.agent.fc_agent_runner import FunctionCallAgentRunner
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.apps.base_app_runner import AppRunner
@@ -12,7 +14,8 @@ from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
-from core.model_runtime.entities.model_entities import ModelFeature
+from core.model_runtime.entities.llm_entities import LLMMode
+from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationError
 from extensions.ext_database import db
@@ -191,7 +194,22 @@ class AgentChatAppRunner(AppRunner):
             raise ValueError("Message not found")
         db.session.close()
 
-        runner = AgentAppRunner(
+        runner_cls: type[FunctionCallAgentRunner] | type[CotChatAgentRunner] | type[CotCompletionAgentRunner]
+        # start agent runner
+        if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
+            # check LLM mode
+            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT:
+                runner_cls = CotChatAgentRunner
+            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION:
+                runner_cls = CotCompletionAgentRunner
+            else:
+                raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")
+        elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
+            runner_cls = FunctionCallAgentRunner
+        else:
+            raise ValueError(f"Invalid agent strategy: {agent_entity.strategy}")
+
+        runner = runner_cls(
             tenant_id=app_config.tenant_id,
             application_generate_entity=application_generate_entity,
             conversation=conversation_result,

api/core/app/apps/common/workflow_response_converter.py

@@ -671,7 +671,7 @@ class WorkflowResponseConverter:
             task_id=task_id,
             data=AgentLogStreamResponse.Data(
                 node_execution_id=event.node_execution_id,
-                message_id=event.id,
+                id=event.id,
                 parent_id=event.parent_id,
                 label=event.label,
                 error=event.error,

api/core/app/apps/workflow/app_generator.py

@@ -8,7 +8,7 @@ from typing import Any, Literal, Union, overload
 from flask import Flask, current_app
 from pydantic import ValidationError
 from sqlalchemy import select
-from sqlalchemy.orm import sessionmaker
+from sqlalchemy.orm import Session, sessionmaker
 
 import contexts
 from configs import dify_config
@@ -23,13 +23,10 @@ from core.app.apps.workflow.generate_response_converter import WorkflowAppGenera
 from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTaskPipeline
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
-from core.app.layers.sandbox_layer import SandboxLayer
-from core.db.session_factory import session_factory
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.repositories import DifyCoreRepositoryFactory
-from core.sandbox.storage.archive_storage import ArchiveSandboxStorage
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
@@ -40,7 +37,6 @@ from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, EndUser, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
-from models.workflow_features import WorkflowFeatures
 from services.workflow_draft_variable_service import DraftVarLoader, WorkflowDraftVariableService
 
 SKIP_PREPARE_USER_INPUTS_KEY = "_skip_prepare_user_inputs"
@@ -480,7 +476,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :return:
         """
         with preserve_flask_contexts(flask_app, context_vars=context):
-            with session_factory.create_session() as session:
+            with Session(db.engine, expire_on_commit=False) as session:
                 workflow = session.scalar(
                     select(Workflow).where(
                         Workflow.tenant_id == application_generate_entity.app_config.tenant_id,
@@ -491,21 +487,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 if workflow is None:
                     raise ValueError("Workflow not found")
 
-                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
-                    graph_engine_layers = (
-                        *graph_engine_layers,
-                        SandboxLayer(
-                            tenant_id=application_generate_entity.app_config.tenant_id,
-                            app_id=application_generate_entity.app_config.app_id,
-                            workflow_version=workflow.version,
-                            sandbox_id=application_generate_entity.workflow_execution_id,
-                            sandbox_storage=ArchiveSandboxStorage(
-                                tenant_id=application_generate_entity.app_config.tenant_id,
-                                sandbox_id=application_generate_entity.workflow_execution_id,
-                            ),
-                        ),
-                    )
-
                 # Determine system_user_id based on invocation source
                 is_external_api_call = application_generate_entity.invoke_from in {
                     InvokeFrom.WEB_APP,

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -13,7 +13,6 @@ from core.app.apps.common.workflow_response_converter import WorkflowResponseCon
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
     AppQueueEvent,
-    ChunkType,
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
@@ -484,33 +483,11 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if delta_text is None:
             return
 
-        tool_call = event.tool_call
-        tool_result = event.tool_result
-        tool_payload = tool_call or tool_result
-        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else None
-        tool_name = tool_payload.name if tool_payload and tool_payload.name else None
-        tool_arguments = tool_call.arguments if tool_call else None
-        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
-        tool_files = tool_result.files if tool_result else []
-        tool_icon = tool_payload.icon if tool_payload else None
-        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
-
         # only publish tts message at text chunk streaming
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        yield self._text_chunk_to_stream_response(
-            text=delta_text,
-            from_variable_selector=event.from_variable_selector,
-            chunk_type=event.chunk_type,
-            tool_call_id=tool_call_id,
-            tool_name=tool_name,
-            tool_arguments=tool_arguments,
-            tool_files=tool_files,
-            tool_elapsed_time=tool_elapsed_time,
-            tool_icon=tool_icon,
-            tool_icon_dark=tool_icon_dark,
-        )
+        yield self._text_chunk_to_stream_response(delta_text, from_variable_selector=event.from_variable_selector)
 
     def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
         """Handle agent log events."""
@@ -673,61 +650,16 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         session.add(workflow_app_log)
 
     def _text_chunk_to_stream_response(
-        self,
-        text: str,
-        from_variable_selector: list[str] | None = None,
-        chunk_type: ChunkType | None = None,
-        tool_call_id: str | None = None,
-        tool_name: str | None = None,
-        tool_arguments: str | None = None,
-        tool_files: list[str] | None = None,
-        tool_error: str | None = None,
-        tool_elapsed_time: float | None = None,
-        tool_icon: str | dict | None = None,
-        tool_icon_dark: str | dict | None = None,
+        self, text: str, from_variable_selector: list[str] | None = None
     ) -> TextChunkStreamResponse:
         """
         Handle completed event.
         :param text: text
         :return:
         """
-        from core.app.entities.task_entities import ChunkType as ResponseChunkType
-
-        response_chunk_type = ResponseChunkType(chunk_type.value) if chunk_type else ResponseChunkType.TEXT
-
-        data = TextChunkStreamResponse.Data(
-            text=text,
-            from_variable_selector=from_variable_selector,
-            chunk_type=response_chunk_type,
-        )
-
-        if response_chunk_type == ResponseChunkType.TOOL_CALL:
-            data = data.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-        elif response_chunk_type == ResponseChunkType.TOOL_RESULT:
-            data = data.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_files": tool_files,
-                    "tool_error": tool_error,
-                    "tool_elapsed_time": tool_elapsed_time,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-
         response = TextChunkStreamResponse(
             task_id=self._application_generate_entity.task_id,
-            data=data,
+            data=TextChunkStreamResponse.Data(text=text, from_variable_selector=from_variable_selector),
         )
 
         return response

api/core/app/apps/workflow_app_runner.py

@@ -463,20 +463,12 @@ class WorkflowBasedAppRunner:
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
-            from core.app.entities.queue_entities import ChunkType as QueueChunkType
-
-            if event.is_final and not event.chunk:
-                return
-
             self._publish_event(
                 QueueTextChunkEvent(
                     text=event.chunk,
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    chunk_type=QueueChunkType(event.chunk_type.value),
-                    tool_call=event.tool_call,
-                    tool_result=event.tool_result,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):

api/core/app/entities/app_asset_entities.py

@@ -1,236 +0,0 @@
-from __future__ import annotations
-
-from collections import defaultdict
-from collections.abc import Generator
-from enum import StrEnum
-
-from pydantic import BaseModel, Field
-
-
-class AssetNodeType(StrEnum):
-    FILE = "file"
-    FOLDER = "folder"
-
-
-class AppAssetNode(BaseModel):
-    id: str = Field(description="Unique identifier for the node")
-    node_type: AssetNodeType = Field(description="Type of node: file or folder")
-    name: str = Field(description="Name of the file or folder")
-    parent_id: str | None = Field(default=None, description="Parent folder ID, None for root level")
-    order: int = Field(default=0, description="Sort order within parent folder, lower values first")
-    extension: str = Field(default="", description="File extension without dot, empty for folders")
-    size: int = Field(default=0, description="File size in bytes, 0 for folders")
-    checksum: str = Field(default="", description="SHA-256 checksum of file content, empty for folders")
-
-    @classmethod
-    def create_folder(cls, node_id: str, name: str, parent_id: str | None = None) -> AppAssetNode:
-        return cls(id=node_id, node_type=AssetNodeType.FOLDER, name=name, parent_id=parent_id)
-
-    @classmethod
-    def create_file(
-        cls, node_id: str, name: str, parent_id: str | None = None, size: int = 0, checksum: str = ""
-    ) -> AppAssetNode:
-        return cls(
-            id=node_id,
-            node_type=AssetNodeType.FILE,
-            name=name,
-            parent_id=parent_id,
-            extension=name.rsplit(".", 1)[-1] if "." in name else "",
-            size=size,
-            checksum=checksum,
-        )
-
-
-class AppAssetNodeView(BaseModel):
-    id: str = Field(description="Unique identifier for the node")
-    node_type: str = Field(description="Type of node: 'file' or 'folder'")
-    name: str = Field(description="Name of the file or folder")
-    path: str = Field(description="Full path from root, e.g. '/folder/file.txt'")
-    extension: str = Field(default="", description="File extension without dot")
-    size: int = Field(default=0, description="File size in bytes")
-    checksum: str = Field(default="", description="SHA-256 checksum of file content")
-    children: list[AppAssetNodeView] = Field(default_factory=list, description="Child nodes for folders")
-
-
-class TreeNodeNotFoundError(Exception):
-    """Tree internal: node not found"""
-
-    pass
-
-
-class TreeParentNotFoundError(Exception):
-    """Tree internal: parent folder not found"""
-
-    pass
-
-
-class TreePathConflictError(Exception):
-    """Tree internal: path already exists"""
-
-    pass
-
-
-class AppAssetFileTree(BaseModel):
-    """
-    File tree structure for app assets using adjacency list pattern.
-
-    Design:
-    - Storage: Flat list with parent_id references (adjacency list)
-    - Path: Computed dynamically via get_path(), not stored
-    - Order: Integer field for user-defined sorting within each folder
-    - API response: transform() builds nested tree with computed paths
-
-    Why adjacency list over nested tree or materialized path:
-    - Simpler CRUD: move/rename only updates one node's parent_id
-    - No path cascade: renaming parent doesn't require updating all descendants
-    - JSON-friendly: flat list serializes cleanly to database JSON column
-    - Trade-off: path lookup is O(depth), acceptable for typical file trees
-    """
-
-    nodes: list[AppAssetNode] = Field(default_factory=list, description="Flat list of all nodes in the tree")
-
-    def get(self, node_id: str) -> AppAssetNode | None:
-        return next((n for n in self.nodes if n.id == node_id), None)
-
-    def get_children(self, parent_id: str | None) -> list[AppAssetNode]:
-        return [n for n in self.nodes if n.parent_id == parent_id]
-
-    def has_child_named(self, parent_id: str | None, name: str) -> bool:
-        return any(n.name == name and n.parent_id == parent_id for n in self.nodes)
-
-    def get_path(self, node_id: str) -> str:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        parts: list[str] = []
-        current: AppAssetNode | None = node
-        while current:
-            parts.append(current.name)
-            current = self.get(current.parent_id) if current.parent_id else None
-        return "/" + "/".join(reversed(parts))
-
-    def get_descendant_ids(self, node_id: str) -> list[str]:
-        result: list[str] = []
-        stack = [node_id]
-        while stack:
-            current_id = stack.pop()
-            for child in self.nodes:
-                if child.parent_id == current_id:
-                    result.append(child.id)
-                    stack.append(child.id)
-        return result
-
-    def add(self, node: AppAssetNode) -> AppAssetNode:
-        if self.get(node.id):
-            raise TreePathConflictError(node.id)
-        if self.has_child_named(node.parent_id, node.name):
-            raise TreePathConflictError(node.name)
-        if node.parent_id:
-            parent = self.get(node.parent_id)
-            if not parent or parent.node_type != AssetNodeType.FOLDER:
-                raise TreeParentNotFoundError(node.parent_id)
-        siblings = self.get_children(node.parent_id)
-        node.order = max((s.order for s in siblings), default=-1) + 1
-        self.nodes.append(node)
-        return node
-
-    def update(self, node_id: str, size: int, checksum: str) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node or node.node_type != AssetNodeType.FILE:
-            raise TreeNodeNotFoundError(node_id)
-        node.size = size
-        node.checksum = checksum
-        return node
-
-    def rename(self, node_id: str, new_name: str) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        if node.name != new_name and self.has_child_named(node.parent_id, new_name):
-            raise TreePathConflictError(new_name)
-        node.name = new_name
-        if node.node_type == AssetNodeType.FILE:
-            node.extension = new_name.rsplit(".", 1)[-1] if "." in new_name else ""
-        return node
-
-    def move(self, node_id: str, new_parent_id: str | None) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        if new_parent_id:
-            parent = self.get(new_parent_id)
-            if not parent or parent.node_type != AssetNodeType.FOLDER:
-                raise TreeParentNotFoundError(new_parent_id)
-        if self.has_child_named(new_parent_id, node.name):
-            raise TreePathConflictError(node.name)
-        node.parent_id = new_parent_id
-        siblings = self.get_children(new_parent_id)
-        node.order = max((s.order for s in siblings if s.id != node_id), default=-1) + 1
-        return node
-
-    def reorder(self, node_id: str, after_node_id: str | None) -> AppAssetNode:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-
-        siblings = sorted(self.get_children(node.parent_id), key=lambda x: x.order)
-        siblings = [s for s in siblings if s.id != node_id]
-
-        if after_node_id is None:
-            insert_idx = 0
-        else:
-            after_node = self.get(after_node_id)
-            if not after_node or after_node.parent_id != node.parent_id:
-                raise TreeNodeNotFoundError(after_node_id)
-            insert_idx = next((i for i, s in enumerate(siblings) if s.id == after_node_id), -1) + 1
-
-        siblings.insert(insert_idx, node)
-        for idx, sibling in enumerate(siblings):
-            sibling.order = idx
-
-        return node
-
-    def remove(self, node_id: str) -> list[str]:
-        node = self.get(node_id)
-        if not node:
-            raise TreeNodeNotFoundError(node_id)
-        ids_to_remove = [node_id] + self.get_descendant_ids(node_id)
-        self.nodes = [n for n in self.nodes if n.id not in ids_to_remove]
-        return ids_to_remove
-
-    def walk_files(self) -> Generator[AppAssetNode, None, None]:
-        return (n for n in self.nodes if n.node_type == AssetNodeType.FILE)
-
-    def transform(self) -> list[AppAssetNodeView]:
-        by_parent: dict[str | None, list[AppAssetNode]] = defaultdict(list)
-        for n in self.nodes:
-            by_parent[n.parent_id].append(n)
-
-        for children in by_parent.values():
-            children.sort(key=lambda x: x.order)
-
-        paths: dict[str, str] = {}
-        tree_views: dict[str, AppAssetNodeView] = {}
-
-        def build_view(node: AppAssetNode, parent_path: str) -> None:
-            path = f"{parent_path}/{node.name}"
-            paths[node.id] = path
-            child_views: list[AppAssetNodeView] = []
-            for child in by_parent.get(node.id, []):
-                build_view(child, path)
-                child_views.append(tree_views[child.id])
-            tree_views[node.id] = AppAssetNodeView(
-                id=node.id,
-                node_type=node.node_type.value,
-                name=node.name,
-                path=path,
-                extension=node.extension,
-                size=node.size,
-                checksum=node.checksum,
-                children=child_views,
-            )
-
-        for root_node in by_parent.get(None, []):
-            build_view(root_node, "")
-
-        return [tree_views[n.id] for n in by_parent.get(None, [])]

