fix: export telemetry routing constants for tests

Export CASE_ROUTING and CASE_TO_TRACE_TASK as module-level constants
to fix import errors in enterprise telemetry tests.

Tests were failing with:
- ImportError: cannot import name 'CASE_ROUTING' from 'core.telemetry.gateway'
- ImportError: cannot import name 'CASE_TO_TRACE_TASK' from 'core.telemetry.gateway'

This fix allows tests to access the routing configuration without breaking
the lazy-loading pattern used internally.

.codex/skills/component-refactoring

@@ -0,0 +1 @@
+../../.agents/skills/component-refactoring

.codex/skills/frontend-code-review

@@ -0,0 +1 @@
+../../.agents/skills/frontend-code-review

.codex/skills/frontend-testing

@@ -0,0 +1 @@
+../../.agents/skills/frontend-testing

.codex/skills/orpc-contract-first

@@ -0,0 +1 @@
+../../.agents/skills/orpc-contract-first

.github/CODEOWNERS

@@ -24,10 +24,6 @@
 /api/services/tools/mcp_tools_manage_service.py @Nov1c444
 /api/controllers/mcp/ @Nov1c444
 /api/controllers/console/app/mcp_server.py @Nov1c444
-
-# Backend - Tests
-/api/tests/ @laipz8200 @QuantumGhost
-
 /api/tests/**/*mcp* @Nov1c444
 
 # Backend - Workflow - Engine (Core graph execution engine)
@@ -238,9 +234,6 @@
 # Frontend - Base Components
 /web/app/components/base/ @iamjoel @zxhlyh
 
-# Frontend - Base Components Tests
-/web/app/components/base/**/*.spec.tsx @hyoban @CodingOnStar
-
 # Frontend - Utils and Hooks
 /web/utils/classnames.ts @iamjoel @zxhlyh
 /web/utils/time.ts @iamjoel @zxhlyh

.github/workflows/autofix.yml

@@ -79,6 +79,29 @@ jobs:
           find . -name "*.py" -type f -exec sed -i.bak -E 's/"([^"]+)" \| None/Optional["\1"]/g; s/'"'"'([^'"'"']+)'"'"' \| None/Optional['"'"'\1'"'"']/g' {} \;
           find . -name "*.py.bak" -type f -delete
 
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          package_json_file: web/package.json
+          run_install: false
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: 24
+          cache: pnpm
+          cache-dependency-path: ./web/pnpm-lock.yaml
+
+      - name: Install web dependencies
+        run: |
+          cd web
+          pnpm install --frozen-lockfile
+
+      - name: ESLint autofix
+        run: |
+          cd web
+          pnpm lint:fix || true
+
       # mdformat breaks YAML front matter in markdown files. Add --exclude for directories containing YAML front matter.
       - name: mdformat
         run: |

.github/workflows/build-push.yml

@@ -8,7 +8,6 @@ on:
       - "build/**"
       - "release/e-*"
       - "hotfix/**"
-      - "feat/hitl-backend"
     tags:
       - "*"
 

.github/workflows/deploy-hitl.yml

@@ -4,7 +4,8 @@ on:
   workflow_run:
     workflows: ["Build and Push API & Web"]
     branches:
-      - "build/feat/hitl"
+      - "feat/hitl-frontend"
+      - "feat/hitl-backend"
     types:
       - completed
 
@@ -13,7 +14,10 @@ jobs:
     runs-on: ubuntu-latest
     if: |
       github.event.workflow_run.conclusion == 'success' &&
-      github.event.workflow_run.head_branch == 'build/feat/hitl'
+      (
+        github.event.workflow_run.head_branch == 'feat/hitl-frontend' ||
+        github.event.workflow_run.head_branch == 'feat/hitl-backend'
+      )
     steps:
       - name: Deploy to server
         uses: appleboy/ssh-action@v1

.github/workflows/web-tests.yml

@@ -39,7 +39,7 @@ jobs:
         run: pnpm install --frozen-lockfile
 
       - name: Run tests
-        run: pnpm test:ci
+        run: pnpm test:coverage
 
       - name: Coverage Summary
         if: always()

.vscode/launch.json.template

@@ -37,7 +37,7 @@
                 "-c",
                 "1",
                 "-Q",
-                "dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention,workflow_based_app_execution",
+                "dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention",
                 "--loglevel",
                 "INFO"
             ],

api/.env.example

@@ -715,31 +715,5 @@ ANNOTATION_IMPORT_MAX_CONCURRENT=5
 # Sandbox expired records clean configuration
 SANDBOX_EXPIRED_RECORDS_CLEAN_GRACEFUL_PERIOD=21
 SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_SIZE=1000
-SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_MAX_INTERVAL=200
 SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS=30
 SANDBOX_EXPIRED_RECORDS_CLEAN_TASK_LOCK_TTL=90000
-
-
-# Redis URL used for PubSub between API and
-# celery worker
-# defaults to url constructed from `REDIS_*`
-# configurations
-PUBSUB_REDIS_URL=
-# Pub/sub channel type for streaming events.
-# valid options are:
-#
-# - pubsub: for normal Pub/Sub
-# - sharded: for sharded Pub/Sub
-#
-# It's highly recommended to use sharded Pub/Sub AND redis cluster
-# for large deployments.
-PUBSUB_REDIS_CHANNEL_TYPE=pubsub
-# Whether to use Redis cluster mode while running
-# PubSub.
-#  It's highly recommended to enable this for large deployments.
-PUBSUB_REDIS_USE_CLUSTERS=false
-
-# Whether to Enable human input timeout check task
-ENABLE_HUMAN_INPUT_TIMEOUT_TASK=true
-# Human input timeout check interval in minutes
-HUMAN_INPUT_TIMEOUT_TASK_INTERVAL=1

api/.importlinter

@@ -36,8 +36,6 @@ ignore_imports =
     core.workflow.nodes.loop.loop_node -> core.workflow.graph_engine
     core.workflow.nodes.loop.loop_node -> core.workflow.graph
     core.workflow.nodes.loop.loop_node -> core.workflow.graph_engine.command_channels
-    # TODO(QuantumGhost): fix the import violation later
-    core.workflow.entities.pause_reason -> core.workflow.nodes.human_input.entities
 
 [importlinter:contract:workflow-infrastructure-dependencies]
 name = Workflow Infrastructure Dependencies
@@ -52,14 +50,14 @@ ignore_imports =
     core.workflow.nodes.agent.agent_node -> extensions.ext_database
     core.workflow.nodes.datasource.datasource_node -> extensions.ext_database
     core.workflow.nodes.knowledge_index.knowledge_index_node -> extensions.ext_database
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_database
     core.workflow.nodes.llm.file_saver -> extensions.ext_database
     core.workflow.nodes.llm.llm_utils -> extensions.ext_database
     core.workflow.nodes.llm.node -> extensions.ext_database
     core.workflow.nodes.tool.tool_node -> extensions.ext_database
     core.workflow.graph_engine.command_channels.redis_channel -> extensions.ext_redis
     core.workflow.graph_engine.manager -> extensions.ext_redis
-    # TODO(QuantumGhost): use DI to avoid depending on global DB.
-    core.workflow.nodes.human_input.human_input_node -> extensions.ext_database
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_redis
 
 [importlinter:contract:workflow-external-imports]
 name = Workflow External Imports
@@ -124,6 +122,11 @@ ignore_imports =
     core.workflow.nodes.http_request.node -> core.tools.tool_file_manager
     core.workflow.nodes.iteration.iteration_node -> core.app.workflow.node_factory
     core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.index_processor.index_processor_factory
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.datasource.retrieval_service
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.retrieval.dataset_retrieval
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> models.dataset
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> services.feature_service
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.model_runtime.model_providers.__base.large_language_model
     core.workflow.nodes.llm.llm_utils -> configs
     core.workflow.nodes.llm.llm_utils -> core.app.entities.app_invoke_entities
     core.workflow.nodes.llm.llm_utils -> core.file.models
@@ -133,6 +136,7 @@ ignore_imports =
     core.workflow.nodes.llm.llm_utils -> models.provider
     core.workflow.nodes.llm.llm_utils -> services.credit_pool_service
     core.workflow.nodes.llm.node -> core.tools.signature
+    core.workflow.nodes.template_transform.template_transform_node -> configs
     core.workflow.nodes.tool.tool_node -> core.callback_handler.workflow_tool_callback_handler
     core.workflow.nodes.tool.tool_node -> core.tools.tool_engine
     core.workflow.nodes.tool.tool_node -> core.tools.tool_manager
@@ -141,9 +145,9 @@ ignore_imports =
     core.workflow.nodes.agent.agent_node -> core.agent.entities
     core.workflow.nodes.agent.agent_node -> core.agent.plugin_entities
     core.workflow.nodes.base.node -> core.app.entities.app_invoke_entities
-    core.workflow.nodes.human_input.human_input_node -> core.app.entities.app_invoke_entities
     core.workflow.nodes.knowledge_index.knowledge_index_node -> core.app.entities.app_invoke_entities
     core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.app.app_config.entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.app.entities.app_invoke_entities
     core.workflow.nodes.llm.node -> core.app.entities.app_invoke_entities
     core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.app.entities.app_invoke_entities
     core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.advanced_prompt_transform
@@ -159,6 +163,9 @@ ignore_imports =
     core.workflow.workflow_entry -> core.app.workflow.node_factory
     core.workflow.nodes.datasource.datasource_node -> core.datasource.datasource_manager
     core.workflow.nodes.datasource.datasource_node -> core.datasource.utils.message_transformer
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.entities.agent_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.entities.model_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.model_manager
     core.workflow.nodes.llm.llm_utils -> core.entities.provider_entities
     core.workflow.nodes.parameter_extractor.parameter_extractor_node -> core.model_manager
     core.workflow.nodes.question_classifier.question_classifier_node -> core.model_manager
@@ -207,6 +214,7 @@ ignore_imports =
     core.workflow.nodes.llm.node -> core.llm_generator.output_parser.structured_output
     core.workflow.nodes.llm.node -> core.model_manager
     core.workflow.nodes.agent.entities -> core.prompt.entities.advanced_prompt_entities
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.prompt.simple_prompt_transform
     core.workflow.nodes.llm.entities -> core.prompt.entities.advanced_prompt_entities
     core.workflow.nodes.llm.llm_utils -> core.prompt.entities.advanced_prompt_entities
     core.workflow.nodes.llm.node -> core.prompt.entities.advanced_prompt_entities
@@ -222,6 +230,7 @@ ignore_imports =
     core.workflow.nodes.knowledge_index.knowledge_index_node -> services.summary_index_service
     core.workflow.nodes.knowledge_index.knowledge_index_node -> tasks.generate_summary_index_task
     core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.index_processor.processor.paragraph_index_processor
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.llm.node -> models.dataset
     core.workflow.nodes.agent.agent_node -> core.tools.utils.message_transformer
     core.workflow.nodes.llm.file_saver -> core.tools.signature
@@ -239,7 +248,6 @@ ignore_imports =
     core.workflow.nodes.document_extractor.node -> core.variables.segments
     core.workflow.nodes.http_request.executor -> core.variables.segments
     core.workflow.nodes.http_request.node -> core.variables.segments
-    core.workflow.nodes.human_input.entities -> core.variables.consts
     core.workflow.nodes.iteration.iteration_node -> core.variables
     core.workflow.nodes.iteration.iteration_node -> core.variables.segments
     core.workflow.nodes.iteration.iteration_node -> core.variables.variables
@@ -280,12 +288,12 @@ ignore_imports =
     core.workflow.nodes.agent.agent_node -> extensions.ext_database
     core.workflow.nodes.datasource.datasource_node -> extensions.ext_database
     core.workflow.nodes.knowledge_index.knowledge_index_node -> extensions.ext_database
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_database
+    core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_redis
     core.workflow.nodes.llm.file_saver -> extensions.ext_database
     core.workflow.nodes.llm.llm_utils -> extensions.ext_database
     core.workflow.nodes.llm.node -> extensions.ext_database
     core.workflow.nodes.tool.tool_node -> extensions.ext_database
-    core.workflow.nodes.human_input.human_input_node -> extensions.ext_database
-    core.workflow.nodes.human_input.human_input_node -> core.repositories.human_input_repository
     core.workflow.workflow_entry -> extensions.otel.runtime
     core.workflow.nodes.agent.agent_node -> models
     core.workflow.nodes.base.node -> models.enums

api/.ruff.toml

@@ -106,10 +106,10 @@ ignore = [
     "N803", # invalid-argument-name
 ]
 "tests/*" = [
-    "F811", # redefined-while-unused
-    "T201", # allow print in tests,
-    "S110", # allow ignoring exceptions in tests code (currently)
-
+    "F811",  # redefined-while-unused
+    "T201",  # allow print in tests,
+    "S110",  # allow ignoring exceptions in tests code (currently)
+    "PT019", # @patch-injected params look like unused fixtures
 ]
 "controllers/console/explore/trial.py" = ["TID251"]
 "controllers/console/human_input_form.py" = ["TID251"]

api/.vscode/launch.json.example

@@ -54,7 +54,7 @@
                 "--loglevel",
                 "DEBUG",
                 "-Q",
-                "dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,workflow_based_app_execution,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor"
+                "dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor"
             ]
         }
     ]

api/README.md

@@ -122,7 +122,8 @@ These commands assume you start from the repository root.
 
    ```bash
    cd api
-   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q api_token,dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
+   # Note: enterprise_telemetry queue is only used in Enterprise Edition
+   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention,enterprise_telemetry
    ```
 
 1. Optional: start Celery Beat (scheduled tasks, in a new terminal).

api/app_factory.py

@@ -1,16 +1,45 @@
 import logging
 import time
 
+from flask import request
 from opentelemetry.trace import get_current_span
 from opentelemetry.trace.span import INVALID_SPAN_ID, INVALID_TRACE_ID
 
 from configs import dify_config
 from contexts.wrapper import RecyclableContextVar
+from controllers.console.error import UnauthorizedAndForceLogout
 from core.logging.context import init_request_context
 from dify_app import DifyApp
+from services.enterprise.enterprise_service import EnterpriseService
+from services.feature_service import LicenseStatus
 
 logger = logging.getLogger(__name__)
 
+# Console bootstrap APIs exempt from license check.
+# Defined at module level to avoid per-request tuple construction.
+# - system-features: license status for expiry UI (GlobalPublicStoreProvider)
+# - setup: install/setup status check (AppInitializer)
+# - init: init password validation for fresh install (InitPasswordPopup)
+# - login: auto-login after setup completion (InstallForm)
+# - features: billing/plan features (ProviderContextProvider)
+# - account/profile: login check + user profile (AppContextProvider, useIsLogin)
+# - workspaces/current: workspace + model providers (AppContextProvider)
+# - version: version check (AppContextProvider)
+# - activate/check: invitation link validation (signin page)
+# Without these exemptions, the signin page triggers location.reload()
+# on unauthorized_and_force_logout, causing an infinite loop.
+_CONSOLE_EXEMPT_PREFIXES = (
+    "/console/api/system-features",
+    "/console/api/setup",
+    "/console/api/init",
+    "/console/api/login",
+    "/console/api/features",
+    "/console/api/account/profile",
+    "/console/api/workspaces/current",
+    "/console/api/version",
+    "/console/api/activate/check",
+)
+
 
 # ----------------------------
 # Application Factory Function
@@ -31,6 +60,39 @@ def create_flask_app_with_configs() -> DifyApp:
         init_request_context()
         RecyclableContextVar.increment_thread_recycles()
 
+        # Enterprise license validation for API endpoints (both console and webapp)
+        # When license expires, block all API access except bootstrap endpoints needed
+        # for the frontend to load the license expiration page without infinite reloads.
+        if dify_config.ENTERPRISE_ENABLED:
+            is_console_api = request.path.startswith("/console/api/")
+            is_webapp_api = request.path.startswith("/api/") and not is_console_api
+
+            if is_console_api or is_webapp_api:
+                if is_console_api:
+                    is_exempt = any(request.path.startswith(p) for p in _CONSOLE_EXEMPT_PREFIXES)
+                else:  # webapp API
+                    is_exempt = request.path.startswith("/api/system-features")
+
+                if not is_exempt:
+                    try:
+                        # Check license status with caching (10 min TTL)
+                        license_status = EnterpriseService.get_cached_license_status()
+                        if license_status in (LicenseStatus.INACTIVE, LicenseStatus.EXPIRED, LicenseStatus.LOST):
+                            # Cookie clearing is handled by register_external_error_handlers
+                            # in libs/external_api.py which detects the error code and calls
+                            # build_force_logout_cookie_headers(). Frontend then checks
+                            # code === 'unauthorized_and_force_logout' and calls location.reload().
+                            raise UnauthorizedAndForceLogout(
+                                f"Enterprise license is {license_status}. Please contact your administrator."
+                            )
+                    except UnauthorizedAndForceLogout:
+                        raise
+                    except Exception:
+                        # If license check fails, log but don't block the request.
+                        # This prevents service disruption if enterprise API is temporarily
+                        # unavailable.
+                        logger.exception("Failed to check enterprise license status")
+
     # add after request hook for injecting trace headers from OpenTelemetry span context
     # Only adds headers when OTEL is enabled and has valid context
     @dify_app.after_request
@@ -81,6 +143,7 @@ def initialize_extensions(app: DifyApp):
         ext_commands,
         ext_compress,
         ext_database,
+        ext_enterprise_telemetry,
         ext_fastopenapi,
         ext_forward_refs,
         ext_hosting_provider,
@@ -131,6 +194,7 @@ def initialize_extensions(app: DifyApp):
         ext_commands,
         ext_fastopenapi,
         ext_otel,
+        ext_enterprise_telemetry,
         ext_request_logging,
         ext_session_factory,
     ]

api/commands.py

@@ -30,6 +30,7 @@ from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
 from extensions.storage.opendal_storage import OpenDALStorage
 from extensions.storage.storage_type import StorageType
+from libs.db_migration_lock import DbMigrationAutoRenewLock
 from libs.helper import email as email_validate
 from libs.password import hash_password, password_pattern, valid_password
 from libs.rsa import generate_key_pair
@@ -54,6 +55,8 @@ from tasks.remove_app_and_related_data_task import delete_draft_variables_batch
 
 logger = logging.getLogger(__name__)
 
+DB_UPGRADE_LOCK_TTL_SECONDS = 60
+
 
 @click.command("reset-password", help="Reset the account password.")
 @click.option("--email", prompt=True, help="Account email to reset password for")
@@ -727,8 +730,15 @@ def create_tenant(email: str, language: str | None = None, name: str | None = No
 @click.command("upgrade-db", help="Upgrade the database")
 def upgrade_db():
     click.echo("Preparing database migration...")
-    lock = redis_client.lock(name="db_upgrade_lock", timeout=60)
+    lock = DbMigrationAutoRenewLock(
+        redis_client=redis_client,
+        name="db_upgrade_lock",
+        ttl_seconds=DB_UPGRADE_LOCK_TTL_SECONDS,
+        logger=logger,
+        log_context="db_migration",
+    )
     if lock.acquire(blocking=False):
+        migration_succeeded = False
         try:
             click.echo(click.style("Starting database migration.", fg="green"))
 
@@ -737,6 +747,7 @@ def upgrade_db():
 
             flask_migrate.upgrade()
 
+            migration_succeeded = True
             click.echo(click.style("Database migration successful!", fg="green"))
 
         except Exception as e:
@@ -744,7 +755,8 @@ def upgrade_db():
             click.echo(click.style(f"Database migration failed: {e}", fg="red"))
             raise SystemExit(1)
         finally:
-            lock.release()
+            status = "successful" if migration_succeeded else "failed"
+            lock.release_safely(status=status)
     else:
         click.echo("Database migration skipped")
 

api/configs/app_config.py

@@ -8,7 +8,7 @@ from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, Settings
 from libs.file_utils import search_file_upwards
 
 from .deploy import DeploymentConfig
-from .enterprise import EnterpriseFeatureConfig
+from .enterprise import EnterpriseFeatureConfig, EnterpriseTelemetryConfig
 from .extra import ExtraServiceConfig
 from .feature import FeatureConfig
 from .middleware import MiddlewareConfig
@@ -73,6 +73,8 @@ class DifyConfig(
     # Enterprise feature configs
     # **Before using, please contact business@dify.ai by email to inquire about licensing matters.**
     EnterpriseFeatureConfig,
+    # Enterprise telemetry configs
+    EnterpriseTelemetryConfig,
 ):
     model_config = SettingsConfigDict(
         # read from dotenv format config file

api/configs/enterprise/__init__.py

@@ -18,3 +18,49 @@ class EnterpriseFeatureConfig(BaseSettings):
         description="Allow customization of the enterprise logo.",
         default=False,
     )
+
+
+class EnterpriseTelemetryConfig(BaseSettings):
+    """
+    Configuration for enterprise telemetry.
+    """
+
+    ENTERPRISE_TELEMETRY_ENABLED: bool = Field(
+        description="Enable enterprise telemetry collection (also requires ENTERPRISE_ENABLED=true).",
+        default=False,
+    )
+
+    ENTERPRISE_OTLP_ENDPOINT: str = Field(
+        description="Enterprise OTEL collector endpoint.",
+        default="",
+    )
+
+    ENTERPRISE_OTLP_HEADERS: str = Field(
+        description="Auth headers for OTLP export (key=value,key2=value2).",
+        default="",
+    )
+
+    ENTERPRISE_OTLP_PROTOCOL: str = Field(
+        description="OTLP protocol: 'http' or 'grpc' (default: http).",
+        default="http",
+    )
+
+    ENTERPRISE_OTLP_API_KEY: str = Field(
+        description="Bearer token for enterprise OTLP export authentication.",
+        default="",
+    )
+
+    ENTERPRISE_INCLUDE_CONTENT: bool = Field(
+        description="Include input/output content in traces (privacy toggle).",
+        default=True,
+    )
+
+    ENTERPRISE_SERVICE_NAME: str = Field(
+        description="Service name for OTEL resource.",
+        default="dify",
+    )
+
+    ENTERPRISE_OTEL_SAMPLING_RATE: float = Field(
+        description="Sampling rate for enterprise traces (0.0 to 1.0, default 1.0 = 100%).",
+        default=1.0,
+    )

api/configs/feature/__init__.py

@@ -1,4 +1,3 @@
-from datetime import timedelta
 from enum import StrEnum
 from typing import Literal
 
@@ -49,16 +48,6 @@ class SecurityConfig(BaseSettings):
         default=5,
     )
 
-    WEB_FORM_SUBMIT_RATE_LIMIT_MAX_ATTEMPTS: PositiveInt = Field(
-        description="Maximum number of web form submissions allowed per IP within the rate limit window",
-        default=30,
-    )
-
-    WEB_FORM_SUBMIT_RATE_LIMIT_WINDOW_SECONDS: PositiveInt = Field(
-        description="Time window in seconds for web form submission rate limiting",
-        default=60,
-    )
-
     LOGIN_DISABLED: bool = Field(
         description="Whether to disable login checks",
         default=False,
@@ -93,12 +82,6 @@ class AppExecutionConfig(BaseSettings):
         default=0,
     )
 
-    HUMAN_INPUT_GLOBAL_TIMEOUT_SECONDS: PositiveInt = Field(
-        description="Maximum seconds a workflow run can stay paused waiting for human input before global timeout.",
-        default=int(timedelta(days=7).total_seconds()),
-        ge=1,
-    )
-
 
 class CodeExecutionSandboxConfig(BaseSettings):
     """
@@ -1151,14 +1134,6 @@ class CeleryScheduleTasksConfig(BaseSettings):
         description="Enable queue monitor task",
         default=False,
     )
-    ENABLE_HUMAN_INPUT_TIMEOUT_TASK: bool = Field(
-        description="Enable human input timeout check task",
-        default=True,
-    )
-    HUMAN_INPUT_TIMEOUT_TASK_INTERVAL: PositiveInt = Field(
-        description="Human input timeout check interval in minutes",
-        default=1,
-    )
     ENABLE_CHECK_UPGRADABLE_PLUGIN_TASK: bool = Field(
         description="Enable check upgradable plugin task",
         default=True,
@@ -1180,16 +1155,6 @@ class CeleryScheduleTasksConfig(BaseSettings):
         default=0,
     )
 
-    # API token last_used_at batch update
-    ENABLE_API_TOKEN_LAST_USED_UPDATE_TASK: bool = Field(
-        description="Enable periodic batch update of API token last_used_at timestamps",
-        default=True,
-    )
-    API_TOKEN_LAST_USED_UPDATE_INTERVAL: int = Field(
-        description="Interval in minutes for batch updating API token last_used_at (default 30)",
-        default=30,
-    )
-
     # Trigger provider refresh (simple version)
     ENABLE_TRIGGER_PROVIDER_REFRESH_TASK: bool = Field(
         description="Enable trigger provider refresh poller",
@@ -1344,10 +1309,6 @@ class SandboxExpiredRecordsCleanConfig(BaseSettings):
         description="Maximum number of records to process in each batch",
         default=1000,
     )
-    SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_MAX_INTERVAL: PositiveInt = Field(
-        description="Maximum interval in milliseconds between batches",
-        default=200,
-    )
     SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS: PositiveInt = Field(
         description="Retention days for sandbox expired workflow_run records and message records",
         default=30,

api/configs/middleware/__init__.py

@@ -6,7 +6,6 @@ from pydantic import Field, NonNegativeFloat, NonNegativeInt, PositiveFloat, Pos
 from pydantic_settings import BaseSettings
 
 from .cache.redis_config import RedisConfig
-from .cache.redis_pubsub_config import RedisPubSubConfig
 from .storage.aliyun_oss_storage_config import AliyunOSSStorageConfig
 from .storage.amazon_s3_storage_config import S3StorageConfig
 from .storage.azure_blob_storage_config import AzureBlobStorageConfig
@@ -259,20 +258,11 @@ class CeleryConfig(DatabaseConfig):
         description="Password of the Redis Sentinel master.",
         default=None,
     )
-
     CELERY_SENTINEL_SOCKET_TIMEOUT: PositiveFloat | None = Field(
         description="Timeout for Redis Sentinel socket operations in seconds.",
         default=0.1,
     )
 
-    CELERY_TASK_ANNOTATIONS: dict[str, Any] | None = Field(
-        description=(
-            "Annotations for Celery tasks as a JSON mapping of task name -> options "
-            "(for example, rate limits or other task-specific settings)."
-        ),
-        default=None,
-    )
-
     @computed_field
     def CELERY_RESULT_BACKEND(self) -> str | None:
         if self.CELERY_BACKEND in ("database", "rabbitmq"):
@@ -327,7 +317,6 @@ class MiddlewareConfig(
     CeleryConfig,  # Note: CeleryConfig already inherits from DatabaseConfig
     KeywordStoreConfig,
     RedisConfig,
-    RedisPubSubConfig,
     # configs of storage and storage providers
     StorageConfig,
     AliyunOSSStorageConfig,

api/configs/middleware/cache/redis_pubsub_config.py

@@ -1,96 +0,0 @@
-from typing import Literal, Protocol
-from urllib.parse import quote_plus, urlunparse
-
-from pydantic import Field
-from pydantic_settings import BaseSettings
-
-
-class RedisConfigDefaults(Protocol):
-    REDIS_HOST: str
-    REDIS_PORT: int
-    REDIS_USERNAME: str | None
-    REDIS_PASSWORD: str | None
-    REDIS_DB: int
-    REDIS_USE_SSL: bool
-    REDIS_USE_SENTINEL: bool | None
-    REDIS_USE_CLUSTERS: bool
-
-
-class RedisConfigDefaultsMixin:
-    def _redis_defaults(self: RedisConfigDefaults) -> RedisConfigDefaults:
-        return self
-
-
-class RedisPubSubConfig(BaseSettings, RedisConfigDefaultsMixin):
-    """
-    Configuration settings for Redis pub/sub streaming.
-    """
-
-    PUBSUB_REDIS_URL: str | None = Field(
-        alias="PUBSUB_REDIS_URL",
-        description=(
-            "Redis connection URL for pub/sub streaming events between API "
-            "and celery worker, defaults to url constructed from "
-            "`REDIS_*` configurations"
-        ),
-        default=None,
-    )
-
-    PUBSUB_REDIS_USE_CLUSTERS: bool = Field(
-        description=(
-            "Enable Redis Cluster mode for pub/sub streaming. It's highly "
-            "recommended to enable this for large deployments."
-        ),
-        default=False,
-    )
-
-    PUBSUB_REDIS_CHANNEL_TYPE: Literal["pubsub", "sharded"] = Field(
-        description=(
-            "Pub/sub channel type for streaming events. "
-            "Valid options are:\n"
-            "\n"
-            " - pubsub: for normal Pub/Sub\n"
-            " - sharded: for sharded Pub/Sub\n"
-            "\n"
-            "It's highly recommended to use sharded Pub/Sub AND redis cluster "
-            "for large deployments."
-        ),
-        default="pubsub",
-    )
-
-    def _build_default_pubsub_url(self) -> str:
-        defaults = self._redis_defaults()
-        if not defaults.REDIS_HOST or not defaults.REDIS_PORT:
-            raise ValueError("PUBSUB_REDIS_URL must be set when default Redis URL cannot be constructed")
-
-        scheme = "rediss" if defaults.REDIS_USE_SSL else "redis"
-        username = defaults.REDIS_USERNAME or None
-        password = defaults.REDIS_PASSWORD or None
-
-        userinfo = ""
-        if username:
-            userinfo = quote_plus(username)
-        if password:
-            password_part = quote_plus(password)
-            userinfo = f"{userinfo}:{password_part}" if userinfo else f":{password_part}"
-        if userinfo:
-            userinfo = f"{userinfo}@"
-
-        host = defaults.REDIS_HOST
-        port = defaults.REDIS_PORT
-        db = defaults.REDIS_DB
-
-        netloc = f"{userinfo}{host}:{port}"
-        return urlunparse((scheme, netloc, f"/{db}", "", "", ""))
-
-    @property
-    def normalized_pubsub_redis_url(self) -> str:
-        pubsub_redis_url = self.PUBSUB_REDIS_URL
-        if pubsub_redis_url:
-            cleaned = pubsub_redis_url.strip()
-            pubsub_redis_url = cleaned or None
-
-        if pubsub_redis_url:
-            return pubsub_redis_url
-
-        return self._build_default_pubsub_url()

api/constants/languages.py

@@ -21,7 +21,6 @@ language_timezone_mapping = {
     "th-TH": "Asia/Bangkok",
     "id-ID": "Asia/Jakarta",
     "ar-TN": "Africa/Tunis",
-    "nl-NL": "Europe/Amsterdam",
 }
 
 languages = list(language_timezone_mapping.keys())

api/controllers/common/schema.py

@@ -5,6 +5,8 @@ from enum import StrEnum
 from flask_restx import Namespace
 from pydantic import BaseModel, TypeAdapter
 
+from controllers.console import console_ns
+
 DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
@@ -22,9 +24,6 @@ def register_schema_models(namespace: Namespace, *models: type[BaseModel]) -> No
 
 
 def get_or_create_model(model_name: str, field_def):
-    # Import lazily to avoid circular imports between console controllers and schema helpers.
-    from controllers.console import console_ns
-
     existing = console_ns.models.get(model_name)
     if existing is None:
         existing = console_ns.model(model_name, field_def)

api/controllers/console/__init__.py

@@ -37,7 +37,6 @@ from . import (
     apikey,
     extension,
     feature,
-    human_input_form,
     init_validate,
     ping,
     setup,
@@ -172,7 +171,6 @@ __all__ = [
     "forgot_password",
     "generator",
     "hit_testing",
-    "human_input_form",
     "init_validate",
     "installed_app",
     "load_balancing_config",

api/controllers/console/apikey.py

@@ -10,7 +10,6 @@ from libs.helper import TimestampField
 from libs.login import current_account_with_tenant, login_required
 from models.dataset import Dataset
 from models.model import ApiToken, App
-from services.api_token_service import ApiTokenCache
 
 from . import console_ns
 from .wraps import account_initialization_required, edit_permission_required, setup_required
@@ -132,11 +131,6 @@ class BaseApiKeyResource(Resource):
         if key is None:
             flask_restx.abort(HTTPStatus.NOT_FOUND, message="API key not found")
 
-        # Invalidate cache before deleting from database
-        # Type assertion: key is guaranteed to be non-None here because abort() raises
-        assert key is not None  # nosec - for type checker only
-        ApiTokenCache.delete(key.token, key.type)
-
         db.session.query(ApiToken).where(ApiToken.id == api_key_id).delete()
         db.session.commit()
 

api/controllers/console/app/app.py

@@ -660,6 +660,19 @@ class AppCopyApi(Resource):
             )
             session.commit()
 
+            # Inherit web app permission from original app
+            if result.app_id and FeatureService.get_system_features().webapp_auth.enabled:
+                try:
+                    # Get the original app's access mode
+                    original_settings = EnterpriseService.WebAppAuth.get_app_access_mode_by_id(app_model.id)
+                    access_mode = original_settings.access_mode
+                except Exception:
+                    # If original app has no settings (old app), default to public to match fallback behavior
+                    access_mode = "public"
+
+                # Apply the same access mode to the copied app
+                EnterpriseService.WebAppAuth.update_app_access_mode(result.app_id, access_mode)
+
             stmt = select(App).where(App.id == result.app_id)
             app = session.scalar(stmt)
 

api/controllers/console/app/conversation.py

@@ -89,7 +89,6 @@ status_count_model = console_ns.model(
         "success": fields.Integer,
         "failed": fields.Integer,
         "partial_success": fields.Integer,
-        "paused": fields.Integer,
     },
 )
 
@@ -599,12 +598,7 @@ def _get_conversation(app_model, conversation_id):
     db.session.execute(
         sa.update(Conversation)
         .where(Conversation.id == conversation_id, Conversation.read_at.is_(None))
-        # Keep updated_at unchanged when only marking a conversation as read.
-        .values(
-            read_at=naive_utc_now(),
-            read_account_id=current_user.id,
-            updated_at=Conversation.updated_at,
-        )
+        .values(read_at=naive_utc_now(), read_account_id=current_user.id)
     )
     db.session.commit()
     db.session.refresh(conversation)

api/controllers/console/app/generator.py

@@ -35,6 +35,7 @@ class InstructionGeneratePayload(BaseModel):
     instruction: str = Field(..., description="Instruction for generation")
     model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
     ideal_output: str = Field(default="", description="Expected ideal output")
+    app_id: str | None = Field(default=None, description="App ID for prompt generation tracing")
 
 
 class InstructionTemplatePayload(BaseModel):
@@ -66,10 +67,17 @@ class RuleGenerateApi(Resource):
     @account_initialization_required
     def post(self):
         args = RuleGeneratePayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
+        account, current_tenant_id = current_account_with_tenant()
 
         try:
-            rules = LLMGenerator.generate_rule_config(tenant_id=current_tenant_id, args=args)
+            rules = LLMGenerator.generate_rule_config(
+                tenant_id=current_tenant_id,
+                instruction=args.instruction,
+                model_config=args.model_config_data,
+                no_variable=args.no_variable,
+                user_id=account.id,
+                app_id=args.app_id,
+            )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
@@ -95,12 +103,16 @@ class RuleCodeGenerateApi(Resource):
     @account_initialization_required
     def post(self):
         args = RuleCodeGeneratePayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
+        account, current_tenant_id = current_account_with_tenant()
 
         try:
             code_result = LLMGenerator.generate_code(
                 tenant_id=current_tenant_id,
-                args=args,
+                instruction=args.instruction,
+                model_config=args.model_config_data,
+                code_language=args.code_language,
+                user_id=account.id,
+                app_id=args.app_id,
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -127,12 +139,15 @@ class RuleStructuredOutputGenerateApi(Resource):
     @account_initialization_required
     def post(self):
         args = RuleStructuredOutputPayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
+        account, current_tenant_id = current_account_with_tenant()
 
         try:
             structured_output = LLMGenerator.generate_structured_output(
                 tenant_id=current_tenant_id,
-                args=args,
+                instruction=args.instruction,
+                model_config=args.model_config_data,
+                user_id=account.id,
+                app_id=args.app_id,
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -159,14 +174,14 @@ class InstructionGenerateApi(Resource):
     @account_initialization_required
     def post(self):
         args = InstructionGeneratePayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
+        account, current_tenant_id = current_account_with_tenant()
+        app_id = args.app_id or args.flow_id
         providers: list[type[CodeNodeProvider]] = [Python3CodeProvider, JavascriptCodeProvider]
         code_provider: type[CodeNodeProvider] | None = next(
             (p for p in providers if p.is_accept_language(args.language)), None
         )
         code_template = code_provider.get_default_code() if code_provider else ""
         try:
-            # Generate from nothing for a workflow node
             if (args.current in (code_template, "")) and args.node_id != "":
                 app = db.session.query(App).where(App.id == args.flow_id).first()
                 if not app:
@@ -183,33 +198,33 @@ class InstructionGenerateApi(Resource):
                     case "llm":
                         return LLMGenerator.generate_rule_config(
                             current_tenant_id,
-                            args=RuleGeneratePayload(
-                                instruction=args.instruction,
-                                model_config=args.model_config_data,
-                                no_variable=True,
-                            ),
+                            instruction=args.instruction,
+                            model_config=args.model_config_data,
+                            no_variable=True,
+                            user_id=account.id,
+                            app_id=app_id,
                         )
                     case "agent":
                         return LLMGenerator.generate_rule_config(
                             current_tenant_id,
-                            args=RuleGeneratePayload(
-                                instruction=args.instruction,
-                                model_config=args.model_config_data,
-                                no_variable=True,
-                            ),
+                            instruction=args.instruction,
+                            model_config=args.model_config_data,
+                            no_variable=True,
+                            user_id=account.id,
+                            app_id=app_id,
                         )
                     case "code":
                         return LLMGenerator.generate_code(
                             tenant_id=current_tenant_id,
-                            args=RuleCodeGeneratePayload(
-                                instruction=args.instruction,
-                                model_config=args.model_config_data,
-                                code_language=args.language,
-                            ),
+                            instruction=args.instruction,
+                            model_config=args.model_config_data,
+                            code_language=args.language,
+                            user_id=account.id,
+                            app_id=app_id,
                         )
                     case _:
                         return {"error": f"invalid node type: {node_type}"}
-            if args.node_id == "" and args.current != "":  # For legacy app without a workflow
+            if args.node_id == "" and args.current != "":
                 return LLMGenerator.instruction_modify_legacy(
                     tenant_id=current_tenant_id,
                     flow_id=args.flow_id,
@@ -217,8 +232,10 @@ class InstructionGenerateApi(Resource):
                     instruction=args.instruction,
                     model_config=args.model_config_data,
                     ideal_output=args.ideal_output,
+                    user_id=account.id,
+                    app_id=app_id,
                 )
-            if args.node_id != "" and args.current != "":  # For workflow node
+            if args.node_id != "" and args.current != "":
                 return LLMGenerator.instruction_modify_workflow(
                     tenant_id=current_tenant_id,
                     flow_id=args.flow_id,
@@ -228,6 +245,8 @@ class InstructionGenerateApi(Resource):
                     model_config=args.model_config_data,
                     ideal_output=args.ideal_output,
                     workflow_service=WorkflowService(),
+                    user_id=account.id,
+                    app_id=app_id,
                 )
             return {"error": "incompatible parameters"}, 400
         except ProviderTokenNotInitError as ex:

api/controllers/console/app/message.py

@@ -33,7 +33,7 @@ from libs.login import current_account_with_tenant, login_required
 from models.model import AppMode, Conversation, Message, MessageAnnotation, MessageFeedback
 from services.errors.conversation import ConversationNotExistsError
 from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
-from services.message_service import MessageService, attach_message_extra_contents
+from services.message_service import MessageService
 
 logger = logging.getLogger(__name__)
 
@@ -207,7 +207,6 @@ message_detail_model = console_ns.model(
         "created_at": TimestampField,
         "agent_thoughts": fields.List(fields.Nested(agent_thought_model)),
         "message_files": fields.List(fields.Nested(message_file_model)),
-        "extra_contents": fields.List(fields.Raw),
         "metadata": fields.Raw(attribute="message_metadata_dict"),
         "status": fields.String,
         "error": fields.String,
@@ -300,7 +299,6 @@ class ChatMessageListApi(Resource):
             has_more = False
 
         history_messages = list(reversed(history_messages))
-        attach_message_extra_contents(history_messages)
 
         return InfiniteScrollPagination(data=history_messages, limit=args.limit, has_more=has_more)
 
@@ -483,5 +481,4 @@ class MessageApi(Resource):
         if not message:
             raise NotFound("Message Not Exists.")
 
