chore(deps): bump google-auth in /api in the google group

Bumps the google group in /api with 1 update: [google-auth](https://github.com/googleapis/google-auth-library-python).


Updates `google-auth` from 2.49.0 to 2.49.1
- [Release notes](https://github.com/googleapis/google-auth-library-python/releases)
- [Changelog](https://github.com/googleapis/google-auth-library-python/blob/main/CHANGELOG.md)
- [Commits](https://github.com/googleapis/google-auth-library-python/commits)

---
updated-dependencies:
- dependency-name: google-auth
  dependency-version: 2.49.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: google
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/workflows/api-tests.yml

@@ -27,7 +27,7 @@ jobs:
           persist-credentials: false
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+        uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
         with:
           enable-cache: true
           python-version: ${{ matrix.python-version }}

.github/workflows/autofix.yml

@@ -39,7 +39,7 @@ jobs:
         with:
           python-version: "3.11"
 
-      - uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+      - uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
 
       - name: Generate Docker Compose
         if: steps.docker-compose-changes.outputs.any_changed == 'true'

.github/workflows/build-push.yml

@@ -113,7 +113,7 @@ jobs:
             context: "web"
     steps:
       - name: Download digests
-        uses: actions/download-artifact@70fc10c6e5e1ce46ad2ea6f2b72d43f7d47b13c3 # v8.0.0
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
         with:
           path: /tmp/digests
           pattern: digests-${{ matrix.context }}-*

.github/workflows/db-migration-test.yml

@@ -19,7 +19,7 @@ jobs:
           persist-credentials: false
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+        uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
         with:
           enable-cache: true
           python-version: "3.12"
@@ -69,7 +69,7 @@ jobs:
           persist-credentials: false
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+        uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
         with:
           enable-cache: true
           python-version: "3.12"

.github/workflows/main-ci.yml

@@ -28,7 +28,7 @@ jobs:
       migration-changed: ${{ steps.changes.outputs.migration }}
     steps:
       - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
-      - uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3.0.2
+      - uses: dorny/paths-filter@fbd0ab8f3e69293af611ebaee6363fc25e6d187d # v4.0.1
         id: changes
         with:
           filters: |

.github/workflows/pyrefly-diff.yml

@@ -22,7 +22,7 @@ jobs:
           fetch-depth: 0
 
       - name: Setup Python & UV
-        uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+        uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
         with:
           enable-cache: true
 

.github/workflows/style.yml

@@ -33,7 +33,7 @@ jobs:
 
       - name: Setup UV and Python
         if: steps.changed-files.outputs.any_changed == 'true'
-        uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+        uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
         with:
           enable-cache: false
           python-version: "3.12"

.github/workflows/translate-i18n-claude.yml

@@ -120,7 +120,7 @@ jobs:
 
       - name: Run Claude Code for Translation Sync
         if: steps.detect_changes.outputs.CHANGED_FILES != ''
-        uses: anthropics/claude-code-action@26ec041249acb0a944c0a47b6c0c13f05dbc5b44 # v1.0.70
+        uses: anthropics/claude-code-action@cd77b50d2b0808657f8e6774085c8bf54484351c # v1.0.72
         with:
           anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           github_token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/vdb-tests.yml

@@ -31,7 +31,7 @@ jobs:
           remove_tool_cache: true
 
       - name: Setup UV and Python
-        uses: astral-sh/setup-uv@6ee6290f1cbc4156c0bdd66691b2c144ef8df19a # v7.4.0
+        uses: astral-sh/setup-uv@e06108dd0aef18192324c70427afc47652e63a82 # v7.5.0
         with:
           enable-cache: true
           python-version: ${{ matrix.python-version }}

.github/workflows/web-tests.yml

@@ -26,8 +26,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        shardIndex: [1, 2, 3, 4, 5, 6]
-        shardTotal: [6]
+        shardIndex: [1, 2, 3, 4]
+        shardTotal: [4]
     defaults:
       run:
         shell: bash
@@ -77,7 +77,7 @@ jobs:
         uses: ./.github/actions/setup-web
 
       - name: Download blob reports
-        uses: actions/download-artifact@70fc10c6e5e1ce46ad2ea6f2b72d43f7d47b13c3 # v8.0.0
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1
         with:
           path: web/.vitest-reports
           pattern: blob-report-*

.gitignore

@@ -237,3 +237,6 @@ scripts/stress-test/reports/
 # settings
 *.local.json
 *.local.md
+
+# Code Agent Folder
+.qoder/*

api/.env.example

@@ -22,10 +22,10 @@ APP_WEB_URL=http://localhost:3000
 # Files URL
 FILES_URL=http://localhost:5001
 
-# INTERNAL_FILES_URL is used for plugin daemon communication within Docker network.
-# Set this to the internal Docker service URL for proper plugin file access.
-# Example: INTERNAL_FILES_URL=http://api:5001
-INTERNAL_FILES_URL=http://127.0.0.1:5001
+# INTERNAL_FILES_URL is used by services running in Docker to reach the API file endpoints.
+# For Docker Desktop (Mac/Windows), use http://host.docker.internal:5001 when the API runs on the host.
+# For Docker Compose on Linux, use http://api:5001 when the API runs inside the Docker network.
+INTERNAL_FILES_URL=http://host.docker.internal:5001
 
 # TRIGGER URL
 TRIGGER_URL=http://localhost:5001
@@ -180,7 +180,7 @@ CONSOLE_CORS_ALLOW_ORIGINS=http://localhost:3000,*
 COOKIE_DOMAIN=
 