api/core/app/entities/app_invoke_entities.py

@@ -36,9 +36,6 @@ class InvokeFrom(StrEnum):
     # this is used for plugin trigger and webhook trigger.
     TRIGGER = "trigger"
 
-    # AGENT indicates that this invocation is from an agent.
-    AGENT = "agent"
-
     # EXPLORE indicates that this invocation is from
     # the workflow (or chatflow) explore page.
     EXPLORE = "explore"

api/core/app/entities/llm_generation_entities.py

@@ -1,70 +0,0 @@
-"""
-LLM Generation Detail entities.
-
-Defines the structure for storing and transmitting LLM generation details
-including reasoning content, tool calls, and their sequence.
-"""
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-
-class ContentSegment(BaseModel):
-    """Represents a content segment in the generation sequence."""
-
-    type: Literal["content"] = "content"
-    start: int = Field(..., description="Start position in the text")
-    end: int = Field(..., description="End position in the text")
-
-
-class ReasoningSegment(BaseModel):
-    """Represents a reasoning segment in the generation sequence."""
-
-    type: Literal["reasoning"] = "reasoning"
-    index: int = Field(..., description="Index into reasoning_content array")
-
-
-class ToolCallSegment(BaseModel):
-    """Represents a tool call segment in the generation sequence."""
-
-    type: Literal["tool_call"] = "tool_call"
-    index: int = Field(..., description="Index into tool_calls array")
-
-
-SequenceSegment = ContentSegment | ReasoningSegment | ToolCallSegment
-
-
-class ToolCallDetail(BaseModel):
-    """Represents a tool call with its arguments and result."""
-
-    id: str = Field(default="", description="Unique identifier for the tool call")
-    name: str = Field(..., description="Name of the tool")
-    arguments: str = Field(default="", description="JSON string of tool arguments")
-    result: str = Field(default="", description="Result from the tool execution")
-    elapsed_time: float | None = Field(default=None, description="Elapsed time in seconds")
-
-
-class LLMGenerationDetailData(BaseModel):
-    """
-    Domain model for LLM generation detail.
-
-    Contains the structured data for reasoning content, tool calls,
-    and their display sequence.
-    """
-
-    reasoning_content: list[str] = Field(default_factory=list, description="List of reasoning segments")
-    tool_calls: list[ToolCallDetail] = Field(default_factory=list, description="List of tool call details")
-    sequence: list[SequenceSegment] = Field(default_factory=list, description="Display order of segments")
-
-    def is_empty(self) -> bool:
-        """Check if there's any meaningful generation detail."""
-        return not self.reasoning_content and not self.tool_calls
-
-    def to_response_dict(self) -> dict:
-        """Convert to dictionary for API response."""
-        return {
-            "reasoning_content": self.reasoning_content,
-            "tool_calls": [tc.model_dump() for tc in self.tool_calls],
-            "sequence": [seg.model_dump() for seg in self.sequence],
-        }

api/core/app/entities/queue_entities.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
+from core.workflow.entities import AgentNodeStrategyInit
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes import NodeType
 
@@ -177,17 +177,6 @@ class QueueLoopCompletedEvent(AppQueueEvent):
     error: str | None = None
 
 
-class ChunkType(StrEnum):
-    """Stream chunk type for LLM-related events."""
-
-    TEXT = "text"  # Normal text streaming
-    TOOL_CALL = "tool_call"  # Tool call arguments streaming
-    TOOL_RESULT = "tool_result"  # Tool execution result
-    THOUGHT = "thought"  # Agent thinking process (ReAct)
-    THOUGHT_START = "thought_start"  # Agent thought start
-    THOUGHT_END = "thought_end"  # Agent thought end
-
-
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
@@ -202,16 +191,6 @@ class QueueTextChunkEvent(AppQueueEvent):
     in_loop_id: str | None = None
     """loop id if node is in loop"""
 
-    # Extended fields for Agent/Tool streaming
-    chunk_type: ChunkType = ChunkType.TEXT
-    """type of the chunk"""
-
-    # Tool streaming payloads
-    tool_call: ToolCall | None = None
-    """structured tool call info"""
-    tool_result: ToolResult | None = None
-    """structured tool result info"""
-
 
 class QueueAgentMessageEvent(AppQueueEvent):
     """

api/core/app/entities/task_entities.py

@@ -113,38 +113,6 @@ class MessageStreamResponse(StreamResponse):
     answer: str
     from_variable_selector: list[str] | None = None
 
-    # Extended fields for Agent/Tool streaming (imported at runtime to avoid circular import)
-    chunk_type: str | None = None
-    """type of the chunk: text, tool_call, tool_result, thought"""
-
-    # Tool call fields (when chunk_type == "tool_call")
-    tool_call_id: str | None = None
-    """unique identifier for this tool call"""
-    tool_name: str | None = None
-    """name of the tool being called"""
-    tool_arguments: str | None = None
-    """accumulated tool arguments JSON"""
-
-    # Tool result fields (when chunk_type == "tool_result")
-    tool_files: list[str] | None = None
-    """file IDs produced by tool"""
-    tool_error: str | None = None
-    """error message if tool failed"""
-    tool_elapsed_time: float | None = None
-    """elapsed time spent executing the tool"""
-    tool_icon: str | dict | None = None
-    """icon of the tool"""
-    tool_icon_dark: str | dict | None = None
-    """dark theme icon of the tool"""
-
-    def model_dump(self, *args, **kwargs) -> dict[str, object]:
-        kwargs.setdefault("exclude_none", True)
-        return super().model_dump(*args, **kwargs)
-
-    def model_dump_json(self, *args, **kwargs) -> str:
-        kwargs.setdefault("exclude_none", True)
-        return super().model_dump_json(*args, **kwargs)
-
 
 class MessageAudioStreamResponse(StreamResponse):
     """
@@ -614,17 +582,6 @@ class LoopNodeCompletedStreamResponse(StreamResponse):
     data: Data
 
 
-class ChunkType(StrEnum):
-    """Stream chunk type for LLM-related events."""
-
-    TEXT = "text"  # Normal text streaming
-    TOOL_CALL = "tool_call"  # Tool call arguments streaming
-    TOOL_RESULT = "tool_result"  # Tool execution result
-    THOUGHT = "thought"  # Agent thinking process (ReAct)
-    THOUGHT_START = "thought_start"  # Agent thought start
-    THOUGHT_END = "thought_end"  # Agent thought end
-
-
 class TextChunkStreamResponse(StreamResponse):
     """
     TextChunkStreamResponse entity
@@ -638,36 +595,6 @@ class TextChunkStreamResponse(StreamResponse):
         text: str
         from_variable_selector: list[str] | None = None
 
-        # Extended fields for Agent/Tool streaming
-        chunk_type: ChunkType = ChunkType.TEXT
-        """type of the chunk"""
-
-        # Tool call fields (when chunk_type == TOOL_CALL)
-        tool_call_id: str | None = None
-        """unique identifier for this tool call"""
-        tool_name: str | None = None
-        """name of the tool being called"""
-        tool_arguments: str | None = None
-        """accumulated tool arguments JSON"""
-
-        # Tool result fields (when chunk_type == TOOL_RESULT)
-        tool_files: list[str] | None = None
-        """file IDs produced by tool"""
-        tool_error: str | None = None
-        """error message if tool failed"""
-
-        # Tool elapsed time fields (when chunk_type == TOOL_RESULT)
-        tool_elapsed_time: float | None = None
-        """elapsed time spent executing the tool"""
-
-        def model_dump(self, *args, **kwargs) -> dict[str, object]:
-            kwargs.setdefault("exclude_none", True)
-            return super().model_dump(*args, **kwargs)
-
-        def model_dump_json(self, *args, **kwargs) -> str:
-            kwargs.setdefault("exclude_none", True)
-            return super().model_dump_json(*args, **kwargs)
-
     event: StreamEvent = StreamEvent.TEXT_CHUNK
     data: Data
 
@@ -816,7 +743,7 @@ class AgentLogStreamResponse(StreamResponse):
         """
 
         node_execution_id: str
-        message_id: str
+        id: str
         label: str
         parent_id: str | None = None
         error: str | None = None

api/core/app/layers/sandbox_layer.py

@@ -1,119 +0,0 @@
-import logging
-
-from core.sandbox import AppAssetsInitializer, DifyCliInitializer, SandboxManager
-from core.sandbox.storage.sandbox_storage import SandboxStorage
-from core.virtual_environment.__base.virtual_environment import VirtualEnvironment
-from core.workflow.graph_engine.layers.base import GraphEngineLayer
-from core.workflow.graph_events.base import GraphEngineEvent
-from core.workflow.graph_events.graph import GraphRunPausedEvent
-from models.workflow import Workflow
-from services.app_asset_service import AppAssetService
-from services.sandbox.sandbox_provider_service import SandboxProviderService
-
-logger = logging.getLogger(__name__)
-
-
-class SandboxInitializationError(Exception):
-    pass
-
-
-class SandboxLayer(GraphEngineLayer):
-    def __init__(
-        self,
-        tenant_id: str,
-        app_id: str,
-        workflow_version: str,
-        sandbox_id: str,
-        sandbox_storage: SandboxStorage,
-    ) -> None:
-        super().__init__()
-        self._tenant_id = tenant_id
-        self._app_id = app_id
-        self._workflow_version = workflow_version
-        self._sandbox_id = sandbox_id
-        self._sandbox_storage = sandbox_storage
-
-    @property
-    def sandbox(self) -> VirtualEnvironment:
-        sandbox = SandboxManager.get(self._sandbox_id)
-        if sandbox is None:
-            raise RuntimeError(f"Sandbox not found or not initialized for sandbox_id={self._sandbox_id}")
-        return sandbox
-
-    def on_graph_start(self) -> None:
-        try:
-            is_draft = self._workflow_version == Workflow.VERSION_DRAFT
-            assets = AppAssetService.get_assets(self._tenant_id, self._app_id, is_draft=is_draft)
-            if not assets:
-                raise ValueError(
-                    f"No assets found for tid={self._tenant_id}, app_id={self._app_id}, wf={self._workflow_version}"
-                )
-            if is_draft:
-                logger.info(
-                    "Building draft assets for tenant_id=%s, app_id=%s, workflow_version=%s, assets_id=%s",
-                    self._tenant_id,
-                    self._app_id,
-                    self._workflow_version,
-                    assets.id,
-                )
-                AppAssetService.build_assets(self._tenant_id, self._app_id, assets)
-
-            logger.info(
-                "Initializing sandbox for tenant_id=%s, app_id=%s, workflow_version=%s, assets_id=%s",
-                self._tenant_id,
-                self._app_id,
-                self._workflow_version,
-                assets.id,
-            )
-
-            builder = (
-                SandboxProviderService.create_sandbox_builder(self._tenant_id)
-                .initializer(AppAssetsInitializer(self._tenant_id, self._app_id, assets.id))
-                .initializer(DifyCliInitializer(self._tenant_id, self._app_id, assets.id))
-            )
-            sandbox = builder.build()
-
-            SandboxManager.register(self._sandbox_id, sandbox)
-            logger.info(
-                "Sandbox initialized, workflow_execution_id=%s, sandbox_id=%s, sandbox_arch=%s",
-                self._sandbox_id,
-                sandbox.metadata.id,
-                sandbox.metadata.arch,
-            )
-
-            # Check if sandbox is initialized
-            if self._sandbox_storage.mount(sandbox):
-                logger.info("Sandbox files restored, sandbox_id=%s", self._sandbox_id)
-        except Exception as e:
-            logger.exception("Failed to initialize sandbox")
-            raise SandboxInitializationError(f"Failed to initialize sandbox: {e}") from e
-
-    def on_event(self, event: GraphEngineEvent) -> None:
-        # TODO: handle graph run paused event
-        if not isinstance(event, GraphRunPausedEvent):
-            return
-
-    def on_graph_end(self, error: Exception | None) -> None:
-        sandbox = SandboxManager.unregister(self._sandbox_id)
-        if sandbox is None:
-            logger.debug("No sandbox to release for sandbox_id=%s", self._sandbox_id)
-            return
-
-        sandbox_id = sandbox.metadata.id
-        logger.info(
-            "Releasing sandbox, workflow_execution_id=%s, sandbox_id=%s",
-            self._sandbox_id,
-            sandbox_id,
-        )
-
-        try:
-            self._sandbox_storage.unmount(sandbox)
-            logger.info("Sandbox files persisted, sandbox_id=%s", self._sandbox_id)
-        except Exception:
-            logger.exception("Failed to persist sandbox files, sandbox_id=%s", self._sandbox_id)
-
-        try:
-            sandbox.release_environment()
-            logger.info("Sandbox released, sandbox_id=%s", sandbox_id)
-        except Exception:
-            logger.exception("Failed to release sandbox, sandbox_id=%s", sandbox_id)