-        attach_message_extra_contents([message])
         return message

api/controllers/console/app/ops_trace.py

@@ -1,6 +1,7 @@
 from typing import Any
 
 from flask import request
+from flask_login import current_user
 from flask_restx import Resource, fields
 from pydantic import BaseModel, Field
 from werkzeug.exceptions import BadRequest
@@ -77,7 +78,10 @@ class TraceAppConfigApi(Resource):
 
         try:
             result = OpsService.create_tracing_app_config(
-                app_id=app_id, tracing_provider=args.tracing_provider, tracing_config=args.tracing_config
+                app_id=app_id,
+                tracing_provider=args.tracing_provider,
+                tracing_config=args.tracing_config,
+                account_id=current_user.id,
             )
             if not result:
                 raise TracingConfigIsExist()
@@ -102,7 +106,10 @@ class TraceAppConfigApi(Resource):
 
         try:
             result = OpsService.update_tracing_app_config(
-                app_id=app_id, tracing_provider=args.tracing_provider, tracing_config=args.tracing_config
+                app_id=app_id,
+                tracing_provider=args.tracing_provider,
+                tracing_config=args.tracing_config,
+                account_id=current_user.id,
             )
             if not result:
                 raise TracingConfigNotExist()
@@ -124,7 +131,9 @@ class TraceAppConfigApi(Resource):
         args = TraceProviderQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
 
         try:
-            result = OpsService.delete_tracing_app_config(app_id=app_id, tracing_provider=args.tracing_provider)
+            result = OpsService.delete_tracing_app_config(
+                app_id=app_id, tracing_provider=args.tracing_provider, account_id=current_user.id
+            )
             if not result:
                 raise TracingConfigNotExist()
             return {"result": "success"}, 204

api/controllers/console/app/workflow.py

@@ -507,179 +507,6 @@ class WorkflowDraftRunLoopNodeApi(Resource):
             raise InternalServerError()
 
 
-class HumanInputFormPreviewPayload(BaseModel):
-    inputs: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Values used to fill missing upstream variables referenced in form_content",
-    )
-
-
-class HumanInputFormSubmitPayload(BaseModel):
-    form_inputs: dict[str, Any] = Field(..., description="Values the user provides for the form's own fields")
-    inputs: dict[str, Any] = Field(
-        ...,
-        description="Values used to fill missing upstream variables referenced in form_content",
-    )
-    action: str = Field(..., description="Selected action ID")
-
-
-class HumanInputDeliveryTestPayload(BaseModel):
-    delivery_method_id: str = Field(..., description="Delivery method ID")
-    inputs: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Values used to fill missing upstream variables referenced in form_content",
-    )
-
-
-reg(HumanInputFormPreviewPayload)
-reg(HumanInputFormSubmitPayload)
-reg(HumanInputDeliveryTestPayload)
-
-
-@console_ns.route("/apps/<uuid:app_id>/advanced-chat/workflows/draft/human-input/nodes/<string:node_id>/form/preview")
-class AdvancedChatDraftHumanInputFormPreviewApi(Resource):
-    @console_ns.doc("get_advanced_chat_draft_human_input_form")
-    @console_ns.doc(description="Get human input form preview for advanced chat workflow")
-    @console_ns.doc(params={"app_id": "Application ID", "node_id": "Node ID"})
-    @console_ns.expect(console_ns.models[HumanInputFormPreviewPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT])
-    @edit_permission_required
-    def post(self, app_model: App, node_id: str):
-        """
-        Preview human input form content and placeholders
-        """
-        current_user, _ = current_account_with_tenant()
-        args = HumanInputFormPreviewPayload.model_validate(console_ns.payload or {})
-        inputs = args.inputs
-
-        workflow_service = WorkflowService()
-        preview = workflow_service.get_human_input_form_preview(
-            app_model=app_model,
-            account=current_user,
-            node_id=node_id,
-            inputs=inputs,
-        )
-        return jsonable_encoder(preview)
-
-
-@console_ns.route("/apps/<uuid:app_id>/advanced-chat/workflows/draft/human-input/nodes/<string:node_id>/form/run")
-class AdvancedChatDraftHumanInputFormRunApi(Resource):
-    @console_ns.doc("submit_advanced_chat_draft_human_input_form")
-    @console_ns.doc(description="Submit human input form preview for advanced chat workflow")
-    @console_ns.doc(params={"app_id": "Application ID", "node_id": "Node ID"})
-    @console_ns.expect(console_ns.models[HumanInputFormSubmitPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT])
-    @edit_permission_required
-    def post(self, app_model: App, node_id: str):
-        """
-        Submit human input form preview
-        """
-        current_user, _ = current_account_with_tenant()
-        args = HumanInputFormSubmitPayload.model_validate(console_ns.payload or {})
-        workflow_service = WorkflowService()
-        result = workflow_service.submit_human_input_form_preview(
-            app_model=app_model,
-            account=current_user,
-            node_id=node_id,
-            form_inputs=args.form_inputs,
-            inputs=args.inputs,
-            action=args.action,
-        )
-        return jsonable_encoder(result)
-
-
-@console_ns.route("/apps/<uuid:app_id>/workflows/draft/human-input/nodes/<string:node_id>/form/preview")
-class WorkflowDraftHumanInputFormPreviewApi(Resource):
-    @console_ns.doc("get_workflow_draft_human_input_form")
-    @console_ns.doc(description="Get human input form preview for workflow")
-    @console_ns.doc(params={"app_id": "Application ID", "node_id": "Node ID"})
-    @console_ns.expect(console_ns.models[HumanInputFormPreviewPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.WORKFLOW])
-    @edit_permission_required
-    def post(self, app_model: App, node_id: str):
-        """
-        Preview human input form content and placeholders
-        """
-        current_user, _ = current_account_with_tenant()
-        args = HumanInputFormPreviewPayload.model_validate(console_ns.payload or {})
-        inputs = args.inputs
-
-        workflow_service = WorkflowService()
-        preview = workflow_service.get_human_input_form_preview(
-            app_model=app_model,
-            account=current_user,
-            node_id=node_id,
-            inputs=inputs,
-        )
-        return jsonable_encoder(preview)
-
-
-@console_ns.route("/apps/<uuid:app_id>/workflows/draft/human-input/nodes/<string:node_id>/form/run")
-class WorkflowDraftHumanInputFormRunApi(Resource):
-    @console_ns.doc("submit_workflow_draft_human_input_form")
-    @console_ns.doc(description="Submit human input form preview for workflow")
-    @console_ns.doc(params={"app_id": "Application ID", "node_id": "Node ID"})
-    @console_ns.expect(console_ns.models[HumanInputFormSubmitPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.WORKFLOW])
-    @edit_permission_required
-    def post(self, app_model: App, node_id: str):
-        """
-        Submit human input form preview
-        """
-        current_user, _ = current_account_with_tenant()
-        workflow_service = WorkflowService()
-        args = HumanInputFormSubmitPayload.model_validate(console_ns.payload or {})
-        result = workflow_service.submit_human_input_form_preview(
-            app_model=app_model,
-            account=current_user,
-            node_id=node_id,
-            form_inputs=args.form_inputs,
-            inputs=args.inputs,
-            action=args.action,
-        )
-        return jsonable_encoder(result)
-
-
-@console_ns.route("/apps/<uuid:app_id>/workflows/draft/human-input/nodes/<string:node_id>/delivery-test")
-class WorkflowDraftHumanInputDeliveryTestApi(Resource):
-    @console_ns.doc("test_workflow_draft_human_input_delivery")
-    @console_ns.doc(description="Test human input delivery for workflow")
-    @console_ns.doc(params={"app_id": "Application ID", "node_id": "Node ID"})
-    @console_ns.expect(console_ns.models[HumanInputDeliveryTestPayload.__name__])
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.WORKFLOW, AppMode.ADVANCED_CHAT])
-    @edit_permission_required
-    def post(self, app_model: App, node_id: str):
-        """
-        Test human input delivery
-        """
-        current_user, _ = current_account_with_tenant()
-        workflow_service = WorkflowService()
-        args = HumanInputDeliveryTestPayload.model_validate(console_ns.payload or {})
-        workflow_service.test_human_input_delivery(
-            app_model=app_model,
-            account=current_user,
-            node_id=node_id,
-            delivery_method_id=args.delivery_method_id,
-            inputs=args.inputs,
-        )
-        return jsonable_encoder({})
-
-
 @console_ns.route("/apps/<uuid:app_id>/workflows/draft/run")
 class DraftWorkflowRunApi(Resource):
     @console_ns.doc("run_draft_workflow")

api/controllers/console/app/workflow_run.py

@@ -5,15 +5,10 @@ from flask import request
 from flask_restx import Resource, fields, marshal_with
 from pydantic import BaseModel, Field, field_validator
 from sqlalchemy import select
-from sqlalchemy.orm import sessionmaker
 
-from configs import dify_config
 from controllers.console import console_ns
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
-from controllers.web.error import NotFoundError
-from core.workflow.entities.pause_reason import HumanInputRequired
-from core.workflow.enums import WorkflowExecutionStatus
 from extensions.ext_database import db
 from fields.end_user_fields import simple_end_user_fields
 from fields.member_fields import simple_account_fields
@@ -32,21 +27,9 @@ from libs.custom_inputs import time_duration
 from libs.helper import uuid_value
 from libs.login import current_user, login_required
 from models import Account, App, AppMode, EndUser, WorkflowArchiveLog, WorkflowRunTriggeredFrom
-from models.workflow import WorkflowRun
-from repositories.factory import DifyAPIRepositoryFactory
 from services.retention.workflow_run.constants import ARCHIVE_BUNDLE_NAME
 from services.workflow_run_service import WorkflowRunService
 
-
-def _build_backstage_input_url(form_token: str | None) -> str | None:
-    if not form_token:
-        return None
-    base_url = dify_config.APP_WEB_URL
-    if not base_url:
-        return None
-    return f"{base_url.rstrip('/')}/form/{form_token}"
-
-
 # Workflow run status choices for filtering
 WORKFLOW_RUN_STATUS_CHOICES = ["running", "succeeded", "failed", "stopped", "partial-succeeded"]
 EXPORT_SIGNED_URL_EXPIRE_SECONDS = 3600
@@ -457,68 +440,3 @@ class WorkflowRunNodeExecutionListApi(Resource):
         )
 
         return {"data": node_executions}
-
-
-@console_ns.route("/workflow/<string:workflow_run_id>/pause-details")
-class ConsoleWorkflowPauseDetailsApi(Resource):
-    """Console API for getting workflow pause details."""
-
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def get(self, workflow_run_id: str):
-        """
-        Get workflow pause details.
-
-        GET /console/api/workflow/<workflow_run_id>/pause-details
-
-        Returns information about why and where the workflow is paused.
-        """
-
-        # Query WorkflowRun to determine if workflow is suspended
-        session_maker = sessionmaker(bind=db.engine)
-        workflow_run_repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker=session_maker)
-
-        workflow_run = db.session.get(WorkflowRun, workflow_run_id)
-        if not workflow_run:
-            raise NotFoundError("Workflow run not found")
-
-        if workflow_run.tenant_id != current_user.current_tenant_id:
-            raise NotFoundError("Workflow run not found")
-
-        # Check if workflow is suspended
-        is_paused = workflow_run.status == WorkflowExecutionStatus.PAUSED
-        if not is_paused:
-            return {
-                "paused_at": None,
-                "paused_nodes": [],
-            }, 200
-
-        pause_entity = workflow_run_repo.get_workflow_pause(workflow_run_id)
-        pause_reasons = pause_entity.get_pause_reasons() if pause_entity else []
-
-        # Build response
-        paused_at = pause_entity.paused_at if pause_entity else None
-        paused_nodes = []
-        response = {
-            "paused_at": paused_at.isoformat() + "Z" if paused_at else None,
-            "paused_nodes": paused_nodes,
-        }
-
-        for reason in pause_reasons:
-            if isinstance(reason, HumanInputRequired):
-                paused_nodes.append(
-                    {
-                        "node_id": reason.node_id,
-                        "node_title": reason.node_title,
-                        "pause_type": {
-                            "type": "human_input",
-                            "form_id": reason.form_id,
-                            "backstage_input_url": _build_backstage_input_url(reason.form_token),
-                        },
-                    }
-                )
-            else:
-                raise AssertionError("unimplemented.")
-
-        return response, 200

api/controllers/console/datasets/datasets.py

@@ -55,7 +55,6 @@ from libs.login import current_account_with_tenant, login_required
 from models import ApiToken, Dataset, Document, DocumentSegment, UploadFile
 from models.dataset import DatasetPermissionEnum
 from models.provider_ids import ModelProviderID
-from services.api_token_service import ApiTokenCache
 from services.dataset_service import DatasetPermissionService, DatasetService, DocumentService
 
 # Register models for flask_restx to avoid dict type issues in Swagger
@@ -821,11 +820,6 @@ class DatasetApiDeleteApi(Resource):
         if key is None:
             console_ns.abort(404, message="API key not found")
 
-        # Invalidate cache before deleting from database
-        # Type assertion: key is guaranteed to be non-None here because abort() raises
-        assert key is not None  # nosec - for type checker only
-        ApiTokenCache.delete(key.token, key.type)
-
         db.session.query(ApiToken).where(ApiToken.id == api_key_id).delete()
         db.session.commit()
 

api/controllers/console/explore/trial.py

@@ -1,16 +1,15 @@
 import logging
-from typing import Any, Literal, cast
+from typing import Any, cast
 
 from flask import request
-from flask_restx import Resource, fields, marshal, marshal_with
-from pydantic import BaseModel
+from flask_restx import Resource, fields, marshal, marshal_with, reqparse
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
 
 import services
 from controllers.common.fields import Parameters as ParametersResponse
 from controllers.common.fields import Site as SiteResponse
 from controllers.common.schema import get_or_create_model
-from controllers.console import api, console_ns
+from controllers.console import api
 from controllers.console.app.error import (
     AppUnavailableError,
     AudioTooLargeError,
@@ -118,56 +117,7 @@ workflow_fields_copy["rag_pipeline_variables"] = fields.List(fields.Nested(pipel
 workflow_model = get_or_create_model("TrialWorkflow", workflow_fields_copy)
 
 
-# Pydantic models for request validation
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
-
-
-class WorkflowRunRequest(BaseModel):
-    inputs: dict
-    files: list | None = None
-
-
-class ChatRequest(BaseModel):
-    inputs: dict
-    query: str
-    files: list | None = None
-    conversation_id: str | None = None
-    parent_message_id: str | None = None
-    retriever_from: str = "explore_app"
-
-
-class TextToSpeechRequest(BaseModel):
-    message_id: str | None = None
-    voice: str | None = None
-    text: str | None = None
-    streaming: bool | None = None
-
-
-class CompletionRequest(BaseModel):
-    inputs: dict
-    query: str = ""
-    files: list | None = None
-    response_mode: Literal["blocking", "streaming"] | None = None
-    retriever_from: str = "explore_app"
-
-
-# Register schemas for Swagger documentation
-console_ns.schema_model(
-    WorkflowRunRequest.__name__, WorkflowRunRequest.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
-)
-console_ns.schema_model(
-    ChatRequest.__name__, ChatRequest.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
-)
-console_ns.schema_model(
-    TextToSpeechRequest.__name__, TextToSpeechRequest.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
-)
-console_ns.schema_model(
-    CompletionRequest.__name__, CompletionRequest.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
-)
-
-
 class TrialAppWorkflowRunApi(TrialAppResource):
-    @console_ns.expect(console_ns.models[WorkflowRunRequest.__name__])
     def post(self, trial_app):
         """
         Run workflow
@@ -179,8 +129,10 @@ class TrialAppWorkflowRunApi(TrialAppResource):
         if app_mode != AppMode.WORKFLOW:
             raise NotWorkflowAppError()
 
-        request_data = WorkflowRunRequest.model_validate(console_ns.payload)
-        args = request_data.model_dump()
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, nullable=False, location="json")
+        parser.add_argument("files", type=list, required=False, location="json")
+        args = parser.parse_args()
         assert current_user is not None
         try:
             app_id = app_model.id
@@ -231,7 +183,6 @@ class TrialAppWorkflowTaskStopApi(TrialAppResource):
 
 
 class TrialChatApi(TrialAppResource):
-    @console_ns.expect(console_ns.models[ChatRequest.__name__])
     @trial_feature_enable
     def post(self, trial_app):
         app_model = trial_app
@@ -239,14 +190,14 @@ class TrialChatApi(TrialAppResource):
         if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
-        request_data = ChatRequest.model_validate(console_ns.payload)
-        args = request_data.model_dump()
-
-        # Validate UUID values if provided
-        if args.get("conversation_id"):
-            args["conversation_id"] = uuid_value(args["conversation_id"])
-        if args.get("parent_message_id"):
-            args["parent_message_id"] = uuid_value(args["parent_message_id"])
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, location="json")
+        parser.add_argument("query", type=str, required=True, location="json")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("conversation_id", type=uuid_value, location="json")
+        parser.add_argument("parent_message_id", type=uuid_value, required=False, location="json")
+        parser.add_argument("retriever_from", type=str, required=False, default="explore_app", location="json")
+        args = parser.parse_args()
 
         args["auto_generate_name"] = False
 
@@ -369,16 +320,20 @@ class TrialChatAudioApi(TrialAppResource):
 
 
 class TrialChatTextApi(TrialAppResource):
-    @console_ns.expect(console_ns.models[TextToSpeechRequest.__name__])
     @trial_feature_enable
     def post(self, trial_app):
         app_model = trial_app
         try:
-            request_data = TextToSpeechRequest.model_validate(console_ns.payload)
+            parser = reqparse.RequestParser()
+            parser.add_argument("message_id", type=str, required=False, location="json")
+            parser.add_argument("voice", type=str, location="json")
+            parser.add_argument("text", type=str, location="json")
+            parser.add_argument("streaming", type=bool, location="json")
+            args = parser.parse_args()
 
-            message_id = request_data.message_id
-            text = request_data.text
-            voice = request_data.voice
+            message_id = args.get("message_id", None)
+            text = args.get("text", None)
+            voice = args.get("voice", None)
             if not isinstance(current_user, Account):
                 raise ValueError("current_user must be an Account instance")
 
@@ -416,15 +371,19 @@ class TrialChatTextApi(TrialAppResource):
 
 
 class TrialCompletionApi(TrialAppResource):
-    @console_ns.expect(console_ns.models[CompletionRequest.__name__])
     @trial_feature_enable
     def post(self, trial_app):
         app_model = trial_app
         if app_model.mode != "completion":
             raise NotCompletionAppError()
 
-        request_data = CompletionRequest.model_validate(console_ns.payload)
-        args = request_data.model_dump()
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, location="json")
+        parser.add_argument("query", type=str, location="json", default="")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
+        parser.add_argument("retriever_from", type=str, required=False, default="explore_app", location="json")
+        args = parser.parse_args()
 
         streaming = args["response_mode"] == "streaming"
         args["auto_generate_name"] = False

api/controllers/console/human_input_form.py

@@ -1,217 +0,0 @@
-"""
-Console/Studio Human Input Form APIs.
-"""
-
-import json
-import logging
-from collections.abc import Generator
-
-from flask import Response, jsonify, request
-from flask_restx import Resource, reqparse
-from sqlalchemy import select
-from sqlalchemy.orm import Session, sessionmaker
-
-from controllers.console import console_ns
-from controllers.console.wraps import account_initialization_required, setup_required
-from controllers.web.error import InvalidArgumentError, NotFoundError
-from core.app.apps.advanced_chat.app_generator import AdvancedChatAppGenerator
-from core.app.apps.common.workflow_response_converter import WorkflowResponseConverter
-from core.app.apps.message_generator import MessageGenerator
-from core.app.apps.workflow.app_generator import WorkflowAppGenerator
-from extensions.ext_database import db
-from libs.login import current_account_with_tenant, login_required
-from models import App
-from models.enums import CreatorUserRole
-from models.human_input import RecipientType
-from models.model import AppMode
-from models.workflow import WorkflowRun
-from repositories.factory import DifyAPIRepositoryFactory
-from services.human_input_service import Form, HumanInputService
-from services.workflow_event_snapshot_service import build_workflow_event_stream
-
-logger = logging.getLogger(__name__)
-
-
-def _jsonify_form_definition(form: Form) -> Response:
-    payload = form.get_definition().model_dump()
-    payload["expiration_time"] = int(form.expiration_time.timestamp())
-    return Response(json.dumps(payload, ensure_ascii=False), mimetype="application/json")
-
-
-@console_ns.route("/form/human_input/<string:form_token>")
-class ConsoleHumanInputFormApi(Resource):
-    """Console API for getting human input form definition."""
-
-    @staticmethod
-    def _ensure_console_access(form: Form):
-        _, current_tenant_id = current_account_with_tenant()
-
-        if form.tenant_id != current_tenant_id:
-            raise NotFoundError("App not found")
-
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def get(self, form_token: str):
-        """
-        Get human input form definition by form token.
-
-        GET /console/api/form/human_input/<form_token>
-        """
-        service = HumanInputService(db.engine)
-        form = service.get_form_definition_by_token_for_console(form_token)
-        if form is None:
-            raise NotFoundError(f"form not found, token={form_token}")
-
-        self._ensure_console_access(form)
-
-        return _jsonify_form_definition(form)
-
-    @account_initialization_required
-    @login_required
-    def post(self, form_token: str):
-        """
-        Submit human input form by form token.
-
-        POST /console/api/form/human_input/<form_token>
-
-        Request body:
-        {
-            "inputs": {
-                "content": "User input content"
-            },
-            "action": "Approve"
-        }
-        """
-        parser = reqparse.RequestParser()
-        parser.add_argument("inputs", type=dict, required=True, location="json")
-        parser.add_argument("action", type=str, required=True, location="json")
-        args = parser.parse_args()
-        current_user, _ = current_account_with_tenant()
-
-        service = HumanInputService(db.engine)
-        form = service.get_form_by_token(form_token)
-        if form is None:
-            raise NotFoundError(f"form not found, token={form_token}")
-
-        self._ensure_console_access(form)
-
-        recipient_type = form.recipient_type
-        if recipient_type not in {RecipientType.CONSOLE, RecipientType.BACKSTAGE}:
-            raise NotFoundError(f"form not found, token={form_token}")
-        # The type checker is not smart enought to validate the following invariant.
-        # So we need to assert it manually.
-        assert recipient_type is not None, "recipient_type cannot be None here."
-
-        service.submit_form_by_token(
-            recipient_type=recipient_type,
-            form_token=form_token,
-            selected_action_id=args["action"],
-            form_data=args["inputs"],
-            submission_user_id=current_user.id,
-        )
-
-        return jsonify({})
-
-
-@console_ns.route("/workflow/<string:workflow_run_id>/events")
-class ConsoleWorkflowEventsApi(Resource):
-    """Console API for getting workflow execution events after resume."""
-
-    @account_initialization_required
-    @login_required
-    def get(self, workflow_run_id: str):
-        """
-        Get workflow execution events stream after resume.
-
-        GET /console/api/workflow/<workflow_run_id>/events
-
-        Returns Server-Sent Events stream.
-        """
-
-        user, tenant_id = current_account_with_tenant()
-        session_maker = sessionmaker(db.engine)
-        repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
-        workflow_run = repo.get_workflow_run_by_id_and_tenant_id(
-            tenant_id=tenant_id,
-            run_id=workflow_run_id,
-        )
-        if workflow_run is None:
-            raise NotFoundError(f"WorkflowRun not found, id={workflow_run_id}")
-
-        if workflow_run.created_by_role != CreatorUserRole.ACCOUNT:
-            raise NotFoundError(f"WorkflowRun not created by account, id={workflow_run_id}")
-
-        if workflow_run.created_by != user.id:
-            raise NotFoundError(f"WorkflowRun not created by the current account, id={workflow_run_id}")
-
-        with Session(expire_on_commit=False, bind=db.engine) as session:
-            app = _retrieve_app_for_workflow_run(session, workflow_run)
-
-        if workflow_run.finished_at is not None:
-            # TODO(QuantumGhost): should we modify the handling for finished workflow run here?
-            response = WorkflowResponseConverter.workflow_run_result_to_finish_response(
-                task_id=workflow_run.id,
-                workflow_run=workflow_run,
-                creator_user=user,
-            )
-
-            payload = response.model_dump(mode="json")
-            payload["event"] = response.event.value
-
-            def _generate_finished_events() -> Generator[str, None, None]:
-                yield f"data: {json.dumps(payload)}\n\n"
-
-            event_generator = _generate_finished_events
-
-        else:
-            msg_generator = MessageGenerator()
-            if app.mode == AppMode.ADVANCED_CHAT:
-                generator = AdvancedChatAppGenerator()
-            elif app.mode == AppMode.WORKFLOW:
-                generator = WorkflowAppGenerator()
-            else:
-                raise InvalidArgumentError(f"cannot subscribe to workflow run, workflow_run_id={workflow_run.id}")
-
-            include_state_snapshot = request.args.get("include_state_snapshot", "false").lower() == "true"
-
-            def _generate_stream_events():
-                if include_state_snapshot:
-                    return generator.convert_to_event_stream(
-                        build_workflow_event_stream(
-                            app_mode=AppMode(app.mode),
-                            workflow_run=workflow_run,
-                            tenant_id=workflow_run.tenant_id,
-                            app_id=workflow_run.app_id,
-                            session_maker=session_maker,
-                        )
-                    )
-                return generator.convert_to_event_stream(
-                    msg_generator.retrieve_events(AppMode(app.mode), workflow_run.id),
-                )
-
-            event_generator = _generate_stream_events
-
-        return Response(
-            event_generator(),
-            mimetype="text/event-stream",
-            headers={
-                "Cache-Control": "no-cache",
-                "Connection": "keep-alive",
-            },
-        )
-
-
-def _retrieve_app_for_workflow_run(session: Session, workflow_run: WorkflowRun):
-    query = select(App).where(
-        App.id == workflow_run.app_id,
-        App.tenant_id == workflow_run.tenant_id,
-    )
-    app = session.scalars(query).first()
-    if app is None:
-        raise AssertionError(
-            f"App not found for WorkflowRun, workflow_run_id={workflow_run.id}, "
-            f"app_id={workflow_run.app_id}, tenant_id={workflow_run.tenant_id}"
-        )
-
-    return app

api/controllers/console/remote_files.py

@@ -1,7 +1,6 @@
 import urllib.parse
 
 import httpx
-from flask_restx import Resource
 from pydantic import BaseModel, Field
 
 import services
@@ -11,12 +10,12 @@ from controllers.common.errors import (
     RemoteFileUploadError,
     UnsupportedFileTypeError,
 )
-from controllers.console import console_ns
+from controllers.fastopenapi import console_router
 from core.file import helpers as file_helpers
 from core.helper import ssrf_proxy
 from extensions.ext_database import db
 from fields.file_fields import FileWithSignedUrl, RemoteFileInfo
-from libs.login import current_account_with_tenant, login_required
+from libs.login import current_account_with_tenant
 from services.file_service import FileService
 
 
@@ -24,73 +23,69 @@ class RemoteFileUploadPayload(BaseModel):
     url: str = Field(..., description="URL to fetch")
 
 
-@console_ns.route("/remote-files/<path:url>")
-class GetRemoteFileInfo(Resource):
-    @login_required
-    def get(self, url: str):
-        decoded_url = urllib.parse.unquote(url)
-        resp = ssrf_proxy.head(decoded_url)
+@console_router.get(
+    "/remote-files/<path:url>",
+    response_model=RemoteFileInfo,
+    tags=["console"],
+)
+def get_remote_file_info(url: str) -> RemoteFileInfo:
+    decoded_url = urllib.parse.unquote(url)
+    resp = ssrf_proxy.head(decoded_url)
+    if resp.status_code != httpx.codes.OK:
+        resp = ssrf_proxy.get(decoded_url, timeout=3)
+    resp.raise_for_status()
+    return RemoteFileInfo(
+        file_type=resp.headers.get("Content-Type", "application/octet-stream"),
+        file_length=int(resp.headers.get("Content-Length", 0)),
+    )
+
+
+@console_router.post(
+    "/remote-files/upload",
+    response_model=FileWithSignedUrl,
+    tags=["console"],
+    status_code=201,
+)
+def upload_remote_file(payload: RemoteFileUploadPayload) -> FileWithSignedUrl:
+    url = payload.url
+
+    try:
+        resp = ssrf_proxy.head(url=url)
         if resp.status_code != httpx.codes.OK:
-            resp = ssrf_proxy.get(decoded_url, timeout=3)
-        resp.raise_for_status()
-        return RemoteFileInfo(
-            file_type=resp.headers.get("Content-Type", "application/octet-stream"),
-            file_length=int(resp.headers.get("Content-Length", 0)),
-        ).model_dump(mode="json")
+            resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
+        if resp.status_code != httpx.codes.OK:
+            raise RemoteFileUploadError(f"Failed to fetch file from {url}: {resp.text}")
+    except httpx.RequestError as e:
+        raise RemoteFileUploadError(f"Failed to fetch file from {url}: {str(e)}")
 
+    file_info = helpers.guess_file_info_from_response(resp)
 
-@console_ns.route("/remote-files/upload")
-class RemoteFileUpload(Resource):
-    @login_required
-    def post(self):
-        payload = RemoteFileUploadPayload.model_validate(console_ns.payload)
-        url = payload.url
+    if not FileService.is_file_size_within_limit(extension=file_info.extension, file_size=file_info.size):
+        raise FileTooLargeError
 
-        # Try to fetch remote file metadata/content first
-        try:
-            resp = ssrf_proxy.head(url=url)
-            if resp.status_code != httpx.codes.OK:
-                resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
-            if resp.status_code != httpx.codes.OK:
-                # Normalize into a user-friendly error message expected by tests
-                raise RemoteFileUploadError(f"Failed to fetch file from {url}: {resp.text}")
-        except httpx.RequestError as e:
-            raise RemoteFileUploadError(f"Failed to fetch file from {url}: {str(e)}")
+    content = resp.content if resp.request.method == "GET" else ssrf_proxy.get(url).content
 
-        file_info = helpers.guess_file_info_from_response(resp)
-
-        # Enforce file size limit with 400 (Bad Request) per tests' expectation
-        if not FileService.is_file_size_within_limit(extension=file_info.extension, file_size=file_info.size):
-            raise FileTooLargeError()
-
-        # Load content if needed
-        content = resp.content if resp.request.method == "GET" else ssrf_proxy.get(url).content
-
-        try:
-            user, _ = current_account_with_tenant()
-            upload_file = FileService(db.engine).upload_file(
-                filename=file_info.filename,
-                content=content,
-                mimetype=file_info.mimetype,
-                user=user,
-                source_url=url,
-            )
-        except services.errors.file.FileTooLargeError as file_too_large_error:
-            raise FileTooLargeError(file_too_large_error.description)
-        except services.errors.file.UnsupportedFileTypeError:
-            raise UnsupportedFileTypeError()
-
-        # Success: return created resource with 201 status
-        return (
-            FileWithSignedUrl(
-                id=upload_file.id,
-                name=upload_file.name,
-                size=upload_file.size,
-                extension=upload_file.extension,
-                url=file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
-                mime_type=upload_file.mime_type,
-                created_by=upload_file.created_by,
-                created_at=int(upload_file.created_at.timestamp()),
-            ).model_dump(mode="json"),
-            201,
+    try:
+        user, _ = current_account_with_tenant()
+        upload_file = FileService(db.engine).upload_file(
+            filename=file_info.filename,
+            content=content,
+            mimetype=file_info.mimetype,
+            user=user,
+            source_url=url,
         )
+    except services.errors.file.FileTooLargeError as file_too_large_error:
+        raise FileTooLargeError(file_too_large_error.description)
+    except services.errors.file.UnsupportedFileTypeError:
+        raise UnsupportedFileTypeError()
+
+    return FileWithSignedUrl(
+        id=upload_file.id,
+        name=upload_file.name,
+        size=upload_file.size,
+        extension=upload_file.extension,
+        url=file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
+        mime_type=upload_file.mime_type,
+        created_by=upload_file.created_by,
+        created_at=int(upload_file.created_at.timestamp()),
+    )

api/controllers/console/setup.py

@@ -42,15 +42,7 @@ class SetupResponse(BaseModel):
     tags=["console"],
 )
 def get_setup_status_api() -> SetupStatusResponse:
-    """Get system setup status.
-
-    NOTE: This endpoint is unauthenticated by design.
-
-    During first-time bootstrap there is no admin account yet, so frontend initialization must be
-    able to query setup progress before any login flow exists.
-
-    Only bootstrap-safe status information should be returned by this endpoint.
-    """
+    """Get system setup status."""
     if dify_config.EDITION == "SELF_HOSTED":
         setup_status = get_setup_status()
         if setup_status and not isinstance(setup_status, bool):
@@ -69,12 +61,7 @@ def get_setup_status_api() -> SetupStatusResponse:
 )
 @only_edition_self_hosted
 def setup_system(payload: SetupRequestPayload) -> SetupResponse:
-    """Initialize system setup with admin account.
-
-    NOTE: This endpoint is unauthenticated by design for first-time bootstrap.
-    Access is restricted by deployment mode (`SELF_HOSTED`), one-time setup guards,
-    and init-password validation rather than user session authentication.
-    """
+    """Initialize system setup with admin account."""
     if get_setup_status():
         raise AlreadySetupError()
 

api/controllers/console/tag/tags.py

@@ -120,7 +120,7 @@ class TagUpdateDeleteApi(Resource):
 