 # Vector database configuration
-# Supported values are `weaviate`, `oceanbase`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`,  `opengauss`, `tablestore`,`vastbase`,`tidb`,`tidb_on_qdrant`,`baidu`,`lindorm`,`huawei_cloud`,`upstash`, `matrixone`.
+# Supported values are `weaviate`, `oceanbase`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`,  `opengauss`, `tablestore`,`vastbase`,`tidb`,`tidb_on_qdrant`,`baidu`,`lindorm`,`huawei_cloud`,`upstash`, `matrixone`, `hologres`.
 VECTOR_STORE=weaviate
 # Prefix used to create collection name in vector database
 VECTOR_INDEX_NAME_PREFIX=Vector_index
@@ -217,6 +217,20 @@ COUCHBASE_PASSWORD=password
 COUCHBASE_BUCKET_NAME=Embeddings
 COUCHBASE_SCOPE_NAME=_default
 
+# Hologres configuration
+# access_key_id is used as the PG username, access_key_secret is used as the PG password
+HOLOGRES_HOST=
+HOLOGRES_PORT=80
+HOLOGRES_DATABASE=
+HOLOGRES_ACCESS_KEY_ID=
+HOLOGRES_ACCESS_KEY_SECRET=
+HOLOGRES_SCHEMA=public
+HOLOGRES_TOKENIZER=jieba
+HOLOGRES_DISTANCE_METHOD=Cosine
+HOLOGRES_BASE_QUANTIZATION_TYPE=rabitq
+HOLOGRES_MAX_DEGREE=64
+HOLOGRES_EF_CONSTRUCTION=400
+
 # Milvus configuration
 MILVUS_URI=http://127.0.0.1:19530
 MILVUS_TOKEN=

api/.importlinter

@@ -96,7 +96,6 @@ ignore_imports =
     dify_graph.nodes.tool.tool_node -> core.callback_handler.workflow_tool_callback_handler
     dify_graph.nodes.tool.tool_node -> core.tools.tool_engine
     dify_graph.nodes.tool.tool_node -> core.tools.tool_manager
-    dify_graph.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.app.app_config.entities
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.advanced_prompt_transform
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.simple_prompt_transform
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> dify_graph.model_runtime.model_providers.__base.large_language_model
@@ -104,7 +103,6 @@ ignore_imports =
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.model_manager
     dify_graph.nodes.question_classifier.question_classifier_node -> core.model_manager
     dify_graph.nodes.tool.tool_node -> core.tools.utils.message_transformer
-    dify_graph.nodes.llm.node -> core.helper.code_executor
     dify_graph.nodes.llm.node -> core.llm_generator.output_parser.errors
     dify_graph.nodes.llm.node -> core.llm_generator.output_parser.structured_output
     dify_graph.nodes.llm.node -> core.model_manager
@@ -116,7 +114,6 @@ ignore_imports =
     dify_graph.nodes.parameter_extractor.parameter_extractor_node -> core.prompt.utils.prompt_message_util
     dify_graph.nodes.question_classifier.entities -> core.prompt.entities.advanced_prompt_entities
     dify_graph.nodes.question_classifier.question_classifier_node -> core.prompt.utils.prompt_message_util
-    dify_graph.nodes.knowledge_index.entities -> core.rag.retrieval.retrieval_methods
     dify_graph.nodes.llm.node -> models.dataset
     dify_graph.nodes.llm.file_saver -> core.tools.signature
     dify_graph.nodes.llm.file_saver -> core.tools.tool_file_manager

api/Dockerfile

@@ -97,7 +97,7 @@ ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
 
 # Download nltk data
 RUN mkdir -p /usr/local/share/nltk_data \
-    && NLTK_DATA=/usr/local/share/nltk_data python -c "import nltk; from unstructured.nlp.tokenize import download_nltk_packages; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger'); nltk.download('stopwords'); download_nltk_packages()" \
+    && NLTK_DATA=/usr/local/share/nltk_data python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger'); nltk.download('stopwords')" \
     && chmod -R 755 /usr/local/share/nltk_data
 
 ENV TIKTOKEN_CACHE_DIR=/app/api/.tiktoken_cache

api/commands/vector.py

@@ -160,6 +160,7 @@ def migrate_knowledge_vector_database():
     }
     lower_collection_vector_types = {
         VectorType.ANALYTICDB,
+        VectorType.HOLOGRES,
         VectorType.CHROMA,
         VectorType.MYSCALE,
         VectorType.PGVECTO_RS,

api/configs/middleware/__init__.py

@@ -26,6 +26,7 @@ from .vdb.chroma_config import ChromaConfig
 from .vdb.clickzetta_config import ClickzettaConfig
 from .vdb.couchbase_config import CouchbaseConfig
 from .vdb.elasticsearch_config import ElasticsearchConfig
+from .vdb.hologres_config import HologresConfig
 from .vdb.huawei_cloud_config import HuaweiCloudConfig
 from .vdb.iris_config import IrisVectorConfig
 from .vdb.lindorm_config import LindormConfig
@@ -347,6 +348,7 @@ class MiddlewareConfig(
     AnalyticdbConfig,
     ChromaConfig,
     ClickzettaConfig,
+    HologresConfig,
     HuaweiCloudConfig,
     IrisVectorConfig,
     MilvusConfig,