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -1,5 +1,4 @@
 import logging
-import re
 import time
 from collections.abc import Generator
 from threading import Thread
@@ -59,7 +58,7 @@ from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.model import AppMode, Conversation, LLMGenerationDetail, Message, MessageAgentThought
+from models.model import AppMode, Conversation, Message, MessageAgentThought
 
 logger = logging.getLogger(__name__)
 
@@ -69,8 +68,6 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
-    _THINK_PATTERN = re.compile(r"<think[^>]*>(.*?)</think>", re.IGNORECASE | re.DOTALL)
-
     _task_state: EasyUITaskState
     _application_generate_entity: Union[ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity]
 
@@ -412,136 +409,11 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
                 )
             )
 
-        # Save LLM generation detail if there's reasoning_content
-        self._save_generation_detail(session=session, message=message, llm_result=llm_result)
-
         message_was_created.send(
             message,
             application_generate_entity=self._application_generate_entity,
         )
 
-    def _save_generation_detail(self, *, session: Session, message: Message, llm_result: LLMResult) -> None:
-        """
-        Save LLM generation detail for Completion/Chat/Agent-Chat applications.
-        For Agent-Chat, also merges MessageAgentThought records.
-        """
-        import json
-
-        reasoning_list: list[str] = []
-        tool_calls_list: list[dict] = []
-        sequence: list[dict] = []
-        answer = message.answer or ""
-
-        # Check if this is Agent-Chat mode by looking for agent thoughts
-        agent_thoughts = (
-            session.query(MessageAgentThought)
-            .filter_by(message_id=message.id)
-            .order_by(MessageAgentThought.position.asc())
-            .all()
-        )
-
-        if agent_thoughts:
-            # Agent-Chat mode: merge MessageAgentThought records
-            content_pos = 0
-            cleaned_answer_parts: list[str] = []
-            for thought in agent_thoughts:
-                # Add thought/reasoning
-                if thought.thought:
-                    reasoning_text = thought.thought
-                    if "<think" in reasoning_text.lower():
-                        clean_text, extracted_reasoning = self._split_reasoning_from_answer(reasoning_text)
-                        if extracted_reasoning:
-                            reasoning_text = extracted_reasoning
-                            thought.thought = clean_text or extracted_reasoning
-                    reasoning_list.append(reasoning_text)
-                    sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
-
-                # Add tool calls
-                if thought.tool:
-                    tool_calls_list.append(
-                        {
-                            "name": thought.tool,
-                            "arguments": thought.tool_input or "",
-                            "result": thought.observation or "",
-                        }
-                    )
-                    sequence.append({"type": "tool_call", "index": len(tool_calls_list) - 1})
-
-                # Add answer content if present
-                if thought.answer:
-                    content_text = thought.answer
-                    if "<think" in content_text.lower():
-                        clean_answer, extracted_reasoning = self._split_reasoning_from_answer(content_text)
-                        if extracted_reasoning:
-                            reasoning_list.append(extracted_reasoning)
-                            sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
-                        content_text = clean_answer
-                        thought.answer = clean_answer or content_text
-
-                    if content_text:
-                        start = content_pos
-                        end = content_pos + len(content_text)
-                        sequence.append({"type": "content", "start": start, "end": end})
-                        content_pos = end
-                        cleaned_answer_parts.append(content_text)
-
-            if cleaned_answer_parts:
-                merged_answer = "".join(cleaned_answer_parts)
-                message.answer = merged_answer
-                llm_result.message.content = merged_answer
-        else:
-            # Completion/Chat mode: use reasoning_content from llm_result
-            reasoning_content = llm_result.reasoning_content
-            if not reasoning_content and answer:
-                # Extract reasoning from <think> blocks and clean the final answer
-                clean_answer, reasoning_content = self._split_reasoning_from_answer(answer)
-                if reasoning_content:
-                    answer = clean_answer
-                    llm_result.message.content = clean_answer
-                    llm_result.reasoning_content = reasoning_content
-                    message.answer = clean_answer
-            if reasoning_content:
-                reasoning_list = [reasoning_content]
-                # Content comes first, then reasoning
-                if answer:
-                    sequence.append({"type": "content", "start": 0, "end": len(answer)})
-                sequence.append({"type": "reasoning", "index": 0})
-
-        # Only save if there's meaningful generation detail
-        if not reasoning_list and not tool_calls_list:
-            return
-
-        # Check if generation detail already exists
-        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
-
-        if existing:
-            existing.reasoning_content = json.dumps(reasoning_list) if reasoning_list else None
-            existing.tool_calls = json.dumps(tool_calls_list) if tool_calls_list else None
-            existing.sequence = json.dumps(sequence) if sequence else None
-        else:
-            generation_detail = LLMGenerationDetail(
-                tenant_id=self._application_generate_entity.app_config.tenant_id,
-                app_id=self._application_generate_entity.app_config.app_id,
-                message_id=message.id,
-                reasoning_content=json.dumps(reasoning_list) if reasoning_list else None,
-                tool_calls=json.dumps(tool_calls_list) if tool_calls_list else None,
-                sequence=json.dumps(sequence) if sequence else None,
-            )
-            session.add(generation_detail)
-
-    @classmethod
-    def _split_reasoning_from_answer(cls, text: str) -> tuple[str, str]:
-        """
-        Extract reasoning segments from <think> blocks and return (clean_text, reasoning).
-        """
-        matches = cls._THINK_PATTERN.findall(text)
-        reasoning_content = "\n".join(match.strip() for match in matches) if matches else ""
-
-        clean_text = cls._THINK_PATTERN.sub("", text)
-        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
-
-        return clean_text, reasoning_content or ""
-
     def _handle_stop(self, event: QueueStopEvent):
         """
         Handle stop.

api/core/app/task_pipeline/message_cycle_manager.py

@@ -232,31 +232,15 @@ class MessageCycleManager:
         answer: str,
         message_id: str,
         from_variable_selector: list[str] | None = None,
-        chunk_type: str | None = None,
-        tool_call_id: str | None = None,
-        tool_name: str | None = None,
-        tool_arguments: str | None = None,
-        tool_files: list[str] | None = None,
-        tool_error: str | None = None,
-        tool_elapsed_time: float | None = None,
-        tool_icon: str | dict | None = None,
-        tool_icon_dark: str | dict | None = None,
         event_type: StreamEvent | None = None,
     ) -> MessageStreamResponse:
         """
         Message to stream response.
         :param answer: answer
         :param message_id: message id
-        :param from_variable_selector: from variable selector
-        :param chunk_type: type of the chunk (text, function_call, tool_result, thought)
-        :param tool_call_id: unique identifier for this tool call
-        :param tool_name: name of the tool being called
-        :param tool_arguments: accumulated tool arguments JSON
-        :param tool_files: file IDs produced by tool
-        :param tool_error: error message if tool failed
         :return:
         """
-        response = MessageStreamResponse(
+        return MessageStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=message_id,
             answer=answer,
@@ -264,35 +248,6 @@ class MessageCycleManager:
             event=event_type or StreamEvent.MESSAGE,
         )
 
-        if chunk_type:
-            response = response.model_copy(update={"chunk_type": chunk_type})
-
-        if chunk_type == "tool_call":
-            response = response.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-        elif chunk_type == "tool_result":
-            response = response.model_copy(
-                update={
-                    "tool_call_id": tool_call_id,
-                    "tool_name": tool_name,
-                    "tool_arguments": tool_arguments,
-                    "tool_files": tool_files,
-                    "tool_error": tool_error,
-                    "tool_elapsed_time": tool_elapsed_time,
-                    "tool_icon": tool_icon,
-                    "tool_icon_dark": tool_icon_dark,
-                }
-            )
-
-        return response
-
     def message_replace_to_stream_response(self, answer: str, reason: str = "") -> MessageReplaceStreamResponse:
         """
         Message replace to stream response.

api/core/app_assets/__init__.py

@@ -1,35 +0,0 @@
-from .entities import (
-    AssetItem,
-    FileAsset,
-    FileReference,
-    SkillAsset,
-    SkillMetadata,
-    ToolConfiguration,
-    ToolDefinition,
-    ToolFieldConfig,
-    ToolReference,
-    ToolType,
-)
-from .packager import AssetPackager, ZipPackager
-from .parser import AssetItemParser, AssetParser, FileAssetParser, SkillAssetParser
-from .paths import AssetPaths
-
-__all__ = [
-    "AssetItem",
-    "AssetItemParser",
-    "AssetPackager",
-    "AssetParser",
-    "AssetPaths",
-    "FileAsset",
-    "FileAssetParser",
-    "FileReference",
-    "SkillAsset",
-    "SkillAssetParser",
-    "SkillMetadata",
-    "ToolConfiguration",
-    "ToolDefinition",
-    "ToolFieldConfig",
-    "ToolReference",
-    "ToolType",
-    "ZipPackager",
-]

api/core/app_assets/entities/__init__.py

@@ -1,24 +0,0 @@
-from .assets import AssetItem, FileAsset
-from .skill import (
-    FileReference,
-    SkillAsset,
-    SkillMetadata,
-    ToolConfiguration,
-    ToolDefinition,
-    ToolFieldConfig,
-    ToolReference,
-    ToolType,
-)
-
-__all__ = [
-    "AssetItem",
-    "FileAsset",
-    "FileReference",
-    "SkillAsset",
-    "SkillMetadata",
-    "ToolConfiguration",
-    "ToolDefinition",
-    "ToolFieldConfig",
-    "ToolReference",
-    "ToolType",
-]

api/core/app_assets/entities/assets.py

@@ -1,22 +0,0 @@
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-
-
-@dataclass
-class AssetItem(ABC):
-    node_id: str
-    path: str
-    file_name: str
-    extension: str
-
-    @abstractmethod
-    def get_storage_key(self) -> str:
-        raise NotImplementedError
-
-
-@dataclass
-class FileAsset(AssetItem):
-    storage_key: str
-
-    def get_storage_key(self) -> str:
-        return self.storage_key

api/core/app_assets/entities/skill.py

@@ -1,68 +0,0 @@
-from dataclasses import dataclass, field
-from enum import StrEnum
-from typing import Any
-
-from pydantic import BaseModel, ConfigDict, Field
-
-from .assets import AssetItem
-
-
-class ToolType(StrEnum):
-    MCP = "mcp"
-    BUILTIN = "builtin"
-
-
-class ToolFieldConfig(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    id: str
-    value: Any
-    auto: bool = False
-
-
-class ToolConfiguration(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    fields: list[ToolFieldConfig] = Field(default_factory=list)
-
-
-class ToolDefinition(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    type: ToolType
-    credential_id: str | None = None
-    configuration: ToolConfiguration = Field(default_factory=ToolConfiguration)
-
-
-class ToolReference(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    provider: str
-    tool_name: str
-    uuid: str
-    raw: str
-
-
-class FileReference(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    source: str
-    uuid: str
-    raw: str
-
-
-class SkillMetadata(BaseModel):
-    model_config = ConfigDict(extra="allow")
-
-    tools: dict[str, ToolDefinition] = Field(default_factory=dict)
-
-
-@dataclass
-class SkillAsset(AssetItem):
-    storage_key: str
-    metadata: SkillMetadata
-    tool_references: list[ToolReference] = field(default_factory=list)
-    file_references: list[FileReference] = field(default_factory=list)
-
-    def get_storage_key(self) -> str:
-        return self.storage_key

api/core/app_assets/packager/__init__.py

@@ -1,7 +0,0 @@
-from .base import AssetPackager
-from .zip_packager import ZipPackager
-
-__all__ = [
-    "AssetPackager",
-    "ZipPackager",
-]