         TagService.delete_tag(tag_id)
 
-        return "", 204
+        return 204
 
 
 @console_ns.route("/tag-bindings/create")

api/controllers/console/workspace/tool_providers.py

@@ -878,7 +878,11 @@ class ToolBuiltinProviderSetDefaultApi(Resource):
         current_user, current_tenant_id = current_account_with_tenant()
         payload = BuiltinProviderDefaultCredentialPayload.model_validate(console_ns.payload or {})
         return BuiltinToolManageService.set_default_provider(
-            tenant_id=current_tenant_id, user_id=current_user.id, provider=provider, id=payload.id
+            tenant_id=current_tenant_id,
+            user_id=current_user.id,
+            provider=provider,
+            id=payload.id,
+            account=current_user,
         )
 
 

api/controllers/service_api/__init__.py

@@ -34,8 +34,6 @@ from .dataset import (
     metadata,
     segment,
 )
-from .dataset.rag_pipeline import rag_pipeline_workflow
-from .end_user import end_user
 from .workspace import models
 
 __all__ = [
@@ -46,7 +44,6 @@ __all__ = [
     "conversation",
     "dataset",
     "document",
-    "end_user",
     "file",
     "file_preview",
     "hit_testing",
@@ -54,7 +51,6 @@ __all__ = [
     "message",
     "metadata",
     "models",
-    "rag_pipeline_workflow",
     "segment",
     "site",
     "workflow",

api/controllers/service_api/app/workflow.py

@@ -33,9 +33,8 @@ from core.workflow.graph_engine.manager import GraphEngineManager
 from extensions.ext_database import db
 from fields.workflow_app_log_fields import build_workflow_app_log_pagination_model
 from libs import helper
-from libs.helper import OptionalTimestampField, TimestampField
+from libs.helper import TimestampField
 from models.model import App, AppMode, EndUser
-from models.workflow import WorkflowRun
 from repositories.factory import DifyAPIRepositoryFactory
 from services.app_generate_service import AppGenerateService
 from services.errors.app import IsDraftWorkflowError, WorkflowIdFormatError, WorkflowNotFoundError
@@ -64,32 +63,17 @@ class WorkflowLogQuery(BaseModel):
 
 register_schema_models(service_api_ns, WorkflowRunPayload, WorkflowLogQuery)
 
-
-class WorkflowRunStatusField(fields.Raw):
-    def output(self, key, obj: WorkflowRun, **kwargs):
-        return obj.status.value
-
-
-class WorkflowRunOutputsField(fields.Raw):
-    def output(self, key, obj: WorkflowRun, **kwargs):
-        if obj.status == WorkflowExecutionStatus.PAUSED:
-            return {}
-
-        outputs = obj.outputs_dict
-        return outputs or {}
-
-
 workflow_run_fields = {
     "id": fields.String,
     "workflow_id": fields.String,
-    "status": WorkflowRunStatusField,
+    "status": fields.String,
     "inputs": fields.Raw,
-    "outputs": WorkflowRunOutputsField,
+    "outputs": fields.Raw,
     "error": fields.String,
     "total_steps": fields.Integer,
     "total_tokens": fields.Integer,
     "created_at": TimestampField,
-    "finished_at": OptionalTimestampField,
+    "finished_at": TimestampField,
     "elapsed_time": fields.Float,
 }
 

api/controllers/service_api/dataset/dataset.py

@@ -396,7 +396,7 @@ class DatasetApi(DatasetApiResource):
         try:
             if DatasetService.delete_dataset(dataset_id_str, current_user):
                 DatasetPermissionService.clear_partial_member_list(dataset_id_str)
-                return "", 204
+                return 204
             else:
                 raise NotFound("Dataset not found.")
         except services.errors.dataset.DatasetInUseError:
@@ -557,7 +557,7 @@ class DatasetTagsApi(DatasetApiResource):
         payload = TagDeletePayload.model_validate(service_api_ns.payload or {})
         TagService.delete_tag(payload.tag_id)
 
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/tags/binding")
@@ -581,7 +581,7 @@ class DatasetTagBindingApi(DatasetApiResource):
         payload = TagBindingPayload.model_validate(service_api_ns.payload or {})
         TagService.save_tag_binding({"tag_ids": payload.tag_ids, "target_id": payload.target_id, "type": "knowledge"})
 
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/tags/unbinding")
@@ -605,7 +605,7 @@ class DatasetTagUnbindingApi(DatasetApiResource):
         payload = TagUnbindingPayload.model_validate(service_api_ns.payload or {})
         TagService.delete_tag_binding({"tag_id": payload.tag_id, "target_id": payload.target_id, "type": "knowledge"})
 
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/<uuid:dataset_id>/tags")

api/controllers/service_api/dataset/document.py

@@ -746,4 +746,4 @@ class DocumentApi(DatasetApiResource):
         except services.errors.document.DocumentIndexingError:
             raise DocumentIndexingError("Cannot delete document during indexing.")
 
-        return "", 204
+        return 204

api/controllers/service_api/dataset/metadata.py

@@ -128,7 +128,7 @@ class DatasetMetadataServiceApi(DatasetApiResource):
         DatasetService.check_dataset_permission(dataset, current_user)
 
         MetadataService.delete_metadata(dataset_id_str, metadata_id_str)
-        return "", 204
+        return 204
 
 
 @service_api_ns.route("/datasets/<uuid:dataset_id>/metadata/built-in")

api/controllers/service_api/dataset/rag_pipeline/rag_pipeline_workflow.py

@@ -1,3 +1,5 @@
+import string
+import uuid
 from collections.abc import Generator
 from typing import Any
 
@@ -10,7 +12,6 @@ from controllers.common.errors import FilenameNotExistsError, NoFileUploadedErro
 from controllers.common.schema import register_schema_model
 from controllers.service_api import service_api_ns
 from controllers.service_api.dataset.error import PipelineRunError
-from controllers.service_api.dataset.rag_pipeline.serializers import serialize_upload_file
 from controllers.service_api.wraps import DatasetApiResource
 from core.app.apps.pipeline.pipeline_generator import PipelineGenerator
 from core.app.entities.app_invoke_entities import InvokeFrom
@@ -40,7 +41,7 @@ register_schema_model(service_api_ns, DatasourceNodeRunPayload)
 register_schema_model(service_api_ns, PipelineRunApiEntity)
 
 
-@service_api_ns.route("/datasets/<uuid:dataset_id>/pipeline/datasource-plugins")
+@service_api_ns.route(f"/datasets/{uuid:dataset_id}/pipeline/datasource-plugins")
 class DatasourcePluginsApi(DatasetApiResource):
     """Resource for datasource plugins."""
 
@@ -75,7 +76,7 @@ class DatasourcePluginsApi(DatasetApiResource):
         return datasource_plugins, 200
 
 
-@service_api_ns.route("/datasets/<uuid:dataset_id>/pipeline/datasource/nodes/<string:node_id>/run")
+@service_api_ns.route(f"/datasets/{uuid:dataset_id}/pipeline/datasource/nodes/{string:node_id}/run")
 class DatasourceNodeRunApi(DatasetApiResource):
     """Resource for datasource node run."""
 
@@ -130,7 +131,7 @@ class DatasourceNodeRunApi(DatasetApiResource):
         )
 
 
-@service_api_ns.route("/datasets/<uuid:dataset_id>/pipeline/run")
+@service_api_ns.route(f"/datasets/{uuid:dataset_id}/pipeline/run")
 class PipelineRunApi(DatasetApiResource):
     """Resource for datasource node run."""
 
@@ -231,4 +232,12 @@ class KnowledgebasePipelineFileUploadApi(DatasetApiResource):
         except services.errors.file.UnsupportedFileTypeError:
             raise UnsupportedFileTypeError()
 
-        return serialize_upload_file(upload_file), 201
+        return {
+            "id": upload_file.id,
+            "name": upload_file.name,
+            "size": upload_file.size,
+            "extension": upload_file.extension,
+            "mime_type": upload_file.mime_type,
+            "created_by": upload_file.created_by,
+            "created_at": upload_file.created_at,
+        }, 201

api/controllers/service_api/dataset/rag_pipeline/serializers.py

@@ -1,22 +0,0 @@
-"""
-Serialization helpers for Service API knowledge pipeline endpoints.
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from models.model import UploadFile
-
-
-def serialize_upload_file(upload_file: UploadFile) -> dict[str, Any]:
-    return {
-        "id": upload_file.id,
-        "name": upload_file.name,
-        "size": upload_file.size,
-        "extension": upload_file.extension,
-        "mime_type": upload_file.mime_type,
-        "created_by": upload_file.created_by,
-        "created_at": upload_file.created_at.isoformat() if upload_file.created_at else None,
-    }

api/controllers/service_api/dataset/segment.py

@@ -233,7 +233,7 @@ class DatasetSegmentApi(DatasetApiResource):
         if not segment:
             raise NotFound("Segment not found.")
         SegmentService.delete_segment(segment, document, dataset)
-        return "", 204
+        return 204
 
     @service_api_ns.expect(service_api_ns.models[SegmentUpdatePayload.__name__])
     @service_api_ns.doc("update_segment")
@@ -499,7 +499,7 @@ class DatasetChildChunkApi(DatasetApiResource):
         except ChildChunkDeleteIndexServiceError as e:
             raise ChildChunkDeleteIndexError(str(e))
 
-        return "", 204
+        return 204
 
     @service_api_ns.expect(service_api_ns.models[ChildChunkUpdatePayload.__name__])
     @service_api_ns.doc("update_child_chunk")

api/controllers/service_api/end_user/__init__.py

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

api/controllers/service_api/end_user/end_user.py

@@ -1,41 +0,0 @@
-from uuid import UUID
-
-from flask_restx import Resource
-
-from controllers.service_api import service_api_ns
-from controllers.service_api.end_user.error import EndUserNotFoundError
-from controllers.service_api.wraps import validate_app_token
-from fields.end_user_fields import EndUserDetail
-from models.model import App
-from services.end_user_service import EndUserService
-
-
-@service_api_ns.route("/end-users/<uuid:end_user_id>")
-class EndUserApi(Resource):
-    """Resource for retrieving end user details by ID."""
-
-    @service_api_ns.doc("get_end_user")
-    @service_api_ns.doc(description="Get an end user by ID")
-    @service_api_ns.doc(
-        params={"end_user_id": "End user ID"},
-        responses={
-            200: "End user retrieved successfully",
-            401: "Unauthorized - invalid API token",
-            404: "End user not found",
-        },
-    )
-    @validate_app_token
-    def get(self, app_model: App, end_user_id: UUID):
-        """Get end user detail.
-
-        This endpoint is scoped to the current app token's tenant/app to prevent
-        cross-tenant/app access when an end-user ID is known.
-        """
-
-        end_user = EndUserService.get_end_user_by_id(
-            tenant_id=app_model.tenant_id, app_id=app_model.id, end_user_id=str(end_user_id)
-        )
-        if end_user is None:
-            raise EndUserNotFoundError()
-
-        return EndUserDetail.model_validate(end_user).model_dump(mode="json")

api/controllers/service_api/end_user/error.py

@@ -1,7 +0,0 @@
-from libs.exception import BaseHTTPException
-
-
-class EndUserNotFoundError(BaseHTTPException):
-    error_code = "end_user_not_found"
-    description = "End user not found."
-    code = 404

api/controllers/service_api/wraps.py

@@ -1,24 +1,27 @@
 import logging
 import time
 from collections.abc import Callable
+from datetime import timedelta
 from enum import StrEnum, auto
 from functools import wraps
-from typing import Concatenate, ParamSpec, TypeVar, cast
+from typing import Concatenate, ParamSpec, TypeVar
 
 from flask import current_app, request
 from flask_login import user_logged_in
 from flask_restx import Resource
 from pydantic import BaseModel
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, NotFound, Unauthorized
 
 from enums.cloud_plan import CloudPlan
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
+from libs.datetime_utils import naive_utc_now
 from libs.login import current_user
 from models import Account, Tenant, TenantAccountJoin, TenantStatus
 from models.dataset import Dataset, RateLimitLog
 from models.model import ApiToken, App
-from services.api_token_service import ApiTokenCache, fetch_token_with_single_flight, record_token_usage
 from services.end_user_service import EndUserService
 from services.feature_service import FeatureService
 
@@ -217,8 +220,6 @@ def validate_dataset_token(view: Callable[Concatenate[T, P], R] | None = None):
     def decorator(view: Callable[Concatenate[T, P], R]):
         @wraps(view)
         def decorated(*args: P.args, **kwargs: P.kwargs):
-            api_token = validate_and_get_api_token("dataset")
-
             # get url path dataset_id from positional args or kwargs
             # Flask passes URL path parameters as positional arguments
             dataset_id = None
@@ -255,18 +256,12 @@ def validate_dataset_token(view: Callable[Concatenate[T, P], R] | None = None):
             # Validate dataset if dataset_id is provided
             if dataset_id:
                 dataset_id = str(dataset_id)
-                dataset = (
-                    db.session.query(Dataset)
-                    .where(
-                        Dataset.id == dataset_id,
-                        Dataset.tenant_id == api_token.tenant_id,
-                    )
-                    .first()
-                )
+                dataset = db.session.query(Dataset).where(Dataset.id == dataset_id).first()
                 if not dataset:
                     raise NotFound("Dataset not found.")
                 if not dataset.enable_api:
                     raise Forbidden("Dataset api access is not enabled.")
+            api_token = validate_and_get_api_token("dataset")
             tenant_account_join = (
                 db.session.query(Tenant, TenantAccountJoin)
                 .where(Tenant.id == api_token.tenant_id)
@@ -301,14 +296,7 @@ def validate_dataset_token(view: Callable[Concatenate[T, P], R] | None = None):
 
 def validate_and_get_api_token(scope: str | None = None):
     """
-    Validate and get API token with Redis caching.
-
-    This function uses a two-tier approach:
-    1. First checks Redis cache for the token
-    2. If not cached, queries database and caches the result
-
-    The last_used_at field is updated asynchronously via Celery task
-    to avoid blocking the request.
+    Validate and get API token.
     """
     auth_header = request.headers.get("Authorization")
     if auth_header is None or " " not in auth_header:
@@ -320,18 +308,29 @@ def validate_and_get_api_token(scope: str | None = None):
     if auth_scheme != "bearer":
         raise Unauthorized("Authorization scheme must be 'Bearer'")
 
-    # Try to get token from cache first
-    # Returns a CachedApiToken (plain Python object), not a SQLAlchemy model
-    cached_token = ApiTokenCache.get(auth_token, scope)
-    if cached_token is not None:
-        logger.debug("Token validation served from cache for scope: %s", scope)
-        # Record usage in Redis for later batch update (no Celery task per request)
-        record_token_usage(auth_token, scope)
-        return cast(ApiToken, cached_token)
+    current_time = naive_utc_now()
+    cutoff_time = current_time - timedelta(minutes=1)
+    with Session(db.engine, expire_on_commit=False) as session:
+        update_stmt = (
+            update(ApiToken)
+            .where(
+                ApiToken.token == auth_token,
+                (ApiToken.last_used_at.is_(None) | (ApiToken.last_used_at < cutoff_time)),
+                ApiToken.type == scope,
+            )
+            .values(last_used_at=current_time)
+        )
+        stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
+        result = session.execute(update_stmt)
+        api_token = session.scalar(stmt)
 
-    # Cache miss - use Redis lock for single-flight mode
-    # This ensures only one request queries DB for the same token concurrently
-    return fetch_token_with_single_flight(auth_token, scope)
+        if hasattr(result, "rowcount") and result.rowcount > 0:
+            session.commit()
+
+        if not api_token:
+            raise Unauthorized("Access token is invalid")
+
+    return api_token
 
 
 class DatasetApiResource(Resource):

api/controllers/web/__init__.py

@@ -23,7 +23,6 @@ from . import (
     feature,
     files,
     forgot_password,
-    human_input_form,
     login,
     message,
     passport,
@@ -31,7 +30,6 @@ from . import (
     saved_message,
     site,
     workflow,
-    workflow_events,
 )
 
 api.add_namespace(web_ns)
@@ -46,7 +44,6 @@ __all__ = [
     "feature",
     "files",
     "forgot_password",
-    "human_input_form",
     "login",
     "message",
     "passport",
@@ -55,5 +52,4 @@ __all__ = [
     "site",
     "web_ns",
     "workflow",
-    "workflow_events",
 ]

api/controllers/web/error.py

@@ -117,12 +117,6 @@ class InvokeRateLimitError(BaseHTTPException):
     code = 429
 
 
-class WebFormRateLimitExceededError(BaseHTTPException):
-    error_code = "web_form_rate_limit_exceeded"
-    description = "Too many form requests. Please try again later."
-    code = 429
-
-
 class NotFoundError(BaseHTTPException):
     error_code = "not_found"
     code = 404

api/controllers/web/human_input_form.py

@@ -1,161 +0,0 @@
-"""
-Web App Human Input Form APIs.
-"""
-
-import json
-import logging
-from datetime import datetime
-
-from flask import Response, request
-from flask_restx import Resource, reqparse
-from werkzeug.exceptions import Forbidden
-
-from configs import dify_config
-from controllers.web import web_ns
-from controllers.web.error import NotFoundError, WebFormRateLimitExceededError
-from controllers.web.site import serialize_app_site_payload
-from extensions.ext_database import db
-from libs.helper import RateLimiter, extract_remote_ip
-from models.account import TenantStatus
-from models.model import App, Site
-from services.human_input_service import Form, FormNotFoundError, HumanInputService
-
-logger = logging.getLogger(__name__)
-
-_FORM_SUBMIT_RATE_LIMITER = RateLimiter(
-    prefix="web_form_submit_rate_limit",
-    max_attempts=dify_config.WEB_FORM_SUBMIT_RATE_LIMIT_MAX_ATTEMPTS,
-    time_window=dify_config.WEB_FORM_SUBMIT_RATE_LIMIT_WINDOW_SECONDS,
-)
-_FORM_ACCESS_RATE_LIMITER = RateLimiter(
-    prefix="web_form_access_rate_limit",
-    max_attempts=dify_config.WEB_FORM_SUBMIT_RATE_LIMIT_MAX_ATTEMPTS,
-    time_window=dify_config.WEB_FORM_SUBMIT_RATE_LIMIT_WINDOW_SECONDS,
-)
-
-
-def _stringify_default_values(values: dict[str, object]) -> dict[str, str]:
-    result: dict[str, str] = {}
-    for key, value in values.items():
-        if value is None:
-            result[key] = ""
-        elif isinstance(value, (dict, list)):
-            result[key] = json.dumps(value, ensure_ascii=False)
-        else:
-            result[key] = str(value)
-    return result
-
-
-def _to_timestamp(value: datetime) -> int:
-    return int(value.timestamp())
-
-
-def _jsonify_form_definition(form: Form, site_payload: dict | None = None) -> Response:
-    """Return the form payload (optionally with site) as a JSON response."""
-    definition_payload = form.get_definition().model_dump()
-    payload = {
-        "form_content": definition_payload["rendered_content"],
-        "inputs": definition_payload["inputs"],
-        "resolved_default_values": _stringify_default_values(definition_payload["default_values"]),
-        "user_actions": definition_payload["user_actions"],
-        "expiration_time": _to_timestamp(form.expiration_time),
-    }
-    if site_payload is not None:
-        payload["site"] = site_payload
-    return Response(json.dumps(payload, ensure_ascii=False), mimetype="application/json")
-
-
-@web_ns.route("/form/human_input/<string:form_token>")
-class HumanInputFormApi(Resource):
-    """API for getting and submitting human input forms via the web app."""
-
-    # NOTE(QuantumGhost): this endpoint is unauthenticated on purpose for now.
-
-    # def get(self, _app_model: App, _end_user: EndUser, form_token: str):
-    def get(self, form_token: str):
-        """
-        Get human input form definition by token.
-
-        GET /api/form/human_input/<form_token>
-        """
-        ip_address = extract_remote_ip(request)
-        if _FORM_ACCESS_RATE_LIMITER.is_rate_limited(ip_address):
-            raise WebFormRateLimitExceededError()
-        _FORM_ACCESS_RATE_LIMITER.increment_rate_limit(ip_address)
-
-        service = HumanInputService(db.engine)
-        # TODO(QuantumGhost): forbid submision for form tokens
-        # that are only for console.
-        form = service.get_form_by_token(form_token)
-
-        if form is None:
-            raise NotFoundError("Form not found")
-
-        service.ensure_form_active(form)
-        app_model, site = _get_app_site_from_form(form)
-
-        return _jsonify_form_definition(form, site_payload=serialize_app_site_payload(app_model, site, None))
-
-    # def post(self, _app_model: App, _end_user: EndUser, form_token: str):
-    def post(self, form_token: str):
-        """
-        Submit human input form by token.
-
-        POST /api/form/human_input/<form_token>
-
-        Request body:
-        {
-            "inputs": {
-                "content": "User input content"
-            },
-            "action": "Approve"
-        }
-        """
-        parser = reqparse.RequestParser()
-        parser.add_argument("inputs", type=dict, required=True, location="json")
-        parser.add_argument("action", type=str, required=True, location="json")
-        args = parser.parse_args()
-
-        ip_address = extract_remote_ip(request)
-        if _FORM_SUBMIT_RATE_LIMITER.is_rate_limited(ip_address):
-            raise WebFormRateLimitExceededError()
-        _FORM_SUBMIT_RATE_LIMITER.increment_rate_limit(ip_address)
-
-        service = HumanInputService(db.engine)
-        form = service.get_form_by_token(form_token)
-        if form is None:
-            raise NotFoundError("Form not found")
-
-        if (recipient_type := form.recipient_type) is None:
-            logger.warning("Recipient type is None for form, form_id=%", form.id)
-            raise AssertionError("Recipient type is None")
-
-        try:
-            service.submit_form_by_token(
-                recipient_type=recipient_type,
-                form_token=form_token,
-                selected_action_id=args["action"],
-                form_data=args["inputs"],
-                submission_end_user_id=None,
-                # submission_end_user_id=_end_user.id,
-            )
-        except FormNotFoundError:
-            raise NotFoundError("Form not found")
-
-        return {}, 200
-
-
-def _get_app_site_from_form(form: Form) -> tuple[App, Site]:
-    """Resolve App/Site for the form's app and validate tenant status."""
-    app_model = db.session.query(App).where(App.id == form.app_id).first()
-    if app_model is None or app_model.tenant_id != form.tenant_id:
-        raise NotFoundError("Form not found")
-
-    site = db.session.query(Site).where(Site.app_id == app_model.id).first()
-    if site is None:
-        raise Forbidden()
-
-    if app_model.tenant and app_model.tenant.status == TenantStatus.ARCHIVE:
-        raise Forbidden()
-
-    return app_model, site

api/controllers/web/site.py

@@ -1,6 +1,4 @@
-from typing import cast
-
-from flask_restx import fields, marshal, marshal_with
+from flask_restx import fields, marshal_with
 from werkzeug.exceptions import Forbidden
 
 from configs import dify_config
@@ -9,7 +7,7 @@ from controllers.web.wraps import WebApiResource
 from extensions.ext_database import db
 from libs.helper import AppIconUrlField
 from models.account import TenantStatus
-from models.model import App, Site
+from models.model import Site
 from services.feature_service import FeatureService
 
 
@@ -110,14 +108,3 @@ class AppSiteInfo:
                 "remove_webapp_brand": remove_webapp_brand,
                 "replace_webapp_logo": replace_webapp_logo,
             }
-
-
-def serialize_site(site: Site) -> dict:
-    """Serialize Site model using the same schema as AppSiteApi."""
-    return cast(dict, marshal(site, AppSiteApi.site_fields))
-
-
-def serialize_app_site_payload(app_model: App, site: Site, end_user_id: str | None) -> dict:
-    can_replace_logo = FeatureService.get_features(app_model.tenant_id).can_replace_logo
-    app_site_info = AppSiteInfo(app_model.tenant, app_model, site, end_user_id, can_replace_logo)
-    return cast(dict, marshal(app_site_info, AppSiteApi.app_fields))

api/controllers/web/workflow_events.py

@@ -1,112 +0,0 @@
-"""
-Web App Workflow Resume APIs.
-"""
-
-import json
-from collections.abc import Generator
-
-from flask import Response, request
-from sqlalchemy.orm import sessionmaker
-
-from controllers.web import api
-from controllers.web.error import InvalidArgumentError, NotFoundError
-from controllers.web.wraps import WebApiResource
-from core.app.apps.advanced_chat.app_generator import AdvancedChatAppGenerator
-from core.app.apps.base_app_generator import BaseAppGenerator
-from core.app.apps.common.workflow_response_converter import WorkflowResponseConverter
-from core.app.apps.message_generator import MessageGenerator
-from core.app.apps.workflow.app_generator import WorkflowAppGenerator
-from extensions.ext_database import db
-from models.enums import CreatorUserRole
-from models.model import App, AppMode, EndUser
-from repositories.factory import DifyAPIRepositoryFactory
-from services.workflow_event_snapshot_service import build_workflow_event_stream
-
-
-class WorkflowEventsApi(WebApiResource):
-    """API for getting workflow execution events after resume."""
-
-    def get(self, app_model: App, end_user: EndUser, task_id: str):
-        """
-        Get workflow execution events stream after resume.
-
-        GET /api/workflow/<task_id>/events
-
-        Returns Server-Sent Events stream.
-        """
-        workflow_run_id = task_id
-        session_maker = sessionmaker(db.engine)
-        repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
-        workflow_run = repo.get_workflow_run_by_id_and_tenant_id(
-            tenant_id=app_model.tenant_id,
-            run_id=workflow_run_id,
-        )
-
-        if workflow_run is None:
-            raise NotFoundError(f"WorkflowRun not found, id={workflow_run_id}")
-
-        if workflow_run.app_id != app_model.id:
-            raise NotFoundError(f"WorkflowRun not found, id={workflow_run_id}")
-
-        if workflow_run.created_by_role != CreatorUserRole.END_USER:
-            raise NotFoundError(f"WorkflowRun not created by end user, id={workflow_run_id}")
-
-        if workflow_run.created_by != end_user.id:
-            raise NotFoundError(f"WorkflowRun not created by the current end user, id={workflow_run_id}")
-
-        if workflow_run.finished_at is not None:
-            response = WorkflowResponseConverter.workflow_run_result_to_finish_response(
-                task_id=workflow_run.id,
-                workflow_run=workflow_run,
-                creator_user=end_user,
-            )
-
-            payload = response.model_dump(mode="json")
-            payload["event"] = response.event.value
-
-            def _generate_finished_events() -> Generator[str, None, None]:
-                yield f"data: {json.dumps(payload)}\n\n"
-
-            event_generator = _generate_finished_events
-        else:
-            app_mode = AppMode.value_of(app_model.mode)
-            msg_generator = MessageGenerator()
-            generator: BaseAppGenerator
-            if app_mode == AppMode.ADVANCED_CHAT:
-                generator = AdvancedChatAppGenerator()
-            elif app_mode == AppMode.WORKFLOW:
-                generator = WorkflowAppGenerator()
-            else:
-                raise InvalidArgumentError(f"cannot subscribe to workflow run, workflow_run_id={workflow_run.id}")
-
-            include_state_snapshot = request.args.get("include_state_snapshot", "false").lower() == "true"
-
-            def _generate_stream_events():
-                if include_state_snapshot:
-                    return generator.convert_to_event_stream(
-                        build_workflow_event_stream(
-                            app_mode=app_mode,
-                            workflow_run=workflow_run,
-                            tenant_id=app_model.tenant_id,
-                            app_id=app_model.id,
-                            session_maker=session_maker,
-                        )
-                    )
-                return generator.convert_to_event_stream(
-                    msg_generator.retrieve_events(app_mode, workflow_run.id),
-                )
-
-            event_generator = _generate_stream_events
-
-        return Response(
-            event_generator(),
-            mimetype="text/event-stream",
-            headers={
-                "Cache-Control": "no-cache",
-                "Connection": "keep-alive",
-            },
-        )
-
-
-# Register the APIs
-api.add_resource(WorkflowEventsApi, "/workflow/<string:task_id>/events")

api/core/agent/base_agent_runner.py

@@ -79,7 +79,7 @@ class BaseAgentRunner(AppRunner):
         self.model_instance = model_instance
 
         # init callback
-        self.agent_callback = DifyAgentCallbackHandler()
+        self.agent_callback = DifyAgentCallbackHandler(tenant_id=tenant_id)
         # init dataset tools
         hit_callback = DatasetIndexToolCallbackHandler(
             queue_manager=queue_manager,

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

@@ -4,8 +4,8 @@ import contextvars
 import logging
 import threading
 import uuid
-from collections.abc import Generator, Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Literal, TypeVar, Union, overload
+from collections.abc import Generator, Mapping
+from typing import TYPE_CHECKING, Any, Literal, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -29,25 +29,21 @@ 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.pause_state_persist_layer import PauseStateLayerConfig, PauseStatePersistenceLayer
 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.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
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
-from core.workflow.runtime import GraphRuntimeState
 from core.workflow.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader
 from extensions.ext_database import db
 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.base import Base
 from models.enums import WorkflowRunTriggeredFrom
 from services.conversation_service import ConversationService
 from services.workflow_draft_variable_service import (
@@ -69,9 +65,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         user: Union[Account, EndUser],
         args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        workflow_run_id: str,
         streaming: Literal[False],
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Mapping[str, Any]: ...
 
     @overload
@@ -80,11 +74,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
-        args: Mapping[str, Any],
+        args: Mapping,
         invoke_from: InvokeFrom,
-        workflow_run_id: str,
         streaming: Literal[True],
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Generator[Mapping | str, None, None]: ...
 
     @overload
@@ -93,11 +85,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
-        args: Mapping[str, Any],
+        args: Mapping,
         invoke_from: InvokeFrom,
-        workflow_run_id: str,
         streaming: bool,
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Mapping[str, Any] | Generator[str | Mapping, None, None]: ...
 
     def generate(
@@ -105,11 +95,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
-        args: Mapping[str, Any],
+        args: Mapping,
         invoke_from: InvokeFrom,
-        workflow_run_id: str,
         streaming: bool = True,
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Mapping[str, Any] | Generator[str | Mapping, None, None]:
         """
         Generate App response.
@@ -173,6 +161,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             # always enable retriever resource in debugger mode
             app_config.additional_features.show_retrieve_source = True  # type: ignore
 
+        workflow_run_id = str(uuid.uuid4())
         # init application generate entity
         application_generate_entity = AdvancedChatAppGenerateEntity(
             task_id=str(uuid.uuid4()),
@@ -190,7 +179,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=invoke_from,
             extras=extras,
             trace_manager=trace_manager,
-            workflow_run_id=str(workflow_run_id),
+            workflow_run_id=workflow_run_id,
         )
         contexts.plugin_tool_providers.set({})
         contexts.plugin_tool_providers_lock.set(threading.Lock())
@@ -227,38 +216,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             workflow_node_execution_repository=workflow_node_execution_repository,
             conversation=conversation,
             stream=streaming,
-            pause_state_config=pause_state_config,
-        )
-
-    def resume(
-        self,
-        *,
-        app_model: App,
-        workflow: Workflow,
-        user: Union[Account, EndUser],
-        conversation: Conversation,
-        message: Message,
-        application_generate_entity: AdvancedChatAppGenerateEntity,
-        workflow_execution_repository: WorkflowExecutionRepository,
-        workflow_node_execution_repository: WorkflowNodeExecutionRepository,
-        graph_runtime_state: GraphRuntimeState,
-        pause_state_config: PauseStateLayerConfig | None = None,
-    ):
-        """
-        Resume a paused advanced chat execution.
-        """
-        return self._generate(
-            workflow=workflow,
-            user=user,
-            invoke_from=application_generate_entity.invoke_from,
-            application_generate_entity=application_generate_entity,
-            workflow_execution_repository=workflow_execution_repository,
-            workflow_node_execution_repository=workflow_node_execution_repository,
-            conversation=conversation,
-            message=message,
-            stream=application_generate_entity.stream,
-            pause_state_config=pause_state_config,
-            graph_runtime_state=graph_runtime_state,
         )
 
     def single_iteration_generate(
@@ -439,12 +396,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         conversation: Conversation | None = None,
-        message: Message | None = None,
         stream: bool = True,
         variable_loader: VariableLoader = DUMMY_VARIABLE_LOADER,
-        pause_state_config: PauseStateLayerConfig | None = None,
-        graph_runtime_state: GraphRuntimeState | None = None,
-        graph_engine_layers: Sequence[GraphEngineLayer] = (),
     ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], Any, None]:
         """
         Generate App response.
@@ -458,12 +411,12 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         :param conversation: conversation
         :param stream: is stream
         """
-        is_first_conversation = conversation is None
+        is_first_conversation = False
+        if not conversation:
+            is_first_conversation = True
 
-        if conversation is not None and message is not None:
-            pass
-        else:
-            conversation, message = self._init_generate_records(application_generate_entity, conversation)
+        # init generate records
+        (conversation, message) = self._init_generate_records(application_generate_entity, conversation)
 
         if is_first_conversation:
             # update conversation features
@@ -486,16 +439,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             message_id=message.id,
         )
 
-        graph_layers: list[GraphEngineLayer] = list(graph_engine_layers)
-        if pause_state_config is not None:
-            graph_layers.append(
-                PauseStatePersistenceLayer(
-                    session_factory=pause_state_config.session_factory,
-                    generate_entity=application_generate_entity,
-                    state_owner_user_id=pause_state_config.state_owner_user_id,
-                )
-            )
-
         # new thread with request context and contextvars
         context = contextvars.copy_context()
 
@@ -511,25 +454,14 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 "variable_loader": variable_loader,
                 "workflow_execution_repository": workflow_execution_repository,
                 "workflow_node_execution_repository": workflow_node_execution_repository,
-                "graph_engine_layers": tuple(graph_layers),
-                "graph_runtime_state": graph_runtime_state,
             },
         )
 
         worker_thread.start()
 
         # release database connection, because the following new thread operations may take a long time
-        with Session(bind=db.engine, expire_on_commit=False) as session:
-            workflow = _refresh_model(session, workflow)
-            message = _refresh_model(session, message)
-        #     workflow_ = session.get(Workflow, workflow.id)
-        #     assert workflow_ is not None
-        #     workflow = workflow_
-        #     message_ = session.get(Message, message.id)
-        #     assert message_ is not None
-        #     message = message_
-        # db.session.refresh(workflow)
-        # db.session.refresh(message)
+        db.session.refresh(workflow)
+        db.session.refresh(message)
         # db.session.refresh(user)
         db.session.close()
 
@@ -558,8 +490,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         variable_loader: VariableLoader,
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
-        graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        graph_runtime_state: GraphRuntimeState | None = None,
     ):
         """
         Generate worker in a new thread.
@@ -617,8 +547,6 @@ 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,
-                graph_runtime_state=graph_runtime_state,
             )
 
             try:
@@ -686,13 +614,3 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             else:
                 logger.exception("Failed to process generate task pipeline, conversation_id: %s", conversation.id)
                 raise e
-
-
-_T = TypeVar("_T", bound=Base)
-
-
-def _refresh_model(session, model: _T) -> _T:
-    with Session(bind=db.engine, expire_on_commit=False) as session:
-        detach_model = session.get(type(model), model.id)
-        assert detach_model is not None
-        return detach_model

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

@@ -66,7 +66,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        graph_runtime_state: GraphRuntimeState | None = None,
     ):
         super().__init__(
             queue_manager=queue_manager,
@@ -83,7 +82,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         self._app = app
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
-        self._resume_graph_runtime_state = graph_runtime_state
 
     @trace_span(WorkflowAppRunnerHandler)
     def run(self):
@@ -112,21 +110,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
             invoke_from = InvokeFrom.DEBUGGER
         user_from = self._resolve_user_from(invoke_from)
 