api/configs/middleware/vdb/hologres_config.py

@@ -0,0 +1,68 @@
+from holo_search_sdk.types import BaseQuantizationType, DistanceType, TokenizerType
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class HologresConfig(BaseSettings):
+    """
+    Configuration settings for Hologres vector database.
+
+    Hologres is compatible with PostgreSQL protocol.
+    access_key_id is used as the PostgreSQL username,
+    and access_key_secret is used as the PostgreSQL password.
+    """
+
+    HOLOGRES_HOST: str | None = Field(
+        description="Hostname or IP address of the Hologres instance.",
+        default=None,
+    )
+
+    HOLOGRES_PORT: int = Field(
+        description="Port number for connecting to the Hologres instance.",
+        default=80,
+    )
+
+    HOLOGRES_DATABASE: str | None = Field(
+        description="Name of the Hologres database to connect to.",
+        default=None,
+    )
+
+    HOLOGRES_ACCESS_KEY_ID: str | None = Field(
+        description="Alibaba Cloud AccessKey ID, also used as the PostgreSQL username.",
+        default=None,
+    )
+
+    HOLOGRES_ACCESS_KEY_SECRET: str | None = Field(
+        description="Alibaba Cloud AccessKey Secret, also used as the PostgreSQL password.",
+        default=None,
+    )
+
+    HOLOGRES_SCHEMA: str = Field(
+        description="Schema name in the Hologres database.",
+        default="public",
+    )
+
+    HOLOGRES_TOKENIZER: TokenizerType = Field(
+        description="Tokenizer for full-text search index (e.g., 'jieba', 'ik', 'standard', 'simple').",
+        default="jieba",
+    )
+
+    HOLOGRES_DISTANCE_METHOD: DistanceType = Field(
+        description="Distance method for vector index (e.g., 'Cosine', 'Euclidean', 'InnerProduct').",
+        default="Cosine",
+    )
+
+    HOLOGRES_BASE_QUANTIZATION_TYPE: BaseQuantizationType = Field(
+        description="Base quantization type for vector index (e.g., 'rabitq', 'sq8', 'fp16', 'fp32').",
+        default="rabitq",
+    )
+
+    HOLOGRES_MAX_DEGREE: int = Field(
+        description="Max degree (M) parameter for HNSW vector index.",
+        default=64,
+    )
+
+    HOLOGRES_EF_CONSTRUCTION: int = Field(
+        description="ef_construction parameter for HNSW vector index.",
+        default=400,
+    )

api/controllers/console/app/app.py

@@ -25,7 +25,8 @@ from controllers.console.wraps import (
 )
 from core.ops.ops_trace_manager import OpsTraceManager
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
-from dify_graph.enums import NodeType, WorkflowExecutionStatus
+from core.trigger.constants import TRIGGER_NODE_TYPES
+from dify_graph.enums import WorkflowExecutionStatus
 from dify_graph.file import helpers as file_helpers
 from extensions.ext_database import db
 from libs.login import current_account_with_tenant, login_required
@@ -508,11 +509,7 @@ class AppListApi(Resource):
                 .scalars()
                 .all()
             )
-            trigger_node_types = {
-                NodeType.TRIGGER_WEBHOOK,
-                NodeType.TRIGGER_SCHEDULE,
-                NodeType.TRIGGER_PLUGIN,
-            }
+            trigger_node_types = TRIGGER_NODE_TYPES
             for workflow in draft_workflows:
                 node_id = None
                 try:

api/controllers/console/app/workflow.py

@@ -22,6 +22,7 @@ from core.app.apps.workflow.app_generator import SKIP_PREPARE_USER_INPUTS_KEY
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.helper.trace_id_helper import get_external_trace_id
 from core.plugin.impl.exc import PluginInvokeError
+from core.trigger.constants import TRIGGER_SCHEDULE_NODE_TYPE
 from core.trigger.debug.event_selectors import (
     TriggerDebugEvent,
     TriggerDebugEventPoller,
@@ -1209,7 +1210,7 @@ class DraftWorkflowTriggerNodeApi(Resource):
         node_type: NodeType = draft_workflow.get_node_type_from_node_config(node_config)
         event: TriggerDebugEvent | None = None
         # for schedule trigger, when run single node, just execute directly
-        if node_type == NodeType.TRIGGER_SCHEDULE:
+        if node_type == TRIGGER_SCHEDULE_NODE_TYPE:
             event = TriggerDebugEvent(
                 workflow_args={},
                 node_id=node_id,

api/controllers/console/datasets/datasets.py

@@ -263,6 +263,7 @@ def _get_retrieval_methods_by_vector_type(vector_type: str | None, is_mock: bool
         VectorType.BAIDU,
         VectorType.ALIBABACLOUD_MYSQL,
         VectorType.IRIS,
+        VectorType.HOLOGRES,
     }
 
     semantic_methods = {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}

api/controllers/service_api/wraps.py

@@ -3,7 +3,7 @@ import time
 from collections.abc import Callable
 from enum import StrEnum, auto
 from functools import wraps
-from typing import Concatenate, ParamSpec, TypeVar, cast
+from typing import Concatenate, ParamSpec, TypeVar, cast, overload
 
 from flask import current_app, request
 from flask_login import user_logged_in
@@ -44,10 +44,22 @@ class FetchUserArg(BaseModel):
     required: bool = False
 
 
-def validate_app_token(view: Callable[P, R] | None = None, *, fetch_user_arg: FetchUserArg | None = None):
-    def decorator(view_func: Callable[P, R]):
+@overload
+def validate_app_token(view: Callable[P, R]) -> Callable[P, R]: ...
+
+
+@overload
+def validate_app_token(
+    view: None = None, *, fetch_user_arg: FetchUserArg | None = None
+) -> Callable[[Callable[P, R]], Callable[P, R]]: ...
+
+
+def validate_app_token(
+    view: Callable[P, R] | None = None, *, fetch_user_arg: FetchUserArg | None = None
+) -> Callable[P, R] | Callable[[Callable[P, R]], Callable[P, R]]:
+    def decorator(view_func: Callable[P, R]) -> Callable[P, R]:
         @wraps(view_func)
-        def decorated_view(*args: P.args, **kwargs: P.kwargs):
+        def decorated_view(*args: P.args, **kwargs: P.kwargs) -> R:
             api_token = validate_and_get_api_token("app")
 
             app_model = db.session.query(App).where(App.id == api_token.app_id).first()
@@ -213,10 +225,20 @@ def cloud_edition_billing_rate_limit_check(resource: str, api_token_type: str):
     return interceptor
 
 
-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):
+@overload
+def validate_dataset_token(view: Callable[Concatenate[T, P], R]) -> Callable[P, R]: ...
+
+
+@overload
+def validate_dataset_token(view: None = None) -> Callable[[Callable[Concatenate[T, P], R]], Callable[P, R]]: ...
+
+
+def validate_dataset_token(
+    view: Callable[Concatenate[T, P], R] | None = None,
+) -> Callable[P, R] | Callable[[Callable[Concatenate[T, P], R]], Callable[P, R]]:
+    def decorator(view_func: Callable[Concatenate[T, P], R]) -> Callable[P, R]:
+        @wraps(view_func)
+        def decorated(*args: P.args, **kwargs: P.kwargs) -> R:
             api_token = validate_and_get_api_token("dataset")
 