api/core/app_assets/packager/base.py

@@ -1,9 +0,0 @@
-from abc import ABC, abstractmethod
-
-from core.app_assets.entities import AssetItem
-
-
-class AssetPackager(ABC):
-    @abstractmethod
-    def package(self, assets: list[AssetItem]) -> bytes:
-        raise NotImplementedError

api/core/app_assets/packager/zip_packager.py

@@ -1,27 +0,0 @@
-import io
-import zipfile
-from typing import TYPE_CHECKING
-
-from core.app_assets.entities import AssetItem
-
-from .base import AssetPackager
-
-if TYPE_CHECKING:
-    from extensions.ext_storage import Storage
-
-
-class ZipPackager(AssetPackager):
-    _storage: "Storage"
-
-    def __init__(self, storage: "Storage") -> None:
-        self._storage = storage
-
-    def package(self, assets: list[AssetItem]) -> bytes:
-        zip_buffer = io.BytesIO()
-
-        with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zf:
-            for asset in assets:
-                content = self._storage.load_once(asset.get_storage_key())
-                zf.writestr(asset.path, content)
-
-        return zip_buffer.getvalue()

api/core/app_assets/parser/__init__.py

@@ -1,10 +0,0 @@
-from .asset_parser import AssetParser
-from .base import AssetItemParser, FileAssetParser
-from .skill_parser import SkillAssetParser
-
-__all__ = [
-    "AssetItemParser",
-    "AssetParser",
-    "FileAssetParser",
-    "SkillAssetParser",
-]

api/core/app_assets/parser/asset_parser.py

@@ -1,36 +0,0 @@
-from core.app.entities.app_asset_entities import AppAssetFileTree
-from core.app_assets.entities import AssetItem
-from core.app_assets.paths import AssetPaths
-
-from .base import AssetItemParser, FileAssetParser
-
-
-class AssetParser:
-    def __init__(
-        self,
-        tree: AppAssetFileTree,
-        tenant_id: str,
-        app_id: str,
-    ) -> None:
-        self._tree = tree
-        self._tenant_id = tenant_id
-        self._app_id = app_id
-        self._parsers = {}
-        self._default_parser = FileAssetParser()
-
-    def register(self, extension: str, parser: AssetItemParser) -> None:
-        self._parsers[extension] = parser
-
-    def parse(self) -> list[AssetItem]:
-        assets: list[AssetItem] = []
-
-        for node in self._tree.walk_files():
-            path = self._tree.get_path(node.id).lstrip("/")
-            storage_key = AssetPaths.draft_file(self._tenant_id, self._app_id, node.id)
-            extension = node.extension or ""
-
-            parser = self._parsers.get(extension, self._default_parser)
-            asset = parser.parse(node.id, path, node.name, extension, storage_key)
-            assets.append(asset)
-
-        return assets

api/core/app_assets/parser/base.py

@@ -1,34 +0,0 @@
-from abc import ABC, abstractmethod
-
-from core.app_assets.entities import AssetItem, FileAsset
-
-
-class AssetItemParser(ABC):
-    @abstractmethod
-    def parse(
-        self,
-        node_id: str,
-        path: str,
-        file_name: str,
-        extension: str,
-        storage_key: str,
-    ) -> AssetItem:
-        raise NotImplementedError
-
-
-class FileAssetParser(AssetItemParser):
-    def parse(
-        self,
-        node_id: str,
-        path: str,
-        file_name: str,
-        extension: str,
-        storage_key: str,
-    ) -> FileAsset:
-        return FileAsset(
-            node_id=node_id,
-            path=path,
-            file_name=file_name,
-            extension=extension,
-            storage_key=storage_key,
-        )

api/core/app_assets/parser/skill_parser.py

@@ -1,146 +0,0 @@
-import json
-import logging
-import re
-from typing import Any
-
-from core.app_assets.entities import (
-    FileReference,
-    SkillAsset,
-    SkillMetadata,
-    ToolReference,
-)
-from core.app_assets.paths import AssetPaths
-from extensions.ext_storage import storage
-
-from .base import AssetItemParser
-
-TOOL_REFERENCE_PATTERN = re.compile(r"§\[tool\]\.\[([^\]]+)\]\.\[([^\]]+)\]\.\[([^\]]+)\]§")
-FILE_REFERENCE_PATTERN = re.compile(r"§\[file\]\.\[([^\]]+)\]\.\[([^\]]+)\]§")
-
-logger = logging.getLogger(__name__)
-
-
-class SkillAssetParser(AssetItemParser):
-    def __init__(
-        self,
-        tenant_id: str,
-        app_id: str,
-        assets_id: str,
-    ) -> None:
-        self._tenant_id = tenant_id
-        self._app_id = app_id
-        self._assets_id = assets_id
-
-    def parse(
-        self,
-        node_id: str,
-        path: str,
-        file_name: str,
-        extension: str,
-        storage_key: str,
-    ) -> SkillAsset:
-        try:
-            return self._parse_skill_asset(node_id, path, file_name, extension, storage_key)
-        except Exception:
-            logger.exception("Failed to parse skill asset %s: %s", node_id)
-            # handle as plain text
-            return SkillAsset(
-                node_id=node_id,
-                path=path,
-                file_name=file_name,
-                extension=extension,
-                storage_key=storage_key,
-                metadata=SkillMetadata(),
-                tool_references=[],
-                file_references=[],
-            )
-
-    def _parse_skill_asset(
-        self, node_id: str, path: str, file_name: str, extension: str, storage_key: str
-    ) -> SkillAsset:
-        try:
-            data = json.loads(storage.load_once(storage_key))
-        except (json.JSONDecodeError, UnicodeDecodeError):
-            # handle as plain text
-            return SkillAsset(
-                node_id=node_id,
-                path=path,
-                file_name=file_name,
-                extension=extension,
-                storage_key=storage_key,
-                metadata=SkillMetadata(),
-                tool_references=[],
-                file_references=[],
-            )
-
-        if not isinstance(data, dict):
-            raise ValueError(f"Skill document {node_id} must be a JSON object")
-
-        data_dict: dict[str, Any] = data
-        metadata_raw = data_dict.get("metadata", {})
-        content = data_dict.get("content", "")
-
-        if not isinstance(content, str):
-            raise ValueError(f"Skill document {node_id} 'content' must be a string")
-
-        metadata = SkillMetadata.model_validate(metadata_raw)
-
-        tool_references: list[ToolReference] = self._parse_tool_references(content)
-        file_references: list[FileReference] = self._parse_file_references(content)
-
-        resolved_content = self._resolve_content(content, tool_references, file_references)
-        resolved_key = AssetPaths.build_resolved_file(self._tenant_id, self._app_id, self._assets_id, node_id)
-        storage.save(resolved_key, resolved_content.encode("utf-8"))
-
-        return SkillAsset(
-            node_id=node_id,
-            path=path,
-            file_name=file_name,
-            extension=extension,
-            storage_key=resolved_key,
-            metadata=metadata,
-            tool_references=tool_references,
-            file_references=file_references,
-        )
-
-    def _resolve_content(
-        self,
-        content: str,
-        tool_references: list[ToolReference],
-        file_references: list[FileReference],
-    ) -> str:
-        for ref in tool_references:
-            replacement = f"{ref.tool_name}"
-            content = content.replace(ref.raw, replacement)
-
-        for ref in file_references:
-            replacement = f"[file:{ref.uuid}]"
-            content = content.replace(ref.raw, replacement)
-
-        return content
-
-    def _parse_tool_references(self, content: str) -> list[ToolReference]:
-        tool_references: list[ToolReference] = []
-        for match in TOOL_REFERENCE_PATTERN.finditer(content):
-            tool_references.append(
-                ToolReference(
-                    provider=match.group(1),
-                    tool_name=match.group(2),
-                    uuid=match.group(3),
-                    raw=match.group(0),
-                )
-            )
-
-        return tool_references
-
-    def _parse_file_references(self, content: str) -> list[FileReference]:
-        file_references: list[FileReference] = []
-        for match in FILE_REFERENCE_PATTERN.finditer(content):
-            file_references.append(
-                FileReference(
-                    source=match.group(1),
-                    uuid=match.group(2),
-                    raw=match.group(0),
-                )
-            )
-        return file_references

api/core/app_assets/paths.py

@@ -1,18 +0,0 @@
-class AssetPaths:
-    _BASE = "app_assets"
-
-    @staticmethod
-    def draft_file(tenant_id: str, app_id: str, node_id: str) -> str:
-        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/draft/{node_id}"
-
-    @staticmethod
-    def build_zip(tenant_id: str, app_id: str, assets_id: str) -> str:
-        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/build/{assets_id}.zip"
-
-    @staticmethod
-    def build_resolved_file(tenant_id: str, app_id: str, assets_id: str, node_id: str) -> str:
-        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/build/{assets_id}/resolved/{node_id}"
-
-    @staticmethod
-    def build_tool_manifest(tenant_id: str, app_id: str, assets_id: str) -> str:
-        return f"{AssetPaths._BASE}/{tenant_id}/{app_id}/build/{assets_id}/tools.json"

api/core/callback_handler/index_tool_callback_handler.py

@@ -5,6 +5,7 @@ from sqlalchemy import select
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.models.document import Document
@@ -89,8 +90,6 @@ class DatasetIndexToolCallbackHandler:
     # TODO(-LAN-): Improve type check
     def return_retriever_resource_info(self, resource: Sequence[RetrievalSourceMetadata]):
         """Handle return_retriever_resource_info."""
-        from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
-
         self._queue_manager.publish(
             QueueRetrieverResourcesEvent(retriever_resources=resource), PublishFrom.APPLICATION_MANAGER
         )

api/core/entities/knowledge_entities.py

@@ -3,6 +3,7 @@ from pydantic import BaseModel, Field, field_validator
 
 class PreviewDetail(BaseModel):
     content: str
+    summary: str | None = None
     child_chunks: list[str] | None = None
 
 

api/core/indexing_runner.py

@@ -311,14 +311,18 @@ class IndexingRunner:
         qa_preview_texts: list[QAPreviewDetail] = []
 
         total_segments = 0
+        # doc_form represents the segmentation method (general, parent-child, QA)
         index_type = doc_form
         index_processor = IndexProcessorFactory(index_type).init_index_processor()
+        # one extract_setting is one source document
         for extract_setting in extract_settings:
             # extract
             processing_rule = DatasetProcessRule(
                 mode=tmp_processing_rule["mode"], rules=json.dumps(tmp_processing_rule["rules"])
             )
+            # Extract document content
             text_docs = index_processor.extract(extract_setting, process_rule_mode=tmp_processing_rule["mode"])
+            # Cleaning and segmentation
             documents = index_processor.transform(
                 text_docs,
                 current_user=None,
@@ -361,6 +365,12 @@ class IndexingRunner:
 
         if doc_form and doc_form == "qa_model":
             return IndexingEstimate(total_segments=total_segments * 20, qa_preview=qa_preview_texts, preview=[])
+
+        # Generate summary preview
+        summary_index_setting = tmp_processing_rule.get("summary_index_setting")
+        if summary_index_setting and summary_index_setting.get("enable") and preview_texts:
+            preview_texts = index_processor.generate_summary_preview(tenant_id, preview_texts, summary_index_setting)
+
         return IndexingEstimate(total_segments=total_segments, preview=preview_texts)
 
     def _extract(

api/core/llm_generator/llm_generator.py

@@ -72,7 +72,7 @@ class LLMGenerator:
                 prompt_messages=list(prompts), model_parameters={"max_tokens": 500, "temperature": 1}, stream=False
             )
         answer = response.message.get_text_content()
-        if answer == "":
+        if answer is None:
             return ""
         try:
             result_dict = json.loads(answer)
@@ -113,9 +113,11 @@ class LLMGenerator:
         output_parser = SuggestedQuestionsAfterAnswerOutputParser()
         format_instructions = output_parser.get_format_instructions()
 
-        prompt_template = PromptTemplateParser(template="{{histories}}\n{{format_instructions}}\nquestions:\n")
+        prompt_template = PromptTemplateParser(
+            template="{{histories}}\n{{format_instructions}}\nquestions:\n")
 
-        prompt = prompt_template.format({"histories": histories, "format_instructions": format_instructions})
+        prompt = prompt_template.format(
+            {"histories": histories, "format_instructions": format_instructions})
 
         try:
             model_manager = ModelManager()
@@ -141,11 +143,13 @@ class LLMGenerator:
             )
 
             text_content = response.message.get_text_content()
-            questions = output_parser.parse(text_content) if text_content else []
+            questions = output_parser.parse(
+                text_content) if text_content else []
         except InvokeError:
             questions = []
         except Exception:
-            logger.exception("Failed to generate suggested questions after answer")
+            logger.exception(
+                "Failed to generate suggested questions after answer")
             questions = []
 
         return questions
@@ -156,10 +160,12 @@ class LLMGenerator:
 
         error = ""
         error_step = ""
-        rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
+        rule_config = {"prompt": "", "variables": [],
+                       "opening_statement": "", "error": ""}
         model_parameters = model_config.get("completion_params", {})
         if no_variable:
-            prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
+            prompt_template = PromptTemplateParser(
+                WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
 
             prompt_generate = prompt_template.format(
                 inputs={
@@ -190,7 +196,8 @@ class LLMGenerator:
                 error = str(e)
                 error_step = "generate rule config"
             except Exception as e:
-                logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
+                logger.exception(
+                    "Failed to generate rule config, model: %s", model_config.get("name"))
                 rule_config["error"] = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -245,7 +252,8 @@ class LLMGenerator:
                 },
                 remove_template_variables=False,
             )
-            parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
+            parameter_messages = [UserPromptMessage(
+                content=parameter_generate_prompt)]
 