-        resume_state = self._resume_graph_runtime_state
-
-        if resume_state is not None:
-            graph_runtime_state = resume_state
-            variable_pool = graph_runtime_state.variable_pool
-            graph = self._init_graph(
-                graph_config=self._workflow.graph_dict,
-                graph_runtime_state=graph_runtime_state,
-                workflow_id=self._workflow.id,
-                tenant_id=self._workflow.tenant_id,
-                user_id=self.application_generate_entity.user_id,
-                invoke_from=invoke_from,
-                user_from=user_from,
-            )
-        elif self.application_generate_entity.single_iteration_run or self.application_generate_entity.single_loop_run:
+        if self.application_generate_entity.single_iteration_run or self.application_generate_entity.single_loop_run:
             # Handle single iteration or single loop run
             graph, variable_pool, graph_runtime_state = self._prepare_single_node_execution(
                 workflow=self._workflow,

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

@@ -24,8 +24,6 @@ from core.app.entities.queue_entities import (
     QueueAgentLogEvent,
     QueueAnnotationReplyEvent,
     QueueErrorEvent,
-    QueueHumanInputFormFilledEvent,
-    QueueHumanInputFormTimeoutEvent,
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
@@ -44,7 +42,6 @@ from core.app.entities.queue_entities import (
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
     QueueWorkflowPartialSuccessEvent,
-    QueueWorkflowPausedEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
     WorkflowQueueMessage,
@@ -66,8 +63,8 @@ from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.repositories.human_input_repository import HumanInputFormRepositoryImpl
-from core.workflow.entities.pause_reason import HumanInputRequired
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
 from core.workflow.enums import WorkflowExecutionStatus
 from core.workflow.nodes import NodeType
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
@@ -76,8 +73,7 @@ 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, Message, MessageFile
-from models.enums import CreatorUserRole, MessageStatus
-from models.execution_extra_content import HumanInputContent
+from models.enums import CreatorUserRole
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
@@ -134,7 +130,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         )
 
         self._task_state = WorkflowTaskState()
-        self._seed_task_state_from_message(message)
         self._message_cycle_manager = MessageCycleManager(
             application_generate_entity=application_generate_entity, task_state=self._task_state
         )
@@ -142,7 +137,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._application_generate_entity = application_generate_entity
         self._workflow_id = workflow.id
         self._workflow_features_dict = workflow.features_dict
-        self._workflow_tenant_id = workflow.tenant_id
         self._conversation_id = conversation.id
         self._conversation_mode = conversation.mode
         self._message_id = message.id
@@ -152,13 +146,8 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._workflow_run_id: str = ""
         self._draft_var_saver_factory = draft_var_saver_factory
         self._graph_runtime_state: GraphRuntimeState | None = None
-        self._message_saved_on_pause = False
         self._seed_graph_runtime_state_from_queue_manager()
 
-    def _seed_task_state_from_message(self, message: Message) -> None:
-        if message.status == MessageStatus.PAUSED and message.answer:
-            self._task_state.answer = message.answer
-
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
         """
         Process generate task pipeline.
@@ -321,7 +310,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             task_id=self._application_generate_entity.task_id,
             workflow_run_id=run_id,
             workflow_id=self._workflow_id,
-            reason=event.reason,
         )
 
         yield workflow_start_resp
@@ -539,35 +527,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         )
 
         yield workflow_finish_resp
-
-    def _handle_workflow_paused_event(
-        self,
-        event: QueueWorkflowPausedEvent,
-        **kwargs,
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle workflow paused events."""
-        validated_state = self._ensure_graph_runtime_initialized()
-        responses = self._workflow_response_converter.workflow_pause_to_stream_response(
-            event=event,
-            task_id=self._application_generate_entity.task_id,
-            graph_runtime_state=validated_state,
-        )
-        for reason in event.reasons:
-            if isinstance(reason, HumanInputRequired):
-                self._persist_human_input_extra_content(form_id=reason.form_id, node_id=reason.node_id)
-        yield from responses
-        resolved_state: GraphRuntimeState | None = None
-        try:
-            resolved_state = self._ensure_graph_runtime_initialized()
-        except ValueError:
-            resolved_state = None
-
-        with self._database_session() as session:
-            self._save_message(session=session, graph_runtime_state=resolved_state)
-            message = self._get_message(session=session)
-            if message is not None:
-                message.status = MessageStatus.PAUSED
-            self._message_saved_on_pause = True
         self._base_task_pipeline.queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
 
     def _handle_workflow_failed_event(
@@ -607,7 +566,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
         """Handle stop events."""
-        _ = trace_manager
         resolved_state = None
         if self._workflow_run_id:
             resolved_state = self._resolve_graph_runtime_state(graph_runtime_state)
@@ -622,8 +580,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             )
 
             with self._database_session() as session:
-                # Save message
-                self._save_message(session=session, graph_runtime_state=resolved_state)
+                self._save_message(session=session, graph_runtime_state=resolved_state, trace_manager=trace_manager)
 
             yield workflow_finish_resp
         elif event.stopped_by in (
@@ -632,8 +589,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ):
             # When hitting input-moderation or annotation-reply, the workflow will not start
             with self._database_session() as session:
-                # Save message
-                self._save_message(session=session)
+                self._save_message(session=session, trace_manager=trace_manager)
 
         yield self._message_end_to_stream_response()
 
@@ -642,6 +598,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         event: QueueAdvancedChatMessageEndEvent,
         *,
         graph_runtime_state: GraphRuntimeState | None = None,
+        trace_manager: TraceQueueManager | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
         """Handle advanced chat message end events."""
@@ -657,10 +614,9 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
                 reason=QueueMessageReplaceEvent.MessageReplaceReason.OUTPUT_MODERATION,
             )
 
-        # Save message unless it has already been persisted on pause.
-        if not self._message_saved_on_pause:
-            with self._database_session() as session:
-                self._save_message(session=session, graph_runtime_state=resolved_state)
+        # Save message
+        with self._database_session() as session:
+            self._save_message(session=session, graph_runtime_state=resolved_state, trace_manager=trace_manager)
 
         yield self._message_end_to_stream_response()
 
@@ -686,65 +642,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         """Handle message replace events."""
         yield self._message_cycle_manager.message_replace_to_stream_response(answer=event.text, reason=event.reason)
 
-    def _handle_human_input_form_filled_event(
-        self, event: QueueHumanInputFormFilledEvent, **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle human input form filled events."""
-        self._persist_human_input_extra_content(node_id=event.node_id)
-        yield self._workflow_response_converter.human_input_form_filled_to_stream_response(
-            event=event, task_id=self._application_generate_entity.task_id
-        )
-
-    def _handle_human_input_form_timeout_event(
-        self, event: QueueHumanInputFormTimeoutEvent, **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle human input form timeout events."""
-        yield self._workflow_response_converter.human_input_form_timeout_to_stream_response(
-            event=event, task_id=self._application_generate_entity.task_id
-        )
-
-    def _persist_human_input_extra_content(self, *, node_id: str | None = None, form_id: str | None = None) -> None:
-        if not self._workflow_run_id or not self._message_id:
-            return
-
-        if form_id is None:
-            if node_id is None:
-                return
-            form_id = self._load_human_input_form_id(node_id=node_id)
-            if form_id is None:
-                logger.warning(
-                    "HumanInput form not found for workflow run %s node %s",
-                    self._workflow_run_id,
-                    node_id,
-                )
-                return
-
-        with self._database_session() as session:
-            exists_stmt = select(HumanInputContent).where(
-                HumanInputContent.workflow_run_id == self._workflow_run_id,
-                HumanInputContent.message_id == self._message_id,
-                HumanInputContent.form_id == form_id,
-            )
-            if session.scalar(exists_stmt) is not None:
-                return
-
-            content = HumanInputContent(
-                workflow_run_id=self._workflow_run_id,
-                message_id=self._message_id,
-                form_id=form_id,
-            )
-            session.add(content)
-
-    def _load_human_input_form_id(self, *, node_id: str) -> str | None:
-        form_repository = HumanInputFormRepositoryImpl(
-            session_factory=db.engine,
-            tenant_id=self._workflow_tenant_id,
-        )
-        form = form_repository.get_form(self._workflow_run_id, node_id)
-        if form is None:
-            return None
-        return form.id
-
     def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
         """Handle agent log events."""
         yield self._workflow_response_converter.handle_agent_log(
@@ -762,7 +659,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             QueueWorkflowStartedEvent: self._handle_workflow_started_event,
             QueueWorkflowSucceededEvent: self._handle_workflow_succeeded_event,
             QueueWorkflowPartialSuccessEvent: self._handle_workflow_partial_success_event,
-            QueueWorkflowPausedEvent: self._handle_workflow_paused_event,
             QueueWorkflowFailedEvent: self._handle_workflow_failed_event,
             # Node events
             QueueNodeRetryEvent: self._handle_node_retry_event,
@@ -784,8 +680,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             QueueMessageReplaceEvent: self._handle_message_replace_event,
             QueueAdvancedChatMessageEndEvent: self._handle_advanced_chat_message_end_event,
             QueueAgentLogEvent: self._handle_agent_log_event,
-            QueueHumanInputFormFilledEvent: self._handle_human_input_form_filled_event,
-            QueueHumanInputFormTimeoutEvent: self._handle_human_input_form_timeout_event,
         }
 
     def _dispatch_event(
@@ -853,9 +747,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
                 case QueueWorkflowFailedEvent():
                     yield from self._handle_workflow_failed_event(event, trace_manager=trace_manager)
                     break
-                case QueueWorkflowPausedEvent():
-                    yield from self._handle_workflow_paused_event(event)
-                    break
 
                 case QueueStopEvent():
                     yield from self._handle_stop_event(event, graph_runtime_state=None, trace_manager=trace_manager)
@@ -879,13 +770,14 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if self._conversation_name_generate_thread:
             logger.debug("Conversation name generation running as daemon thread")
 
-    def _save_message(self, *, session: Session, graph_runtime_state: GraphRuntimeState | None = None):
+    def _save_message(
+        self,
+        *,
+        session: Session,
+        graph_runtime_state: GraphRuntimeState | None = None,
+        trace_manager: TraceQueueManager | None = None,
+    ):
         message = self._get_message(session=session)
-        if message is None:
-            return
-
-        if message.status == MessageStatus.PAUSED:
-            message.status = MessageStatus.NORMAL
 
         # If there are assistant files, remove markdown image links from answer
         answer_text = self._task_state.answer
@@ -940,6 +832,22 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
+        if trace_manager:
+            telemetry_emit(
+                TelemetryEvent(
+                    name=TraceTaskName.MESSAGE_TRACE,
+                    context=TelemetryContext(
+                        tenant_id=self._application_generate_entity.app_config.tenant_id,
+                        app_id=self._application_generate_entity.app_config.app_id,
+                    ),
+                    payload={
+                        "conversation_id": str(message.conversation_id),
+                        "message_id": str(message.id),
+                    },
+                ),
+                trace_manager=trace_manager,
+            )
+
     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/common/workflow_response_converter.py

@@ -5,14 +5,9 @@ from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, NewType, Union
 
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
     QueueAgentLogEvent,
-    QueueHumanInputFormFilledEvent,
-    QueueHumanInputFormTimeoutEvent,
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
@@ -24,13 +19,9 @@ from core.app.entities.queue_entities import (
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
-    QueueWorkflowPausedEvent,
 )
 from core.app.entities.task_entities import (
     AgentLogStreamResponse,
-    HumanInputFormFilledResponse,
-    HumanInputFormTimeoutResponse,
-    HumanInputRequiredResponse,
     IterationNodeCompletedStreamResponse,
     IterationNodeNextStreamResponse,
     IterationNodeStartStreamResponse,
@@ -40,9 +31,7 @@ from core.app.entities.task_entities import (
     NodeFinishStreamResponse,
     NodeRetryStreamResponse,
     NodeStartStreamResponse,
-    StreamResponse,
     WorkflowFinishStreamResponse,
-    WorkflowPauseStreamResponse,
     WorkflowStartStreamResponse,
 )
 from core.file import FILE_MODEL_IDENTITY, File
@@ -51,8 +40,6 @@ from core.tools.entities.tool_entities import ToolProviderType
 from core.tools.tool_manager import ToolManager
 from core.trigger.trigger_manager import TriggerManager
 from core.variables.segments import ArrayFileSegment, FileSegment, Segment
-from core.workflow.entities.pause_reason import HumanInputRequired
-from core.workflow.entities.workflow_start_reason import WorkflowStartReason
 from core.workflow.enums import (
     NodeType,
     SystemVariableKey,
@@ -64,11 +51,8 @@ from core.workflow.runtime import GraphRuntimeState
 from core.workflow.system_variable import SystemVariable
 from core.workflow.workflow_entry import WorkflowEntry
 from core.workflow.workflow_type_encoder import WorkflowRuntimeTypeConverter
-from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
 from models import Account, EndUser
-from models.human_input import HumanInputForm
-from models.workflow import WorkflowRun
 from services.variable_truncator import BaseTruncator, DummyVariableTruncator, VariableTruncator
 
 NodeExecutionId = NewType("NodeExecutionId", str)
@@ -207,7 +191,6 @@ class WorkflowResponseConverter:
         task_id: str,
         workflow_run_id: str,
         workflow_id: str,
-        reason: WorkflowStartReason,
     ) -> WorkflowStartStreamResponse:
         run_id = self._ensure_workflow_run_id(workflow_run_id)
         started_at = naive_utc_now()
@@ -221,7 +204,6 @@ class WorkflowResponseConverter:
                 workflow_id=workflow_id,
                 inputs=self._workflow_inputs,
                 created_at=int(started_at.timestamp()),
-                reason=reason,
             ),
         )
 
@@ -282,160 +264,6 @@ class WorkflowResponseConverter:
             ),
         )
 
-    def workflow_pause_to_stream_response(
-        self,
-        *,
-        event: QueueWorkflowPausedEvent,
-        task_id: str,
-        graph_runtime_state: GraphRuntimeState,
-    ) -> list[StreamResponse]:
-        run_id = self._ensure_workflow_run_id()
-        started_at = self._workflow_started_at
-        if started_at is None:
-            raise ValueError(
-                "workflow_pause_to_stream_response called before workflow_start_to_stream_response",
-            )
-        paused_at = naive_utc_now()
-        elapsed_time = (paused_at - started_at).total_seconds()
-        encoded_outputs = self._encode_outputs(event.outputs) or {}
-        if self._application_generate_entity.invoke_from == InvokeFrom.SERVICE_API:
-            encoded_outputs = {}
-        pause_reasons = [reason.model_dump(mode="json") for reason in event.reasons]
-        human_input_form_ids = [reason.form_id for reason in event.reasons if isinstance(reason, HumanInputRequired)]
-        expiration_times_by_form_id: dict[str, datetime] = {}
-        if human_input_form_ids:
-            stmt = select(HumanInputForm.id, HumanInputForm.expiration_time).where(
-                HumanInputForm.id.in_(human_input_form_ids)
-            )
-            with Session(bind=db.engine) as session:
-                for form_id, expiration_time in session.execute(stmt):
-                    expiration_times_by_form_id[str(form_id)] = expiration_time
-
-        responses: list[StreamResponse] = []
-
-        for reason in event.reasons:
-            if isinstance(reason, HumanInputRequired):
-                expiration_time = expiration_times_by_form_id.get(reason.form_id)
-                if expiration_time is None:
-                    raise ValueError(f"HumanInputForm not found for pause reason, form_id={reason.form_id}")
-                responses.append(
-                    HumanInputRequiredResponse(
-                        task_id=task_id,
-                        workflow_run_id=run_id,
-                        data=HumanInputRequiredResponse.Data(
-                            form_id=reason.form_id,
-                            node_id=reason.node_id,
-                            node_title=reason.node_title,
-                            form_content=reason.form_content,
-                            inputs=reason.inputs,
-                            actions=reason.actions,
-                            display_in_ui=reason.display_in_ui,
-                            form_token=reason.form_token,
-                            resolved_default_values=reason.resolved_default_values,
-                            expiration_time=int(expiration_time.timestamp()),
-                        ),
-                    )
-                )
-
-        responses.append(
-            WorkflowPauseStreamResponse(
-                task_id=task_id,
-                workflow_run_id=run_id,
-                data=WorkflowPauseStreamResponse.Data(
-                    workflow_run_id=run_id,
-                    paused_nodes=list(event.paused_nodes),
-                    outputs=encoded_outputs,
-                    reasons=pause_reasons,
-                    status=WorkflowExecutionStatus.PAUSED,
-                    created_at=int(started_at.timestamp()),
-                    elapsed_time=elapsed_time,
-                    total_tokens=graph_runtime_state.total_tokens,
-                    total_steps=graph_runtime_state.node_run_steps,
-                ),
-            )
-        )
-
-        return responses
-
-    def human_input_form_filled_to_stream_response(
-        self, *, event: QueueHumanInputFormFilledEvent, task_id: str
-    ) -> HumanInputFormFilledResponse:
-        run_id = self._ensure_workflow_run_id()
-        return HumanInputFormFilledResponse(
-            task_id=task_id,
-            workflow_run_id=run_id,
-            data=HumanInputFormFilledResponse.Data(
-                node_id=event.node_id,
-                node_title=event.node_title,
-                rendered_content=event.rendered_content,
-                action_id=event.action_id,
-                action_text=event.action_text,
-            ),
-        )
-
-    def human_input_form_timeout_to_stream_response(
-        self, *, event: QueueHumanInputFormTimeoutEvent, task_id: str
-    ) -> HumanInputFormTimeoutResponse:
-        run_id = self._ensure_workflow_run_id()
-        return HumanInputFormTimeoutResponse(
-            task_id=task_id,
-            workflow_run_id=run_id,
-            data=HumanInputFormTimeoutResponse.Data(
-                node_id=event.node_id,
-                node_title=event.node_title,
-                expiration_time=int(event.expiration_time.timestamp()),
-            ),
-        )
-
-    @classmethod
-    def workflow_run_result_to_finish_response(
-        cls,
-        *,
-        task_id: str,
-        workflow_run: WorkflowRun,
-        creator_user: Account | EndUser,
-    ) -> WorkflowFinishStreamResponse:
-        run_id = workflow_run.id
-        elapsed_time = workflow_run.elapsed_time
-
-        encoded_outputs = workflow_run.outputs_dict
-        finished_at = workflow_run.finished_at
-        assert finished_at is not None
-
-        created_by: Mapping[str, object]
-        user = creator_user
-        if isinstance(user, Account):
-            created_by = {
-                "id": user.id,
-                "name": user.name,
-                "email": user.email,
-            }
-        else:
-            created_by = {
-                "id": user.id,
-                "user": user.session_id,
-            }
-
-        return WorkflowFinishStreamResponse(
-            task_id=task_id,
-            workflow_run_id=run_id,
-            data=WorkflowFinishStreamResponse.Data(
-                id=run_id,
-                workflow_id=workflow_run.workflow_id,
-                status=workflow_run.status,
-                outputs=encoded_outputs,
-                error=workflow_run.error,
-                elapsed_time=elapsed_time,
-                total_tokens=workflow_run.total_tokens,
-                total_steps=workflow_run.total_steps,
-                created_by=created_by,
-                created_at=int(workflow_run.created_at.timestamp()),
-                finished_at=int(finished_at.timestamp()),
-                files=cls.fetch_files_from_node_outputs(encoded_outputs),
-                exceptions_count=workflow_run.exceptions_count,
-            ),
-        )
-
     def workflow_node_start_to_stream_response(
         self,
         *,
@@ -764,8 +592,7 @@ class WorkflowResponseConverter:
             ),
         )
 
-    @classmethod
-    def fetch_files_from_node_outputs(cls, outputs_dict: Mapping[str, Any] | None) -> Sequence[Mapping[str, Any]]:
+    def fetch_files_from_node_outputs(self, outputs_dict: Mapping[str, Any] | None) -> Sequence[Mapping[str, Any]]:
         """
         Fetch files from node outputs
         :param outputs_dict: node outputs dict
@@ -774,7 +601,7 @@ class WorkflowResponseConverter:
         if not outputs_dict:
             return []
 
-        files = [cls._fetch_files_from_variable_value(output_value) for output_value in outputs_dict.values()]
+        files = [self._fetch_files_from_variable_value(output_value) for output_value in outputs_dict.values()]
         # Remove None
         files = [file for file in files if file]
         # Flatten list

api/core/app/apps/message_based_app_generator.py

@@ -1,6 +1,6 @@
 import json
 import logging
-from collections.abc import Callable, Generator, Mapping
+from collections.abc import Generator
 from typing import Union, cast
 
 from sqlalchemy import select
@@ -10,14 +10,12 @@ from core.app.app_config.entities import EasyUIBasedAppConfig, EasyUIBasedAppMod
 from core.app.apps.base_app_generator import BaseAppGenerator
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.apps.exc import GenerateTaskStoppedError
-from core.app.apps.streaming_utils import stream_topic_events
 from core.app.entities.app_invoke_entities import (
     AdvancedChatAppGenerateEntity,
     AgentChatAppGenerateEntity,
     AppGenerateEntity,
     ChatAppGenerateEntity,
     CompletionAppGenerateEntity,
-    ConversationAppGenerateEntity,
     InvokeFrom,
 )
 from core.app.entities.task_entities import (
@@ -29,8 +27,6 @@ from core.app.entities.task_entities import (
 from core.app.task_pipeline.easy_ui_based_generate_task_pipeline import EasyUIBasedGenerateTaskPipeline
 from core.prompt.utils.prompt_template_parser import PromptTemplateParser
 from extensions.ext_database import db
-from extensions.ext_redis import get_pubsub_broadcast_channel
-from libs.broadcast_channel.channel import Topic
 from libs.datetime_utils import naive_utc_now
 from models import Account
 from models.enums import CreatorUserRole
@@ -160,7 +156,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
         query = application_generate_entity.query or "New conversation"
         conversation_name = (query[:20] + "…") if len(query) > 20 else query
 
-        created_new_conversation = conversation is None
         try:
             if not conversation:
                 conversation = Conversation(
@@ -237,10 +232,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
                 db.session.add_all(message_files)
 
             db.session.commit()
-
-            if isinstance(application_generate_entity, ConversationAppGenerateEntity):
-                application_generate_entity.conversation_id = conversation.id
-                application_generate_entity.is_new_conversation = created_new_conversation
             return conversation, message
         except Exception:
             db.session.rollback()
@@ -293,29 +284,3 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             raise MessageNotExistsError("Message not exists")
 
         return message
-
-    @staticmethod
-    def _make_channel_key(app_mode: AppMode, workflow_run_id: str):
-        return f"channel:{app_mode}:{workflow_run_id}"
-
-    @classmethod
-    def get_response_topic(cls, app_mode: AppMode, workflow_run_id: str) -> Topic:
-        key = cls._make_channel_key(app_mode, workflow_run_id)
-        channel = get_pubsub_broadcast_channel()
-        topic = channel.topic(key)
-        return topic
-
-    @classmethod
-    def retrieve_events(
-        cls,
-        app_mode: AppMode,
-        workflow_run_id: str,
-        idle_timeout=300,
-        on_subscribe: Callable[[], None] | None = None,
-    ) -> Generator[Mapping | str, None, None]:
-        topic = cls.get_response_topic(app_mode, workflow_run_id)
-        return stream_topic_events(
-            topic=topic,
-            idle_timeout=idle_timeout,
-            on_subscribe=on_subscribe,
-        )

api/core/app/apps/message_generator.py

@@ -1,36 +0,0 @@
-from collections.abc import Callable, Generator, Mapping
-
-from core.app.apps.streaming_utils import stream_topic_events
-from extensions.ext_redis import get_pubsub_broadcast_channel
-from libs.broadcast_channel.channel import Topic
-from models.model import AppMode
-
-
-class MessageGenerator:
-    @staticmethod
-    def _make_channel_key(app_mode: AppMode, workflow_run_id: str):
-        return f"channel:{app_mode}:{str(workflow_run_id)}"
-
-    @classmethod
-    def get_response_topic(cls, app_mode: AppMode, workflow_run_id: str) -> Topic:
-        key = cls._make_channel_key(app_mode, workflow_run_id)
-        channel = get_pubsub_broadcast_channel()
-        topic = channel.topic(key)
-        return topic
-
-    @classmethod
-    def retrieve_events(
-        cls,
-        app_mode: AppMode,
-        workflow_run_id: str,
-        idle_timeout=300,
-        ping_interval: float = 10.0,
-        on_subscribe: Callable[[], None] | None = None,
-    ) -> Generator[Mapping | str, None, None]:
-        topic = cls.get_response_topic(app_mode, workflow_run_id)
-        return stream_topic_events(
-            topic=topic,
-            idle_timeout=idle_timeout,
-            ping_interval=ping_interval,
-            on_subscribe=on_subscribe,
-        )

api/core/app/apps/streaming_utils.py

@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-import json
-import time
-from collections.abc import Callable, Generator, Iterable, Mapping
-from typing import Any
-
-from core.app.entities.task_entities import StreamEvent
-from libs.broadcast_channel.channel import Topic
-from libs.broadcast_channel.exc import SubscriptionClosedError
-
-
-def stream_topic_events(
-    *,
-    topic: Topic,
-    idle_timeout: float,
-    ping_interval: float | None = None,
-    on_subscribe: Callable[[], None] | None = None,
-    terminal_events: Iterable[str | StreamEvent] | None = None,
-) -> Generator[Mapping[str, Any] | str, None, None]:
-    # send a PING event immediately to prevent the connection staying in pending state for a long time.
-    #
-    # This simplify the debugging process as the DevTools in Chrome does not
-    # provide complete curl command for pending connections.
-    yield StreamEvent.PING.value
-
-    terminal_values = _normalize_terminal_events(terminal_events)
-    last_msg_time = time.time()
-    last_ping_time = last_msg_time
-    with topic.subscribe() as sub:
-        # on_subscribe fires only after the Redis subscription is active.
-        # This is used to gate task start and reduce pub/sub race for the first event.
-        if on_subscribe is not None:
-            on_subscribe()
-        while True:
-            try:
-                msg = sub.receive(timeout=1)
-            except SubscriptionClosedError:
-                return
-            if msg is None:
-                current_time = time.time()
-                if current_time - last_msg_time > idle_timeout:
-                    return
-                if ping_interval is not None and current_time - last_ping_time >= ping_interval:
-                    yield StreamEvent.PING.value
-                    last_ping_time = current_time
-                continue
-
-            last_msg_time = time.time()
-            last_ping_time = last_msg_time
-            event = json.loads(msg)
-            yield event
-            if not isinstance(event, dict):
-                continue
-
-            event_type = event.get("event")
-            if event_type in terminal_values:
-                return
-
-
-def _normalize_terminal_events(terminal_events: Iterable[str | StreamEvent] | None) -> set[str]:
-    if not terminal_events:
-        return {StreamEvent.WORKFLOW_FINISHED.value, StreamEvent.WORKFLOW_PAUSED.value}
-    values: set[str] = set()
-    for item in terminal_events:
-        if isinstance(item, StreamEvent):
-            values.add(item.value)
-        else:
-            values.add(str(item))
-    return values

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

@@ -25,7 +25,6 @@ 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.pause_state_persist_layer import PauseStateLayerConfig, PauseStatePersistenceLayer
 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
@@ -35,15 +34,12 @@ 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
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
-from core.workflow.runtime import GraphRuntimeState
 from core.workflow.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader
 from extensions.ext_database import db
 from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
-from models.account import Account
+from models import Account, App, EndUser, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.enums import WorkflowRunTriggeredFrom
-from models.model import App, EndUser
-from models.workflow import Workflow, WorkflowNodeExecutionTriggeredFrom
 from services.workflow_draft_variable_service import DraftVarLoader, WorkflowDraftVariableService
 
 if TYPE_CHECKING:
@@ -70,11 +66,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: Literal[True],
         call_depth: int,
-        workflow_run_id: str | uuid.UUID | None = None,
         triggered_from: WorkflowRunTriggeredFrom | None = None,
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Generator[Mapping[str, Any] | str, None, None]: ...
 
     @overload
@@ -88,11 +82,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: Literal[False],
         call_depth: int,
-        workflow_run_id: str | uuid.UUID | None = None,
         triggered_from: WorkflowRunTriggeredFrom | None = None,
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Mapping[str, Any]: ...
 
     @overload
@@ -106,11 +98,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: bool,
         call_depth: int,
-        workflow_run_id: str | uuid.UUID | None = None,
         triggered_from: WorkflowRunTriggeredFrom | None = None,
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Union[Mapping[str, Any], Generator[Mapping[str, Any] | str, None, None]]: ...
 
     def generate(
@@ -123,11 +113,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         streaming: bool = True,
         call_depth: int = 0,
-        workflow_run_id: str | uuid.UUID | None = None,
         triggered_from: WorkflowRunTriggeredFrom | None = None,
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Union[Mapping[str, Any], Generator[Mapping[str, Any] | str, None, None]]:
         files: Sequence[Mapping[str, Any]] = args.get("files") or []
 
@@ -159,10 +147,13 @@ class WorkflowAppGenerator(BaseAppGenerator):
 
         inputs: Mapping[str, Any] = args["inputs"]
 
-        extras = {
+        extras: dict[str, Any] = {
             **extract_external_trace_id_from_args(args),
         }
-        workflow_run_id = str(workflow_run_id or uuid.uuid4())
+        parent_trace_context = args.get("_parent_trace_context")
+        if parent_trace_context:
+            extras["parent_trace_context"] = parent_trace_context
+        workflow_run_id = str(uuid.uuid4())
         # FIXME (Yeuoly): we need to remove the SKIP_PREPARE_USER_INPUTS_KEY from the args
         # trigger shouldn't prepare user inputs
         if self._should_prepare_user_inputs(args):
@@ -228,40 +219,13 @@ class WorkflowAppGenerator(BaseAppGenerator):
             streaming=streaming,
             root_node_id=root_node_id,
             graph_engine_layers=graph_engine_layers,
-            pause_state_config=pause_state_config,
         )
 
-    def resume(
-        self,
-        *,
-        app_model: App,
-        workflow: Workflow,
-        user: Union[Account, EndUser],
-        application_generate_entity: WorkflowAppGenerateEntity,
-        graph_runtime_state: GraphRuntimeState,
-        workflow_execution_repository: WorkflowExecutionRepository,
-        workflow_node_execution_repository: WorkflowNodeExecutionRepository,
-        graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        pause_state_config: PauseStateLayerConfig | None = None,
-        variable_loader: VariableLoader = DUMMY_VARIABLE_LOADER,
-    ) -> Union[Mapping[str, Any], Generator[str | Mapping[str, Any], None, None]]:
+    def resume(self, *, workflow_run_id: str) -> None:
         """
-        Resume a paused workflow execution using the persisted runtime state.
+        @TBD
         """
-        return self._generate(
-            app_model=app_model,
-            workflow=workflow,
-            user=user,
-            application_generate_entity=application_generate_entity,
-            invoke_from=application_generate_entity.invoke_from,
-            workflow_execution_repository=workflow_execution_repository,
-            workflow_node_execution_repository=workflow_node_execution_repository,
-            streaming=application_generate_entity.stream,
-            variable_loader=variable_loader,
-            graph_engine_layers=graph_engine_layers,
-            graph_runtime_state=graph_runtime_state,
-            pause_state_config=pause_state_config,
-        )
+        pass
 
     def _generate(
         self,
@@ -277,8 +241,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         variable_loader: VariableLoader = DUMMY_VARIABLE_LOADER,
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        graph_runtime_state: GraphRuntimeState | None = None,
-        pause_state_config: PauseStateLayerConfig | None = None,
     ) -> Union[Mapping[str, Any], Generator[str | Mapping[str, Any], None, None]]:
         """
         Generate App response.
@@ -292,8 +254,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :param workflow_node_execution_repository: repository for workflow node execution
         :param streaming: is stream
         """
-        graph_layers: list[GraphEngineLayer] = list(graph_engine_layers)
-
         # init queue manager
         queue_manager = WorkflowAppQueueManager(
             task_id=application_generate_entity.task_id,
@@ -302,15 +262,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
             app_mode=app_model.mode,
         )
 
-        if pause_state_config is not None:
-            graph_layers.append(
-                PauseStatePersistenceLayer(
-                    session_factory=pause_state_config.session_factory,
-                    generate_entity=application_generate_entity,
-                    state_owner_user_id=pause_state_config.state_owner_user_id,
-                )
-            )
-
         # new thread with request context and contextvars
         context = contextvars.copy_context()
 
@@ -328,8 +279,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 "root_node_id": root_node_id,
                 "workflow_execution_repository": workflow_execution_repository,
                 "workflow_node_execution_repository": workflow_node_execution_repository,
-                "graph_engine_layers": tuple(graph_layers),
-                "graph_runtime_state": graph_runtime_state,
+                "graph_engine_layers": graph_engine_layers,
             },
         )
 
@@ -431,7 +381,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
             workflow_node_execution_repository=workflow_node_execution_repository,
             streaming=streaming,
             variable_loader=var_loader,
-            pause_state_config=None,
         )
 
     def single_loop_generate(
@@ -513,7 +462,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
             workflow_node_execution_repository=workflow_node_execution_repository,
             streaming=streaming,
             variable_loader=var_loader,
-            pause_state_config=None,
         )
 
     def _generate_worker(
@@ -527,7 +475,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        graph_runtime_state: GraphRuntimeState | None = None,
     ) -> None:
         """
         Generate worker in a new thread.
@@ -573,7 +520,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 workflow_node_execution_repository=workflow_node_execution_repository,
                 root_node_id=root_node_id,
                 graph_engine_layers=graph_engine_layers,
-                graph_runtime_state=graph_runtime_state,
             )
 
             try:

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

@@ -42,7 +42,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
-        graph_runtime_state: GraphRuntimeState | None = None,
     ):
         super().__init__(
             queue_manager=queue_manager,
@@ -56,7 +55,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         self._root_node_id = root_node_id
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
-        self._resume_graph_runtime_state = graph_runtime_state
 
     @trace_span(WorkflowAppRunnerHandler)
     def run(self):
@@ -65,28 +63,23 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         """
         app_config = self.application_generate_entity.app_config
         app_config = cast(WorkflowAppConfig, app_config)
+
+        system_inputs = SystemVariable(
+            files=self.application_generate_entity.files,
+            user_id=self._sys_user_id,
+            app_id=app_config.app_id,
+            timestamp=int(naive_utc_now().timestamp()),
+            workflow_id=app_config.workflow_id,
+            workflow_execution_id=self.application_generate_entity.workflow_execution_id,
+        )
+
         invoke_from = self.application_generate_entity.invoke_from
         # if only single iteration or single loop run is requested
         if self.application_generate_entity.single_iteration_run or self.application_generate_entity.single_loop_run:
             invoke_from = InvokeFrom.DEBUGGER
         user_from = self._resolve_user_from(invoke_from)
 
-        resume_state = self._resume_graph_runtime_state
-
-        if resume_state is not None:
-            graph_runtime_state = resume_state
-            variable_pool = graph_runtime_state.variable_pool
-            graph = self._init_graph(
-                graph_config=self._workflow.graph_dict,
-                graph_runtime_state=graph_runtime_state,
-                workflow_id=self._workflow.id,
-                tenant_id=self._workflow.tenant_id,
-                user_id=self.application_generate_entity.user_id,
-                user_from=user_from,
-                invoke_from=invoke_from,
-                root_node_id=self._root_node_id,
-            )
-        elif self.application_generate_entity.single_iteration_run or self.application_generate_entity.single_loop_run:
+        if self.application_generate_entity.single_iteration_run or self.application_generate_entity.single_loop_run:
             graph, variable_pool, graph_runtime_state = self._prepare_single_node_execution(
                 workflow=self._workflow,
                 single_iteration_run=self.application_generate_entity.single_iteration_run,
@@ -96,14 +89,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
             inputs = self.application_generate_entity.inputs
 
             # Create a variable pool.
-            system_inputs = SystemVariable(
-                files=self.application_generate_entity.files,
-                user_id=self._sys_user_id,
-                app_id=app_config.app_id,
-                timestamp=int(naive_utc_now().timestamp()),
-                workflow_id=app_config.workflow_id,
-                workflow_execution_id=self.application_generate_entity.workflow_execution_id,
-            )
+
             variable_pool = VariablePool(
                 system_variables=system_inputs,
                 user_inputs=inputs,
@@ -112,6 +98,8 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
             )
 
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
+
+            # init graph
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
                 graph_runtime_state=graph_runtime_state,