             # get url path dataset_id from positional args or kwargs
@@ -287,7 +309,7 @@ def validate_dataset_token(view: Callable[Concatenate[T, P], R] | None = None):
                     raise Unauthorized("Tenant owner account does not exist.")
             else:
                 raise Unauthorized("Tenant does not exist.")
-            return view(api_token.tenant_id, *args, **kwargs)
+            return view_func(api_token.tenant_id, *args, **kwargs)  # type: ignore[arg-type]
 
         return decorated
 

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

@@ -69,7 +69,7 @@ from dify_graph.entities.pause_reason import HumanInputRequired
 from dify_graph.enums import WorkflowExecutionStatus
 from dify_graph.model_runtime.entities.llm_entities import LLMUsage
 from dify_graph.model_runtime.utils.encoders import jsonable_encoder
-from dify_graph.nodes import NodeType
+from dify_graph.nodes import BuiltinNodeTypes
 from dify_graph.repositories.draft_variable_repository import DraftVariableSaverFactory
 from dify_graph.runtime import GraphRuntimeState
 from dify_graph.system_variable import SystemVariable
@@ -357,7 +357,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     ) -> Generator[StreamResponse, None, None]:
         """Handle node succeeded events."""
         # Record files if it's an answer node or end node
-        if event.node_type in [NodeType.ANSWER, NodeType.END, NodeType.LLM]:
+        if event.node_type in [BuiltinNodeTypes.ANSWER, BuiltinNodeTypes.END, BuiltinNodeTypes.LLM]:
             self._recorded_files.extend(
                 self._workflow_response_converter.fetch_files_from_node_outputs(event.outputs or {})
             )

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

@@ -48,12 +48,13 @@ from core.app.entities.task_entities import (
 from core.plugin.impl.datasource import PluginDatasourceManager
 from core.tools.entities.tool_entities import ToolProviderType
 from core.tools.tool_manager import ToolManager
+from core.trigger.constants import TRIGGER_PLUGIN_NODE_TYPE
 from core.trigger.trigger_manager import TriggerManager
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities.pause_reason import HumanInputRequired
 from dify_graph.entities.workflow_start_reason import WorkflowStartReason
 from dify_graph.enums import (
-    NodeType,
+    BuiltinNodeTypes,
     SystemVariableKey,
     WorkflowExecutionStatus,
     WorkflowNodeExecutionMetadataKey,
@@ -442,7 +443,7 @@ class WorkflowResponseConverter:
         event: QueueNodeStartedEvent,
         task_id: str,
     ) -> NodeStartStreamResponse | None:
-        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+        if event.node_type in {BuiltinNodeTypes.ITERATION, BuiltinNodeTypes.LOOP}:
             return None
         run_id = self._ensure_workflow_run_id()
         snapshot = self._store_snapshot(event)
@@ -464,13 +465,13 @@ class WorkflowResponseConverter:
         )
 
         try:
-            if event.node_type == NodeType.TOOL:
+            if event.node_type == BuiltinNodeTypes.TOOL:
                 response.data.extras["icon"] = ToolManager.get_tool_icon(
                     tenant_id=self._application_generate_entity.app_config.tenant_id,
                     provider_type=ToolProviderType(event.provider_type),
                     provider_id=event.provider_id,
                 )
-            elif event.node_type == NodeType.DATASOURCE:
+            elif event.node_type == BuiltinNodeTypes.DATASOURCE:
                 manager = PluginDatasourceManager()
                 provider_entity = manager.fetch_datasource_provider(
                     self._application_generate_entity.app_config.tenant_id,
@@ -479,7 +480,7 @@ class WorkflowResponseConverter:
                 response.data.extras["icon"] = provider_entity.declaration.identity.generate_datasource_icon_url(
                     self._application_generate_entity.app_config.tenant_id
                 )
-            elif event.node_type == NodeType.TRIGGER_PLUGIN:
+            elif event.node_type == TRIGGER_PLUGIN_NODE_TYPE:
                 response.data.extras["icon"] = TriggerManager.get_trigger_plugin_icon(
                     self._application_generate_entity.app_config.tenant_id,
                     event.provider_id,
@@ -496,7 +497,7 @@ class WorkflowResponseConverter:
         event: QueueNodeSucceededEvent | QueueNodeFailedEvent | QueueNodeExceptionEvent,
         task_id: str,
     ) -> NodeFinishStreamResponse | None:
-        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+        if event.node_type in {BuiltinNodeTypes.ITERATION, BuiltinNodeTypes.LOOP}:
             return None
         run_id = self._ensure_workflow_run_id()
         snapshot = self._pop_snapshot(event.node_execution_id)
@@ -554,7 +555,7 @@ class WorkflowResponseConverter:
         event: QueueNodeRetryEvent,
         task_id: str,
     ) -> NodeRetryStreamResponse | None:
-        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+        if event.node_type in {BuiltinNodeTypes.ITERATION, BuiltinNodeTypes.LOOP}:
             return None
         run_id = self._ensure_workflow_run_id()
 