             # the second step to generate the task_parameter and task_statement
             statement_generate_prompt = statement_template.format(
@@ -255,13 +263,15 @@ class LLMGenerator:
                 },
                 remove_template_variables=False,
             )
-            statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
+            statement_messages = [UserPromptMessage(
+                content=statement_generate_prompt)]
 
             try:
                 parameter_content: LLMResult = model_instance.invoke_llm(
                     prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
                 )
-                rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.get_text_content())
+                rule_config["variables"] = re.findall(
+                    r'"\s*([^"]+)\s*"', parameter_content.message.get_text_content())
             except InvokeError as e:
                 error = str(e)
                 error_step = "generate variables"
@@ -270,13 +280,15 @@ class LLMGenerator:
                 statement_content: LLMResult = model_instance.invoke_llm(
                     prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
                 )
-                rule_config["opening_statement"] = statement_content.message.get_text_content()
+                rule_config["opening_statement"] = statement_content.message.get_text_content(
+                )
             except InvokeError as e:
                 error = str(e)
                 error_step = "generate conversation opener"
 
         except Exception as e:
-            logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
+            logger.exception(
+                "Failed to generate rule config, model: %s", model_config.get("name"))
             rule_config["error"] = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -286,9 +298,11 @@ class LLMGenerator:
     @classmethod
     def generate_code(cls, tenant_id: str, instruction: str, model_config: dict, code_language: str = "javascript"):
         if code_language == "python":
-            prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
+            prompt_template = PromptTemplateParser(
+                PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
         else:
-            prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
+            prompt_template = PromptTemplateParser(
+                JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
 
         prompt = prompt_template.format(
             inputs={
@@ -321,7 +335,8 @@ class LLMGenerator:
             return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
             logger.exception(
-                "Failed to invoke LLM model, model: %s, language: %s", model_config.get("name"), code_language
+                "Failed to invoke LLM model, model: %s, language: %s", model_config.get(
+                    "name"), code_language
             )
             return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
 
@@ -335,7 +350,8 @@ class LLMGenerator:
             model_type=ModelType.LLM,
         )
 
-        prompt_messages: list[PromptMessage] = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
+        prompt_messages: list[PromptMessage] = [SystemPromptMessage(
+            content=prompt), UserPromptMessage(content=query)]
 
         # Explicitly use the non-streaming overload
         result = model_instance.invoke_llm(
@@ -381,16 +397,19 @@ class LLMGenerator:
                 parsed_content = json_repair.loads(raw_content)
 
             if not isinstance(parsed_content, dict | list):
-                raise ValueError(f"Failed to parse structured output from llm: {raw_content}")
+                raise ValueError(
+                    f"Failed to parse structured output from llm: {raw_content}")
 
-            generated_json_schema = json.dumps(parsed_content, indent=2, ensure_ascii=False)
+            generated_json_schema = json.dumps(
+                parsed_content, indent=2, ensure_ascii=False)
             return {"output": generated_json_schema, "error": ""}
 
         except InvokeError as e:
             error = str(e)
             return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
         except Exception as e:
-            logger.exception("Failed to invoke LLM model, model: %s", model_config.get("name"))
+            logger.exception(
+                "Failed to invoke LLM model, model: %s", model_config.get("name"))
             return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
 
     @staticmethod
@@ -398,7 +417,8 @@ class LLMGenerator:
         tenant_id: str, flow_id: str, current: str, instruction: str, model_config: dict, ideal_output: str | None
     ):
         last_run: Message | None = (
-            db.session.query(Message).where(Message.app_id == flow_id).order_by(Message.created_at.desc()).first()
+            db.session.query(Message).where(Message.app_id == flow_id).order_by(
+                Message.created_at.desc()).first()
         )
         if not last_run:
             return LLMGenerator.__instruction_modify_common(
@@ -446,7 +466,8 @@ class LLMGenerator:
         workflow = workflow_service.get_draft_workflow(app_model=app)
         if not workflow:
             raise ValueError("Workflow not found for the given app model.")
-        last_run = workflow_service.get_node_last_run(app_model=app, workflow=workflow, node_id=node_id)
+        last_run = workflow_service.get_node_last_run(
+            app_model=app, workflow=workflow, node_id=node_id)
         try:
             node_type = cast(WorkflowNodeExecutionModel, last_run).node_type
         except Exception:
@@ -470,7 +491,8 @@ class LLMGenerator:
             )
 
         def agent_log_of(node_execution: WorkflowNodeExecutionModel) -> Sequence:
-            raw_agent_log = node_execution.execution_metadata_dict.get(WorkflowNodeExecutionMetadataKey.AGENT_LOG, [])
+            raw_agent_log = node_execution.execution_metadata_dict.get(
+                WorkflowNodeExecutionMetadataKey.AGENT_LOG, [])
             if not raw_agent_log:
                 return []
 
@@ -518,11 +540,14 @@ class LLMGenerator:
         ERROR_MESSAGE = "{{#error_message#}}"
         injected_instruction = instruction
         if LAST_RUN in injected_instruction:
-            injected_instruction = injected_instruction.replace(LAST_RUN, json.dumps(last_run))
+            injected_instruction = injected_instruction.replace(
+                LAST_RUN, json.dumps(last_run))
         if CURRENT in injected_instruction:
-            injected_instruction = injected_instruction.replace(CURRENT, current or "null")
+            injected_instruction = injected_instruction.replace(
+                CURRENT, current or "null")
         if ERROR_MESSAGE in injected_instruction:
-            injected_instruction = injected_instruction.replace(ERROR_MESSAGE, error_message or "null")
+            injected_instruction = injected_instruction.replace(
+                ERROR_MESSAGE, error_message or "null")
         model_instance = ModelManager().get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
@@ -560,11 +585,13 @@ class LLMGenerator:
             first_brace = generated_raw.find("{")
             last_brace = generated_raw.rfind("}")
             if first_brace == -1 or last_brace == -1 or last_brace < first_brace:
-                raise ValueError(f"Could not find a valid JSON object in response: {generated_raw}")
-            json_str = generated_raw[first_brace : last_brace + 1]
+                raise ValueError(
+                    f"Could not find a valid JSON object in response: {generated_raw}")
+            json_str = generated_raw[first_brace: last_brace + 1]
             data = json_repair.loads(json_str)
             if not isinstance(data, dict):
-                raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
+                raise TypeError(
+                    f"Expected a JSON object, but got {type(data).__name__}")
             return data
         except InvokeError as e:
             error = str(e)

api/core/llm_generator/prompts.py

@@ -434,3 +434,20 @@ INSTRUCTION_GENERATE_TEMPLATE_PROMPT = """The output of this prompt is not as ex
 You should edit the prompt according to the IDEAL OUTPUT."""
 
 INSTRUCTION_GENERATE_TEMPLATE_CODE = """Please fix the errors in the {{#error_message#}}."""
+
+DEFAULT_GENERATOR_SUMMARY_PROMPT = (
+    """Summarize the following content. Extract only the key information and main points. """
+    """Remove redundant details.
+
+Requirements:
+1. Write a concise summary in plain text
+2. Use the same language as the input content
+3. Focus on important facts, concepts, and details
+4. If images are included, describe their key information
+5. Do not use words like "好的", "ok", "I understand", "This text discusses", "The content mentions"
+6. Write directly without extra words
+
+Output only the summary text. Start summarizing now:
+
+"""
+)

api/core/plugin/backwards_invocation/tool.py

@@ -4,7 +4,6 @@ from typing import Any
 from core.callback_handler.workflow_tool_callback_handler import DifyWorkflowCallbackHandler
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
-from core.tools.signature import sign_tool_file
 from core.tools.tool_engine import ToolEngine
 from core.tools.tool_manager import ToolManager
 from core.tools.utils.message_transformer import ToolFileMessageTransformer
@@ -42,43 +41,6 @@ class PluginToolBackwardsInvocation(BaseBackwardsInvocation):
                 response, user_id=user_id, tenant_id=tenant_id
             )
 
-            return cls._sign_tool_file_urls(response)
+            return response
         except Exception as e:
             raise e
-
-    # FIXME: this method should be gracefully deprecated
-    @classmethod
-    def _sign_tool_file_urls(
-        cls, messages: Generator[ToolInvokeMessage, None, None]
-    ) -> Generator[ToolInvokeMessage, None, None]:
-        """
-        Sign file URLs in tool invoke messages for external access.
-        """
-        for message in messages:
-            if message.type in {
-                ToolInvokeMessage.MessageType.IMAGE_LINK,
-                ToolInvokeMessage.MessageType.BINARY_LINK,
-                ToolInvokeMessage.MessageType.FILE,
-            }:
-                if isinstance(message.message, ToolInvokeMessage.TextMessage):
-                    url = message.message.text
-                    # Check if it's an unsigned internal path
-                    if url.startswith("/files/tools/"):
-                        parts = url.split("/")[-1]
-                        if "." in parts:
-                            file_id, ext = parts.rsplit(".", 1)
-                            extension = f".{ext}"
-                        else:
-                            file_id = parts
-                            extension = ".bin"
-
-                        signed_url = sign_tool_file(tool_file_id=file_id, extension=extension)
-
-                        yield ToolInvokeMessage(
-                            type=message.type,
-                            message=ToolInvokeMessage.TextMessage(text=signed_url),
-                            meta=message.meta,
-                        )
-                        continue
-
-            yield message

api/core/plugin/entities/request.py

@@ -282,11 +282,3 @@ class TriggerDispatchResponse(BaseModel):
             return deserialize_response(binascii.unhexlify(v.encode()))
         except Exception as e:
             raise ValueError("Failed to deserialize response from hex string") from e
-
-
-class RequestListTools(BaseModel):
-    """
-    Request to list all available tools
-    """
-
-    pass

api/core/plugin/impl/base.py

@@ -320,17 +320,18 @@ class BasePluginClient:
             case PluginInvokeError.__name__:
                 error_object = json.loads(message)
                 invoke_error_type = error_object.get("error_type")
+                args = error_object.get("args")
                 match invoke_error_type:
                     case InvokeRateLimitError.__name__:
-                        raise InvokeRateLimitError(description=error_object.get("message"))
+                        raise InvokeRateLimitError(description=args.get("description"))
                     case InvokeAuthorizationError.__name__:
-                        raise InvokeAuthorizationError(description=error_object.get("message"))
+                        raise InvokeAuthorizationError(description=args.get("description"))
                     case InvokeBadRequestError.__name__:
-                        raise InvokeBadRequestError(description=error_object.get("message"))
+                        raise InvokeBadRequestError(description=args.get("description"))
                     case InvokeConnectionError.__name__:
-                        raise InvokeConnectionError(description=error_object.get("message"))
+                        raise InvokeConnectionError(description=args.get("description"))
                     case InvokeServerUnavailableError.__name__:
-                        raise InvokeServerUnavailableError(description=error_object.get("message"))
+                        raise InvokeServerUnavailableError(description=args.get("description"))
                     case CredentialsValidateFailedError.__name__:
                         raise CredentialsValidateFailedError(error_object.get("message"))
                     case EndpointSetupFailedError.__name__:
@@ -338,11 +339,11 @@ class BasePluginClient:
                     case TriggerProviderCredentialValidationError.__name__:
                         raise TriggerProviderCredentialValidationError(error_object.get("message"))
                     case TriggerPluginInvokeError.__name__:
-                        raise TriggerPluginInvokeError(description=error_object.get("message"))
+                        raise TriggerPluginInvokeError(description=error_object.get("description"))
                     case TriggerInvokeError.__name__:
                         raise TriggerInvokeError(error_object.get("message"))
                     case EventIgnoreError.__name__:
-                        raise EventIgnoreError(description=error_object.get("message"))
+                        raise EventIgnoreError(description=error_object.get("description"))
                     case _:
                         raise PluginInvokeError(description=message)
             case PluginDaemonInternalServerError.__name__:

api/core/rag/datasource/retrieval_service.py

@@ -389,15 +389,14 @@ class RetrievalService:
                 .all()
             }
 
-            records = []
-            include_segment_ids = set()
-            segment_child_map = {}
-
             valid_dataset_documents = {}
             image_doc_ids: list[Any] = []
             child_index_node_ids = []
             index_node_ids = []
             doc_to_document_map = {}
+            summary_segment_ids = set()  # Track segments retrieved via summary
+
+            # First pass: collect all document IDs and identify summary documents
             for document in documents:
                 document_id = document.metadata.get("document_id")
                 if document_id not in dataset_documents:
@@ -408,16 +407,24 @@ class RetrievalService:
                     continue
                 valid_dataset_documents[document_id] = dataset_document
 