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

@@ -1,7 +0,0 @@
-from libs.exception import BaseHTTPException
-
-
-class WorkflowPausedInBlockingModeError(BaseHTTPException):
-    error_code = "workflow_paused_in_blocking_mode"
-    description = "Workflow execution paused for human input; blocking response mode is not supported."
-    code = 400

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

@@ -16,8 +16,6 @@ from core.app.entities.queue_entities import (
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
-    QueueHumanInputFormFilledEvent,
-    QueueHumanInputFormTimeoutEvent,
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
@@ -34,7 +32,6 @@ from core.app.entities.queue_entities import (
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
     QueueWorkflowPartialSuccessEvent,
-    QueueWorkflowPausedEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
     WorkflowQueueMessage,
@@ -49,13 +46,11 @@ from core.app.entities.task_entities import (
     WorkflowAppBlockingResponse,
     WorkflowAppStreamResponse,
     WorkflowFinishStreamResponse,
-    WorkflowPauseStreamResponse,
     WorkflowStartStreamResponse,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.workflow_start_reason import WorkflowStartReason
 from core.workflow.enums import WorkflowExecutionStatus
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.runtime import GraphRuntimeState
@@ -137,25 +132,6 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         for stream_response in generator:
             if isinstance(stream_response, ErrorStreamResponse):
                 raise stream_response.err
-            elif isinstance(stream_response, WorkflowPauseStreamResponse):
-                response = WorkflowAppBlockingResponse(
-                    task_id=self._application_generate_entity.task_id,
-                    workflow_run_id=stream_response.data.workflow_run_id,
-                    data=WorkflowAppBlockingResponse.Data(
-                        id=stream_response.data.workflow_run_id,
-                        workflow_id=self._workflow.id,
-                        status=stream_response.data.status,
-                        outputs=stream_response.data.outputs or {},
-                        error=None,
-                        elapsed_time=stream_response.data.elapsed_time,
-                        total_tokens=stream_response.data.total_tokens,
-                        total_steps=stream_response.data.total_steps,
-                        created_at=stream_response.data.created_at,
-                        finished_at=None,
-                    ),
-                )
-
-                return response
             elif isinstance(stream_response, WorkflowFinishStreamResponse):
                 response = WorkflowAppBlockingResponse(
                     task_id=self._application_generate_entity.task_id,
@@ -170,7 +146,7 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
                         total_tokens=stream_response.data.total_tokens,
                         total_steps=stream_response.data.total_steps,
                         created_at=int(stream_response.data.created_at),
-                        finished_at=int(stream_response.data.finished_at) if stream_response.data.finished_at else None,
+                        finished_at=int(stream_response.data.finished_at),
                     ),
                 )
 
@@ -283,15 +259,13 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         run_id = self._extract_workflow_run_id(runtime_state)
         self._workflow_execution_id = run_id
 
-        if event.reason == WorkflowStartReason.INITIAL:
-            with self._database_session() as session:
-                self._save_workflow_app_log(session=session, workflow_run_id=self._workflow_execution_id)
+        with self._database_session() as session:
+            self._save_workflow_app_log(session=session, workflow_run_id=self._workflow_execution_id)
 
         start_resp = self._workflow_response_converter.workflow_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_run_id=run_id,
             workflow_id=self._workflow.id,
-            reason=event.reason,
         )
         yield start_resp
 
@@ -466,21 +440,6 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         )
         yield workflow_finish_resp
 
-    def _handle_workflow_paused_event(
-        self,
-        event: QueueWorkflowPausedEvent,
-        **kwargs,
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle workflow paused events."""
-        self._ensure_workflow_initialized()
-        validated_state = self._ensure_graph_runtime_initialized()
-        responses = self._workflow_response_converter.workflow_pause_to_stream_response(
-            event=event,
-            task_id=self._application_generate_entity.task_id,
-            graph_runtime_state=validated_state,
-        )
-        yield from responses
-
     def _handle_workflow_failed_and_stop_events(
         self,
         event: Union[QueueWorkflowFailedEvent, QueueStopEvent],
@@ -536,22 +495,6 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             task_id=self._application_generate_entity.task_id, event=event
         )
 
-    def _handle_human_input_form_filled_event(
-        self, event: QueueHumanInputFormFilledEvent, **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle human input form filled events."""
-        yield self._workflow_response_converter.human_input_form_filled_to_stream_response(
-            event=event, task_id=self._application_generate_entity.task_id
-        )
-
-    def _handle_human_input_form_timeout_event(
-        self, event: QueueHumanInputFormTimeoutEvent, **kwargs
-    ) -> Generator[StreamResponse, None, None]:
-        """Handle human input form timeout events."""
-        yield self._workflow_response_converter.human_input_form_timeout_to_stream_response(
-            event=event, task_id=self._application_generate_entity.task_id
-        )
-
     def _get_event_handlers(self) -> dict[type, Callable]:
         """Get mapping of event types to their handlers using fluent pattern."""
         return {
@@ -563,7 +506,6 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             QueueWorkflowStartedEvent: self._handle_workflow_started_event,
             QueueWorkflowSucceededEvent: self._handle_workflow_succeeded_event,
             QueueWorkflowPartialSuccessEvent: self._handle_workflow_partial_success_event,
-            QueueWorkflowPausedEvent: self._handle_workflow_paused_event,
             # Node events
             QueueNodeRetryEvent: self._handle_node_retry_event,
             QueueNodeStartedEvent: self._handle_node_started_event,
@@ -578,8 +520,6 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
             QueueLoopCompletedEvent: self._handle_loop_completed_event,
             # Agent events
             QueueAgentLogEvent: self._handle_agent_log_event,
-            QueueHumanInputFormFilledEvent: self._handle_human_input_form_filled_event,
-            QueueHumanInputFormTimeoutEvent: self._handle_human_input_form_timeout_event,
         }
 
     def _dispatch_event(
@@ -662,9 +602,6 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
                 case QueueWorkflowFailedEvent():
                     yield from self._handle_workflow_failed_and_stop_events(event)
                     break
-                case QueueWorkflowPausedEvent():
-                    yield from self._handle_workflow_paused_event(event)
-                    break
 
                 case QueueStopEvent():
                     yield from self._handle_workflow_failed_and_stop_events(event)

api/core/app/apps/workflow_app_runner.py

@@ -1,4 +1,3 @@
-import logging
 import time
 from collections.abc import Mapping, Sequence
 from typing import Any, cast
@@ -8,8 +7,6 @@ from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
     QueueAgentLogEvent,
-    QueueHumanInputFormFilledEvent,
-    QueueHumanInputFormTimeoutEvent,
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
@@ -25,27 +22,22 @@ from core.app.entities.queue_entities import (
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
     QueueWorkflowPartialSuccessEvent,
-    QueueWorkflowPausedEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
 from core.app.workflow.node_factory import DifyNodeFactory
 from core.workflow.entities import GraphInitParams
-from core.workflow.entities.pause_reason import HumanInputRequired
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.graph_events import (
     GraphEngineEvent,
     GraphRunFailedEvent,
     GraphRunPartialSucceededEvent,
-    GraphRunPausedEvent,
     GraphRunStartedEvent,
     GraphRunSucceededEvent,
     NodeRunAgentLogEvent,
     NodeRunExceptionEvent,
     NodeRunFailedEvent,
-    NodeRunHumanInputFormFilledEvent,
-    NodeRunHumanInputFormTimeoutEvent,
     NodeRunIterationFailedEvent,
     NodeRunIterationNextEvent,
     NodeRunIterationStartedEvent,
@@ -69,9 +61,6 @@ from core.workflow.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader,
 from core.workflow.workflow_entry import WorkflowEntry
 from models.enums import UserFrom
 from models.workflow import Workflow
-from tasks.mail_human_input_delivery_task import dispatch_human_input_email_task
-
-logger = logging.getLogger(__name__)
 
 
 class WorkflowBasedAppRunner:
@@ -338,7 +327,7 @@ class WorkflowBasedAppRunner:
         :param event: event
         """
         if isinstance(event, GraphRunStartedEvent):
-            self._publish_event(QueueWorkflowStartedEvent(reason=event.reason))
+            self._publish_event(QueueWorkflowStartedEvent())
         elif isinstance(event, GraphRunSucceededEvent):
             self._publish_event(QueueWorkflowSucceededEvent(outputs=event.outputs))
         elif isinstance(event, GraphRunPartialSucceededEvent):
@@ -349,38 +338,6 @@ class WorkflowBasedAppRunner:
             self._publish_event(QueueWorkflowFailedEvent(error=event.error, exceptions_count=event.exceptions_count))
         elif isinstance(event, GraphRunAbortedEvent):
             self._publish_event(QueueWorkflowFailedEvent(error=event.reason or "Unknown error", exceptions_count=0))
-        elif isinstance(event, GraphRunPausedEvent):
-            runtime_state = workflow_entry.graph_engine.graph_runtime_state
-            paused_nodes = runtime_state.get_paused_nodes()
-            self._enqueue_human_input_notifications(event.reasons)
-            self._publish_event(
-                QueueWorkflowPausedEvent(
-                    reasons=event.reasons,
-                    outputs=event.outputs,
-                    paused_nodes=paused_nodes,
-                )
-            )
-        elif isinstance(event, NodeRunHumanInputFormFilledEvent):
-            self._publish_event(
-                QueueHumanInputFormFilledEvent(
-                    node_execution_id=event.id,
-                    node_id=event.node_id,
-                    node_type=event.node_type,
-                    node_title=event.node_title,
-                    rendered_content=event.rendered_content,
-                    action_id=event.action_id,
-                    action_text=event.action_text,
-                )
-            )
-        elif isinstance(event, NodeRunHumanInputFormTimeoutEvent):
-            self._publish_event(
-                QueueHumanInputFormTimeoutEvent(
-                    node_id=event.node_id,
-                    node_type=event.node_type,
-                    node_title=event.node_title,
-                    expiration_time=event.expiration_time,
-                )
-            )
         elif isinstance(event, NodeRunRetryEvent):
             node_run_result = event.node_run_result
             inputs = node_run_result.inputs
@@ -587,19 +544,5 @@ class WorkflowBasedAppRunner:
                 )
             )
 
-    def _enqueue_human_input_notifications(self, reasons: Sequence[object]) -> None:
-        for reason in reasons:
-            if not isinstance(reason, HumanInputRequired):
-                continue
-            if not reason.form_id:
-                continue
-            try:
-                dispatch_human_input_email_task.apply_async(
-                    kwargs={"form_id": reason.form_id, "node_title": reason.node_title},
-                    queue="mail",
-                )
-            except Exception:  # pragma: no cover - defensive logging
-                logger.exception("Failed to enqueue human input email task for form %s", reason.form_id)
-
     def _publish_event(self, event: AppQueueEvent):
         self._queue_manager.publish(event, PublishFrom.APPLICATION_MANAGER)

api/core/app/entities/app_invoke_entities.py

@@ -132,7 +132,7 @@ class AppGenerateEntity(BaseModel):
     extras: dict[str, Any] = Field(default_factory=dict)
 
     # tracing instance
-    trace_manager: Optional["TraceQueueManager"] = Field(default=None, exclude=True, repr=False)
+    trace_manager: Optional["TraceQueueManager"] = None
 
 
 class EasyUIBasedAppGenerateEntity(AppGenerateEntity):
@@ -156,7 +156,6 @@ class ConversationAppGenerateEntity(AppGenerateEntity):
     """
 
     conversation_id: str | None = None
-    is_new_conversation: bool = False
     parent_message_id: str | None = Field(
         default=None,
         description=(

api/core/app/entities/queue_entities.py

@@ -8,8 +8,6 @@ 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
-from core.workflow.entities.pause_reason import PauseReason
-from core.workflow.entities.workflow_start_reason import WorkflowStartReason
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes import NodeType
 
@@ -48,9 +46,6 @@ class QueueEvent(StrEnum):
     PING = "ping"
     STOP = "stop"
     RETRY = "retry"
-    PAUSE = "pause"
-    HUMAN_INPUT_FORM_FILLED = "human_input_form_filled"
-    HUMAN_INPUT_FORM_TIMEOUT = "human_input_form_timeout"
 
 
 class AppQueueEvent(BaseModel):
@@ -266,8 +261,6 @@ class QueueWorkflowStartedEvent(AppQueueEvent):
     """QueueWorkflowStartedEvent entity."""
 
     event: QueueEvent = QueueEvent.WORKFLOW_STARTED
-    # Always present; mirrors GraphRunStartedEvent.reason for downstream consumers.
-    reason: WorkflowStartReason = WorkflowStartReason.INITIAL
 
 
 class QueueWorkflowSucceededEvent(AppQueueEvent):
@@ -491,35 +484,6 @@ class QueueStopEvent(AppQueueEvent):
         return reason_mapping.get(self.stopped_by, "Stopped by unknown reason.")
 
 
-class QueueHumanInputFormFilledEvent(AppQueueEvent):
-    """
-    QueueHumanInputFormFilledEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.HUMAN_INPUT_FORM_FILLED
-
-    node_execution_id: str
-    node_id: str
-    node_type: NodeType
-    node_title: str
-    rendered_content: str
-    action_id: str
-    action_text: str
-
-
-class QueueHumanInputFormTimeoutEvent(AppQueueEvent):
-    """
-    QueueHumanInputFormTimeoutEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.HUMAN_INPUT_FORM_TIMEOUT
-
-    node_id: str
-    node_type: NodeType
-    node_title: str
-    expiration_time: datetime
-
-
 class QueueMessage(BaseModel):
     """
     QueueMessage abstract entity
@@ -545,14 +509,3 @@ class WorkflowQueueMessage(QueueMessage):
     """
 
     pass
-
-
-class QueueWorkflowPausedEvent(AppQueueEvent):
-    """
-    QueueWorkflowPausedEvent entity
-    """
-
-    event: QueueEvent = QueueEvent.PAUSE
-    reasons: Sequence[PauseReason] = Field(default_factory=list)
-    outputs: Mapping[str, object] = Field(default_factory=dict)
-    paused_nodes: Sequence[str] = Field(default_factory=list)

api/core/app/entities/task_entities.py

@@ -7,9 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.workflow.entities import AgentNodeStrategyInit
-from core.workflow.entities.workflow_start_reason import WorkflowStartReason
 from core.workflow.enums import WorkflowExecutionStatus, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
-from core.workflow.nodes.human_input.entities import FormInput, UserAction
 
 
 class AnnotationReplyAccount(BaseModel):
@@ -71,7 +69,6 @@ class StreamEvent(StrEnum):
     AGENT_THOUGHT = "agent_thought"
     AGENT_MESSAGE = "agent_message"
     WORKFLOW_STARTED = "workflow_started"
-    WORKFLOW_PAUSED = "workflow_paused"
     WORKFLOW_FINISHED = "workflow_finished"
     NODE_STARTED = "node_started"
     NODE_FINISHED = "node_finished"
@@ -85,9 +82,6 @@ class StreamEvent(StrEnum):
     TEXT_CHUNK = "text_chunk"
     TEXT_REPLACE = "text_replace"
     AGENT_LOG = "agent_log"
-    HUMAN_INPUT_REQUIRED = "human_input_required"
-    HUMAN_INPUT_FORM_FILLED = "human_input_form_filled"
-    HUMAN_INPUT_FORM_TIMEOUT = "human_input_form_timeout"
 
 
 class StreamResponse(BaseModel):
@@ -211,8 +205,6 @@ class WorkflowStartStreamResponse(StreamResponse):
         workflow_id: str
         inputs: Mapping[str, Any]
         created_at: int
-        # Always present; mirrors QueueWorkflowStartedEvent.reason for SSE clients.
-        reason: WorkflowStartReason = WorkflowStartReason.INITIAL
 
     event: StreamEvent = StreamEvent.WORKFLOW_STARTED
     workflow_run_id: str
@@ -239,7 +231,7 @@ class WorkflowFinishStreamResponse(StreamResponse):
         total_steps: int
         created_by: Mapping[str, object] = Field(default_factory=dict)
         created_at: int
-        finished_at: int | None
+        finished_at: int
         exceptions_count: int | None = 0
         files: Sequence[Mapping[str, Any]] | None = []
 
@@ -248,85 +240,6 @@ class WorkflowFinishStreamResponse(StreamResponse):
     data: Data
 
 
-class WorkflowPauseStreamResponse(StreamResponse):
-    """
-    WorkflowPauseStreamResponse entity
-    """
-
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-
-        workflow_run_id: str
-        paused_nodes: Sequence[str] = Field(default_factory=list)
-        outputs: Mapping[str, Any] = Field(default_factory=dict)
-        reasons: Sequence[Mapping[str, Any]] = Field(default_factory=list)
-        status: WorkflowExecutionStatus
-        created_at: int
-        elapsed_time: float
-        total_tokens: int
-        total_steps: int
-
-    event: StreamEvent = StreamEvent.WORKFLOW_PAUSED
-    workflow_run_id: str
-    data: Data
-
-
-class HumanInputRequiredResponse(StreamResponse):
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-
-        form_id: str
-        node_id: str
-        node_title: str
-        form_content: str
-        inputs: Sequence[FormInput] = Field(default_factory=list)
-        actions: Sequence[UserAction] = Field(default_factory=list)
-        display_in_ui: bool = False
-        form_token: str | None = None
-        resolved_default_values: Mapping[str, Any] = Field(default_factory=dict)
-        expiration_time: int = Field(..., description="Unix timestamp in seconds")
-
-    event: StreamEvent = StreamEvent.HUMAN_INPUT_REQUIRED
-    workflow_run_id: str
-    data: Data
-
-
-class HumanInputFormFilledResponse(StreamResponse):
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-
-        node_id: str
-        node_title: str
-        rendered_content: str
-        action_id: str
-        action_text: str
-
-    event: StreamEvent = StreamEvent.HUMAN_INPUT_FORM_FILLED
-    workflow_run_id: str
-    data: Data
-
-
-class HumanInputFormTimeoutResponse(StreamResponse):
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-
-        node_id: str
-        node_title: str
-        expiration_time: int
-
-    event: StreamEvent = StreamEvent.HUMAN_INPUT_FORM_TIMEOUT
-    workflow_run_id: str
-    data: Data
-
-
 class NodeStartStreamResponse(StreamResponse):
     """
     NodeStartStreamResponse entity
@@ -813,7 +726,7 @@ class WorkflowAppBlockingResponse(AppBlockingResponse):
         total_tokens: int
         total_steps: int
         created_at: int
-        finished_at: int | None
+        finished_at: int
 
     workflow_run_id: str
     data: Data

api/core/app/features/rate_limiting/rate_limit.py

@@ -1,4 +1,3 @@
-import contextlib
 import logging
 import time
 import uuid
@@ -104,14 +103,6 @@ class RateLimit:
             )
 
 
-@contextlib.contextmanager
-def rate_limit_context(rate_limit: RateLimit, request_id: str | None):
-    request_id = rate_limit.enter(request_id)
-    yield
-    if request_id is not None:
-        rate_limit.exit(request_id)
-
-
 class RateLimitGenerator:
     def __init__(self, rate_limit: RateLimit, generator: Generator[str, None, None], request_id: str):
         self.rate_limit = rate_limit

api/core/app/layers/pause_state_persist_layer.py

@@ -1,4 +1,3 @@
-from dataclasses import dataclass
 from typing import Annotated, Literal, Self, TypeAlias
 
 from pydantic import BaseModel, Field
@@ -53,14 +52,6 @@ class WorkflowResumptionContext(BaseModel):
         return self.generate_entity.entity
 
 
-@dataclass(frozen=True)
-class PauseStateLayerConfig:
-    """Configuration container for instantiating pause persistence layers."""
-
-    session_factory: Engine | sessionmaker[Session]
-    state_owner_user_id: str
-
-
 class PauseStatePersistenceLayer(GraphEngineLayer):
     def __init__(
         self,

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -54,10 +54,11 @@ from core.model_runtime.entities.message_entities import (
     TextPromptMessageContent,
 )
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.ops.entities.trace_entity import TraceTaskName
-from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
+from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
 from core.prompt.utils.prompt_template_parser import PromptTemplateParser
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
 from core.tools.signature import sign_tool_file
 from events.message_event import message_was_created
 from extensions.ext_database import db
@@ -412,10 +413,19 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
         message.message_metadata = self._task_state.metadata.model_dump_json()
 
         if trace_manager:
-            trace_manager.add_trace_task(
-                TraceTask(
-                    TraceTaskName.MESSAGE_TRACE, conversation_id=self._conversation_id, message_id=self._message_id
-                )
+            telemetry_emit(
+                TelemetryEvent(
+                    name=TraceTaskName.MESSAGE_TRACE,
+                    context=TelemetryContext(
+                        tenant_id=self._application_generate_entity.app_config.tenant_id,
+                        app_id=self._application_generate_entity.app_config.app_id,
+                    ),
+                    payload={
+                        "conversation_id": self._conversation_id,
+                        "message_id": self._message_id,
+                    },
+                ),
+                trace_manager=trace_manager,
             )
 
         message_was_created.send(

api/core/app/task_pipeline/message_cycle_manager.py

@@ -88,11 +88,10 @@ class MessageCycleManager:
         if isinstance(self._application_generate_entity, CompletionAppGenerateEntity):
             return None
 
-        is_first_message = self._application_generate_entity.is_new_conversation
+        is_first_message = self._application_generate_entity.conversation_id is None
         extras = self._application_generate_entity.extras
         auto_generate_conversation_name = extras.get("auto_generate_conversation_name", True)
 
-        thread: Thread | None = None
         if auto_generate_conversation_name and is_first_message:
             # start generate thread
             # time.sleep not block other logic
@@ -108,10 +107,9 @@ class MessageCycleManager:
             thread.daemon = True
             thread.start()
 
-        if is_first_message:
-            self._application_generate_entity.is_new_conversation = False
+            return thread
 
-        return thread
+        return None
 
     def _generate_conversation_name_worker(self, flask_app: Flask, conversation_id: str, query: str):
         with flask_app.app_context():

api/core/app/workflow/layers/persistence.py

@@ -15,8 +15,7 @@ from datetime import datetime
 from typing import Any, Union
 
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity
-from core.ops.entities.trace_entity import TraceTaskName
-from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
+from core.ops.ops_trace_manager import TraceQueueManager
 from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
 from core.workflow.entities import WorkflowExecution, WorkflowNodeExecution
 from core.workflow.enums import (
@@ -373,6 +372,7 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
 
         self._workflow_node_execution_repository.save(domain_execution)
         self._workflow_node_execution_repository.save_execution_data(domain_execution)
+        self._enqueue_node_trace_task(domain_execution)
 
     def _fail_running_node_executions(self, *, error_message: str) -> None:
         now = naive_utc_now()
@@ -390,17 +390,138 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
 
         conversation_id = self._system_variables().get(SystemVariableKey.CONVERSATION_ID.value)
         external_trace_id = None
+        parent_trace_context = None
         if isinstance(self._application_generate_entity, (WorkflowAppGenerateEntity, AdvancedChatAppGenerateEntity)):
             external_trace_id = self._application_generate_entity.extras.get("external_trace_id")
+            parent_trace_context = self._application_generate_entity.extras.get("parent_trace_context")
 
-        trace_task = TraceTask(
-            TraceTaskName.WORKFLOW_TRACE,
-            workflow_execution=execution,
-            conversation_id=conversation_id,
-            user_id=self._trace_manager.user_id,
-            external_trace_id=external_trace_id,
+        from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+        from core.telemetry import emit as telemetry_emit
+
+        telemetry_emit(
+            TelemetryEvent(
+                name=TraceTaskName.WORKFLOW_TRACE,
+                context=TelemetryContext(
+                    tenant_id=self._application_generate_entity.app_config.tenant_id,
+                    user_id=self._trace_manager.user_id,
+                    app_id=self._application_generate_entity.app_config.app_id,
+                ),
+                payload={
+                    "workflow_execution": execution,
+                    "conversation_id": conversation_id,
+                    "user_id": self._trace_manager.user_id,
+                    "external_trace_id": external_trace_id,
+                    "parent_trace_context": parent_trace_context,
+                },
+            ),
+            trace_manager=self._trace_manager,
+        )
+
+    def _enqueue_node_trace_task(self, domain_execution: WorkflowNodeExecution) -> None:
+        if not self._trace_manager:
+            return
+
+        execution = self._get_workflow_execution()
+        meta = domain_execution.metadata or {}
+
+        parent_trace_context = None
+        if isinstance(self._application_generate_entity, (WorkflowAppGenerateEntity, AdvancedChatAppGenerateEntity)):
+            parent_trace_context = self._application_generate_entity.extras.get("parent_trace_context")
+
+        node_data: dict[str, Any] = {
+            "workflow_id": domain_execution.workflow_id,
+            "workflow_execution_id": execution.id_,
+            "tenant_id": self._application_generate_entity.app_config.tenant_id,
+            "app_id": self._application_generate_entity.app_config.app_id,
+            "node_execution_id": domain_execution.id,
+            "node_id": domain_execution.node_id,
+            "node_type": str(domain_execution.node_type.value),
+            "title": domain_execution.title,
+            "status": str(domain_execution.status.value),
+            "error": domain_execution.error,
+            "elapsed_time": domain_execution.elapsed_time,
+            "index": domain_execution.index,
+            "predecessor_node_id": domain_execution.predecessor_node_id,
+            "created_at": domain_execution.created_at,
+            "finished_at": domain_execution.finished_at,
+            "total_tokens": meta.get(WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS, 0),
+            "prompt_tokens": meta.get(WorkflowNodeExecutionMetadataKey.PROMPT_TOKENS),
+            "completion_tokens": meta.get(WorkflowNodeExecutionMetadataKey.COMPLETION_TOKENS),
+            "total_price": meta.get(WorkflowNodeExecutionMetadataKey.TOTAL_PRICE, 0.0),
+            "currency": meta.get(WorkflowNodeExecutionMetadataKey.CURRENCY),
+            "tool_name": (meta.get(WorkflowNodeExecutionMetadataKey.TOOL_INFO) or {}).get("tool_name")
+            if isinstance(meta.get(WorkflowNodeExecutionMetadataKey.TOOL_INFO), dict)
+            else None,
+            "iteration_id": meta.get(WorkflowNodeExecutionMetadataKey.ITERATION_ID),
+            "iteration_index": meta.get(WorkflowNodeExecutionMetadataKey.ITERATION_INDEX),
+            "loop_id": meta.get(WorkflowNodeExecutionMetadataKey.LOOP_ID),
+            "loop_index": meta.get(WorkflowNodeExecutionMetadataKey.LOOP_INDEX),
+            "parallel_id": meta.get(WorkflowNodeExecutionMetadataKey.PARALLEL_ID),
+            "node_inputs": dict(domain_execution.inputs) if domain_execution.inputs else None,
+            "node_outputs": dict(domain_execution.outputs) if domain_execution.outputs else None,
+            "process_data": dict(domain_execution.process_data) if domain_execution.process_data else None,
+        }
+        node_data["invoke_from"] = self._application_generate_entity.invoke_from.value
+        node_data["user_id"] = self._system_variables().get(SystemVariableKey.USER_ID.value)
+
+        # Extract model info from process_data — LLM nodes store provider/model there,
+        if domain_execution.process_data:
+            if mp := domain_execution.process_data.get("model_provider"):
+                node_data["model_provider"] = mp
+            if mn := domain_execution.process_data.get("model_name"):
+                node_data["model_name"] = mn
+
+        if domain_execution.node_type.value == "knowledge-retrieval" and domain_execution.outputs:
+            results = domain_execution.outputs.get("result") or []
+            dataset_ids: list[str] = []
+            dataset_names: list[str] = []
+            for doc in results:
+                if not isinstance(doc, dict):
+                    continue
+                doc_meta = doc.get("metadata") or {}
+                did = doc_meta.get("dataset_id")
+                dname = doc_meta.get("dataset_name")
+                if did and did not in dataset_ids:
+                    dataset_ids.append(did)
+                if dname and dname not in dataset_names:
+                    dataset_names.append(dname)
+            if dataset_ids:
+                node_data["dataset_ids"] = dataset_ids
+            if dataset_names:
+                node_data["dataset_names"] = dataset_names
+
+        tool_info = meta.get(WorkflowNodeExecutionMetadataKey.TOOL_INFO)
+        if isinstance(tool_info, dict):
+            plugin_id = tool_info.get("plugin_unique_identifier")
+            if plugin_id:
+                node_data["plugin_name"] = plugin_id
+            credential_id = tool_info.get("credential_id")
+            if credential_id:
+                node_data["credential_id"] = credential_id
+                node_data["credential_provider_type"] = tool_info.get("provider_type")
+
+        conversation_id = self._system_variables().get(SystemVariableKey.CONVERSATION_ID.value)
+        if conversation_id:
+            node_data["conversation_id"] = conversation_id
+
+        if parent_trace_context:
+            node_data["parent_trace_context"] = parent_trace_context
+
+        from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+        from core.telemetry import emit as telemetry_emit
+
+        telemetry_emit(
+            TelemetryEvent(
+                name=TraceTaskName.NODE_EXECUTION_TRACE,
+                context=TelemetryContext(
+                    tenant_id=node_data.get("tenant_id"),
+                    user_id=node_data.get("user_id"),
+                    app_id=node_data.get("app_id"),
+                ),
+                payload={"node_execution_data": node_data},
+            ),
+            trace_manager=self._trace_manager,
         )
-        self._trace_manager.add_trace_task(trace_task)
 
     def _system_variables(self) -> Mapping[str, Any]:
         runtime_state = self.graph_runtime_state

api/core/app/workflow/node_factory.py

@@ -8,7 +8,6 @@ from core.file.file_manager import file_manager
 from core.helper.code_executor.code_executor import CodeExecutor
 from core.helper.code_executor.code_node_provider import CodeNodeProvider
 from core.helper.ssrf_proxy import ssrf_proxy
-from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from core.tools.tool_file_manager import ToolFileManager
 from core.workflow.entities.graph_config import NodeConfigDict
 from core.workflow.enums import NodeType
@@ -17,7 +16,6 @@ from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.code.code_node import CodeNode
 from core.workflow.nodes.code.limits import CodeNodeLimits
 from core.workflow.nodes.http_request.node import HttpRequestNode
-from core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node import KnowledgeRetrievalNode
 from core.workflow.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
 from core.workflow.nodes.protocols import FileManagerProtocol, HttpClientProtocol
 from core.workflow.nodes.template_transform.template_renderer import (
@@ -49,7 +47,6 @@ class DifyNodeFactory(NodeFactory):
         code_providers: Sequence[type[CodeNodeProvider]] | None = None,
         code_limits: CodeNodeLimits | None = None,
         template_renderer: Jinja2TemplateRenderer | None = None,
-        template_transform_max_output_length: int | None = None,
         http_request_http_client: HttpClientProtocol | None = None,
         http_request_tool_file_manager_factory: Callable[[], ToolFileManager] = ToolFileManager,
         http_request_file_manager: FileManagerProtocol | None = None,
@@ -71,13 +68,9 @@ class DifyNodeFactory(NodeFactory):
             max_object_array_length=dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH,
         )
         self._template_renderer = template_renderer or CodeExecutorJinja2TemplateRenderer()
-        self._template_transform_max_output_length = (
-            template_transform_max_output_length or dify_config.TEMPLATE_TRANSFORM_MAX_LENGTH
-        )
         self._http_request_http_client = http_request_http_client or ssrf_proxy
         self._http_request_tool_file_manager_factory = http_request_tool_file_manager_factory
         self._http_request_file_manager = http_request_file_manager or file_manager
-        self._rag_retrieval = DatasetRetrieval()
 
     @override
     def create_node(self, node_config: NodeConfigDict) -> Node:
@@ -129,7 +122,6 @@ class DifyNodeFactory(NodeFactory):
                 graph_init_params=self.graph_init_params,
                 graph_runtime_state=self.graph_runtime_state,
                 template_renderer=self._template_renderer,
-                max_output_length=self._template_transform_max_output_length,
             )
 
         if node_type == NodeType.HTTP_REQUEST:
@@ -143,15 +135,6 @@ class DifyNodeFactory(NodeFactory):
                 file_manager=self._http_request_file_manager,
             )
 
-        if node_type == NodeType.KNOWLEDGE_RETRIEVAL:
-            return KnowledgeRetrievalNode(
-                id=node_id,
-                config=node_config,
-                graph_init_params=self.graph_init_params,
-                graph_runtime_state=self.graph_runtime_state,
-                rag_retrieval=self._rag_retrieval,
-            )
-
         return node_class(
             id=node_id,
             config=node_config,

api/core/callback_handler/agent_tool_callback_handler.py

@@ -4,8 +4,9 @@ from typing import Any, TextIO, Union
 from pydantic import BaseModel
 
 from configs import dify_config
-from core.ops.entities.trace_entity import TraceTaskName
-from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
+from core.ops.ops_trace_manager import TraceQueueManager
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
 from core.tools.entities.tool_entities import ToolInvokeMessage
 
 _TEXT_COLOR_MAPPING = {
@@ -36,13 +37,15 @@ class DifyAgentCallbackHandler(BaseModel):
 
     color: str | None = ""
     current_loop: int = 1
+    tenant_id: str | None = None
 
-    def __init__(self, color: str | None = None):
+    def __init__(self, color: str | None = None, tenant_id: str | None = None):
         super().__init__()
         """Initialize callback handler."""
         # use a specific color is not specified
         self.color = color or "green"
         self.current_loop = 1
+        self.tenant_id = tenant_id
 
     def on_tool_start(
         self,
@@ -71,15 +74,23 @@ class DifyAgentCallbackHandler(BaseModel):
             print_text("\n")
 
         if trace_manager:
-            trace_manager.add_trace_task(
-                TraceTask(
-                    TraceTaskName.TOOL_TRACE,
-                    message_id=message_id,
-                    tool_name=tool_name,
-                    tool_inputs=tool_inputs,
-                    tool_outputs=tool_outputs,
-                    timer=timer,
-                )
+            telemetry_emit(
+                TelemetryEvent(
+                    name=TraceTaskName.TOOL_TRACE,
+                    context=TelemetryContext(
+                        tenant_id=self.tenant_id,
+                        app_id=trace_manager.app_id,
+                        user_id=trace_manager.user_id,
+                    ),
+                    payload={
+                        "message_id": message_id,
+                        "tool_name": tool_name,
+                        "tool_inputs": tool_inputs,
+                        "tool_outputs": tool_outputs,
+                        "timer": timer,
+                    },
+                ),
+                trace_manager=trace_manager,
             )
 
     def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any):

api/core/entities/execution_extra_content.py

@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Mapping, Sequence
-from typing import Any, TypeAlias
-
-from pydantic import BaseModel, ConfigDict, Field
-
-from core.workflow.nodes.human_input.entities import FormInput, UserAction
-from models.execution_extra_content import ExecutionContentType
-
-
-class HumanInputFormDefinition(BaseModel):
-    model_config = ConfigDict(frozen=True)
-
-    form_id: str
-    node_id: str
-    node_title: str
-    form_content: str
-    inputs: Sequence[FormInput] = Field(default_factory=list)
-    actions: Sequence[UserAction] = Field(default_factory=list)
-    display_in_ui: bool = False
-    form_token: str | None = None
-    resolved_default_values: Mapping[str, Any] = Field(default_factory=dict)
-    expiration_time: int
-
-
-class HumanInputFormSubmissionData(BaseModel):
-    model_config = ConfigDict(frozen=True)
-
-    node_id: str
-    node_title: str
-    rendered_content: str
-    action_id: str
-    action_text: str
-
-
-class HumanInputContent(BaseModel):
-    model_config = ConfigDict(frozen=True)
-
-    workflow_run_id: str
-    submitted: bool
-    form_definition: HumanInputFormDefinition | None = None
-    form_submission_data: HumanInputFormSubmissionData | None = None
-    type: ExecutionContentType = Field(default=ExecutionContentType.HUMAN_INPUT)
-
-
-ExecutionExtraContentDomainModel: TypeAlias = HumanInputContent
-
-__all__ = [
-    "ExecutionExtraContentDomainModel",
-    "HumanInputContent",
-    "HumanInputFormDefinition",
-    "HumanInputFormSubmissionData",
-]

api/core/entities/provider_configuration.py

@@ -28,8 +28,8 @@ from core.model_runtime.entities.provider_entities import (
 )
 from core.model_runtime.model_providers.__base.ai_model import AIModel
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.engine import db
 from models.provider import (
     LoadBalancingModelConfig,
     Provider,

api/core/helper/marketplace.py

@@ -6,8 +6,7 @@ from yarl import URL
 
 from configs import dify_config
 from core.helper.download import download_with_size_limit
-from core.plugin.entities.marketplace import MarketplacePluginDeclaration, MarketplacePluginSnapshot
-from extensions.ext_redis import redis_client
+from core.plugin.entities.marketplace import MarketplacePluginDeclaration
 
 marketplace_api_url = URL(str(dify_config.MARKETPLACE_API_URL))
 logger = logging.getLogger(__name__)
@@ -44,37 +43,28 @@ def batch_fetch_plugin_by_ids(plugin_ids: list[str]) -> list[dict]:
     return data.get("data", {}).get("plugins", [])
 
 
+def batch_fetch_plugin_manifests_ignore_deserialization_error(
+    plugin_ids: list[str],
+) -> Sequence[MarketplacePluginDeclaration]:
+    if len(plugin_ids) == 0:
+        return []
+
+    url = str(marketplace_api_url / "api/v1/plugins/batch")
+    response = httpx.post(url, json={"plugin_ids": plugin_ids}, headers={"X-Dify-Version": dify_config.project.version})
+    response.raise_for_status()
+    result: list[MarketplacePluginDeclaration] = []
+    for plugin in response.json()["data"]["plugins"]:
+        try:
+            result.append(MarketplacePluginDeclaration.model_validate(plugin))
+        except Exception:
+            logger.exception(
+                "Failed to deserialize marketplace plugin manifest for %s", plugin.get("plugin_id", "unknown")
+            )
+
+    return result
+
+
 def record_install_plugin_event(plugin_unique_identifier: str):
     url = str(marketplace_api_url / "api/v1/stats/plugins/install_count")
     response = httpx.post(url, json={"unique_identifier": plugin_unique_identifier})
     response.raise_for_status()
-
-
-def fetch_global_plugin_manifest(cache_key_prefix: str, cache_ttl: int) -> None:
-    """
-    Fetch all plugin manifests from marketplace and cache them in Redis.
-    This should be called once per check cycle to populate the instance-level cache.
-
-    Args:
-        cache_key_prefix: Redis key prefix for caching plugin manifests
-        cache_ttl: Cache TTL in seconds
-
-    Raises:
-        httpx.HTTPError: If the HTTP request fails
-        Exception: If any other error occurs during fetching or caching
-    """
-    url = str(marketplace_api_url / "api/v1/dist/plugins/manifest.json")
-    response = httpx.get(url, headers={"X-Dify-Version": dify_config.project.version}, timeout=30)
-    response.raise_for_status()
-
-    raw_json = response.json()
-    plugins_data = raw_json.get("plugins", [])
-
-    # Parse and cache all plugin snapshots
-    for plugin_data in plugins_data:
-        plugin_snapshot = MarketplacePluginSnapshot.model_validate(plugin_data)
-        redis_client.setex(
-            name=f"{cache_key_prefix}{plugin_snapshot.plugin_id}",
-            time=cache_ttl,
-            value=plugin_snapshot.model_dump_json(),
-        )

api/core/llm_generator/entities.py

@@ -9,6 +9,7 @@ class RuleGeneratePayload(BaseModel):
     instruction: str = Field(..., description="Rule generation instruction")
     model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
     no_variable: bool = Field(default=False, description="Whether to exclude variables")
+    app_id: str | None = Field(default=None, description="App ID for prompt generation tracing")
 
 
 class RuleCodeGeneratePayload(RuleGeneratePayload):
@@ -18,3 +19,4 @@ class RuleCodeGeneratePayload(RuleGeneratePayload):
 class RuleStructuredOutputPayload(BaseModel):
     instruction: str = Field(..., description="Structured output generation instruction")
     model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
+    app_id: str | None = Field(default=None, description="App ID for prompt generation tracing")

api/core/llm_generator/llm_generator.py

@@ -7,7 +7,6 @@ from typing import Protocol, cast
 import json_repair
 
 from core.app.app_config.entities import ModelConfig
-from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.llm_generator.prompts import (
@@ -27,10 +26,11 @@ from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.entities.message_entities import PromptMessage, SystemPromptMessage, UserPromptMessage
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
-from core.ops.entities.trace_entity import TraceTaskName
-from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
+from core.ops.entities.trace_entity import OperationType
 from core.ops.utils import measure_time
 from core.prompt.utils.prompt_template_parser import PromptTemplateParser
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from extensions.ext_storage import storage
@@ -74,7 +74,7 @@ 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)
@@ -96,15 +96,17 @@ class LLMGenerator:
             name = name[:75] + "..."
 