@@ -612,7 +613,7 @@ class WorkflowResponseConverter:
             data=IterationNodeStartStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 created_at=int(time.time()),
                 extras={},
@@ -635,7 +636,7 @@ class WorkflowResponseConverter:
             data=IterationNodeNextStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 index=event.index,
                 created_at=int(time.time()),
@@ -662,7 +663,7 @@ class WorkflowResponseConverter:
             data=IterationNodeCompletedStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 outputs=new_outputs,
                 outputs_truncated=outputs_truncated,
@@ -692,7 +693,7 @@ class WorkflowResponseConverter:
             data=LoopNodeStartStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 created_at=int(time.time()),
                 extras={},
@@ -715,7 +716,7 @@ class WorkflowResponseConverter:
             data=LoopNodeNextStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 index=event.index,
                 # The `pre_loop_output` field is not utilized by the frontend.
@@ -744,7 +745,7 @@ class WorkflowResponseConverter:
             data=LoopNodeCompletedStreamResponse.Data(
                 id=event.node_id,
                 node_id=event.node_id,
-                node_type=event.node_type.value,
+                node_type=event.node_type,
                 title=event.node_title,
                 outputs=new_outputs,
                 outputs_truncated=outputs_truncated,

api/core/app/apps/pipeline/pipeline_runner.py

@@ -12,7 +12,7 @@ from core.app.entities.app_invoke_entities import (
     build_dify_run_context,
 )
 from core.app.workflow.layers.persistence import PersistenceWorkflowInfo, WorkflowPersistenceLayer
-from core.workflow.node_factory import DifyNodeFactory
+from core.workflow.node_factory import DifyNodeFactory, get_default_root_node_id
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities.graph_init_params import GraphInitParams
 from dify_graph.enums import WorkflowType
@@ -274,6 +274,8 @@ class PipelineRunner(WorkflowBasedAppRunner):
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
         )
+        if start_node_id is None:
+            start_node_id = get_default_root_node_id(graph_config)
         graph = Graph.init(graph_config=graph_config, node_factory=node_factory, root_node_id=start_node_id)
 
         if not graph:

api/core/app/apps/workflow_app_runner.py

@@ -32,8 +32,8 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
-from core.workflow.node_factory import DifyNodeFactory
-from core.workflow.node_resolution import resolve_workflow_node_class
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
+from core.workflow.node_factory import DifyNodeFactory, get_default_root_node_id, resolve_workflow_node_class
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities import GraphInitParams
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
@@ -140,6 +140,9 @@ class WorkflowBasedAppRunner:
             graph_runtime_state=graph_runtime_state,
         )
 
+        if root_node_id is None:
+            root_node_id = get_default_root_node_id(graph_config)
+
         # init graph
         graph = Graph.init(graph_config=graph_config, node_factory=node_factory, root_node_id=root_node_id)
 
@@ -505,7 +508,9 @@ class WorkflowBasedAppRunner:
         elif isinstance(event, NodeRunRetrieverResourceEvent):
             self._publish_event(
                 QueueRetrieverResourcesEvent(
-                    retriever_resources=event.retriever_resources,
+                    retriever_resources=[
+                        RetrievalSourceMetadata.model_validate(resource) for resource in event.retriever_resources
+                    ],
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
                 )

api/core/app/entities/queue_entities.py

@@ -9,9 +9,8 @@ from core.app.entities.agent_strategy import AgentStrategyInfo
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from dify_graph.entities.pause_reason import PauseReason
 from dify_graph.entities.workflow_start_reason import WorkflowStartReason
-from dify_graph.enums import WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
 from dify_graph.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
-from dify_graph.nodes import NodeType
 
 
 class QueueEvent(StrEnum):

api/core/app/layers/conversation_variable_persist_layer.py

@@ -2,7 +2,7 @@ import logging
 
 from dify_graph.constants import CONVERSATION_VARIABLE_NODE_ID
 from dify_graph.conversation_variable_updater import ConversationVariableUpdater
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_events import GraphEngineEvent, NodeRunSucceededEvent
 from dify_graph.nodes.variable_assigner.common import helpers as common_helpers
@@ -22,7 +22,7 @@ class ConversationVariablePersistenceLayer(GraphEngineLayer):
     def on_event(self, event: GraphEngineEvent) -> None:
         if not isinstance(event, NodeRunSucceededEvent):
             return
-        if event.node_type != NodeType.VARIABLE_ASSIGNER:
+        if event.node_type != BuiltinNodeTypes.VARIABLE_ASSIGNER:
             return
         if self.graph_runtime_state is None:
             return

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

@@ -12,7 +12,7 @@ from typing_extensions import override
 from core.app.llm import deduct_llm_quota, ensure_llm_quota_available
 from core.errors.error import QuotaExceededError
 from core.model_manager import ModelInstance
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.graph_engine.entities.commands import AbortCommand, CommandType
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_events import GraphEngineEvent, GraphNodeEventBase
@@ -113,11 +113,11 @@ class LLMQuotaLayer(GraphEngineLayer):
     def _extract_model_instance(node: Node) -> ModelInstance | None:
         try:
             match node.node_type:
-                case NodeType.LLM:
+                case BuiltinNodeTypes.LLM:
                     return cast("LLMNode", node).model_instance
-                case NodeType.PARAMETER_EXTRACTOR:
+                case BuiltinNodeTypes.PARAMETER_EXTRACTOR:
                     return cast("ParameterExtractorNode", node).model_instance
-                case NodeType.QUESTION_CLASSIFIER:
+                case BuiltinNodeTypes.QUESTION_CLASSIFIER:
                     return cast("QuestionClassifierNode", node).model_instance
                 case _:
                     return None

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

@@ -16,7 +16,7 @@ from opentelemetry.trace import Span, SpanKind, Tracer, get_tracer, set_span_in_
 from typing_extensions import override
 
 from configs import dify_config
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_events import GraphNodeEventBase
 from dify_graph.nodes.base.node import Node
@@ -74,16 +74,13 @@ class ObservabilityLayer(GraphEngineLayer):
     def _build_parser_registry(self) -> None:
         """Initialize parser registry for node types."""
         self._parsers = {
-            NodeType.TOOL: ToolNodeOTelParser(),
-            NodeType.LLM: LLMNodeOTelParser(),
-            NodeType.KNOWLEDGE_RETRIEVAL: RetrievalNodeOTelParser(),
+            BuiltinNodeTypes.TOOL: ToolNodeOTelParser(),
+            BuiltinNodeTypes.LLM: LLMNodeOTelParser(),
+            BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL: RetrievalNodeOTelParser(),
         }
 
     def _get_parser(self, node: Node) -> NodeOTelParser:
-        node_type = getattr(node, "node_type", None)
-        if isinstance(node_type, NodeType):
-            return self._parsers.get(node_type, self._default_parser)
-        return self._default_parser
+        return self._parsers.get(node.node_type, self._default_parser)
 