+                doc_id = document.metadata.get("doc_id") or ""
+                doc_to_document_map[doc_id] = document
+
+                # Check if this is a summary document
+                is_summary = document.metadata.get("is_summary", False)
+                if is_summary:
+                    # For summary documents, find the original chunk via original_chunk_id
+                    original_chunk_id = document.metadata.get("original_chunk_id")
+                    if original_chunk_id:
+                        summary_segment_ids.add(original_chunk_id)
+                    continue  # Skip adding to other lists for summary documents
+
                 if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
-                    doc_id = document.metadata.get("doc_id") or ""
-                    doc_to_document_map[doc_id] = document
                     if document.metadata.get("doc_type") == DocType.IMAGE:
                         image_doc_ids.append(doc_id)
                     else:
                         child_index_node_ids.append(doc_id)
                 else:
-                    doc_id = document.metadata.get("doc_id") or ""
-                    doc_to_document_map[doc_id] = document
                     if document.metadata.get("doc_type") == DocType.IMAGE:
                         image_doc_ids.append(doc_id)
                     else:
@@ -433,6 +440,7 @@ class RetrievalService:
             attachment_map: dict[str, list[dict[str, Any]]] = {}
             child_chunk_map: dict[str, list[ChildChunk]] = {}
             doc_segment_map: dict[str, list[str]] = {}
+            segment_summary_map: dict[str, str] = {}  # Map segment_id to summary content
 
             with session_factory.create_session() as session:
                 attachments = cls.get_segment_attachment_infos(image_doc_ids, session)
@@ -447,6 +455,7 @@ class RetrievalService:
                         doc_segment_map[attachment["segment_id"]].append(attachment["attachment_id"])
                     else:
                         doc_segment_map[attachment["segment_id"]] = [attachment["attachment_id"]]
+
                 child_chunk_stmt = select(ChildChunk).where(ChildChunk.index_node_id.in_(child_index_node_ids))
                 child_index_nodes = session.execute(child_chunk_stmt).scalars().all()
 
@@ -470,6 +479,7 @@ class RetrievalService:
                     index_node_segments = session.execute(document_segment_stmt).scalars().all()  # type: ignore
                     for index_node_segment in index_node_segments:
                         doc_segment_map[index_node_segment.id] = [index_node_segment.index_node_id]
+
                 if segment_ids:
                     document_segment_stmt = select(DocumentSegment).where(
                         DocumentSegment.enabled == True,
@@ -481,6 +491,42 @@ class RetrievalService:
                 if index_node_segments:
                     segments.extend(index_node_segments)
 
+                # Handle summary documents: query segments by original_chunk_id
+                if summary_segment_ids:
+                    summary_segment_ids_list = list(summary_segment_ids)
+                    summary_segment_stmt = select(DocumentSegment).where(
+                        DocumentSegment.enabled == True,
+                        DocumentSegment.status == "completed",
+                        DocumentSegment.id.in_(summary_segment_ids_list),
+                    )
+                    summary_segments = session.execute(summary_segment_stmt).scalars().all()  # type: ignore
+                    segments.extend(summary_segments)
+                    # Add summary segment IDs to segment_ids for summary query
+                    for seg in summary_segments:
+                        if seg.id not in segment_ids:
+                            segment_ids.append(seg.id)
+
+                # Batch query summaries for segments retrieved via summary (only enabled summaries)
+                if summary_segment_ids:
+                    from models.dataset import DocumentSegmentSummary
+
+                    summaries = (
+                        session.query(DocumentSegmentSummary)
+                        .filter(
+                            DocumentSegmentSummary.chunk_id.in_(list(summary_segment_ids)),
+                            DocumentSegmentSummary.status == "completed",
+                            DocumentSegmentSummary.enabled == True,  # Only retrieve enabled summaries
+                        )
+                        .all()
+                    )
+                    for summary in summaries:
+                        if summary.summary_content:
+                            segment_summary_map[summary.chunk_id] = summary.summary_content
+
+            include_segment_ids = set()
+            segment_child_map: dict[str, dict[str, Any]] = {}
+            records: list[dict[str, Any]] = []
+
             for segment in segments:
                 child_chunks: list[ChildChunk] = child_chunk_map.get(segment.id, [])
                 attachment_infos: list[dict[str, Any]] = attachment_map.get(segment.id, [])
@@ -493,7 +539,7 @@ class RetrievalService:
                             child_chunk_details = []
                             max_score = 0.0
                             for child_chunk in child_chunks:
-                                document = doc_to_document_map[child_chunk.index_node_id]
+                                document = doc_to_document_map.get(child_chunk.index_node_id)
                                 child_chunk_detail = {
                                     "id": child_chunk.id,
                                     "content": child_chunk.content,
@@ -503,7 +549,7 @@ class RetrievalService:
                                 child_chunk_details.append(child_chunk_detail)
                                 max_score = max(max_score, document.metadata.get("score", 0.0) if document else 0.0)
                             for attachment_info in attachment_infos:
-                                file_document = doc_to_document_map[attachment_info["id"]]
+                                file_document = doc_to_document_map.get(attachment_info["id"])
                                 max_score = max(
                                     max_score, file_document.metadata.get("score", 0.0) if file_document else 0.0
                                 )
@@ -576,9 +622,16 @@ class RetrievalService:
                     else None
                 )
 
+                # Extract summary if this segment was retrieved via summary
+                summary_content = segment_summary_map.get(segment.id)
+
                 # Create RetrievalSegments object
                 retrieval_segment = RetrievalSegments(
-                    segment=segment, child_chunks=child_chunks_list, score=score, files=files
+                    segment=segment,
+                    child_chunks=child_chunks_list,
+                    score=score,
+                    files=files,
+                    summary=summary_content,
                 )
                 result.append(retrieval_segment)
 

api/core/rag/embedding/retrieval.py

@@ -20,3 +20,4 @@ class RetrievalSegments(BaseModel):
     child_chunks: list[RetrievalChildChunk] | None = None
     score: float | None = None
     files: list[dict[str, str | int]] | None = None
+    summary: str | None = None  # Summary content if retrieved via summary index

api/core/rag/index_processor/index_processor_base.py

@@ -13,6 +13,7 @@ from urllib.parse import unquote, urlparse
 import httpx
 
 from configs import dify_config
+from core.entities.knowledge_entities import PreviewDetail
 from core.helper import ssrf_proxy
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.index_processor.constant.doc_type import DocType
@@ -45,6 +46,17 @@ class BaseIndexProcessor(ABC):
     def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         raise NotImplementedError
 
+    @abstractmethod
+    def generate_summary_preview(
+        self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict
+    ) -> list[PreviewDetail]:
+        """
+        For each segment in preview_texts, generate a summary using LLM and attach it to the segment.
+        The summary can be stored in a new attribute, e.g., summary.
+        This method should be implemented by subclasses.
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def load(
         self,

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -1,9 +1,25 @@
 """Paragraph index processor."""
 
+import logging
+import re
 import uuid
 from collections.abc import Mapping
 from typing import Any
 
+logger = logging.getLogger(__name__)
+
+from core.entities.knowledge_entities import PreviewDetail
+from core.file import File, FileTransferMethod, FileType, file_manager
+from core.llm_generator.prompts import DEFAULT_GENERATOR_SUMMARY_PROMPT
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.message_entities import (
+    ImagePromptMessageContent,
+    PromptMessageContentUnionTypes,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import ModelFeature, ModelType
+from core.provider_manager import ProviderManager
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -17,12 +33,16 @@ from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import AttachmentDocument, Document, MultimodalGeneralStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
+from extensions.ext_database import db
+from factories.file_factory import build_from_mapping
 from libs import helper
+from models import UploadFile
 from models.account import Account
-from models.dataset import Dataset, DatasetProcessRule
+from models.dataset import Dataset, DatasetProcessRule, DocumentSegment, SegmentAttachmentBinding
 from models.dataset import Document as DatasetDocument
 from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import Rule
+from services.summary_index_service import SummaryIndexService
 
 
 class ParagraphIndexProcessor(BaseIndexProcessor):
@@ -108,6 +128,29 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
                 keyword.add_texts(documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             if node_ids:
@@ -227,3 +270,263 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
             }
         else:
             raise ValueError("Chunks is not a list")
+
+    def generate_summary_preview(
+        self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict
+    ) -> list[PreviewDetail]:
+        """
+        For each segment, concurrently call generate_summary to generate a summary
+        and write it to the summary attribute of PreviewDetail.
+        """
+        import concurrent.futures
+
+        from flask import current_app
+
+        # Capture Flask app context for worker threads
+        flask_app = None
+        try:
+            flask_app = current_app._get_current_object()  # type: ignore
+        except RuntimeError:
+            logger.warning("No Flask application context available, summary generation may fail")
+
+        def process(preview: PreviewDetail) -> None:
+            """Generate summary for a single preview item."""
+            try:
+                if flask_app:
+                    # Ensure Flask app context in worker thread
+                    with flask_app.app_context():
+                        summary = self.generate_summary(tenant_id, preview.content, summary_index_setting)
+                        preview.summary = summary
+                else:
+                    # Fallback: try without app context (may fail)
+                    summary = self.generate_summary(tenant_id, preview.content, summary_index_setting)
+                    preview.summary = summary
+            except Exception:
+                logger.exception("Failed to generate summary for preview")
+                # Don't fail the entire preview if summary generation fails
+                preview.summary = None
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            list(executor.map(process, preview_texts))
+        return preview_texts
+
+    @staticmethod
+    def generate_summary(
+        tenant_id: str,
+        text: str,
+        summary_index_setting: dict | None = None,
+        segment_id: str | None = None,
+    ) -> str:
+        """
+        Generate summary for the given text using ModelInstance.invoke_llm and the default or custom summary prompt,
+        and supports vision models by including images from the segment attachments or text content.
+
+        Args:
+            tenant_id: Tenant ID
+            text: Text content to summarize
+            summary_index_setting: Summary index configuration
+            segment_id: Optional segment ID to fetch attachments from SegmentAttachmentBinding table
+        """
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            raise ValueError("summary_index_setting is required and must be enabled to generate summary.")
+
+        model_name = summary_index_setting.get("model_name")
+        model_provider_name = summary_index_setting.get("model_provider_name")
+        summary_prompt = summary_index_setting.get("summary_prompt")
+
+        # Import default summary prompt
+        if not summary_prompt:
+            summary_prompt = DEFAULT_GENERATOR_SUMMARY_PROMPT
+
+        provider_manager = ProviderManager()
+        provider_model_bundle = provider_manager.get_provider_model_bundle(
+            tenant_id, model_provider_name, ModelType.LLM
+        )
+        model_instance = ModelInstance(provider_model_bundle, model_name)
+
+        # Get model schema to check if vision is supported
+        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
+        supports_vision = model_schema and model_schema.features and ModelFeature.VISION in model_schema.features
+
+        # Extract images if model supports vision
+        image_files = []
+        if supports_vision:
+            # First, try to get images from SegmentAttachmentBinding (preferred method)
+            if segment_id:
+                image_files = ParagraphIndexProcessor._extract_images_from_segment_attachments(tenant_id, segment_id)
+
+            # If no images from attachments, fall back to extracting from text
+            if not image_files:
+                image_files = ParagraphIndexProcessor._extract_images_from_text(tenant_id, text)
+
+        # Build prompt messages
+        prompt_messages = []
+
+        if image_files:
+            # If we have images, create a UserPromptMessage with both text and images
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+
+            # Add images first
+            for file in image_files:
+                try:
+                    file_content = file_manager.to_prompt_message_content(
+                        file, image_detail_config=ImagePromptMessageContent.DETAIL.LOW
+                    )
+                    prompt_message_contents.append(file_content)
+                except Exception as e:
+                    logger.warning("Failed to convert image file to prompt message content: %s", str(e))
+                    continue
+
+            # Add text content
+            if prompt_message_contents:  # Only add text if we successfully added images
+                prompt_message_contents.append(TextPromptMessageContent(data=f"{summary_prompt}\n{text}"))
+                prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+            else:
+                # If image conversion failed, fall back to text-only
+                prompt = f"{summary_prompt}\n{text}"
+                prompt_messages.append(UserPromptMessage(content=prompt))
+        else:
+            # No images, use simple text prompt
+            prompt = f"{summary_prompt}\n{text}"
+            prompt_messages.append(UserPromptMessage(content=prompt))
+
+        result = model_instance.invoke_llm(prompt_messages=prompt_messages, model_parameters={}, stream=False)
+
+        return getattr(result.message, "content", "")
+
+    @staticmethod
+    def _extract_images_from_text(tenant_id: str, text: str) -> list[File]:
+        """
+        Extract images from markdown text and convert them to File objects.
+
+        Args:
+            tenant_id: Tenant ID
+            text: Text content that may contain markdown image links
+
+        Returns:
+            List of File objects representing images found in the text
+        """
+        # Extract markdown images using regex pattern
+        pattern = r"!\[.*?\]\((.*?)\)"
+        images = re.findall(pattern, text)
+
+        if not images:
+            return []
+
+        upload_file_id_list = []
+
+        for image in images:
+            # For data before v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/image-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For data after v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/file-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For tools directory - direct file formats (e.g., .png, .jpg, etc.)
+            pattern = r"/files/tools/([a-f0-9\-]+)\.([a-zA-Z0-9]+)(?:\?[^\s\)\"\']*)?"
+            match = re.search(pattern, image)
+            if match:
+                # Tool files are handled differently, skip for now
+                continue
+
+        if not upload_file_id_list:
+            return []
+
+        # Get unique IDs for database query
+        unique_upload_file_ids = list(set(upload_file_id_list))
+        upload_files = (
+            db.session.query(UploadFile)
+            .where(UploadFile.id.in_(unique_upload_file_ids), UploadFile.tenant_id == tenant_id)
+            .all()
+        )
+
+        # Create File objects from UploadFile records
+        file_objects = []
+        for upload_file in upload_files:
+            # Only process image files
+            if not upload_file.mime_type or "image" not in upload_file.mime_type:
+                continue
+
+            mapping = {
+                "upload_file_id": upload_file.id,
+                "transfer_method": FileTransferMethod.LOCAL_FILE.value,
+                "type": FileType.IMAGE.value,
+            }
+
+            try:
+                file_obj = build_from_mapping(
+                    mapping=mapping,
+                    tenant_id=tenant_id,
+                )
+                file_objects.append(file_obj)
+            except Exception as e:
+                logger.warning("Failed to create File object from UploadFile %s: %s", upload_file.id, str(e))
+                continue
+
+        return file_objects
+
+    @staticmethod
+    def _extract_images_from_segment_attachments(tenant_id: str, segment_id: str) -> list[File]:
+        """
+        Extract images from SegmentAttachmentBinding table (preferred method).
+        This matches how DatasetRetrieval gets segment attachments.
+
+        Args:
+            tenant_id: Tenant ID
+            segment_id: Segment ID to fetch attachments for
+
+        Returns:
+            List of File objects representing images found in segment attachments
+        """
+        from sqlalchemy import select
+
+        # Query attachments from SegmentAttachmentBinding table
+        attachments_with_bindings = db.session.execute(
+            select(SegmentAttachmentBinding, UploadFile)
+            .join(UploadFile, UploadFile.id == SegmentAttachmentBinding.attachment_id)
+            .where(
+                SegmentAttachmentBinding.segment_id == segment_id,
+                SegmentAttachmentBinding.tenant_id == tenant_id,
+            )
+        ).all()
+
+        if not attachments_with_bindings:
+            return []
+
+        file_objects = []
+        for _, upload_file in attachments_with_bindings:
+            # Only process image files
+            if not upload_file.mime_type or "image" not in upload_file.mime_type:
+                continue
+
+            try:
+                # Create File object directly (similar to DatasetRetrieval)
+                file_obj = File(
+                    id=upload_file.id,
+                    filename=upload_file.name,
+                    extension="." + upload_file.extension,
+                    mime_type=upload_file.mime_type,
+                    tenant_id=tenant_id,
+                    type=FileType.IMAGE,
+                    transfer_method=FileTransferMethod.LOCAL_FILE,
+                    remote_url=upload_file.source_url,
+                    related_id=upload_file.id,
+                    size=upload_file.size,
+                    storage_key=upload_file.key,
+                )
+                file_objects.append(file_obj)
+            except Exception as e:
+                logger.warning("Failed to create File object from UploadFile %s: %s", upload_file.id, str(e))
+                continue
+
+        return file_objects