         # get tracing instance
-        trace_manager = TraceQueueManager(app_id=app_id)
-        trace_manager.add_trace_task(
-            TraceTask(
-                TraceTaskName.GENERATE_NAME_TRACE,
-                conversation_id=conversation_id,
-                generate_conversation_name=name,
-                inputs=prompt,
-                timer=timer,
-                tenant_id=tenant_id,
+        telemetry_emit(
+            TelemetryEvent(
+                name=TraceTaskName.GENERATE_NAME_TRACE,
+                context=TelemetryContext(tenant_id=tenant_id, app_id=app_id),
+                payload={
+                    "conversation_id": conversation_id,
+                    "generate_conversation_name": name,
+                    "inputs": prompt,
+                    "timer": timer,
+                    "tenant_id": tenant_id,
+                },
             )
         )
 
@@ -153,19 +155,27 @@ class LLMGenerator:
         return questions
 
     @classmethod
-    def generate_rule_config(cls, tenant_id: str, args: RuleGeneratePayload):
+    def generate_rule_config(
+        cls,
+        tenant_id: str,
+        instruction: str,
+        model_config: ModelConfig,
+        no_variable: bool,
+        user_id: str | None = None,
+        app_id: str | None = None,
+    ):
         output_parser = RuleConfigGeneratorOutputParser()
 
         error = ""
         error_step = ""
         rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
-        model_parameters = args.model_config_data.completion_params
-        if args.no_variable:
+        model_parameters = model_config.completion_params
+        if no_variable:
             prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
 
             prompt_generate = prompt_template.format(
                 inputs={
-                    "TASK_DESCRIPTION": args.instruction,
+                    "TASK_DESCRIPTION": instruction,
                 },
                 remove_template_variables=False,
             )
@@ -177,26 +187,45 @@ class LLMGenerator:
             model_instance = model_manager.get_model_instance(
                 tenant_id=tenant_id,
                 model_type=ModelType.LLM,
-                provider=args.model_config_data.provider,
-                model=args.model_config_data.name,
+                provider=model_config.provider,
+                model=model_config.name,
             )
 
-            try:
-                response: LLMResult = model_instance.invoke_llm(
-                    prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
-                )
+            llm_result = None
+            with measure_time() as timer:
+                try:
+                    llm_result = model_instance.invoke_llm(
+                        prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+                    )
 
-                rule_config["prompt"] = response.message.get_text_content()
+                    rule_config["prompt"] = llm_result.message.get_text_content() or ""
 
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate rule config"
-            except Exception as e:
-                logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
-                rule_config["error"] = str(e)
+                except InvokeError as e:
+                    error = str(e)
+                    error_step = "generate rule config"
+                except Exception as e:
+                    logger.exception("Failed to generate rule config, model: %s", model_config.name)
+                    rule_config["error"] = str(e)
+                    error = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
 
+            if user_id:
+                prompt_value = rule_config.get("prompt", "")
+                generated_output = str(prompt_value) if prompt_value else ""
+                cls._emit_prompt_generation_trace(
+                    tenant_id=tenant_id,
+                    user_id=user_id,
+                    app_id=app_id,
+                    operation_type=OperationType.RULE_GENERATE,
+                    instruction=instruction,
+                    generated_output=generated_output,
+                    llm_result=llm_result,
+                    model_config=model_config,
+                    timer=timer,
+                    error=error or None,
+                )
+
             return rule_config
 
         # get rule config prompt, parameter and statement
@@ -211,7 +240,7 @@ class LLMGenerator:
         # format the prompt_generate_prompt
         prompt_generate_prompt = prompt_template.format(
             inputs={
-                "TASK_DESCRIPTION": args.instruction,
+                "TASK_DESCRIPTION": instruction,
             },
             remove_template_variables=False,
         )
@@ -222,84 +251,125 @@ class LLMGenerator:
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=args.model_config_data.provider,
-            model=args.model_config_data.name,
+            provider=model_config.provider,
+            model=model_config.name,
         )
 
-        try:
+        llm_result = None
+        with measure_time() as timer:
             try:
-                # the first step to generate the task prompt
-                prompt_content: LLMResult = model_instance.invoke_llm(
-                    prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+                try:
+                    # the first step to generate the task prompt
+                    prompt_content: LLMResult = model_instance.invoke_llm(
+                        prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+                    )
+                    llm_result = prompt_content
+                except InvokeError as e:
+                    error = str(e)
+                    error_step = "generate prefix prompt"
+                    rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
+
+                    if user_id:
+                        cls._emit_prompt_generation_trace(
+                            tenant_id=tenant_id,
+                            user_id=user_id,
+                            app_id=app_id,
+                            operation_type=OperationType.RULE_GENERATE,
+                            instruction=instruction,
+                            generated_output="",
+                            llm_result=llm_result,
+                            model_config=model_config,
+                            timer=timer,
+                            error=error,
+                        )
+
+                    return rule_config
+
+                rule_config["prompt"] = prompt_content.message.get_text_content() or ""
+
+                if not isinstance(prompt_content.message.content, str):
+                    raise NotImplementedError("prompt content is not a string")
+                parameter_generate_prompt = parameter_template.format(
+                    inputs={
+                        "INPUT_TEXT": prompt_content.message.content,
+                    },
+                    remove_template_variables=False,
                 )
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate prefix prompt"
-                rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
+                parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
 
-                return rule_config
-
-            rule_config["prompt"] = prompt_content.message.get_text_content()
-
-            parameter_generate_prompt = parameter_template.format(
-                inputs={
-                    "INPUT_TEXT": prompt_content.message.get_text_content(),
-                },
-                remove_template_variables=False,
-            )
-            parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
-
-            # the second step to generate the task_parameter and task_statement
-            statement_generate_prompt = statement_template.format(
-                inputs={
-                    "TASK_DESCRIPTION": args.instruction,
-                    "INPUT_TEXT": prompt_content.message.get_text_content(),
-                },
-                remove_template_variables=False,
-            )
-            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
+                # the second step to generate the task_parameter and task_statement
+                statement_generate_prompt = statement_template.format(
+                    inputs={
+                        "TASK_DESCRIPTION": instruction,
+                        "INPUT_TEXT": prompt_content.message.content,
+                    },
+                    remove_template_variables=False,
                 )
-                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"
+                statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
 
-            try:
-                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()
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate conversation opener"
+                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*"', prompt_content.message.get_text_content() or ""
+                    )
+                except InvokeError as e:
+                    error = str(e)
+                    error_step = "generate variables"
 
-        except Exception as e:
-            logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
-            rule_config["error"] = str(e)
+                try:
+                    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() or ""
+                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.name)
+                rule_config["error"] = str(e)
+                error = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
 
+        if user_id:
+            generated_output = rule_config.get("prompt", "")
+            cls._emit_prompt_generation_trace(
+                tenant_id=tenant_id,
+                user_id=user_id,
+                app_id=app_id,
+                operation_type=OperationType.RULE_GENERATE,
+                instruction=instruction,
+                generated_output=str(generated_output) if generated_output else "",
+                llm_result=llm_result,
+                model_config=model_config,
+                timer=timer,
+                error=error or None,
+            )
+
         return rule_config
 
     @classmethod
     def generate_code(
         cls,
         tenant_id: str,
-        args: RuleCodeGeneratePayload,
+        instruction: str,
+        model_config: ModelConfig,
+        code_language: str = "javascript",
+        user_id: str | None = None,
+        app_id: str | None = None,
     ):
-        if args.code_language == "python":
+        if code_language == "python":
             prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
         else:
             prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
 
         prompt = prompt_template.format(
             inputs={
-                "INSTRUCTION": args.instruction,
-                "CODE_LANGUAGE": args.code_language,
+                "INSTRUCTION": instruction,
+                "CODE_LANGUAGE": code_language,
             },
             remove_template_variables=False,
         )
@@ -308,28 +378,49 @@ class LLMGenerator:
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=args.model_config_data.provider,
-            model=args.model_config_data.name,
+            provider=model_config.provider,
+            model=model_config.name,
         )
 
         prompt_messages = [UserPromptMessage(content=prompt)]
-        model_parameters = args.model_config_data.completion_params
-        try:
-            response: LLMResult = model_instance.invoke_llm(
-                prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+        model_parameters = model_config.completion_params
+
+        llm_result = None
+        error = None
+        with measure_time() as timer:
+            try:
+                llm_result = model_instance.invoke_llm(
+                    prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+                )
+
+                generated_code = llm_result.message.get_text_content() or ""
+                result = {"code": generated_code, "language": code_language, "error": ""}
+
+            except InvokeError as e:
+                error = str(e)
+                result = {"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.name, code_language
+                )
+                error = str(e)
+                result = {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
+
+        if user_id:
+            cls._emit_prompt_generation_trace(
+                tenant_id=tenant_id,
+                user_id=user_id,
+                app_id=app_id,
+                operation_type=OperationType.CODE_GENERATE,
+                instruction=instruction,
+                generated_output=result.get("code", ""),
+                llm_result=llm_result,
+                model_config=model_config,
+                timer=timer,
+                error=error,
             )
 
-            generated_code = response.message.get_text_content()
-            return {"code": generated_code, "language": args.code_language, "error": ""}
-
-        except InvokeError as e:
-            error = str(e)
-            return {"code": "", "language": args.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", args.model_config_data.name, args.code_language
-            )
-            return {"code": "", "language": args.code_language, "error": f"An unexpected error occurred: {str(e)}"}
+        return result
 
     @classmethod
     def generate_qa_document(cls, tenant_id: str, query, document_language: str):
@@ -355,49 +446,81 @@ class LLMGenerator:
             raise TypeError("Expected LLMResult when stream=False")
         response = result
 
-        answer = response.message.get_text_content()
+        answer = response.message.get_text_content() or ""
         return answer.strip()
 
     @classmethod
-    def generate_structured_output(cls, tenant_id: str, args: RuleStructuredOutputPayload):
+    def generate_structured_output(
+        cls,
+        tenant_id: str,
+        instruction: str,
+        model_config: ModelConfig,
+        user_id: str | None = None,
+        app_id: str | None = None,
+    ):
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=args.model_config_data.provider,
-            model=args.model_config_data.name,
+            provider=model_config.provider,
+            model=model_config.name,
         )
 
         prompt_messages = [
             SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
-            UserPromptMessage(content=args.instruction),
+            UserPromptMessage(content=instruction),
         ]
-        model_parameters = args.model_config_data.completion_params
+        model_parameters = model_config.completion_params
 
-        try:
-            response: LLMResult = model_instance.invoke_llm(
-                prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+        llm_result = None
+        error = None
+        result = {"output": "", "error": ""}
+
+        with measure_time() as timer:
+            try:
+                llm_result = model_instance.invoke_llm(
+                    prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+                )
+
+                raw_content = llm_result.message.content
+
+                if not isinstance(raw_content, str):
+                    raise ValueError(f"LLM response content must be a string, got: {type(raw_content)}")
+
+                try:
+                    parsed_content = json.loads(raw_content)
+                except json.JSONDecodeError:
+                    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}")
+
+                generated_json_schema = json.dumps(parsed_content, indent=2, ensure_ascii=False)
+                result = {"output": generated_json_schema, "error": ""}
+
+            except InvokeError as e:
+                error = str(e)
+                result = {"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.name)
+                error = str(e)
+                result = {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
+
+        if user_id:
+            cls._emit_prompt_generation_trace(
+                tenant_id=tenant_id,
+                user_id=user_id,
+                app_id=app_id,
+                operation_type=OperationType.STRUCTURED_OUTPUT,
+                instruction=instruction,
+                generated_output=result.get("output", ""),
+                llm_result=llm_result,
+                model_config=model_config,
+                timer=timer,
+                error=error,
             )
 
-            raw_content = response.message.get_text_content()
-
-            try:
-                parsed_content = json.loads(raw_content)
-            except json.JSONDecodeError:
-                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}")
-
-            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", args.model_config_data.name)
-            return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
+        return result
 
     @staticmethod
     def instruction_modify_legacy(
@@ -407,12 +530,14 @@ class LLMGenerator:
         instruction: str,
         model_config: ModelConfig,
         ideal_output: str | None,
+        user_id: str | None = None,
+        app_id: str | None = None,
     ):
         last_run: Message | None = (
             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(
+            result = LLMGenerator.__instruction_modify_common(
                 tenant_id=tenant_id,
                 model_config=model_config,
                 last_run=None,
@@ -421,22 +546,28 @@ class LLMGenerator:
                 instruction=instruction,
                 node_type="llm",
                 ideal_output=ideal_output,
+                user_id=user_id,
+                app_id=app_id,
             )
-        last_run_dict = {
-            "query": last_run.query,
-            "answer": last_run.answer,
-            "error": last_run.error,
-        }
-        return LLMGenerator.__instruction_modify_common(
-            tenant_id=tenant_id,
-            model_config=model_config,
-            last_run=last_run_dict,
-            current=current,
-            error_message=str(last_run.error),
-            instruction=instruction,
-            node_type="llm",
-            ideal_output=ideal_output,
-        )
+        else:
+            last_run_dict = {
+                "query": last_run.query,
+                "answer": last_run.answer,
+                "error": last_run.error,
+            }
+            result = LLMGenerator.__instruction_modify_common(
+                tenant_id=tenant_id,
+                model_config=model_config,
+                last_run=last_run_dict,
+                current=current,
+                error_message=str(last_run.error),
+                instruction=instruction,
+                node_type="llm",
+                ideal_output=ideal_output,
+                user_id=user_id,
+                app_id=app_id,
+            )
+        return result
 
     @staticmethod
     def instruction_modify_workflow(
@@ -448,6 +579,8 @@ class LLMGenerator:
         model_config: ModelConfig,
         ideal_output: str | None,
         workflow_service: WorkflowServiceInterface,
+        user_id: str | None = None,
+        app_id: str | None = None,
     ):
         session = db.session()
 
@@ -478,6 +611,8 @@ class LLMGenerator:
                 instruction=instruction,
                 node_type=node_type,
                 ideal_output=ideal_output,
+                user_id=user_id,
+                app_id=app_id,
             )
 
         def agent_log_of(node_execution: WorkflowNodeExecutionModel) -> Sequence:
@@ -511,6 +646,8 @@ class LLMGenerator:
             instruction=instruction,
             node_type=last_run.node_type,
             ideal_output=ideal_output,
+            user_id=user_id,
+            app_id=app_id,
         )
 
     @staticmethod
@@ -523,6 +660,8 @@ class LLMGenerator:
         instruction: str,
         node_type: str,
         ideal_output: str | None,
+        user_id: str | None = None,
+        app_id: str | None = None,
     ):
         LAST_RUN = "{{#last_run#}}"
         CURRENT = "{{#current#}}"
@@ -562,24 +701,120 @@ class LLMGenerator:
         ]
         model_parameters = {"temperature": 0.4}
 
-        try:
-            response: LLMResult = model_instance.invoke_llm(
-                prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+        llm_result = None
+        error = None
+        result = {}
+
+        with measure_time() as timer:
+            try:
+                llm_result = model_instance.invoke_llm(
+                    prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
+                )
+
+                generated_raw = llm_result.message.get_text_content()
+                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]
+                data = json_repair.loads(json_str)
+                if not isinstance(data, dict):
+                    raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
+                result = data
+            except InvokeError as e:
+                error = str(e)
+                result = {"error": f"Failed to generate code. Error: {error}"}
+            except Exception as e:
+                logger.exception("Failed to invoke LLM model, model: %s", json.dumps(model_config.name), exc_info=True)
+                error = str(e)
+                result = {"error": f"An unexpected error occurred: {str(e)}"}
+
+        if user_id:
+            generated_output = ""
+            if isinstance(result, dict):
+                for key in ["prompt", "code", "output", "modified"]:
+                    if result.get(key):
+                        generated_output = str(result[key])
+                        break
+
+            LLMGenerator._emit_prompt_generation_trace(
+                tenant_id=tenant_id,
+                user_id=user_id,
+                app_id=app_id,
+                operation_type=OperationType.INSTRUCTION_MODIFY,
+                instruction=instruction,
+                generated_output=generated_output,
+                llm_result=llm_result,
+                model_config=model_config,
+                timer=timer,
+                error=error,
             )
 
-            generated_raw = response.message.get_text_content()
-            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]
-            data = json_repair.loads(json_str)
-            if not isinstance(data, dict):
-                raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
-            return data
-        except InvokeError as e:
-            error = str(e)
-            return {"error": f"Failed to generate code. Error: {error}"}
-        except Exception as e:
-            logger.exception("Failed to invoke LLM model, model: %s", json.dumps(model_config.name), exc_info=True)
-            return {"error": f"An unexpected error occurred: {str(e)}"}
+        return result
+
+    @classmethod
+    def _emit_prompt_generation_trace(
+        cls,
+        tenant_id: str,
+        user_id: str,
+        app_id: str | None,
+        operation_type: OperationType,
+        instruction: str,
+        generated_output: str,
+        llm_result: LLMResult | None,
+        model_config: ModelConfig | None = None,
+        timer=None,
+        error: str | None = None,
+    ):
+        if llm_result:
+            prompt_tokens = llm_result.usage.prompt_tokens
+            completion_tokens = llm_result.usage.completion_tokens
+            total_tokens = llm_result.usage.total_tokens
+            model_name = llm_result.model
+            # Extract provider from model_config if available, otherwise fall back to parsing model name
+            if model_config and model_config.provider:
+                model_provider = model_config.provider
+            else:
+                model_provider = model_name.split("/")[0] if "/" in model_name else ""
+            latency = llm_result.usage.latency
+            total_price = float(llm_result.usage.total_price) if llm_result.usage.total_price else None
+            currency = llm_result.usage.currency
+        else:
+            prompt_tokens = 0
+            completion_tokens = 0
+            total_tokens = 0
+            model_provider = model_config.provider if model_config else ""
+            model_name = model_config.name if model_config else ""
+            latency = 0.0
+            if timer:
+                start_time = timer.get("start")
+                end_time = timer.get("end")
+                if start_time and end_time:
+                    latency = (end_time - start_time).total_seconds()
+            total_price = None
+            currency = None
+
+        telemetry_emit(
+            TelemetryEvent(
+                name=TraceTaskName.PROMPT_GENERATION_TRACE,
+                context=TelemetryContext(tenant_id=tenant_id, user_id=user_id, app_id=app_id),
+                payload={
+                    "tenant_id": tenant_id,
+                    "user_id": user_id,
+                    "app_id": app_id,
+                    "operation_type": operation_type,
+                    "instruction": instruction,
+                    "generated_output": generated_output,
+                    "prompt_tokens": prompt_tokens,
+                    "completion_tokens": completion_tokens,
+                    "total_tokens": total_tokens,
+                    "model_provider": model_provider,
+                    "model_name": model_name,
+                    "latency": latency,
+                    "total_price": total_price,
+                    "currency": currency,
+                    "timer": timer,
+                    "error": error,
+                },
+            )
+        )

api/core/logging/filters.py

@@ -15,16 +15,23 @@ class TraceContextFilter(logging.Filter):
     """
 
     def filter(self, record: logging.LogRecord) -> bool:
-        # Get trace context from OpenTelemetry
-        trace_id, span_id = self._get_otel_context()
+        # Preserve explicit trace_id set by the caller (e.g. emit_metric_only_event)
+        existing_trace_id = getattr(record, "trace_id", "")
+        if not existing_trace_id:
+            # Get trace context from OpenTelemetry
+            trace_id, span_id = self._get_otel_context()
 
-        # Set trace_id (fallback to ContextVar if no OTEL context)
-        if trace_id:
-            record.trace_id = trace_id
+            # Set trace_id (fallback to ContextVar if no OTEL context)
+            if trace_id:
+                record.trace_id = trace_id
+            else:
+                record.trace_id = get_trace_id()
+
+            record.span_id = span_id or ""
         else:
-            record.trace_id = get_trace_id()
-
-        record.span_id = span_id or ""
+            # Keep existing trace_id; only fill span_id if missing
+            if not getattr(record, "span_id", ""):
+                record.span_id = ""
 
         # For backward compatibility, also set req_id
         record.req_id = get_request_id()
@@ -55,9 +62,12 @@ class IdentityContextFilter(logging.Filter):
 
     def filter(self, record: logging.LogRecord) -> bool:
         identity = self._extract_identity()
-        record.tenant_id = identity.get("tenant_id", "")
-        record.user_id = identity.get("user_id", "")
-        record.user_type = identity.get("user_type", "")
+        if not getattr(record, "tenant_id", ""):
+            record.tenant_id = identity.get("tenant_id", "")
+        if not getattr(record, "user_id", ""):
+            record.user_id = identity.get("user_id", "")
+        if not getattr(record, "user_type", ""):
+            record.user_type = identity.get("user_type", "")
         return True
 
     def _extract_identity(self) -> dict[str, str]:

api/core/moderation/input_moderation.py

@@ -5,9 +5,10 @@ from typing import Any
 from core.app.app_config.entities import AppConfig
 from core.moderation.base import ModerationAction, ModerationError
 from core.moderation.factory import ModerationFactory
-from core.ops.entities.trace_entity import TraceTaskName
-from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
+from core.ops.ops_trace_manager import TraceQueueManager
 from core.ops.utils import measure_time
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
 
 logger = logging.getLogger(__name__)
 
@@ -49,14 +50,18 @@ class InputModeration:
             moderation_result = moderation_factory.moderation_for_inputs(inputs, query)
 
         if trace_manager:
-            trace_manager.add_trace_task(
-                TraceTask(
-                    TraceTaskName.MODERATION_TRACE,
-                    message_id=message_id,
-                    moderation_result=moderation_result,
-                    inputs=inputs,
-                    timer=timer,
-                )
+            telemetry_emit(
+                TelemetryEvent(
+                    name=TraceTaskName.MODERATION_TRACE,
+                    context=TelemetryContext(tenant_id=tenant_id, app_id=app_id),
+                    payload={
+                        "message_id": message_id,
+                        "moderation_result": moderation_result,
+                        "inputs": inputs,
+                        "timer": timer,
+                    },
+                ),
+                trace_manager=trace_manager,
             )
 
         if not moderation_result.flagged:

api/core/ops/entities/trace_entity.py

@@ -9,8 +9,8 @@ from pydantic import BaseModel, ConfigDict, field_serializer, field_validator
 class BaseTraceInfo(BaseModel):
     message_id: str | None = None
     message_data: Any | None = None
-    inputs: Union[str, dict[str, Any], list] | None = None
-    outputs: Union[str, dict[str, Any], list] | None = None
+    inputs: Union[str, dict[str, Any], list[Any]] | None = None
+    outputs: Union[str, dict[str, Any], list[Any]] | None = None
     start_time: datetime | None = None
     end_time: datetime | None = None
     metadata: dict[str, Any]
@@ -18,7 +18,7 @@ class BaseTraceInfo(BaseModel):
 
     @field_validator("inputs", "outputs")
     @classmethod
-    def ensure_type(cls, v):
+    def ensure_type(cls, v: str | dict[str, Any] | list[Any] | None) -> str | dict[str, Any] | list[Any] | None:
         if v is None:
             return None
         if isinstance(v, str | dict | list):
@@ -27,6 +27,48 @@ class BaseTraceInfo(BaseModel):
 
     model_config = ConfigDict(protected_namespaces=())
 
+    @property
+    def resolved_trace_id(self) -> str | None:
+        """Get trace_id with intelligent fallback.
+
+        Priority:
+        1. External trace_id (from X-Trace-Id header)
+        2. workflow_run_id (if this trace type has it)
+        3. message_id (as final fallback)
+        """
+        if self.trace_id:
+            return self.trace_id
+
+        # Try workflow_run_id (only exists on workflow-related traces)
+        workflow_run_id = getattr(self, "workflow_run_id", None)
+        if workflow_run_id:
+            return workflow_run_id
+
+        # Final fallback to message_id
+        return str(self.message_id) if self.message_id else None
+
+    @property
+    def resolved_parent_context(self) -> tuple[str | None, str | None]:
+        """Resolve cross-workflow parent linking from metadata.
+
+        Extracts typed parent IDs from the untyped ``parent_trace_context``
+        metadata dict (set by tool_node when invoking nested workflows).
+
+        Returns:
+            (trace_correlation_override, parent_span_id_source) where
+            trace_correlation_override is the outer workflow_run_id and
+            parent_span_id_source is the outer node_execution_id.
+        """
+        parent_ctx = self.metadata.get("parent_trace_context")
+        if not isinstance(parent_ctx, dict):
+            return None, None
+        trace_override = parent_ctx.get("parent_workflow_run_id")
+        parent_span = parent_ctx.get("parent_node_execution_id")
+        return (
+            trace_override if isinstance(trace_override, str) else None,
+            parent_span if isinstance(parent_span, str) else None,
+        )
+
     @field_serializer("start_time", "end_time")
     def serialize_datetime(self, dt: datetime | None) -> str | None:
         if dt is None:
@@ -48,10 +90,14 @@ class WorkflowTraceInfo(BaseTraceInfo):
     workflow_run_version: str
     error: str | None = None
     total_tokens: int
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
     file_list: list[str]
     query: str
     metadata: dict[str, Any]
 
+    invoked_by: str | None = None
+
 
 class MessageTraceInfo(BaseTraceInfo):
     conversation_model: str
@@ -59,7 +105,7 @@ class MessageTraceInfo(BaseTraceInfo):
     answer_tokens: int
     total_tokens: int
     error: str | None = None
-    file_list: Union[str, dict[str, Any], list] | None = None
+    file_list: Union[str, dict[str, Any], list[Any]] | None = None
     message_file_data: Any | None = None
     conversation_mode: str
     gen_ai_server_time_to_first_token: float | None = None
@@ -106,7 +152,7 @@ class ToolTraceInfo(BaseTraceInfo):
     tool_config: dict[str, Any]
     time_cost: Union[int, float]
     tool_parameters: dict[str, Any]
-    file_url: Union[str, None, list] = None
+    file_url: Union[str, None, list[str]] = None
 
 
 class GenerateNameTraceInfo(BaseTraceInfo):
@@ -114,6 +160,79 @@ class GenerateNameTraceInfo(BaseTraceInfo):
     tenant_id: str
 
 
+class PromptGenerationTraceInfo(BaseTraceInfo):
+    """Trace information for prompt generation operations (rule-generate, code-generate, etc.)."""
+
+    tenant_id: str
+    user_id: str
+    app_id: str | None = None
+
+    operation_type: str
+    instruction: str
+
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+    model_provider: str
+    model_name: str
+
+    latency: float
+
+    total_price: float | None = None
+    currency: str | None = None
+
+    error: str | None = None
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class WorkflowNodeTraceInfo(BaseTraceInfo):
+    workflow_id: str
+    workflow_run_id: str
+    tenant_id: str
+    node_execution_id: str
+    node_id: str
+    node_type: str
+    title: str
+
+    status: str
+    error: str | None = None
+    elapsed_time: float
+
+    index: int
+    predecessor_node_id: str | None = None
+
+    total_tokens: int = 0
+    total_price: float = 0.0
+    currency: str | None = None
+
+    model_provider: str | None = None
+    model_name: str | None = None
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
+
+    tool_name: str | None = None
+
+    iteration_id: str | None = None
+    iteration_index: int | None = None
+    loop_id: str | None = None
+    loop_index: int | None = None
+    parallel_id: str | None = None
+
+    node_inputs: Mapping[str, Any] | None = None
+    node_outputs: Mapping[str, Any] | None = None
+    process_data: Mapping[str, Any] | None = None
+
+    invoked_by: str | None = None
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class DraftNodeExecutionTrace(WorkflowNodeTraceInfo):
+    pass
+
+
 class TaskData(BaseModel):
     app_id: str
     trace_info_type: str
@@ -128,16 +247,38 @@ trace_info_info_map = {
     "DatasetRetrievalTraceInfo": DatasetRetrievalTraceInfo,
     "ToolTraceInfo": ToolTraceInfo,
     "GenerateNameTraceInfo": GenerateNameTraceInfo,
+    "PromptGenerationTraceInfo": PromptGenerationTraceInfo,
+    "WorkflowNodeTraceInfo": WorkflowNodeTraceInfo,
+    "DraftNodeExecutionTrace": DraftNodeExecutionTrace,
 }
 
 
+class OperationType(StrEnum):
+    """Operation type for token metric labels.
+
+    Used as a metric attribute on ``dify.tokens.input`` / ``dify.tokens.output``
+    counters so consumers can break down token usage by operation.
+    """
+
+    WORKFLOW = "workflow"
+    NODE_EXECUTION = "node_execution"
+    MESSAGE = "message"
+    RULE_GENERATE = "rule_generate"
+    CODE_GENERATE = "code_generate"
+    STRUCTURED_OUTPUT = "structured_output"
+    INSTRUCTION_MODIFY = "instruction_modify"
+
+
 class TraceTaskName(StrEnum):
     CONVERSATION_TRACE = "conversation"
     WORKFLOW_TRACE = "workflow"
+    DRAFT_NODE_EXECUTION_TRACE = "draft_node_execution"
     MESSAGE_TRACE = "message"
     MODERATION_TRACE = "moderation"
     SUGGESTED_QUESTION_TRACE = "suggested_question"
     DATASET_RETRIEVAL_TRACE = "dataset_retrieval"
     TOOL_TRACE = "tool"
     GENERATE_NAME_TRACE = "generate_conversation_name"
+    PROMPT_GENERATION_TRACE = "prompt_generation"
     DATASOURCE_TRACE = "datasource"
+    NODE_EXECUTION_TRACE = "node_execution"

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -3,6 +3,7 @@ import os
 from datetime import datetime, timedelta
 
 from langfuse import Langfuse
+from sqlalchemy import select
 from sqlalchemy.orm import sessionmaker
 
 from core.ops.base_trace_instance import BaseTraceInstance
@@ -30,7 +31,7 @@ from core.ops.utils import filter_none_values
 from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.enums import NodeType
 from extensions.ext_database import db
-from models import EndUser, WorkflowNodeExecutionTriggeredFrom
+from models import EndUser, Message, WorkflowNodeExecutionTriggeredFrom
 from models.enums import MessageStatus
 
 logger = logging.getLogger(__name__)
@@ -71,7 +72,50 @@ class LangFuseDataTrace(BaseTraceInstance):
         metadata = trace_info.metadata
         metadata["workflow_app_log_id"] = trace_info.workflow_app_log_id
 
-        if trace_info.message_id:
+        # Check for parent_trace_context to detect nested workflow
+        parent_trace_context = trace_info.metadata.get("parent_trace_context")
+
+        if parent_trace_context:
+            # Nested workflow: create span under outer trace
+            outer_trace_id = parent_trace_context.get("trace_id")
+            parent_node_execution_id = parent_trace_context.get("parent_node_execution_id")
+            parent_conversation_id = parent_trace_context.get("parent_conversation_id")
+            parent_workflow_run_id = parent_trace_context.get("parent_workflow_run_id")
+
+            # Resolve outer trace_id: try message_id lookup first, fallback to workflow_run_id
+            if parent_conversation_id:
+                session_factory = sessionmaker(bind=db.engine)
+                with session_factory() as session:
+                    message_data_stmt = select(Message.id).where(
+                        Message.conversation_id == parent_conversation_id,
+                        Message.workflow_run_id == parent_workflow_run_id,
+                    )
+                    resolved_message_id = session.scalar(message_data_stmt)
+                    if resolved_message_id:
+                        outer_trace_id = resolved_message_id
+                    else:
+                        outer_trace_id = parent_workflow_run_id
+            else:
+                outer_trace_id = parent_workflow_run_id
+
+            # Create inner workflow span under outer trace
+            workflow_span_data = LangfuseSpan(
+                id=trace_info.workflow_run_id,
+                name=TraceTaskName.WORKFLOW_TRACE,
+                input=dict(trace_info.workflow_run_inputs),
+                output=dict(trace_info.workflow_run_outputs),
+                trace_id=outer_trace_id,
+                parent_observation_id=parent_node_execution_id,
+                start_time=trace_info.start_time,
+                end_time=trace_info.end_time,
+                metadata=metadata,
+                level=LevelEnum.DEFAULT if trace_info.error == "" else LevelEnum.ERROR,
+                status_message=trace_info.error or "",
+            )
+            self.add_span(langfuse_span_data=workflow_span_data)
+            # Use outer_trace_id for all node spans/generations
+            trace_id = outer_trace_id
+        elif trace_info.message_id:
             trace_id = trace_info.trace_id or trace_info.message_id
             name = TraceTaskName.MESSAGE_TRACE
             trace_data = LangfuseTrace(
@@ -174,6 +218,11 @@ class LangFuseDataTrace(BaseTraceInstance):
                     }
                 )
 