     @override
     def on_graph_start(self) -> None:

api/core/datasource/datasource_manager.py

@@ -24,12 +24,12 @@ from core.datasource.utils.message_transformer import DatasourceFileMessageTrans
 from core.datasource.website_crawl.website_crawl_provider import WebsiteCrawlDatasourcePluginProviderController
 from core.db.session_factory import session_factory
 from core.plugin.impl.datasource import PluginDatasourceManager
+from core.workflow.nodes.datasource.entities import DatasourceParameter, OnlineDriveDownloadFileParam
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from dify_graph.enums import WorkflowNodeExecutionMetadataKey
 from dify_graph.file import File
 from dify_graph.file.enums import FileTransferMethod, FileType
 from dify_graph.node_events import NodeRunResult, StreamChunkEvent, StreamCompletedEvent
-from dify_graph.repositories.datasource_manager_protocol import DatasourceParameter, OnlineDriveDownloadFileParam
 from factories import file_factory
 from models.model import UploadFile
 from models.tools import ToolFile

api/core/ops/aliyun_trace/aliyun_trace.py

@@ -58,7 +58,7 @@ from core.ops.entities.trace_entity import (
 )
 from core.repositories import DifyCoreRepositoryFactory
 from dify_graph.entities import WorkflowNodeExecution
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import WorkflowNodeExecutionTriggeredFrom
 
@@ -302,11 +302,11 @@ class AliyunDataTrace(BaseTraceInstance):
         self, node_execution: WorkflowNodeExecution, trace_info: WorkflowTraceInfo, trace_metadata: TraceMetadata
     ):
         try:
-            if node_execution.node_type == NodeType.LLM:
+            if node_execution.node_type == BuiltinNodeTypes.LLM:
                 node_span = self.build_workflow_llm_span(trace_info, node_execution, trace_metadata)
-            elif node_execution.node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+            elif node_execution.node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                 node_span = self.build_workflow_retrieval_span(trace_info, node_execution, trace_metadata)
-            elif node_execution.node_type == NodeType.TOOL:
+            elif node_execution.node_type == BuiltinNodeTypes.TOOL:
                 node_span = self.build_workflow_tool_span(trace_info, node_execution, trace_metadata)
             else:
                 node_span = self.build_workflow_task_span(trace_info, node_execution, trace_metadata)

api/core/ops/arize_phoenix_trace/arize_phoenix_trace.py

@@ -155,8 +155,8 @@ def wrap_span_metadata(metadata, **kwargs):
     return metadata
 
 
-# Mapping from NodeType string values to OpenInference span kinds.
-# NodeType values not listed here default to CHAIN.
+# Mapping from built-in node type strings to OpenInference span kinds.
+# Node types not listed here default to CHAIN.
 _NODE_TYPE_TO_SPAN_KIND: dict[str, OpenInferenceSpanKindValues] = {
     "llm": OpenInferenceSpanKindValues.LLM,
     "knowledge-retrieval": OpenInferenceSpanKindValues.RETRIEVER,
@@ -168,7 +168,7 @@ _NODE_TYPE_TO_SPAN_KIND: dict[str, OpenInferenceSpanKindValues] = {
 def _get_node_span_kind(node_type: str) -> OpenInferenceSpanKindValues:
     """Return the OpenInference span kind for a given workflow node type.
 
-    Covers every ``NodeType`` enum value.  Nodes that do not have a
+    Covers every built-in node type string. Nodes that do not have a
     specialised span kind (e.g. ``start``, ``end``, ``if-else``,
     ``code``, ``loop``, ``iteration``, etc.) are mapped to ``CHAIN``.
     """

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -28,7 +28,7 @@ from core.ops.langfuse_trace.entities.langfuse_trace_entity import (
 )
 from core.ops.utils import filter_none_values
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from extensions.ext_database import db
 from models import EndUser, WorkflowNodeExecutionTriggeredFrom
 from models.enums import MessageStatus
@@ -141,7 +141,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -28,7 +28,7 @@ from core.ops.langsmith_trace.entities.langsmith_trace_entity import (
 )
 from core.ops.utils import filter_none_values, generate_dotted_order
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
@@ -163,7 +163,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}
@@ -197,7 +197,7 @@ class LangSmithDataTrace(BaseTraceInstance):
                         "ls_model_name": process_data.get("model_name", ""),
                     }
                 )
-            elif node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+            elif node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                 run_type = LangSmithRunType.retriever
             else:
                 run_type = LangSmithRunType.tool

api/core/ops/mlflow_trace/mlflow_trace.py

@@ -23,7 +23,7 @@ from core.ops.entities.trace_entity import (
     TraceTaskName,
     WorkflowTraceInfo,
 )
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from extensions.ext_database import db
 from models import EndUser
 from models.workflow import WorkflowNodeExecutionModel
@@ -145,10 +145,10 @@ class MLflowDataTrace(BaseTraceInstance):
                     "app_name": node.title,
                 }
 