api/core/rag/index_processor/processor/parent_child_index_processor.py

@@ -1,11 +1,13 @@
 """Paragraph index processor."""
 
 import json
+import logging
 import uuid
 from collections.abc import Mapping
 from typing import Any
 
 from configs import dify_config
+from core.entities.knowledge_entities import PreviewDetail
 from core.model_manager import ModelInstance
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -25,6 +27,9 @@ from models.dataset import ChildChunk, Dataset, DatasetProcessRule, DocumentSegm
 from models.dataset import Document as DatasetDocument
 from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import ParentMode, Rule
+from services.summary_index_service import SummaryIndexService
+
+logger = logging.getLogger(__name__)
 
 
 class ParentChildIndexProcessor(BaseIndexProcessor):
@@ -135,6 +140,29 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
         # node_ids is segment's node_ids
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         if dataset.indexing_technique == "high_quality":
             delete_child_chunks = kwargs.get("delete_child_chunks") or False
             precomputed_child_node_ids = kwargs.get("precomputed_child_node_ids")
@@ -326,3 +354,57 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
             "preview": preview,
             "total_segments": len(parent_childs.parent_child_chunks),
         }
+
+    def generate_summary_preview(
+        self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict
+    ) -> list[PreviewDetail]:
+        """
+        For each parent chunk in preview_texts, concurrently call generate_summary to generate a summary
+        and write it to the summary attribute of PreviewDetail.
+        
+        Note: For parent-child structure, we only generate summaries for parent chunks.
+        """
+        import concurrent.futures
+
+        from flask import current_app
+
+        # Capture Flask app context for worker threads
+        flask_app = None
+        try:
+            flask_app = current_app._get_current_object()  # type: ignore
+        except RuntimeError:
+            logger.warning("No Flask application context available, summary generation may fail")
+
+        def process(preview: PreviewDetail) -> None:
+            """Generate summary for a single preview item (parent chunk)."""
+            try:
+                if flask_app:
+                    # Ensure Flask app context in worker thread
+                    with flask_app.app_context():
+                        # Use ParagraphIndexProcessor's generate_summary method
+                        from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+                        summary = ParagraphIndexProcessor.generate_summary(
+                            tenant_id=tenant_id,
+                            text=preview.content,
+                            summary_index_setting=summary_index_setting,
+                        )
+                        if summary:
+                            preview.summary = summary
+                else:
+                    # Fallback: try without app context (may fail)
+                    from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+                    summary = ParagraphIndexProcessor.generate_summary(
+                        tenant_id=tenant_id,
+                        text=preview.content,
+                        summary_index_setting=summary_index_setting,
+                    )
+                    if summary:
+                        preview.summary = summary
+            except Exception:
+                logger.exception("Failed to generate summary for preview")
+                # Don't fail the entire preview if summary generation fails
+                preview.summary = None
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            list(executor.map(process, preview_texts))
+        return preview_texts

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -11,6 +11,7 @@ import pandas as pd
 from flask import Flask, current_app
 from werkzeug.datastructures import FileStorage
 
+from core.entities.knowledge_entities import PreviewDetail
 from core.llm_generator.llm_generator import LLMGenerator
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -25,9 +26,10 @@ from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
 from models.account import Account
-from models.dataset import Dataset
+from models.dataset import Dataset, DocumentSegment
 from models.dataset import Document as DatasetDocument
 from services.entities.knowledge_entities.knowledge_entities import Rule
+from services.summary_index_service import SummaryIndexService
 
 logger = logging.getLogger(__name__)
 
@@ -144,6 +146,30 @@ class QAIndexProcessor(BaseIndexProcessor):
                 vector.create_multimodal(multimodal_documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Note: qa_model doesn't generate summaries, but we clean them for completeness
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         vector = Vector(dataset)
         if node_ids:
             vector.delete_by_ids(node_ids)
@@ -212,6 +238,17 @@ class QAIndexProcessor(BaseIndexProcessor):
             "total_segments": len(qa_chunks.qa_chunks),
         }
 
+    def generate_summary_preview(
+        self, tenant_id: str, preview_texts: list[PreviewDetail], summary_index_setting: dict
+    ) -> list[PreviewDetail]:
+        """
+        QA model doesn't generate summaries, so this method returns preview_texts unchanged.
+        
+        Note: QA model uses question-answer pairs, which don't require summary generation.
+        """
+        # QA model doesn't generate summaries, return as-is
+        return preview_texts
+
     def _format_qa_document(self, flask_app: Flask, tenant_id: str, document_node, all_qa_documents, document_language):
         format_documents = []
         if document_node.page_content is None or not document_node.page_content.strip():

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -29,7 +29,6 @@ from models import (
     Account,
     CreatorUserRole,
     EndUser,
-    LLMGenerationDetail,
     WorkflowNodeExecutionModel,
     WorkflowNodeExecutionTriggeredFrom,
 )
@@ -458,113 +457,6 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             session.merge(db_model)
             session.flush()
 
-            # Save LLMGenerationDetail for LLM nodes with successful execution
-            if (
-                domain_model.node_type == NodeType.LLM
-                and domain_model.status == WorkflowNodeExecutionStatus.SUCCEEDED
-                and domain_model.outputs is not None
-            ):
-                self._save_llm_generation_detail(session, domain_model)
-
-    def _save_llm_generation_detail(self, session, execution: WorkflowNodeExecution) -> None:
-        """
-        Save LLM generation detail for LLM nodes.
-        Extracts reasoning_content, tool_calls, and sequence from outputs and metadata.
-        """
-        outputs = execution.outputs or {}
-        metadata = execution.metadata or {}
-
-        reasoning_list = self._extract_reasoning(outputs)
-        tool_calls_list = self._extract_tool_calls(metadata.get(WorkflowNodeExecutionMetadataKey.AGENT_LOG))
-
-        if not reasoning_list and not tool_calls_list:
-            return
-
-        sequence = self._build_generation_sequence(outputs.get("text", ""), reasoning_list, tool_calls_list)
-        self._upsert_generation_detail(session, execution, reasoning_list, tool_calls_list, sequence)
-
-    def _extract_reasoning(self, outputs: Mapping[str, Any]) -> list[str]:
-        """Extract reasoning_content as a clean list of non-empty strings."""
-        reasoning_content = outputs.get("reasoning_content")
-        if isinstance(reasoning_content, str):
-            trimmed = reasoning_content.strip()
-            return [trimmed] if trimmed else []
-        if isinstance(reasoning_content, list):
-            return [item.strip() for item in reasoning_content if isinstance(item, str) and item.strip()]
-        return []
-
-    def _extract_tool_calls(self, agent_log: Any) -> list[dict[str, str]]:
-        """Extract tool call records from agent logs."""
-        if not agent_log or not isinstance(agent_log, list):
-            return []
-
-        tool_calls: list[dict[str, str]] = []
-        for log in agent_log:
-            log_data = log.data if hasattr(log, "data") else (log.get("data", {}) if isinstance(log, dict) else {})
-            tool_name = log_data.get("tool_name")
-            if tool_name and str(tool_name).strip():
-                tool_calls.append(
-                    {
-                        "id": log_data.get("tool_call_id", ""),
-                        "name": tool_name,
-                        "arguments": json.dumps(log_data.get("tool_args", {})),
-                        "result": str(log_data.get("output", "")),
-                    }
-                )
-        return tool_calls
-
-    def _build_generation_sequence(
-        self, text: str, reasoning_list: list[str], tool_calls_list: list[dict[str, str]]
-    ) -> list[dict[str, Any]]:
-        """Build a simple content/reasoning/tool_call sequence."""
-        sequence: list[dict[str, Any]] = []
-        if text:
-            sequence.append({"type": "content", "start": 0, "end": len(text)})
-        for index in range(len(reasoning_list)):
-            sequence.append({"type": "reasoning", "index": index})
-        for index in range(len(tool_calls_list)):
-            sequence.append({"type": "tool_call", "index": index})
-        return sequence
-
-    def _upsert_generation_detail(
-        self,
-        session,
-        execution: WorkflowNodeExecution,
-        reasoning_list: list[str],
-        tool_calls_list: list[dict[str, str]],
-        sequence: list[dict[str, Any]],
-    ) -> None:
-        """Insert or update LLMGenerationDetail with serialized fields."""
-        existing = (
-            session.query(LLMGenerationDetail)
-            .filter_by(
-                workflow_run_id=execution.workflow_execution_id,
-                node_id=execution.node_id,
-            )
-            .first()
-        )
-
-        reasoning_json = json.dumps(reasoning_list) if reasoning_list else None
-        tool_calls_json = json.dumps(tool_calls_list) if tool_calls_list else None
-        sequence_json = json.dumps(sequence) if sequence else None
-
-        if existing:
-            existing.reasoning_content = reasoning_json
-            existing.tool_calls = tool_calls_json
-            existing.sequence = sequence_json
-            return
-
-        generation_detail = LLMGenerationDetail(
-            tenant_id=self._tenant_id,
-            app_id=self._app_id,
-            workflow_run_id=execution.workflow_execution_id,
-            node_id=execution.node_id,
-            reasoning_content=reasoning_json,
-            tool_calls=tool_calls_json,
-            sequence=sequence_json,
-        )
-        session.add(generation_detail)
-
     def get_db_models_by_workflow_run(
         self,
         workflow_run_id: str,

api/core/sandbox/__init__.py

@@ -1,53 +0,0 @@
-from .bash.dify_cli import (
-    DifyCliBinary,
-    DifyCliConfig,
-    DifyCliEnvConfig,
-    DifyCliLocator,
-    DifyCliToolConfig,
-)
-from .constants import (
-    APP_ASSETS_PATH,
-    APP_ASSETS_ZIP_PATH,
-    DIFY_CLI_CONFIG_FILENAME,
-    DIFY_CLI_GLOBAL_TOOLS_PATH,
-    DIFY_CLI_PATH,
-    DIFY_CLI_PATH_PATTERN,
-    DIFY_CLI_ROOT,
-    DIFY_CLI_TOOLS_ROOT,
-)
-from .initializer import AppAssetsInitializer, DifyCliInitializer, SandboxInitializer
-from .manager import SandboxManager
-from .session import SandboxSession
-from .storage import ArchiveSandboxStorage, SandboxStorage
-from .utils.debug import sandbox_debug
-from .utils.encryption import create_sandbox_config_encrypter, masked_config
-from .vm import SandboxBuilder, SandboxType, VMConfig
-
-__all__ = [
-    "APP_ASSETS_PATH",
-    "APP_ASSETS_ZIP_PATH",
-    "DIFY_CLI_CONFIG_FILENAME",
-    "DIFY_CLI_GLOBAL_TOOLS_PATH",
-    "DIFY_CLI_PATH",
-    "DIFY_CLI_PATH_PATTERN",
-    "DIFY_CLI_ROOT",
-    "DIFY_CLI_TOOLS_ROOT",
-    "AppAssetsInitializer",
-    "ArchiveSandboxStorage",
-    "DifyCliBinary",
-    "DifyCliConfig",
-    "DifyCliEnvConfig",
-    "DifyCliInitializer",
-    "DifyCliLocator",
-    "DifyCliToolConfig",
-    "SandboxBuilder",
-    "SandboxInitializer",
-    "SandboxManager",
-    "SandboxSession",
-    "SandboxStorage",
-    "SandboxType",
-    "VMConfig",
-    "create_sandbox_config_encrypter",
-    "masked_config",
-    "sandbox_debug",
-]