+            # Determine parent_observation_id for nested workflows
+            node_parent_observation_id = None
+            if parent_trace_context or trace_info.message_id:
+                node_parent_observation_id = trace_info.workflow_run_id
+
             # add generation span
             if process_data and process_data.get("model_mode") == "chat":
                 total_token = metadata.get("total_tokens", 0)
@@ -206,7 +255,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
                     status_message=trace_info.error or "",
-                    parent_observation_id=trace_info.workflow_run_id if trace_info.message_id else None,
+                    parent_observation_id=node_parent_observation_id,
                     usage=generation_usage,
                 )
 
@@ -225,7 +274,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
                     status_message=trace_info.error or "",
-                    parent_observation_id=trace_info.workflow_run_id if trace_info.message_id else None,
+                    parent_observation_id=node_parent_observation_id,
                 )
 
                 self.add_span(langfuse_span_data=span_data)

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -6,6 +6,7 @@ from typing import cast
 
 from langsmith import Client
 from langsmith.schemas import RunBase
+from sqlalchemy import select
 from sqlalchemy.orm import sessionmaker
 
 from core.ops.base_trace_instance import BaseTraceInstance
@@ -30,7 +31,7 @@ from core.ops.utils import filter_none_values, generate_dotted_order
 from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
-from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
+from models import EndUser, Message, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
 logger = logging.getLogger(__name__)
 
@@ -64,7 +65,35 @@ class LangSmithDataTrace(BaseTraceInstance):
             self.generate_name_trace(trace_info)
 
     def workflow_trace(self, trace_info: WorkflowTraceInfo):
-        trace_id = trace_info.trace_id or trace_info.message_id or trace_info.workflow_run_id
+        # Check for parent_trace_context for cross-workflow linking
+        parent_trace_context = trace_info.metadata.get("parent_trace_context")
+
+        if parent_trace_context:
+            # Inner workflow: resolve outer trace_id and link to parent node
+            outer_trace_id = parent_trace_context.get("parent_workflow_run_id")
+
+            # Try to resolve message_id from conversation_id if available
+            if parent_trace_context.get("parent_conversation_id"):
+                try:
+                    session_factory = sessionmaker(bind=db.engine)
+                    with session_factory() as session:
+                        message_data_stmt = select(Message.id).where(
+                            Message.conversation_id == parent_trace_context["parent_conversation_id"],
+                            Message.workflow_run_id == parent_trace_context["parent_workflow_run_id"],
+                        )
+                        resolved_message_id = session.scalar(message_data_stmt)
+                        if resolved_message_id:
+                            outer_trace_id = resolved_message_id
+                except Exception as e:
+                    logger.debug("Failed to resolve message_id from conversation_id: %s", str(e))
+
+            trace_id = outer_trace_id
+            parent_run_id = parent_trace_context.get("parent_node_execution_id")
+        else:
+            # Outer workflow: existing behavior
+            trace_id = trace_info.trace_id or trace_info.message_id or trace_info.workflow_run_id
+            parent_run_id = trace_info.message_id or None
+
         if trace_info.start_time is None:
             trace_info.start_time = datetime.now()
         message_dotted_order = (
@@ -78,7 +107,8 @@ class LangSmithDataTrace(BaseTraceInstance):
         metadata = trace_info.metadata
         metadata["workflow_app_log_id"] = trace_info.workflow_app_log_id
 
-        if trace_info.message_id:
+        # Only create message_run for outer workflows (no parent_trace_context)
+        if trace_info.message_id and not parent_trace_context:
             message_run = LangSmithRunModel(
                 id=trace_info.message_id,
                 name=TraceTaskName.MESSAGE_TRACE,
@@ -121,9 +151,9 @@ class LangSmithDataTrace(BaseTraceInstance):
             },
             error=trace_info.error,
             tags=["workflow"],
-            parent_run_id=trace_info.message_id or None,
+            parent_run_id=parent_run_id,
             trace_id=trace_id,
-            dotted_order=workflow_dotted_order,
+            dotted_order=None if parent_trace_context else workflow_dotted_order,
             serialized=None,
             events=[],
             session_id=None,

api/core/ops/ops_trace_manager.py

@@ -15,22 +15,32 @@ from sqlalchemy import select
 from sqlalchemy.orm import Session, sessionmaker
 
 from core.helper.encrypter import batch_decrypt_token, encrypt_token, obfuscated_token
-from core.ops.entities.config_entity import OPS_FILE_PATH, TracingProviderEnum
+from core.ops.entities.config_entity import (
+    OPS_FILE_PATH,
+    TracingProviderEnum,
+)
 from core.ops.entities.trace_entity import (
     DatasetRetrievalTraceInfo,
+    DraftNodeExecutionTrace,
     GenerateNameTraceInfo,
     MessageTraceInfo,
     ModerationTraceInfo,
+    PromptGenerationTraceInfo,
     SuggestedQuestionTraceInfo,
     TaskData,
     ToolTraceInfo,
     TraceTaskName,
+    WorkflowNodeTraceInfo,
     WorkflowTraceInfo,
 )
 from core.ops.utils import get_message_data
+from extensions.ext_database import db
 from extensions.ext_storage import storage
-from models.engine import db
+from models.account import Tenant
+from models.dataset import Dataset
 from models.model import App, AppModelConfig, Conversation, Message, MessageFile, TraceAppConfig
+from models.provider import Provider, ProviderCredential, ProviderModel, ProviderModelCredential, ProviderType
+from models.tools import ApiToolProvider, BuiltinToolProvider, MCPToolProvider, WorkflowToolProvider
 from models.workflow import WorkflowAppLog
 from tasks.ops_trace_task import process_trace_tasks
 
@@ -40,6 +50,139 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
+def _lookup_app_and_workspace_names(app_id: str | None, tenant_id: str | None) -> tuple[str, str]:
+    """Return (app_name, workspace_name) for the given IDs. Falls back to empty strings."""
+    app_name = ""
+    workspace_name = ""
+    if not app_id and not tenant_id:
+        return app_name, workspace_name
+    with Session(db.engine) as session:
+        if app_id:
+            name = session.scalar(select(App.name).where(App.id == app_id))
+            if name:
+                app_name = name
+        if tenant_id:
+            name = session.scalar(select(Tenant.name).where(Tenant.id == tenant_id))
+            if name:
+                workspace_name = name
+    return app_name, workspace_name
+
+
+_PROVIDER_TYPE_TO_MODEL: dict[str, type] = {
+    "builtin": BuiltinToolProvider,
+    "plugin": BuiltinToolProvider,
+    "api": ApiToolProvider,
+    "workflow": WorkflowToolProvider,
+    "mcp": MCPToolProvider,
+}
+
+
+def _lookup_credential_name(credential_id: str | None, provider_type: str | None) -> str:
+    if not credential_id:
+        return ""
+    model_cls = _PROVIDER_TYPE_TO_MODEL.get(provider_type or "")
+    if not model_cls:
+        return ""
+    with Session(db.engine) as session:
+        name = session.scalar(select(model_cls.name).where(model_cls.id == credential_id))
+        return str(name) if name else ""
+
+
+def _lookup_llm_credential_info(
+    tenant_id: str | None, provider: str | None, model: str | None, model_type: str | None = "llm"
+) -> tuple[str | None, str]:
+    """
+    Lookup LLM credential ID and name for the given provider and model.
+    Returns (credential_id, credential_name).
+
+    Handles async timing issues gracefully - if credential is deleted between lookups,
+    returns the ID but empty name rather than failing.
+    """
+    if not tenant_id or not provider:
+        return None, ""
+
+    try:
+        with Session(db.engine) as session:
+            # Try to find provider-level or model-level configuration
+            provider_record = session.scalar(
+                select(Provider).where(
+                    Provider.tenant_id == tenant_id,
+                    Provider.provider_name == provider,
+                    Provider.provider_type == ProviderType.CUSTOM,
+                )
+            )
+
+            if not provider_record:
+                return None, ""
+
+            # Check if there's a model-specific config
+            credential_id = None
+            credential_name = ""
+            is_model_level = False
+
+            if model and provider_record.credential_id:
+                # Try model-level first
+                model_record = session.scalar(
+                    select(ProviderModel).where(
+                        ProviderModel.tenant_id == tenant_id,
+                        ProviderModel.provider_name == provider,
+                        ProviderModel.model_name == model,
+                        ProviderModel.model_type == model_type,
+                    )
+                )
+
+                if model_record and model_record.credential_id:
+                    credential_id = model_record.credential_id
+                    is_model_level = True
+
+            if not credential_id and provider_record.credential_id:
+                # Fall back to provider-level credential
+                credential_id = provider_record.credential_id
+                is_model_level = False
+
+            # Lookup credential_name if we have credential_id
+            if credential_id:
+                try:
+                    if is_model_level:
+                        # Query ProviderModelCredential
+                        cred_name = session.scalar(
+                            select(ProviderModelCredential.credential_name).where(
+                                ProviderModelCredential.id == credential_id
+                            )
+                        )
+                    else:
+                        # Query ProviderCredential
+                        cred_name = session.scalar(
+                            select(ProviderCredential.credential_name).where(ProviderCredential.id == credential_id)
+                        )
+
+                    if cred_name:
+                        credential_name = str(cred_name)
+                except Exception as e:
+                    # Credential might have been deleted between lookups (async timing)
+                    # Return ID but empty name rather than failing
+                    logger.warning(
+                        "Failed to lookup credential name for credential_id=%s (provider=%s, model=%s): %s",
+                        credential_id,
+                        provider,
+                        model,
+                        str(e),
+                    )
+
+            return credential_id, credential_name
+    except Exception as e:
+        # Database query failed or other unexpected error
+        # Return empty rather than propagating error to telemetry emission
+        logger.warning(
+            "Failed to lookup LLM credential info for tenant_id=%s, provider=%s, model=%s: %s",
+            tenant_id,
+            provider,
+            model,
+            str(e),
+        )
+        return None, ""
+
+
 class OpsTraceProviderConfigMap(collections.UserDict[str, dict[str, Any]]):
     def __getitem__(self, provider: str) -> dict[str, Any]:
         match provider:
@@ -314,6 +457,10 @@ class OpsTraceManager:
         if app_id is None:
             return None
 
+        # Handle storage_id format (tenant-{uuid}) - not a real app_id
+        if isinstance(app_id, str) and app_id.startswith("tenant-"):
+            return None
+
         app: App | None = db.session.query(App).where(App.id == app_id).first()
 
         if app is None:
@@ -466,8 +613,6 @@ class TraceTask:
 
     @classmethod
     def _get_workflow_run_repo(cls):
-        from repositories.factory import DifyAPIRepositoryFactory
-
         if cls._workflow_run_repo is None:
             with cls._repo_lock:
                 if cls._workflow_run_repo is None:
@@ -478,6 +623,56 @@ class TraceTask:
                     cls._workflow_run_repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
         return cls._workflow_run_repo
 
+    @classmethod
+    def _get_user_id_from_metadata(cls, metadata: dict[str, Any]) -> str:
+        """Extract user ID from metadata, prioritizing end_user over account.
+
+        Returns the actual user ID (end_user or account) who invoked the workflow,
+        regardless of invoke_from context.
+        """
+        # Priority 1: End user (external users via API/WebApp)
+        if user_id := metadata.get("from_end_user_id"):
+            return f"end_user:{user_id}"
+
+        # Priority 2: Account user (internal users via console/debugger)
+        if user_id := metadata.get("from_account_id"):
+            return f"account:{user_id}"
+
+        # Priority 3: User (internal users via console/debugger)
+        if user_id := metadata.get("user_id"):
+            return f"user:{user_id}"
+
+        return "anonymous"
+
+    @classmethod
+    def _calculate_workflow_token_split(cls, workflow_run_id: str, tenant_id: str) -> tuple[int, int]:
+        from core.workflow.enums import WorkflowNodeExecutionMetadataKey
+        from models.workflow import WorkflowNodeExecutionModel
+
+        with Session(db.engine) as session:
+            node_executions = session.scalars(
+                select(WorkflowNodeExecutionModel).where(
+                    WorkflowNodeExecutionModel.tenant_id == tenant_id,
+                    WorkflowNodeExecutionModel.workflow_run_id == workflow_run_id,
+                )
+            ).all()
+
+            total_prompt = 0
+            total_completion = 0
+
+            for node_exec in node_executions:
+                metadata = node_exec.execution_metadata_dict
+
+                prompt = metadata.get(WorkflowNodeExecutionMetadataKey.PROMPT_TOKENS)
+                if prompt is not None:
+                    total_prompt += prompt
+
+                completion = metadata.get(WorkflowNodeExecutionMetadataKey.COMPLETION_TOKENS)
+                if completion is not None:
+                    total_completion += completion
+
+            return (total_prompt, total_completion)
+
     def __init__(
         self,
         trace_type: Any,
@@ -498,6 +693,8 @@ class TraceTask:
         self.app_id = None
         self.trace_id = None
         self.kwargs = kwargs
+        if user_id is not None and "user_id" not in self.kwargs:
+            self.kwargs["user_id"] = user_id
         external_trace_id = kwargs.get("external_trace_id")
         if external_trace_id:
             self.trace_id = external_trace_id
@@ -511,7 +708,7 @@ class TraceTask:
             TraceTaskName.WORKFLOW_TRACE: lambda: self.workflow_trace(
                 workflow_run_id=self.workflow_run_id, conversation_id=self.conversation_id, user_id=self.user_id
             ),
-            TraceTaskName.MESSAGE_TRACE: lambda: self.message_trace(message_id=self.message_id),
+            TraceTaskName.MESSAGE_TRACE: lambda: self.message_trace(message_id=self.message_id, **self.kwargs),
             TraceTaskName.MODERATION_TRACE: lambda: self.moderation_trace(
                 message_id=self.message_id, timer=self.timer, **self.kwargs
             ),
@@ -527,6 +724,9 @@ class TraceTask:
             TraceTaskName.GENERATE_NAME_TRACE: lambda: self.generate_name_trace(
                 conversation_id=self.conversation_id, timer=self.timer, **self.kwargs
             ),
+            TraceTaskName.PROMPT_GENERATION_TRACE: lambda: self.prompt_generation_trace(**self.kwargs),
+            TraceTaskName.NODE_EXECUTION_TRACE: lambda: self.node_execution_trace(**self.kwargs),
+            TraceTaskName.DRAFT_NODE_EXECUTION_TRACE: lambda: self.draft_node_execution_trace(**self.kwargs),
         }
 
         return preprocess_map.get(self.trace_type, lambda: None)()
@@ -562,6 +762,10 @@ class TraceTask:
 
         total_tokens = workflow_run.total_tokens
 
+        prompt_tokens, completion_tokens = self._calculate_workflow_token_split(
+            workflow_run_id=workflow_run_id, tenant_id=tenant_id
+        )
+
         file_list = workflow_run_inputs.get("sys.file") or []
         query = workflow_run_inputs.get("query") or workflow_run_inputs.get("sys.query") or ""
 
@@ -582,7 +786,14 @@ class TraceTask:
                 )
                 message_id = session.scalar(message_data_stmt)
 
-        metadata = {
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(workflow_run.app_id, tenant_id)
+        else:
+            app_name, workspace_name = "", ""
+
+        metadata: dict[str, Any] = {
             "workflow_id": workflow_id,
             "conversation_id": conversation_id,
             "workflow_run_id": workflow_run_id,
@@ -595,8 +806,14 @@ class TraceTask:
             "triggered_from": workflow_run.triggered_from,
             "user_id": user_id,
             "app_id": workflow_run.app_id,
+            "app_name": app_name,
+            "workspace_name": workspace_name,
         }
 
+        parent_trace_context = self.kwargs.get("parent_trace_context")
+        if parent_trace_context:
+            metadata["parent_trace_context"] = parent_trace_context
+
         workflow_trace_info = WorkflowTraceInfo(
             trace_id=self.trace_id,
             workflow_data=workflow_run.to_dict(),
@@ -611,6 +828,8 @@ class TraceTask:
             workflow_run_version=workflow_run_version,
             error=error,
             total_tokens=total_tokens,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
             file_list=file_list,
             query=query,
             metadata=metadata,
@@ -618,10 +837,11 @@ class TraceTask:
             message_id=message_id,
             start_time=workflow_run.created_at,
             end_time=workflow_run.finished_at,
+            invoked_by=self._get_user_id_from_metadata(metadata),
         )
         return workflow_trace_info
 
-    def message_trace(self, message_id: str | None):
+    def message_trace(self, message_id: str | None, **kwargs):
         if not message_id:
             return {}
         message_data = get_message_data(message_id)
@@ -644,6 +864,19 @@ class TraceTask:
 
         streaming_metrics = self._extract_streaming_metrics(message_data)
 
+        tenant_id = ""
+        with Session(db.engine) as session:
+            tid = session.scalar(select(App.tenant_id).where(App.id == message_data.app_id))
+            if tid:
+                tenant_id = str(tid)
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(message_data.app_id, tenant_id)
+        else:
+            app_name, workspace_name = "", ""
+
         metadata = {
             "conversation_id": message_data.conversation_id,
             "ls_provider": message_data.model_provider,
@@ -655,7 +888,14 @@ class TraceTask:
             "workflow_run_id": message_data.workflow_run_id,
             "from_source": message_data.from_source,
             "message_id": message_id,
+            "tenant_id": tenant_id,
+            "app_id": message_data.app_id,
+            "user_id": message_data.from_end_user_id or message_data.from_account_id,
+            "app_name": app_name,
+            "workspace_name": workspace_name,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         message_tokens = message_data.message_tokens
 
@@ -672,7 +912,9 @@ class TraceTask:
             outputs=message_data.answer,
             file_list=file_list,
             start_time=created_at,
-            end_time=created_at + timedelta(seconds=message_data.provider_response_latency),
+            end_time=message_data.updated_at
+            if message_data.updated_at and message_data.updated_at > created_at
+            else created_at + timedelta(seconds=message_data.provider_response_latency),
             metadata=metadata,
             message_file_data=message_file_data,
             conversation_mode=conversation_mode,
@@ -697,6 +939,8 @@ class TraceTask:
             "preset_response": moderation_result.preset_response,
             "query": moderation_result.query,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         # get workflow_app_log_id
         workflow_app_log_id = None
@@ -738,6 +982,8 @@ class TraceTask:
             "workflow_run_id": message_data.workflow_run_id,
             "from_source": message_data.from_source,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         # get workflow_app_log_id
         workflow_app_log_id = None
@@ -777,6 +1023,52 @@ class TraceTask:
         if not message_data:
             return {}
 
+        tenant_id = ""
+        with Session(db.engine) as session:
+            tid = session.scalar(select(App.tenant_id).where(App.id == message_data.app_id))
+            if tid:
+                tenant_id = str(tid)
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(message_data.app_id, tenant_id)
+        else:
+            app_name, workspace_name = "", ""
+
+        doc_list = [doc.model_dump() for doc in documents] if documents else []
+        dataset_ids: set[str] = set()
+        for doc in doc_list:
+            doc_meta = doc.get("metadata") or {}
+            did = doc_meta.get("dataset_id")
+            if did:
+                dataset_ids.add(did)
+
+        embedding_models: dict[str, dict[str, str]] = {}
+        if dataset_ids:
+            with Session(db.engine) as session:
+                rows = session.execute(
+                    select(Dataset.id, Dataset.embedding_model, Dataset.embedding_model_provider).where(
+                        Dataset.id.in_(list(dataset_ids))
+                    )
+                ).all()
+                for row in rows:
+                    embedding_models[str(row[0])] = {
+                        "embedding_model": row[1] or "",
+                        "embedding_model_provider": row[2] or "",
+                    }
+
+        # Extract rerank model info from retrieval_model kwargs
+        rerank_model_provider = ""
+        rerank_model_name = ""
+        if "retrieval_model" in kwargs:
+            retrieval_model = kwargs["retrieval_model"]
+            if isinstance(retrieval_model, dict):
+                reranking_model = retrieval_model.get("reranking_model")
+                if isinstance(reranking_model, dict):
+                    rerank_model_provider = reranking_model.get("reranking_provider_name", "")
+                    rerank_model_name = reranking_model.get("reranking_model_name", "")
+
         metadata = {
             "message_id": message_id,
             "ls_provider": message_data.model_provider,
@@ -787,13 +1079,23 @@ class TraceTask:
             "agent_based": message_data.agent_based,
             "workflow_run_id": message_data.workflow_run_id,
             "from_source": message_data.from_source,
+            "tenant_id": tenant_id,
+            "app_id": message_data.app_id,
+            "user_id": message_data.from_end_user_id or message_data.from_account_id,
+            "app_name": app_name,
+            "workspace_name": workspace_name,
+            "embedding_models": embedding_models,
+            "rerank_model_provider": rerank_model_provider,
+            "rerank_model_name": rerank_model_name,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         dataset_retrieval_trace_info = DatasetRetrievalTraceInfo(
             trace_id=self.trace_id,
             message_id=message_id,
             inputs=message_data.query or message_data.inputs,
-            documents=[doc.model_dump() for doc in documents] if documents else [],
+            documents=doc_list,
             start_time=timer.get("start"),
             end_time=timer.get("end"),
             metadata=metadata,
@@ -836,6 +1138,10 @@ class TraceTask:
             "error": error,
             "tool_parameters": tool_parameters,
         }
+        if message_data.workflow_run_id:
+            metadata["workflow_run_id"] = message_data.workflow_run_id
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         file_url = ""
         message_file_data = db.session.query(MessageFile).filter_by(message_id=message_id).first()
@@ -890,6 +1196,8 @@ class TraceTask:
             "conversation_id": conversation_id,
             "tenant_id": tenant_id,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         generate_name_trace_info = GenerateNameTraceInfo(
             trace_id=self.trace_id,
@@ -904,6 +1212,182 @@ class TraceTask:
 
         return generate_name_trace_info
 
+    def prompt_generation_trace(self, **kwargs) -> PromptGenerationTraceInfo | dict:
+        tenant_id = kwargs.get("tenant_id", "")
+        user_id = kwargs.get("user_id", "")
+        app_id = kwargs.get("app_id")
+        operation_type = kwargs.get("operation_type", "")
+        instruction = kwargs.get("instruction", "")
+        generated_output = kwargs.get("generated_output", "")
+
+        prompt_tokens = kwargs.get("prompt_tokens", 0)
+        completion_tokens = kwargs.get("completion_tokens", 0)
+        total_tokens = kwargs.get("total_tokens", 0)
+
+        model_provider = kwargs.get("model_provider", "")
+        model_name = kwargs.get("model_name", "")
+
+        latency = kwargs.get("latency", 0.0)
+
+        timer = kwargs.get("timer")
+        start_time = timer.get("start") if timer else None
+        end_time = timer.get("end") if timer else None
+
+        total_price = kwargs.get("total_price")
+        currency = kwargs.get("currency")
+
+        error = kwargs.get("error")
+
+        app_name = None
+        workspace_name = None
+        if app_id:
+            app_name, workspace_name = _lookup_app_and_workspace_names(app_id, tenant_id)
+
+        metadata = {
+            "tenant_id": tenant_id,
+            "user_id": user_id,
+            "app_id": app_id or "",
+            "app_name": app_name,
+            "workspace_name": workspace_name,
+            "operation_type": operation_type,
+            "model_provider": model_provider,
+            "model_name": model_name,
+        }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
+
+        return PromptGenerationTraceInfo(
+            trace_id=self.trace_id,
+            inputs=instruction,
+            outputs=generated_output,
+            start_time=start_time,
+            end_time=end_time,
+            metadata=metadata,
+            tenant_id=tenant_id,
+            user_id=user_id,
+            app_id=app_id,
+            operation_type=operation_type,
+            instruction=instruction,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=total_tokens,
+            model_provider=model_provider,
+            model_name=model_name,
+            latency=latency,
+            total_price=total_price,
+            currency=currency,
+            error=error,
+        )
+
+    def node_execution_trace(self, **kwargs) -> WorkflowNodeTraceInfo | dict:
+        node_data: dict = kwargs.get("node_execution_data", {})
+        if not node_data:
+            return {}
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(
+                node_data.get("app_id"), node_data.get("tenant_id")
+            )
+        else:
+            app_name, workspace_name = "", ""
+
+        # Try tool credential lookup first
+        credential_id = node_data.get("credential_id")
+        if is_enterprise_telemetry_enabled():
+            credential_name = _lookup_credential_name(credential_id, node_data.get("credential_provider_type"))
+            # If no credential_id found (e.g., LLM nodes), try LLM credential lookup
+            if not credential_id:
+                llm_cred_id, llm_cred_name = _lookup_llm_credential_info(
+                    tenant_id=node_data.get("tenant_id"),
+                    provider=node_data.get("model_provider"),
+                    model=node_data.get("model_name"),
+                    model_type="llm",
+                )
+                if llm_cred_id:
+                    credential_id = llm_cred_id
+                    credential_name = llm_cred_name
+        else:
+            credential_name = ""
+        metadata: dict[str, Any] = {
+            "tenant_id": node_data.get("tenant_id"),
+            "app_id": node_data.get("app_id"),
+            "app_name": app_name,
+            "workspace_name": workspace_name,
+            "user_id": node_data.get("user_id"),
+            "invoke_from": node_data.get("invoke_from"),
+            "credential_id": node_data.get("credential_id"),
+            "credential_name": credential_name,
+            "dataset_ids": node_data.get("dataset_ids"),
+            "dataset_names": node_data.get("dataset_names"),
+            "plugin_name": node_data.get("plugin_name"),
+        }
+
+        parent_trace_context = node_data.get("parent_trace_context")
+        if parent_trace_context:
+            metadata["parent_trace_context"] = parent_trace_context
+
+        message_id: str | None = None
+        conversation_id = node_data.get("conversation_id")
+        workflow_execution_id = node_data.get("workflow_execution_id")
+        if conversation_id and workflow_execution_id and not parent_trace_context:
+            with Session(db.engine) as session:
+                msg_id = session.scalar(
+                    select(Message.id).where(
+                        Message.conversation_id == conversation_id,
+                        Message.workflow_run_id == workflow_execution_id,
+                    )
+                )
+                if msg_id:
+                    message_id = str(msg_id)
+                    metadata["message_id"] = message_id
+            if conversation_id:
+                metadata["conversation_id"] = conversation_id
+
+        return WorkflowNodeTraceInfo(
+            trace_id=self.trace_id,
+            message_id=message_id,
+            start_time=node_data.get("created_at"),
+            end_time=node_data.get("finished_at"),
+            metadata=metadata,
+            workflow_id=node_data.get("workflow_id", ""),
+            workflow_run_id=node_data.get("workflow_execution_id", ""),
+            tenant_id=node_data.get("tenant_id", ""),
+            node_execution_id=node_data.get("node_execution_id", ""),
+            node_id=node_data.get("node_id", ""),
+            node_type=node_data.get("node_type", ""),
+            title=node_data.get("title", ""),
+            status=node_data.get("status", ""),
+            error=node_data.get("error"),
+            elapsed_time=node_data.get("elapsed_time", 0.0),
+            index=node_data.get("index", 0),
+            predecessor_node_id=node_data.get("predecessor_node_id"),
+            total_tokens=node_data.get("total_tokens", 0),
+            total_price=node_data.get("total_price", 0.0),
+            currency=node_data.get("currency"),
+            model_provider=node_data.get("model_provider"),
+            model_name=node_data.get("model_name"),
+            prompt_tokens=node_data.get("prompt_tokens"),
+            completion_tokens=node_data.get("completion_tokens"),
+            tool_name=node_data.get("tool_name"),
+            iteration_id=node_data.get("iteration_id"),
+            iteration_index=node_data.get("iteration_index"),
+            loop_id=node_data.get("loop_id"),
+            loop_index=node_data.get("loop_index"),
+            parallel_id=node_data.get("parallel_id"),
+            node_inputs=node_data.get("node_inputs"),
+            node_outputs=node_data.get("node_outputs"),
+            process_data=node_data.get("process_data"),
+            invoked_by=self._get_user_id_from_metadata(metadata),
+        )
+
+    def draft_node_execution_trace(self, **kwargs) -> DraftNodeExecutionTrace | dict:
+        node_trace = self.node_execution_trace(**kwargs)
+        if not node_trace or not isinstance(node_trace, WorkflowNodeTraceInfo):
+            return node_trace
+        return DraftNodeExecutionTrace(**node_trace.model_dump())
+
     def _extract_streaming_metrics(self, message_data) -> dict:
         if not message_data.message_metadata:
             return {}
@@ -937,13 +1421,17 @@ class TraceQueueManager:
         self.user_id = user_id
         self.trace_instance = OpsTraceManager.get_ops_trace_instance(app_id)
         self.flask_app = current_app._get_current_object()  # type: ignore
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        self._enterprise_telemetry_enabled = is_enterprise_telemetry_enabled()
         if trace_manager_timer is None:
             self.start_timer()
 
     def add_trace_task(self, trace_task: TraceTask):
         global trace_manager_timer, trace_manager_queue
         try:
-            if self.trace_instance:
+            if self._enterprise_telemetry_enabled or self.trace_instance:
                 trace_task.app_id = self.app_id
                 trace_manager_queue.put(trace_task)
         except Exception:
@@ -979,20 +1467,27 @@ class TraceQueueManager:
     def send_to_celery(self, tasks: list[TraceTask]):
         with self.flask_app.app_context():
             for task in tasks:
-                if task.app_id is None:
-                    continue
+                storage_id = task.app_id
+                if storage_id is None:
+                    tenant_id = task.kwargs.get("tenant_id")
+                    if tenant_id:
+                        storage_id = f"tenant-{tenant_id}"
+                    else:
+                        logger.warning("Skipping trace without app_id or tenant_id, trace_type: %s", task.trace_type)
+                        continue
+
                 file_id = uuid4().hex
                 trace_info = task.execute()
 
                 task_data = TaskData(
-                    app_id=task.app_id,
+                    app_id=storage_id,
                     trace_info_type=type(trace_info).__name__,
                     trace_info=trace_info.model_dump() if trace_info else None,
                 )
-                file_path = f"{OPS_FILE_PATH}{task.app_id}/{file_id}.json"
+                file_path = f"{OPS_FILE_PATH}{storage_id}/{file_id}.json"
                 storage.save(file_path, task_data.model_dump_json().encode("utf-8"))
                 file_info = {
                     "file_id": file_id,
-                    "app_id": task.app_id,
+                    "app_id": storage_id,
                 }
                 process_trace_tasks.delay(file_info)  # type: ignore

api/core/ops/utils.py

@@ -5,7 +5,7 @@ from urllib.parse import urlparse
 
 from sqlalchemy import select
 
-from models.engine import db
+from extensions.ext_database import db
 from models.model import Message
 
 

api/core/plugin/backwards_invocation/app.py

@@ -1,4 +1,3 @@
-import uuid
 from collections.abc import Generator, Mapping
 from typing import Union
 
@@ -12,7 +11,6 @@ from core.app.apps.chat.app_generator import ChatAppGenerator
 from core.app.apps.completion.app_generator import CompletionAppGenerator
 from core.app.apps.workflow.app_generator import WorkflowAppGenerator
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.app.layers.pause_state_persist_layer import PauseStateLayerConfig
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
 from extensions.ext_database import db
 from models import Account
@@ -103,11 +101,6 @@ class PluginAppBackwardsInvocation(BaseBackwardsInvocation):
             if not workflow:
                 raise ValueError("unexpected app type")
 
-            pause_config = PauseStateLayerConfig(
-                session_factory=db.engine,
-                state_owner_user_id=workflow.created_by,
-            )
-
             return AdvancedChatAppGenerator().generate(
                 app_model=app,
                 workflow=workflow,
@@ -119,9 +112,7 @@ class PluginAppBackwardsInvocation(BaseBackwardsInvocation):
                     "conversation_id": conversation_id,
                 },
                 invoke_from=InvokeFrom.SERVICE_API,
-                workflow_run_id=str(uuid.uuid4()),
                 streaming=stream,
-                pause_state_config=pause_config,
             )
         elif app.mode == AppMode.AGENT_CHAT:
             return AgentChatAppGenerator().generate(
@@ -168,11 +159,6 @@ class PluginAppBackwardsInvocation(BaseBackwardsInvocation):
         if not workflow:
             raise ValueError("unexpected app type")
 
-        pause_config = PauseStateLayerConfig(
-            session_factory=db.engine,
-            state_owner_user_id=workflow.created_by,
-        )
-
         return WorkflowAppGenerator().generate(
             app_model=app,
             workflow=workflow,
@@ -181,7 +167,6 @@ class PluginAppBackwardsInvocation(BaseBackwardsInvocation):
             invoke_from=InvokeFrom.SERVICE_API,
             streaming=stream,
             call_depth=1,
-            pause_state_config=pause_config,
         )
 
     @classmethod

api/core/plugin/entities/marketplace.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, computed_field, model_validator
+from pydantic import BaseModel, Field, model_validator
 
 from core.model_runtime.entities.provider_entities import ProviderEntity
 from core.plugin.entities.endpoint import EndpointProviderDeclaration
@@ -48,15 +48,3 @@ class MarketplacePluginDeclaration(BaseModel):
         if "tool" in data and not data["tool"]:
             del data["tool"]
         return data
-
-
-class MarketplacePluginSnapshot(BaseModel):
-    org: str
-    name: str
-    latest_version: str
-    latest_package_identifier: str
-    latest_package_url: str
-
-    @computed_field
-    def plugin_id(self) -> str:
-        return f"{self.org}/{self.name}"

api/core/rag/retrieval/dataset_retrieval.py

@@ -1,15 +1,13 @@
 import json
-import logging
 import math
 import re
 import threading
-import time
 from collections import Counter, defaultdict
 from collections.abc import Generator, Mapping
 from typing import Any, Union, cast
 
 from flask import Flask, current_app
-from sqlalchemy import and_, func, literal, or_, select
+from sqlalchemy import and_, literal, or_, select
 from sqlalchemy.orm import Session
 
 from core.app.app_config.entities import (
@@ -20,7 +18,6 @@ from core.app.app_config.entities import (
 )
 from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
-from core.db.session_factory import session_factory
 from core.entities.agent_entities import PlanningStrategy
 from core.entities.model_entities import ModelStatus
 from core.file import File, FileTransferMethod, FileType
@@ -30,8 +27,7 @@ from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
 from core.model_runtime.entities.model_entities import ModelFeature, ModelType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.ops.entities.trace_entity import TraceTaskName
-from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
+from core.ops.ops_trace_manager import TraceQueueManager
 from core.ops.utils import measure_time
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
@@ -59,32 +55,16 @@ from core.rag.retrieval.template_prompts import (
     METADATA_FILTER_USER_PROMPT_2,
     METADATA_FILTER_USER_PROMPT_3,
 )
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
 from core.tools.signature import sign_upload_file
 from core.tools.utils.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
-from core.workflow.nodes.knowledge_retrieval import exc
-from core.workflow.repositories.rag_retrieval_protocol import (
-    KnowledgeRetrievalRequest,
-    Source,
-    SourceChildChunk,
-    SourceMetadata,
-)
 from extensions.ext_database import db
-from extensions.ext_redis import redis_client
 from libs.json_in_md_parser import parse_and_check_json_markdown
 from models import UploadFile
-from models.dataset import (
-    ChildChunk,
-    Dataset,
-    DatasetMetadata,
-    DatasetQuery,
-    DocumentSegment,
-    RateLimitLog,
-    SegmentAttachmentBinding,
-)
+from models.dataset import ChildChunk, Dataset, DatasetMetadata, DatasetQuery, DocumentSegment, SegmentAttachmentBinding
 from models.dataset import Document as DatasetDocument
-from models.dataset import Document as DocumentModel
 from services.external_knowledge_service import ExternalDatasetService
-from services.feature_service import FeatureService
 
 default_retrieval_model: dict[str, Any] = {
     "search_method": RetrievalMethod.SEMANTIC_SEARCH,
@@ -94,8 +74,6 @@ default_retrieval_model: dict[str, Any] = {
     "score_threshold_enabled": False,
 }
 
-logger = logging.getLogger(__name__)
-
 
 class DatasetRetrieval:
     def __init__(self, application_generate_entity=None):
@@ -114,233 +92,6 @@ class DatasetRetrieval:
         else:
             self._llm_usage = self._llm_usage.plus(usage)
 