-                if node.node_type in (NodeType.LLM, NodeType.QUESTION_CLASSIFIER):
+                if node.node_type in (BuiltinNodeTypes.LLM, BuiltinNodeTypes.QUESTION_CLASSIFIER):
                     inputs, llm_attributes = self._parse_llm_inputs_and_attributes(node)
                     attributes.update(llm_attributes)
-                elif node.node_type == NodeType.HTTP_REQUEST:
+                elif node.node_type == BuiltinNodeTypes.HTTP_REQUEST:
                     inputs = node.process_data  # contains request URL
 
                 if not inputs:
@@ -180,9 +180,9 @@ class MLflowDataTrace(BaseTraceInstance):
                 # End node span
                 finished_at = node.created_at + timedelta(seconds=node.elapsed_time)
                 outputs = json.loads(node.outputs) if node.outputs else {}
-                if node.node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+                if node.node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                     outputs = self._parse_knowledge_retrieval_outputs(outputs)
-                elif node.node_type == NodeType.LLM:
+                elif node.node_type == BuiltinNodeTypes.LLM:
                     outputs = outputs.get("text", outputs)
                 node_span.end(
                     outputs=outputs,
@@ -471,13 +471,13 @@ class MLflowDataTrace(BaseTraceInstance):
     def _get_node_span_type(self, node_type: str) -> str:
         """Map Dify node types to MLflow span types"""
         node_type_mapping = {
-            NodeType.LLM: SpanType.LLM,
-            NodeType.QUESTION_CLASSIFIER: SpanType.LLM,
-            NodeType.KNOWLEDGE_RETRIEVAL: SpanType.RETRIEVER,
-            NodeType.TOOL: SpanType.TOOL,
-            NodeType.CODE: SpanType.TOOL,
-            NodeType.HTTP_REQUEST: SpanType.TOOL,
-            NodeType.AGENT: SpanType.AGENT,
+            BuiltinNodeTypes.LLM: SpanType.LLM,
+            BuiltinNodeTypes.QUESTION_CLASSIFIER: SpanType.LLM,
+            BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL: SpanType.RETRIEVER,
+            BuiltinNodeTypes.TOOL: SpanType.TOOL,
+            BuiltinNodeTypes.CODE: SpanType.TOOL,
+            BuiltinNodeTypes.HTTP_REQUEST: SpanType.TOOL,
+            BuiltinNodeTypes.AGENT: SpanType.AGENT,
         }
         return node_type_mapping.get(node_type, "CHAIN")  # type: ignore[arg-type,call-overload]
 

api/core/ops/opik_trace/opik_trace.py

@@ -23,7 +23,7 @@ from core.ops.entities.trace_entity import (
     WorkflowTraceInfo,
 )
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
@@ -187,7 +187,7 @@ class OpikDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}

api/core/ops/tencent_trace/tencent_trace.py

@@ -27,7 +27,7 @@ from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
 from dify_graph.entities.workflow_node_execution import (
     WorkflowNodeExecution,
 )
-from dify_graph.nodes import NodeType
+from dify_graph.nodes import BuiltinNodeTypes
 from extensions.ext_database import db
 from models import Account, App, TenantAccountJoin, WorkflowNodeExecutionTriggeredFrom
 
@@ -179,7 +179,7 @@ class TencentDataTrace(BaseTraceInstance):
                     if node_span:
                         self.trace_client.add_span(node_span)
 
-                        if node_execution.node_type == NodeType.LLM:
+                        if node_execution.node_type == BuiltinNodeTypes.LLM:
                             self._record_llm_metrics(node_execution)
                 except Exception:
                     logger.exception("[Tencent APM] Failed to process node execution: %s", node_execution.id)
@@ -192,15 +192,15 @@ class TencentDataTrace(BaseTraceInstance):
     ) -> SpanData | None:
         """Build span for different node types"""
         try:
-            if node_execution.node_type == NodeType.LLM:
+            if node_execution.node_type == BuiltinNodeTypes.LLM:
                 return TencentSpanBuilder.build_workflow_llm_span(
                     trace_id, workflow_span_id, trace_info, node_execution
                 )
-            elif node_execution.node_type == NodeType.KNOWLEDGE_RETRIEVAL:
+            elif node_execution.node_type == BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL:
                 return TencentSpanBuilder.build_workflow_retrieval_span(
                     trace_id, workflow_span_id, trace_info, node_execution
                 )
-            elif node_execution.node_type == NodeType.TOOL:
+            elif node_execution.node_type == BuiltinNodeTypes.TOOL:
                 return TencentSpanBuilder.build_workflow_tool_span(
                     trace_id, workflow_span_id, trace_info, node_execution
                 )

api/core/ops/weave_trace/weave_trace.py

@@ -31,7 +31,7 @@ from core.ops.entities.trace_entity import (
 )
 from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
 from core.repositories import DifyCoreRepositoryFactory
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey
+from dify_graph.enums import BuiltinNodeTypes, WorkflowNodeExecutionMetadataKey
 from extensions.ext_database import db
 from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
@@ -175,7 +175,7 @@ class WeaveDataTrace(BaseTraceInstance):
             node_name = node_execution.title
             node_type = node_execution.node_type
             status = node_execution.status
-            if node_type == NodeType.LLM:
+            if node_type == BuiltinNodeTypes.LLM:
                 inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
             else:
                 inputs = node_execution.inputs or {}

api/core/plugin/backwards_invocation/node.py

@@ -1,5 +1,5 @@
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.nodes.parameter_extractor.entities import (
     ModelConfig as ParameterExtractorModelConfig,
 )
@@ -52,7 +52,7 @@ class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
             instruction=instruction,  # instruct with variables are not supported
         )
         node_data_dict = node_data.model_dump()
-        node_data_dict["type"] = NodeType.PARAMETER_EXTRACTOR
+        node_data_dict["type"] = BuiltinNodeTypes.PARAMETER_EXTRACTOR
         execution = workflow_service.run_free_workflow_node(
             node_data_dict,
             tenant_id=tenant_id,

api/core/provider_manager.py

@@ -305,9 +305,7 @@ class ProviderManager:
             available_models = provider_configurations.get_models(model_type=model_type, only_active=True)
 
             if available_models:
-                available_model = next(
-                    (model for model in available_models if model.model == "gpt-4"), available_models[0]
-                )
+                available_model = available_models[0]
 