api/core/sandbox/bash/__init__.py

@@ -1,15 +0,0 @@
-from .dify_cli import (
-    DifyCliBinary,
-    DifyCliConfig,
-    DifyCliEnvConfig,
-    DifyCliLocator,
-    DifyCliToolConfig,
-)
-
-__all__ = [
-    "DifyCliBinary",
-    "DifyCliConfig",
-    "DifyCliEnvConfig",
-    "DifyCliLocator",
-    "DifyCliToolConfig",
-]

api/core/sandbox/bash/bash_tool.py

@@ -1,98 +0,0 @@
-from collections.abc import Generator
-from typing import Any
-
-from core.tools.__base.tool import Tool
-from core.tools.__base.tool_runtime import ToolRuntime
-from core.tools.entities.common_entities import I18nObject
-from core.tools.entities.tool_entities import (
-    ToolDescription,
-    ToolEntity,
-    ToolIdentity,
-    ToolInvokeMessage,
-    ToolParameter,
-    ToolProviderType,
-)
-from core.virtual_environment.__base.helpers import submit_command, with_connection
-from core.virtual_environment.__base.virtual_environment import VirtualEnvironment
-
-from ..utils.debug import sandbox_debug
-
-COMMAND_TIMEOUT_SECONDS = 60
-
-
-class SandboxBashTool(Tool):
-    def __init__(self, sandbox: VirtualEnvironment, tenant_id: str, tools_path: str) -> None:
-        self._sandbox = sandbox
-        self._tools_path = tools_path
-
-        entity = ToolEntity(
-            identity=ToolIdentity(
-                author="Dify",
-                name="bash",
-                label=I18nObject(en_US="Bash", zh_Hans="Bash"),
-                provider="sandbox",
-            ),
-            parameters=[
-                ToolParameter.get_simple_instance(
-                    name="command",
-                    llm_description="The bash command to execute in current working directory",
-                    typ=ToolParameter.ToolParameterType.STRING,
-                    required=True,
-                ),
-            ],
-            description=ToolDescription(
-                human=I18nObject(
-                    en_US="Execute bash commands in current working directory",
-                ),
-                llm="Execute bash commands in current working directory. "
-                "Use this tool to run shell commands, scripts, or interact with the system. "
-                "The command will be executed in the current working directory.",
-            ),
-        )
-
-        runtime = ToolRuntime(tenant_id=tenant_id)
-        super().__init__(entity=entity, runtime=runtime)
-
-    def tool_provider_type(self) -> ToolProviderType:
-        return ToolProviderType.BUILT_IN
-
-    def _invoke(
-        self,
-        user_id: str,
-        tool_parameters: dict[str, Any],
-        conversation_id: str | None = None,
-        app_id: str | None = None,
-        message_id: str | None = None,
-    ) -> Generator[ToolInvokeMessage, None, None]:
-        command = tool_parameters.get("command", "")
-        if not command:
-            yield self.create_text_message("Error: No command provided")
-            return
-
-        try:
-            with with_connection(self._sandbox) as conn:
-                cmd_list = ["bash", "-c", command]
-                env_vars = {"PATH": f"{self._tools_path}:/usr/local/bin:/usr/bin:/bin"}
-
-                sandbox_debug("bash_tool", "cmd_list", cmd_list)
-                future = submit_command(self._sandbox, conn, cmd_list, environments=env_vars)
-                timeout = COMMAND_TIMEOUT_SECONDS if COMMAND_TIMEOUT_SECONDS > 0 else None
-                result = future.result(timeout=timeout)
-
-                stdout = result.stdout.decode("utf-8", errors="replace") if result.stdout else ""
-                stderr = result.stderr.decode("utf-8", errors="replace") if result.stderr else ""
-                exit_code = result.exit_code
-
-                output_parts: list[str] = []
-                if stdout:
-                    output_parts.append(f"\n{stdout}")
-                if stderr:
-                    output_parts.append(f"\n{stderr}")
-                output_parts.append(f"\nCommand exited with code {exit_code}")
-
-                yield self.create_text_message("\n".join(output_parts))
-
-        except TimeoutError:
-            yield self.create_text_message(f"Error: Command timed out after {COMMAND_TIMEOUT_SECONDS}s")
-        except Exception as e:
-            yield self.create_text_message(f"Error: {e!s}")

api/core/sandbox/bash/dify_cli.py

@@ -1,134 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-from pydantic import BaseModel, Field
-
-from core.model_runtime.utils.encoders import jsonable_encoder
-from core.session.cli_api import CliApiSession
-from core.tools.entities.tool_entities import ToolParameter, ToolProviderType
-from core.virtual_environment.__base.entities import Arch, OperatingSystem
-
-from ..constants import DIFY_CLI_PATH_PATTERN
-
-if TYPE_CHECKING:
-    from core.tools.__base.tool import Tool
-
-
-class DifyCliBinary(BaseModel):
-    operating_system: OperatingSystem = Field(alias="os")
-    arch: Arch
-    path: Path
-
-    model_config = {
-        "populate_by_name": True,
-        "arbitrary_types_allowed": True,
-    }
-
-
-class DifyCliLocator:
-    def __init__(self, root: str | Path | None = None) -> None:
-        from configs import dify_config
-
-        if root is not None:
-            self._root = Path(root)
-        elif dify_config.SANDBOX_DIFY_CLI_ROOT:
-            self._root = Path(dify_config.SANDBOX_DIFY_CLI_ROOT)
-        else:
-            api_root = Path(__file__).resolve().parents[3]
-            self._root = api_root / "bin"
-
-    def resolve(self, operating_system: OperatingSystem, arch: Arch) -> DifyCliBinary:
-        filename = DIFY_CLI_PATH_PATTERN.format(os=operating_system.value, arch=arch.value)
-        candidate = self._root / filename
-        if not candidate.is_file():
-            raise FileNotFoundError(
-                f"dify CLI binary not found: {candidate}. Configure SANDBOX_DIFY_CLI_ROOT or ensure the file exists."
-            )
-
-        return DifyCliBinary(os=operating_system, arch=arch, path=candidate)
-
-
-class DifyCliEnvConfig(BaseModel):
-    files_url: str
-    cli_api_url: str
-    cli_api_session_id: str
-    cli_api_secret: str
-
-
-class DifyCliToolConfig(BaseModel):
-    provider_type: str
-    identity: dict[str, Any]
-    description: dict[str, Any]
-    parameters: list[dict[str, Any]]
-
-    @classmethod
-    def transform_provider_type(cls, tool_provider_type: ToolProviderType) -> str:
-        provider_type = tool_provider_type
-        match tool_provider_type:
-            case ToolProviderType.BUILT_IN | ToolProviderType.PLUGIN:
-                provider_type = "builtin"
-            case ToolProviderType.MCP | ToolProviderType.WORKFLOW | ToolProviderType.API:
-                provider_type = provider_type
-            case _:
-                raise ValueError(f"Invalid tool provider type: {tool_provider_type}")
-        return provider_type
-
-    @classmethod
-    def create_from_tool(cls, tool: Tool) -> DifyCliToolConfig:
-        return cls(
-            provider_type=cls.transform_provider_type(tool.tool_provider_type()),
-            identity=to_json(tool.entity.identity),
-            description=to_json(tool.entity.description),
-            parameters=[cls.transform_parameter(parameter) for parameter in tool.entity.parameters],
-        )
-
-    @classmethod
-    def transform_parameter(cls, parameter: ToolParameter) -> dict[str, Any]:
-        transformed_parameter = to_json(parameter)
-        transformed_parameter.pop("input_schema", None)
-        transformed_parameter.pop("form", None)
-        match parameter.type:
-            case (
-                ToolParameter.ToolParameterType.SYSTEM_FILES
-                | ToolParameter.ToolParameterType.FILE
-                | ToolParameter.ToolParameterType.FILES
-            ):
-                return transformed_parameter
-            case _:
-                return transformed_parameter
-
-
-class DifyCliConfig(BaseModel):
-    env: DifyCliEnvConfig
-    tools: list[DifyCliToolConfig]
-
-    @classmethod
-    def create(cls, session: CliApiSession, tools: list[Tool]) -> DifyCliConfig:
-        from configs import dify_config
-
-        cli_api_url = dify_config.CLI_API_URL
-
-        return cls(
-            env=DifyCliEnvConfig(
-                files_url=dify_config.FILES_URL,
-                cli_api_url=cli_api_url,
-                cli_api_session_id=session.id,
-                cli_api_secret=session.secret,
-            ),
-            tools=[DifyCliToolConfig.create_from_tool(tool) for tool in tools],
-        )
-
-
-def to_json(obj: Any) -> dict[str, Any]:
-    return jsonable_encoder(obj, exclude_unset=True, exclude_defaults=True, exclude_none=True)
-
-
-__all__ = [
-    "DifyCliBinary",
-    "DifyCliConfig",
-    "DifyCliEnvConfig",
-    "DifyCliLocator",
-    "DifyCliToolConfig",
-]

api/core/sandbox/constants.py

@@ -1,16 +0,0 @@
-from typing import Final
-
-# Dify CLI (absolute path - hidden in /tmp, not in sandbox workdir)
-DIFY_CLI_ROOT: Final[str] = "/tmp/.dify"
-DIFY_CLI_PATH: Final[str] = "/tmp/.dify/bin/dify"
-
-DIFY_CLI_PATH_PATTERN: Final[str] = "dify-cli-{os}-{arch}"
-
-DIFY_CLI_CONFIG_FILENAME: Final[str] = ".dify_cli.json"
-
-DIFY_CLI_TOOLS_ROOT: Final[str] = "/tmp/.dify/tools"
-DIFY_CLI_GLOBAL_TOOLS_PATH: Final[str] = "/tmp/.dify/tools/global"
-
-# App Assets (relative path - stays in sandbox workdir)
-APP_ASSETS_PATH: Final[str] = "assets"
-APP_ASSETS_ZIP_PATH: Final[str] = "/tmp/assets.zip"

api/core/sandbox/entities/__init__.py

@@ -1,3 +0,0 @@
-from .providers import SandboxProviderApiEntity
-
-__all__ = ["SandboxProviderApiEntity"]

api/core/sandbox/entities/providers.py

@@ -1,21 +0,0 @@
-from collections.abc import Mapping
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-
-class SandboxProviderApiEntity(BaseModel):
-    provider_type: str = Field(..., description="Provider type identifier")
-    is_system_configured: bool = Field(default=False)
-    is_tenant_configured: bool = Field(default=False)
-    is_active: bool = Field(default=False)
-    config: Mapping[str, Any] = Field(default_factory=dict)
-    config_schema: list[dict[str, Any]] = Field(default_factory=list)
-
-
-class SandboxProviderEntity(BaseModel):
-    id: str = Field(..., description="Provider identifier")
-    provider_type: str = Field(..., description="Provider type identifier")
-    is_active: bool = Field(default=False)
-    config: Mapping[str, Any] = Field(default_factory=dict)
-    config_schema: list[dict[str, Any]] = Field(default_factory=list)

api/core/sandbox/initializer/__init__.py

@@ -1,9 +0,0 @@
-from .app_assets_initializer import AppAssetsInitializer
-from .base import SandboxInitializer
-from .dify_cli_initializer import DifyCliInitializer
-
-__all__ = [
-    "AppAssetsInitializer",
-    "DifyCliInitializer",
-    "SandboxInitializer",
-]

api/core/sandbox/initializer/app_assets_initializer.py

@@ -1,55 +0,0 @@
-import logging
-from io import BytesIO
-
-from core.app_assets.paths import AssetPaths
-from core.virtual_environment.__base.helpers import execute, with_connection
-from core.virtual_environment.__base.virtual_environment import VirtualEnvironment
-from extensions.ext_storage import storage
-
-from ..constants import APP_ASSETS_PATH, APP_ASSETS_ZIP_PATH
-from .base import SandboxInitializer
-
-logger = logging.getLogger(__name__)
-
-
-class AppAssetsInitializer(SandboxInitializer):
-    def __init__(self, tenant_id: str, app_id: str, assets_id: str) -> None:
-        self._tenant_id = tenant_id
-        self._app_id = app_id
-        self._assets_id = assets_id
-
-    def initialize(self, env: VirtualEnvironment) -> None:
-        zip_key = AssetPaths.build_zip(self._tenant_id, self._app_id, self._assets_id)
-        try:
-            zip_data = storage.load_once(zip_key)
-        except Exception:
-            logger.warning(
-                "Failed to load assets zip for app_id=%s, key=%s",
-                self._app_id,
-                zip_key,
-                exc_info=True,
-            )
-            return
-
-        env.upload_file(APP_ASSETS_ZIP_PATH, BytesIO(zip_data))
-
-        with with_connection(env) as conn:
-            execute(
-                env,
-                ["unzip", "-o", APP_ASSETS_ZIP_PATH, "-d", APP_ASSETS_PATH],
-                connection=conn,
-                timeout=60,
-                error_message="Failed to unzip assets",
-            )
-            execute(
-                env,
-                ["rm", "-f", APP_ASSETS_ZIP_PATH],
-                connection=conn,
-                error_message="Failed to cleanup temp zip file",
-            )
-
-        logger.info(
-            "App assets initialized for app_id=%s, published_id=%s",
-            self._app_id,
-            self._assets_id,
-        )