-    def knowledge_retrieval(self, request: KnowledgeRetrievalRequest) -> list[Source]:
-        self._check_knowledge_rate_limit(request.tenant_id)
-        available_datasets = self._get_available_datasets(request.tenant_id, request.dataset_ids)
-        available_datasets_ids = [i.id for i in available_datasets]
-        if not available_datasets_ids:
-            return []
-
-        if not request.query:
-            return []
-
-        metadata_filter_document_ids, metadata_condition = None, None
-
-        if request.metadata_filtering_mode != "disabled":
-            # Convert workflow layer types to app_config layer types
-            if not request.metadata_model_config:
-                raise ValueError("metadata_model_config is required for this method")
-
-            app_metadata_model_config = ModelConfig.model_validate(request.metadata_model_config.model_dump())
-
-            app_metadata_filtering_conditions = None
-            if request.metadata_filtering_conditions is not None:
-                app_metadata_filtering_conditions = MetadataFilteringCondition.model_validate(
-                    request.metadata_filtering_conditions.model_dump()
-                )
-
-            query = request.query if request.query is not None else ""
-
-            metadata_filter_document_ids, metadata_condition = self.get_metadata_filter_condition(
-                dataset_ids=available_datasets_ids,
-                query=query,
-                tenant_id=request.tenant_id,
-                user_id=request.user_id,
-                metadata_filtering_mode=request.metadata_filtering_mode,
-                metadata_model_config=app_metadata_model_config,
-                metadata_filtering_conditions=app_metadata_filtering_conditions,
-                inputs={},
-            )
-
-        if request.retrieval_mode == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE:
-            planning_strategy = PlanningStrategy.REACT_ROUTER
-            # Ensure required fields are not None for single retrieval mode
-            if request.model_provider is None or request.model_name is None or request.query is None:
-                raise ValueError("model_provider, model_name, and query are required for single retrieval mode")
-
-            model_manager = ModelManager()
-            model_instance = model_manager.get_model_instance(
-                tenant_id=request.tenant_id,
-                model_type=ModelType.LLM,
-                provider=request.model_provider,
-                model=request.model_name,
-            )
-
-            provider_model_bundle = model_instance.provider_model_bundle
-            model_type_instance = model_instance.model_type_instance
-            model_type_instance = cast(LargeLanguageModel, model_type_instance)
-
-            model_credentials = model_instance.credentials
-
-            # check model
-            provider_model = provider_model_bundle.configuration.get_provider_model(
-                model=request.model_name, model_type=ModelType.LLM
-            )
-
-            if provider_model is None:
-                raise exc.ModelNotExistError(f"Model {request.model_name} not exist.")
-
-            if provider_model.status == ModelStatus.NO_CONFIGURE:
-                raise exc.ModelCredentialsNotInitializedError(
-                    f"Model {request.model_name} credentials is not initialized."
-                )
-            elif provider_model.status == ModelStatus.NO_PERMISSION:
-                raise exc.ModelNotSupportedError(f"Dify Hosted OpenAI {request.model_name} currently not support.")
-            elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
-                raise exc.ModelQuotaExceededError(f"Model provider {request.model_provider} quota exceeded.")
-
-            stop = []
-            completion_params = (request.completion_params or {}).copy()
-            if "stop" in completion_params:
-                stop = completion_params["stop"]
-                del completion_params["stop"]
-
-            model_schema = model_type_instance.get_model_schema(request.model_name, model_credentials)
-
-            if not model_schema:
-                raise exc.ModelNotExistError(f"Model {request.model_name} not exist.")
-
-            model_config = ModelConfigWithCredentialsEntity(
-                provider=request.model_provider,
-                model=request.model_name,
-                model_schema=model_schema,
-                mode=request.model_mode or "chat",
-                provider_model_bundle=provider_model_bundle,
-                credentials=model_credentials,
-                parameters=completion_params,
-                stop=stop,
-            )
-            all_documents = self.single_retrieve(
-                request.app_id,
-                request.tenant_id,
-                request.user_id,
-                request.user_from,
-                request.query,
-                available_datasets,
-                model_instance,
-                model_config,
-                planning_strategy,
-                None,  # message_id
-                metadata_filter_document_ids,
-                metadata_condition,
-            )
-        else:
-            all_documents = self.multiple_retrieve(
-                app_id=request.app_id,
-                tenant_id=request.tenant_id,
-                user_id=request.user_id,
-                user_from=request.user_from,
-                available_datasets=available_datasets,
-                query=request.query,
-                top_k=request.top_k,
-                score_threshold=request.score_threshold,
-                reranking_mode=request.reranking_mode,
-                reranking_model=request.reranking_model,
-                weights=request.weights,
-                reranking_enable=request.reranking_enable,
-                metadata_filter_document_ids=metadata_filter_document_ids,
-                metadata_condition=metadata_condition,
-                attachment_ids=request.attachment_ids,
-            )
-
-        dify_documents = [item for item in all_documents if item.provider == "dify"]
-        external_documents = [item for item in all_documents if item.provider == "external"]
-        retrieval_resource_list = []
-        # deal with external documents
-        for item in external_documents:
-            source = Source(
-                metadata=SourceMetadata(
-                    source="knowledge",
-                    dataset_id=item.metadata.get("dataset_id"),
-                    dataset_name=item.metadata.get("dataset_name"),
-                    document_id=item.metadata.get("document_id"),
-                    document_name=item.metadata.get("title"),
-                    data_source_type="external",
-                    retriever_from="workflow",
-                    score=item.metadata.get("score"),
-                    doc_metadata=item.metadata,
-                ),
-                title=item.metadata.get("title"),
-                content=item.page_content,
-            )
-            retrieval_resource_list.append(source)
-        # deal with dify documents
-        if dify_documents:
-            records = RetrievalService.format_retrieval_documents(dify_documents)
-            dataset_ids = [i.segment.dataset_id for i in records]
-            document_ids = [i.segment.document_id for i in records]
-
-            with session_factory.create_session() as session:
-                datasets = session.query(Dataset).where(Dataset.id.in_(dataset_ids)).all()
-                documents = session.query(DatasetDocument).where(DatasetDocument.id.in_(document_ids)).all()
-
-            dataset_map = {i.id: i for i in datasets}
-            document_map = {i.id: i for i in documents}
-
-            if records:
-                for record in records:
-                    segment = record.segment
-                    dataset = dataset_map.get(segment.dataset_id)
-                    document = document_map.get(segment.document_id)
-
-                    if dataset and document:
-                        source = Source(
-                            metadata=SourceMetadata(
-                                source="knowledge",
-                                dataset_id=dataset.id,
-                                dataset_name=dataset.name,
-                                document_id=document.id,
-                                document_name=document.name,
-                                data_source_type=document.data_source_type,
-                                segment_id=segment.id,
-                                retriever_from="workflow",
-                                score=record.score or 0.0,
-                                segment_hit_count=segment.hit_count,
-                                segment_word_count=segment.word_count,
-                                segment_position=segment.position,
-                                segment_index_node_hash=segment.index_node_hash,
-                                doc_metadata=document.doc_metadata,
-                                child_chunks=[
-                                    SourceChildChunk(
-                                        id=str(getattr(chunk, "id", "")),
-                                        content=str(getattr(chunk, "content", "")),
-                                        position=int(getattr(chunk, "position", 0)),
-                                        score=float(getattr(chunk, "score", 0.0)),
-                                    )
-                                    for chunk in (record.child_chunks or [])
-                                ],
-                                position=None,
-                            ),
-                            title=document.name,
-                            files=list(record.files) if record.files else None,
-                            content=segment.get_sign_content(),
-                        )
-                        if segment.answer:
-                            source.content = f"question:{segment.get_sign_content()} \nanswer:{segment.answer}"
-
-                        if record.summary:
-                            source.summary = record.summary
-
-                        retrieval_resource_list.append(source)
-
-        if retrieval_resource_list:
-
-            def _score(item: Source) -> float:
-                meta = item.metadata
-                score = meta.score
-                if isinstance(score, (int, float)):
-                    return float(score)
-                return 0.0
-
-            retrieval_resource_list = sorted(
-                retrieval_resource_list,
-                key=_score,  # type: ignore[arg-type, return-value]
-                reverse=True,
-            )
-            for position, item in enumerate(retrieval_resource_list, start=1):
-                item.metadata.position = position  # type: ignore[index]
-        return retrieval_resource_list
-
     def retrieve(
         self,
         app_id: str,
@@ -400,7 +151,14 @@ class DatasetRetrieval:
         if features:
             if ModelFeature.TOOL_CALL in features or ModelFeature.MULTI_TOOL_CALL in features:
                 planning_strategy = PlanningStrategy.ROUTER
-        available_datasets = self._get_available_datasets(tenant_id, dataset_ids)
+        available_datasets = []
+
+        dataset_stmt = select(Dataset).where(Dataset.tenant_id == tenant_id, Dataset.id.in_(dataset_ids))
+        datasets: list[Dataset] = db.session.execute(dataset_stmt).scalars().all()  # type: ignore
+        for dataset in datasets:
+            if dataset.available_document_count == 0 and dataset.provider != "external":
+                continue
+            available_datasets.append(dataset)
 
         if inputs:
             inputs = {key: str(value) for key, value in inputs.items()}
@@ -971,10 +729,21 @@ class DatasetRetrieval:
             self.application_generate_entity.trace_manager if self.application_generate_entity else None
         )
         if trace_manager:
-            trace_manager.add_trace_task(
-                TraceTask(
-                    TraceTaskName.DATASET_RETRIEVAL_TRACE, message_id=message_id, documents=documents, timer=timer
-                )
+            app_config = self.application_generate_entity.app_config if self.application_generate_entity else None
+            telemetry_emit(
+                TelemetryEvent(
+                    name=TraceTaskName.DATASET_RETRIEVAL_TRACE,
+                    context=TelemetryContext(
+                        tenant_id=app_config.tenant_id if app_config else None,
+                        app_id=app_config.app_id if app_config else None,
+                    ),
+                    payload={
+                        "message_id": message_id,
+                        "documents": documents,
+                        "timer": timer,
+                    },
+                ),
+                trace_manager=trace_manager,
             )
 
     def _on_query(
@@ -1404,6 +1173,7 @@ class DatasetRetrieval:
             query=query or "",
         )
 
+        result_text = ""
         try:
             # handle invoke result
             invoke_result = cast(
@@ -1434,8 +1204,7 @@ class DatasetRetrieval:
                                 "condition": item.get("comparison_operator"),
                             }
                         )
-        except Exception as e:
-            logger.warning(e, exc_info=True)
+        except Exception:
             return None
         return automatic_metadata_filters
 
@@ -1649,12 +1418,7 @@ class DatasetRetrieval:
         usage = None
         for result in invoke_result:
             text = result.delta.message.content
-            if isinstance(text, str):
-                full_text += text
-            elif isinstance(text, list):
-                for i in text:
-                    if i.data:
-                        full_text += i.data
+            full_text += text
 
             if not model:
                 model = result.model
@@ -1772,53 +1536,3 @@ class DatasetRetrieval:
                 cancel_event.set()
             if thread_exceptions is not None:
                 thread_exceptions.append(e)
-
-    def _get_available_datasets(self, tenant_id: str, dataset_ids: list[str]) -> list[Dataset]:
-        with session_factory.create_session() as session:
-            subquery = (
-                session.query(DocumentModel.dataset_id, func.count(DocumentModel.id).label("available_document_count"))
-                .where(
-                    DocumentModel.indexing_status == "completed",
-                    DocumentModel.enabled == True,
-                    DocumentModel.archived == False,
-                    DocumentModel.dataset_id.in_(dataset_ids),
-                )
-                .group_by(DocumentModel.dataset_id)
-                .having(func.count(DocumentModel.id) > 0)
-                .subquery()
-            )
-
-            results = (
-                session.query(Dataset)
-                .outerjoin(subquery, Dataset.id == subquery.c.dataset_id)
-                .where(Dataset.tenant_id == tenant_id, Dataset.id.in_(dataset_ids))
-                .where((subquery.c.available_document_count > 0) | (Dataset.provider == "external"))
-                .all()
-            )
-
-        available_datasets = []
-        for dataset in results:
-            if not dataset:
-                continue
-            available_datasets.append(dataset)
-        return available_datasets
-
-    def _check_knowledge_rate_limit(self, tenant_id: str):
-        knowledge_rate_limit = FeatureService.get_knowledge_rate_limit(tenant_id)
-        if knowledge_rate_limit.enabled:
-            current_time = int(time.time() * 1000)
-            key = f"rate_limit_{tenant_id}"
-            redis_client.zadd(key, {current_time: current_time})
-            redis_client.zremrangebyscore(key, 0, current_time - 60000)
-            request_count = redis_client.zcard(key)
-            if request_count > knowledge_rate_limit.limit:
-                with session_factory.create_session() as session:
-                    rate_limit_log = RateLimitLog(
-                        tenant_id=tenant_id,
-                        subscription_plan=knowledge_rate_limit.subscription_plan,
-                        operation="knowledge",
-                    )
-                    session.add(rate_limit_log)
-                raise exc.RateLimitExceededError(
-                    "you have reached the knowledge base request rate limit of your subscription."
-                )

api/core/repositories/__init__.py

@@ -1,18 +1,19 @@
-"""Repository implementations for data access."""
+"""
+Repository implementations for data access.
 
-from __future__ import annotations
+This package contains concrete implementations of the repository interfaces
+defined in the core.workflow.repository package.
+"""
 
-from .celery_workflow_execution_repository import CeleryWorkflowExecutionRepository
-from .celery_workflow_node_execution_repository import CeleryWorkflowNodeExecutionRepository
-from .factory import DifyCoreRepositoryFactory, RepositoryImportError
-from .sqlalchemy_workflow_execution_repository import SQLAlchemyWorkflowExecutionRepository
-from .sqlalchemy_workflow_node_execution_repository import SQLAlchemyWorkflowNodeExecutionRepository
+from core.repositories.celery_workflow_execution_repository import CeleryWorkflowExecutionRepository
+from core.repositories.celery_workflow_node_execution_repository import CeleryWorkflowNodeExecutionRepository
+from core.repositories.factory import DifyCoreRepositoryFactory, RepositoryImportError
+from core.repositories.sqlalchemy_workflow_node_execution_repository import SQLAlchemyWorkflowNodeExecutionRepository
 
 __all__ = [
     "CeleryWorkflowExecutionRepository",
     "CeleryWorkflowNodeExecutionRepository",
     "DifyCoreRepositoryFactory",
     "RepositoryImportError",
-    "SQLAlchemyWorkflowExecutionRepository",
     "SQLAlchemyWorkflowNodeExecutionRepository",
 ]

api/core/repositories/human_input_repository.py

@@ -1,553 +0,0 @@
-import dataclasses
-import json
-from collections.abc import Mapping, Sequence
-from datetime import datetime
-from typing import Any
-
-from sqlalchemy import Engine, select
-from sqlalchemy.orm import Session, selectinload, sessionmaker
-
-from core.workflow.nodes.human_input.entities import (
-    DeliveryChannelConfig,
-    EmailDeliveryMethod,
-    EmailRecipients,
-    ExternalRecipient,
-    FormDefinition,
-    HumanInputNodeData,
-    MemberRecipient,
-    WebAppDeliveryMethod,
-)
-from core.workflow.nodes.human_input.enums import (
-    DeliveryMethodType,
-    HumanInputFormKind,
-    HumanInputFormStatus,
-)
-from core.workflow.repositories.human_input_form_repository import (
-    FormCreateParams,
-    FormNotFoundError,
-    HumanInputFormEntity,
-    HumanInputFormRecipientEntity,
-)
-from libs.datetime_utils import naive_utc_now
-from libs.uuid_utils import uuidv7
-from models.account import Account, TenantAccountJoin
-from models.human_input import (
-    BackstageRecipientPayload,
-    ConsoleDeliveryPayload,
-    ConsoleRecipientPayload,
-    EmailExternalRecipientPayload,
-    EmailMemberRecipientPayload,
-    HumanInputDelivery,
-    HumanInputForm,
-    HumanInputFormRecipient,
-    RecipientType,
-    StandaloneWebAppRecipientPayload,
-)
-
-
-@dataclasses.dataclass(frozen=True)
-class _DeliveryAndRecipients:
-    delivery: HumanInputDelivery
-    recipients: Sequence[HumanInputFormRecipient]
-
-
-@dataclasses.dataclass(frozen=True)
-class _WorkspaceMemberInfo:
-    user_id: str
-    email: str
-
-
-class _HumanInputFormRecipientEntityImpl(HumanInputFormRecipientEntity):
-    def __init__(self, recipient_model: HumanInputFormRecipient):
-        self._recipient_model = recipient_model
-
-    @property
-    def id(self) -> str:
-        return self._recipient_model.id
-
-    @property
-    def token(self) -> str:
-        if self._recipient_model.access_token is None:
-            raise AssertionError(f"access_token should not be None for recipient {self._recipient_model.id}")
-        return self._recipient_model.access_token
-
-
-class _HumanInputFormEntityImpl(HumanInputFormEntity):
-    def __init__(self, form_model: HumanInputForm, recipient_models: Sequence[HumanInputFormRecipient]):
-        self._form_model = form_model
-        self._recipients = [_HumanInputFormRecipientEntityImpl(recipient) for recipient in recipient_models]
-        self._web_app_recipient = next(
-            (
-                recipient
-                for recipient in recipient_models
-                if recipient.recipient_type == RecipientType.STANDALONE_WEB_APP
-            ),
-            None,
-        )
-        self._console_recipient = next(
-            (recipient for recipient in recipient_models if recipient.recipient_type == RecipientType.CONSOLE),
-            None,
-        )
-        self._submitted_data: Mapping[str, Any] | None = (
-            json.loads(form_model.submitted_data) if form_model.submitted_data is not None else None
-        )
-
-    @property
-    def id(self) -> str:
-        return self._form_model.id
-
-    @property
-    def web_app_token(self):
-        if self._console_recipient is not None:
-            return self._console_recipient.access_token
-        if self._web_app_recipient is None:
-            return None
-        return self._web_app_recipient.access_token
-
-    @property
-    def recipients(self) -> list[HumanInputFormRecipientEntity]:
-        return list(self._recipients)
-
-    @property
-    def rendered_content(self) -> str:
-        return self._form_model.rendered_content
-
-    @property
-    def selected_action_id(self) -> str | None:
-        return self._form_model.selected_action_id
-
-    @property
-    def submitted_data(self) -> Mapping[str, Any] | None:
-        return self._submitted_data
-
-    @property
-    def submitted(self) -> bool:
-        return self._form_model.submitted_at is not None
-
-    @property
-    def status(self) -> HumanInputFormStatus:
-        return self._form_model.status
-
-    @property
-    def expiration_time(self) -> datetime:
-        return self._form_model.expiration_time
-
-
-@dataclasses.dataclass(frozen=True)
-class HumanInputFormRecord:
-    form_id: str
-    workflow_run_id: str | None
-    node_id: str
-    tenant_id: str
-    app_id: str
-    form_kind: HumanInputFormKind
-    definition: FormDefinition
-    rendered_content: str
-    created_at: datetime
-    expiration_time: datetime
-    status: HumanInputFormStatus
-    selected_action_id: str | None
-    submitted_data: Mapping[str, Any] | None
-    submitted_at: datetime | None
-    submission_user_id: str | None
-    submission_end_user_id: str | None
-    completed_by_recipient_id: str | None
-    recipient_id: str | None
-    recipient_type: RecipientType | None
-    access_token: str | None
-
-    @property
-    def submitted(self) -> bool:
-        return self.submitted_at is not None
-
-    @classmethod
-    def from_models(
-        cls, form_model: HumanInputForm, recipient_model: HumanInputFormRecipient | None
-    ) -> "HumanInputFormRecord":
-        definition_payload = json.loads(form_model.form_definition)
-        if "expiration_time" not in definition_payload:
-            definition_payload["expiration_time"] = form_model.expiration_time
-        return cls(
-            form_id=form_model.id,
-            workflow_run_id=form_model.workflow_run_id,
-            node_id=form_model.node_id,
-            tenant_id=form_model.tenant_id,
-            app_id=form_model.app_id,
-            form_kind=form_model.form_kind,
-            definition=FormDefinition.model_validate(definition_payload),
-            rendered_content=form_model.rendered_content,
-            created_at=form_model.created_at,
-            expiration_time=form_model.expiration_time,
-            status=form_model.status,
-            selected_action_id=form_model.selected_action_id,
-            submitted_data=json.loads(form_model.submitted_data) if form_model.submitted_data else None,
-            submitted_at=form_model.submitted_at,
-            submission_user_id=form_model.submission_user_id,
-            submission_end_user_id=form_model.submission_end_user_id,
-            completed_by_recipient_id=form_model.completed_by_recipient_id,
-            recipient_id=recipient_model.id if recipient_model else None,
-            recipient_type=recipient_model.recipient_type if recipient_model else None,
-            access_token=recipient_model.access_token if recipient_model else None,
-        )
-
-
-class _InvalidTimeoutStatusError(ValueError):
-    pass
-
-
-class HumanInputFormRepositoryImpl:
-    def __init__(
-        self,
-        session_factory: sessionmaker | Engine,
-        tenant_id: str,
-    ):
-        if isinstance(session_factory, Engine):
-            session_factory = sessionmaker(bind=session_factory)
-        self._session_factory = session_factory
-        self._tenant_id = tenant_id
-
-    def _delivery_method_to_model(
-        self,
-        session: Session,
-        form_id: str,
-        delivery_method: DeliveryChannelConfig,
-    ) -> _DeliveryAndRecipients:
-        delivery_id = str(uuidv7())
-        delivery_model = HumanInputDelivery(
-            id=delivery_id,
-            form_id=form_id,
-            delivery_method_type=delivery_method.type,
-            delivery_config_id=delivery_method.id,
-            channel_payload=delivery_method.model_dump_json(),
-        )
-        recipients: list[HumanInputFormRecipient] = []
-        if isinstance(delivery_method, WebAppDeliveryMethod):
-            recipient_model = HumanInputFormRecipient(
-                form_id=form_id,
-                delivery_id=delivery_id,
-                recipient_type=RecipientType.STANDALONE_WEB_APP,
-                recipient_payload=StandaloneWebAppRecipientPayload().model_dump_json(),
-            )
-            recipients.append(recipient_model)
-        elif isinstance(delivery_method, EmailDeliveryMethod):
-            email_recipients_config = delivery_method.config.recipients
-            recipients.extend(
-                self._build_email_recipients(
-                    session=session,
-                    form_id=form_id,
-                    delivery_id=delivery_id,
-                    recipients_config=email_recipients_config,
-                )
-            )
-
-        return _DeliveryAndRecipients(delivery=delivery_model, recipients=recipients)
-
-    def _build_email_recipients(
-        self,
-        session: Session,
-        form_id: str,
-        delivery_id: str,
-        recipients_config: EmailRecipients,
-    ) -> list[HumanInputFormRecipient]:
-        member_user_ids = [
-            recipient.user_id for recipient in recipients_config.items if isinstance(recipient, MemberRecipient)
-        ]
-        external_emails = [
-            recipient.email for recipient in recipients_config.items if isinstance(recipient, ExternalRecipient)
-        ]
-        if recipients_config.whole_workspace:
-            members = self._query_all_workspace_members(session=session)
-        else:
-            members = self._query_workspace_members_by_ids(session=session, restrict_to_user_ids=member_user_ids)
-
-        return self._create_email_recipients_from_resolved(
-            form_id=form_id,
-            delivery_id=delivery_id,
-            members=members,
-            external_emails=external_emails,
-        )
-
-    @staticmethod
-    def _create_email_recipients_from_resolved(
-        *,
-        form_id: str,
-        delivery_id: str,
-        members: Sequence[_WorkspaceMemberInfo],
-        external_emails: Sequence[str],
-    ) -> list[HumanInputFormRecipient]:
-        recipient_models: list[HumanInputFormRecipient] = []
-        seen_emails: set[str] = set()
-
-        for member in members:
-            if not member.email:
-                continue
-            if member.email in seen_emails:
-                continue
-            seen_emails.add(member.email)
-            payload = EmailMemberRecipientPayload(user_id=member.user_id, email=member.email)
-            recipient_models.append(
-                HumanInputFormRecipient.new(
-                    form_id=form_id,
-                    delivery_id=delivery_id,
-                    payload=payload,
-                )
-            )
-
-        for email in external_emails:
-            if not email:
-                continue
-            if email in seen_emails:
-                continue
-            seen_emails.add(email)
-            recipient_models.append(
-                HumanInputFormRecipient.new(
-                    form_id=form_id,
-                    delivery_id=delivery_id,
-                    payload=EmailExternalRecipientPayload(email=email),
-                )
-            )
-
-        return recipient_models
-
-    def _query_all_workspace_members(
-        self,
-        session: Session,
-    ) -> list[_WorkspaceMemberInfo]:
-        stmt = (
-            select(Account.id, Account.email)
-            .join(TenantAccountJoin, TenantAccountJoin.account_id == Account.id)
-            .where(TenantAccountJoin.tenant_id == self._tenant_id)
-        )
-        rows = session.execute(stmt).all()
-        return [_WorkspaceMemberInfo(user_id=account_id, email=email) for account_id, email in rows]
-
-    def _query_workspace_members_by_ids(
-        self,
-        session: Session,
-        restrict_to_user_ids: Sequence[str],
-    ) -> list[_WorkspaceMemberInfo]:
-        unique_ids = {user_id for user_id in restrict_to_user_ids if user_id}
-        if not unique_ids:
-            return []
-
-        stmt = (
-            select(Account.id, Account.email)
-            .join(TenantAccountJoin, TenantAccountJoin.account_id == Account.id)
-            .where(TenantAccountJoin.tenant_id == self._tenant_id)
-        )
-        stmt = stmt.where(Account.id.in_(unique_ids))
-
-        rows = session.execute(stmt).all()
-        return [_WorkspaceMemberInfo(user_id=account_id, email=email) for account_id, email in rows]
-
-    def create_form(self, params: FormCreateParams) -> HumanInputFormEntity:
-        form_config: HumanInputNodeData = params.form_config
-
-        with self._session_factory(expire_on_commit=False) as session, session.begin():
-            # Generate unique form ID
-            form_id = str(uuidv7())
-            start_time = naive_utc_now()
-            node_expiration = form_config.expiration_time(start_time)
-            form_definition = FormDefinition(
-                form_content=form_config.form_content,
-                inputs=form_config.inputs,
-                user_actions=form_config.user_actions,
-                rendered_content=params.rendered_content,
-                expiration_time=node_expiration,
-                default_values=dict(params.resolved_default_values),
-                display_in_ui=params.display_in_ui,
-                node_title=form_config.title,
-            )
-            form_model = HumanInputForm(
-                id=form_id,
-                tenant_id=self._tenant_id,
-                app_id=params.app_id,
-                workflow_run_id=params.workflow_execution_id,
-                form_kind=params.form_kind,
-                node_id=params.node_id,
-                form_definition=form_definition.model_dump_json(),
-                rendered_content=params.rendered_content,
-                expiration_time=node_expiration,
-                created_at=start_time,
-            )
-            session.add(form_model)
-            recipient_models: list[HumanInputFormRecipient] = []
-            for delivery in params.delivery_methods:
-                delivery_and_recipients = self._delivery_method_to_model(
-                    session=session,
-                    form_id=form_id,
-                    delivery_method=delivery,
-                )
-                session.add(delivery_and_recipients.delivery)
-                session.add_all(delivery_and_recipients.recipients)
-                recipient_models.extend(delivery_and_recipients.recipients)
-            if params.console_recipient_required and not any(
-                recipient.recipient_type == RecipientType.CONSOLE for recipient in recipient_models
-            ):
-                console_delivery_id = str(uuidv7())
-                console_delivery = HumanInputDelivery(
-                    id=console_delivery_id,
-                    form_id=form_id,
-                    delivery_method_type=DeliveryMethodType.WEBAPP,
-                    delivery_config_id=None,
-                    channel_payload=ConsoleDeliveryPayload().model_dump_json(),
-                )
-                console_recipient = HumanInputFormRecipient(
-                    form_id=form_id,
-                    delivery_id=console_delivery_id,
-                    recipient_type=RecipientType.CONSOLE,
-                    recipient_payload=ConsoleRecipientPayload(
-                        account_id=params.console_creator_account_id,
-                    ).model_dump_json(),
-                )
-                session.add(console_delivery)
-                session.add(console_recipient)
-                recipient_models.append(console_recipient)
-            if params.backstage_recipient_required and not any(
-                recipient.recipient_type == RecipientType.BACKSTAGE for recipient in recipient_models
-            ):
-                backstage_delivery_id = str(uuidv7())
-                backstage_delivery = HumanInputDelivery(
-                    id=backstage_delivery_id,
-                    form_id=form_id,
-                    delivery_method_type=DeliveryMethodType.WEBAPP,
-                    delivery_config_id=None,
-                    channel_payload=ConsoleDeliveryPayload().model_dump_json(),
-                )
-                backstage_recipient = HumanInputFormRecipient(
-                    form_id=form_id,
-                    delivery_id=backstage_delivery_id,
-                    recipient_type=RecipientType.BACKSTAGE,
-                    recipient_payload=BackstageRecipientPayload(
-                        account_id=params.console_creator_account_id,
-                    ).model_dump_json(),
-                )
-                session.add(backstage_delivery)
-                session.add(backstage_recipient)
-                recipient_models.append(backstage_recipient)
-            session.flush()
-
-        return _HumanInputFormEntityImpl(form_model=form_model, recipient_models=recipient_models)
-
-    def get_form(self, workflow_execution_id: str, node_id: str) -> HumanInputFormEntity | None:
-        form_query = select(HumanInputForm).where(
-            HumanInputForm.workflow_run_id == workflow_execution_id,
-            HumanInputForm.node_id == node_id,
-            HumanInputForm.tenant_id == self._tenant_id,
-        )
-        with self._session_factory(expire_on_commit=False) as session:
-            form_model: HumanInputForm | None = session.scalars(form_query).first()
-            if form_model is None:
-                return None
-
-            recipient_query = select(HumanInputFormRecipient).where(HumanInputFormRecipient.form_id == form_model.id)
-            recipient_models = session.scalars(recipient_query).all()
-        return _HumanInputFormEntityImpl(form_model=form_model, recipient_models=recipient_models)
-
-
-class HumanInputFormSubmissionRepository:
-    """Repository for fetching and submitting human input forms."""
-
-    def __init__(self, session_factory: sessionmaker | Engine):
-        if isinstance(session_factory, Engine):
-            session_factory = sessionmaker(bind=session_factory)
-        self._session_factory = session_factory
-
-    def get_by_token(self, form_token: str) -> HumanInputFormRecord | None:
-        query = (
-            select(HumanInputFormRecipient)
-            .options(selectinload(HumanInputFormRecipient.form))
-            .where(HumanInputFormRecipient.access_token == form_token)
-        )
-        with self._session_factory(expire_on_commit=False) as session:
-            recipient_model = session.scalars(query).first()
-            if recipient_model is None or recipient_model.form is None:
-                return None
-            return HumanInputFormRecord.from_models(recipient_model.form, recipient_model)
-
-    def get_by_form_id_and_recipient_type(
-        self,
-        form_id: str,
-        recipient_type: RecipientType,
-    ) -> HumanInputFormRecord | None:
-        query = (
-            select(HumanInputFormRecipient)
-            .options(selectinload(HumanInputFormRecipient.form))
-            .where(
-                HumanInputFormRecipient.form_id == form_id,
-                HumanInputFormRecipient.recipient_type == recipient_type,
-            )
-        )
-        with self._session_factory(expire_on_commit=False) as session:
-            recipient_model = session.scalars(query).first()
-            if recipient_model is None or recipient_model.form is None:
-                return None
-            return HumanInputFormRecord.from_models(recipient_model.form, recipient_model)
-
-    def mark_submitted(
-        self,
-        *,
-        form_id: str,
-        recipient_id: str | None,
-        selected_action_id: str,
-        form_data: Mapping[str, Any],
-        submission_user_id: str | None,
-        submission_end_user_id: str | None,
-    ) -> HumanInputFormRecord:
-        with self._session_factory(expire_on_commit=False) as session, session.begin():
-            form_model = session.get(HumanInputForm, form_id)
-            if form_model is None:
-                raise FormNotFoundError(f"form not found, id={form_id}")
-
-            recipient_model = session.get(HumanInputFormRecipient, recipient_id) if recipient_id else None
-
-            form_model.selected_action_id = selected_action_id
-            form_model.submitted_data = json.dumps(form_data)
-            form_model.submitted_at = naive_utc_now()
-            form_model.status = HumanInputFormStatus.SUBMITTED
-            form_model.submission_user_id = submission_user_id
-            form_model.submission_end_user_id = submission_end_user_id
-            form_model.completed_by_recipient_id = recipient_id
-
-            session.add(form_model)
-            session.flush()
-            session.refresh(form_model)
-            if recipient_model is not None:
-                session.refresh(recipient_model)
-
-            return HumanInputFormRecord.from_models(form_model, recipient_model)
-
-    def mark_timeout(
-        self,
-        *,
-        form_id: str,
-        timeout_status: HumanInputFormStatus,
-        reason: str | None = None,
-    ) -> HumanInputFormRecord:
-        with self._session_factory(expire_on_commit=False) as session, session.begin():
-            form_model = session.get(HumanInputForm, form_id)
-            if form_model is None:
-                raise FormNotFoundError(f"form not found, id={form_id}")
-
-            if timeout_status not in {HumanInputFormStatus.TIMEOUT, HumanInputFormStatus.EXPIRED}:
-                raise _InvalidTimeoutStatusError(f"invalid timeout status: {timeout_status}")
-
-            # already handled or submitted
-            if form_model.status in {HumanInputFormStatus.TIMEOUT, HumanInputFormStatus.EXPIRED}:
-                return HumanInputFormRecord.from_models(form_model, None)
-
-            if form_model.submitted_at is not None or form_model.status == HumanInputFormStatus.SUBMITTED:
-                raise FormNotFoundError(f"form already submitted, id={form_id}")
-
-            form_model.status = timeout_status
-            form_model.selected_action_id = None
-            form_model.submitted_data = None
-            form_model.submission_user_id = None
-            form_model.submission_end_user_id = None
-            form_model.completed_by_recipient_id = None
-            # Reason is recorded in status/error downstream; not stored on form.
-            session.add(form_model)
-            session.flush()
-            session.refresh(form_model)
-
-            return HumanInputFormRecord.from_models(form_model, None)

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -488,7 +488,6 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
                 WorkflowNodeExecutionModel.workflow_run_id == workflow_run_id,
                 WorkflowNodeExecutionModel.tenant_id == self._tenant_id,
                 WorkflowNodeExecutionModel.triggered_from == triggered_from,
-                WorkflowNodeExecutionModel.status != WorkflowNodeExecutionStatus.PAUSED,
             )
 
             if self._app_id:

api/core/telemetry/__init__.py

@@ -0,0 +1,43 @@
+"""Telemetry facade.
+
+Thin public API for emitting telemetry events.  All routing logic
+lives in ``core.telemetry.gateway`` which is shared by both CE and EE.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.ops.entities.trace_entity import TraceTaskName
+from core.telemetry.events import TelemetryContext, TelemetryEvent
+from core.telemetry.gateway import emit as gateway_emit
+from core.telemetry.gateway import get_trace_task_to_case
+
+if TYPE_CHECKING:
+    from core.ops.ops_trace_manager import TraceQueueManager
+
+
+def emit(event: TelemetryEvent, trace_manager: TraceQueueManager | None = None) -> None:
+    """Emit a telemetry event.
+
+    Translates the ``TelemetryEvent`` (keyed by ``TraceTaskName``) into a
+    ``TelemetryCase`` and delegates to ``core.telemetry.gateway.emit()``.
+    """
+    case = get_trace_task_to_case().get(event.name)
+    if case is None:
+        return
+
+    context: dict[str, object] = {
+        "tenant_id": event.context.tenant_id,
+        "user_id": event.context.user_id,
+        "app_id": event.context.app_id,
+    }
+    gateway_emit(case, context, event.payload, trace_manager)
+
+
+__all__ = [
+    "TelemetryContext",
+    "TelemetryEvent",
+    "TraceTaskName",
+    "emit",
+]

api/core/telemetry/events.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from core.ops.entities.trace_entity import TraceTaskName
+
+
+@dataclass(frozen=True)
+class TelemetryContext:
+    tenant_id: str | None = None
+    user_id: str | None = None
+    app_id: str | None = None
+
+
+@dataclass(frozen=True)
+class TelemetryEvent:
+    name: TraceTaskName
+    context: TelemetryContext
+    payload: dict[str, Any]