                 default_model = TenantDefaultModel(
                     tenant_id=tenant_id,

api/core/rag/datasource/vdb/hologres/hologres_vector.py

@@ -0,0 +1,361 @@
+import json
+import logging
+import time
+from typing import Any
+
+import holo_search_sdk as holo  # type: ignore
+from holo_search_sdk.types import BaseQuantizationType, DistanceType, TokenizerType
+from psycopg import sql as psql
+from pydantic import BaseModel, model_validator
+
+from configs import dify_config
+from core.rag.datasource.vdb.vector_base import BaseVector
+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+
+class HologresVectorConfig(BaseModel):
+    """
+    Configuration for Hologres vector database connection.
+
+    In Hologres, access_key_id is used as the PostgreSQL username,
+    and access_key_secret is used as the PostgreSQL password.
+    """
+
+    host: str
+    port: int = 80
+    database: str
+    access_key_id: str
+    access_key_secret: str
+    schema_name: str = "public"
+    tokenizer: TokenizerType = "jieba"
+    distance_method: DistanceType = "Cosine"
+    base_quantization_type: BaseQuantizationType = "rabitq"
+    max_degree: int = 64
+    ef_construction: int = 400
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict):
+        if not values.get("host"):
+            raise ValueError("config HOLOGRES_HOST is required")
+        if not values.get("database"):
+            raise ValueError("config HOLOGRES_DATABASE is required")
+        if not values.get("access_key_id"):
+            raise ValueError("config HOLOGRES_ACCESS_KEY_ID is required")
+        if not values.get("access_key_secret"):
+            raise ValueError("config HOLOGRES_ACCESS_KEY_SECRET is required")
+        return values
+
+
+class HologresVector(BaseVector):
+    """
+    Hologres vector storage implementation using holo-search-sdk.
+
+    Supports semantic search (vector), full-text search, and hybrid search.
+    """
+
+    def __init__(self, collection_name: str, config: HologresVectorConfig):
+        super().__init__(collection_name)
+        self._config = config
+        self._client = self._init_client(config)
+        self.table_name = f"embedding_{collection_name}".lower()
+
+    def _init_client(self, config: HologresVectorConfig):
+        """Initialize and return a holo-search-sdk client."""
+        client = holo.connect(
+            host=config.host,
+            port=config.port,
+            database=config.database,
+            access_key_id=config.access_key_id,
+            access_key_secret=config.access_key_secret,
+            schema=config.schema_name,
+        )
+        client.connect()
+        return client
+
+    def get_type(self) -> str:
+        return VectorType.HOLOGRES
+
+    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        """Create collection table with vector and full-text indexes, then add texts."""
+        dimension = len(embeddings[0])
+        self._create_collection(dimension)
+        self.add_texts(texts, embeddings)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        """Add texts with embeddings to the collection using batch upsert."""
+        if not documents:
+            return []
+
+        pks: list[str] = []
+        batch_size = 100
+        for i in range(0, len(documents), batch_size):
+            batch_docs = documents[i : i + batch_size]
+            batch_embeddings = embeddings[i : i + batch_size]
+
+            values = []
+            column_names = ["id", "text", "meta", "embedding"]
+
+            for j, doc in enumerate(batch_docs):
+                doc_id = doc.metadata.get("doc_id", "") if doc.metadata else ""
+                pks.append(doc_id)
+                values.append(
+                    [
+                        doc_id,
+                        doc.page_content,
+                        json.dumps(doc.metadata or {}),
+                        batch_embeddings[j],
+                    ]
+                )
+
+            table = self._client.open_table(self.table_name)
+            table.upsert_multi(
+                index_column="id",
+                values=values,
+                column_names=column_names,
+                update=True,
+                update_columns=["text", "meta", "embedding"],
+            )
+
+        return pks
+
+    def text_exists(self, id: str) -> bool:
+        """Check if a text with the given doc_id exists in the collection."""
+        if not self._client.check_table_exist(self.table_name):
+            return False
+
+        result = self._client.execute(
+            psql.SQL("SELECT 1 FROM {} WHERE id = {} LIMIT 1").format(
+                psql.Identifier(self.table_name), psql.Literal(id)
+            ),
+            fetch_result=True,
+        )
+        return bool(result)
+
+    def get_ids_by_metadata_field(self, key: str, value: str) -> list[str] | None:
+        """Get document IDs by metadata field key and value."""
+        result = self._client.execute(
+            psql.SQL("SELECT id FROM {} WHERE meta->>{} = {}").format(
+                psql.Identifier(self.table_name), psql.Literal(key), psql.Literal(value)
+            ),
+            fetch_result=True,
+        )
+        if result:
+            return [row[0] for row in result]
+        return None
+
+    def delete_by_ids(self, ids: list[str]):
+        """Delete documents by their doc_id list."""
+        if not ids:
+            return
+        if not self._client.check_table_exist(self.table_name):
+            return
+
+        self._client.execute(
+            psql.SQL("DELETE FROM {} WHERE id IN ({})").format(
+                psql.Identifier(self.table_name),
+                psql.SQL(", ").join(psql.Literal(id) for id in ids),
+            )
+        )
+
+    def delete_by_metadata_field(self, key: str, value: str):
+        """Delete documents by metadata field key and value."""
+        if not self._client.check_table_exist(self.table_name):
+            return
+
+        self._client.execute(
+            psql.SQL("DELETE FROM {} WHERE meta->>{} = {}").format(
+                psql.Identifier(self.table_name), psql.Literal(key), psql.Literal(value)
+            )
+        )
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        """Search for documents by vector similarity."""
+        if not self._client.check_table_exist(self.table_name):
+            return []
+
+        top_k = kwargs.get("top_k", 4)
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+
+        table = self._client.open_table(self.table_name)
+        query = (
+            table.search_vector(
+                vector=query_vector,
+                column="embedding",
+                distance_method=self._config.distance_method,
+                output_name="distance",
+            )
+            .select(["id", "text", "meta"])
+            .limit(top_k)
+        )
+
+        # Apply document_ids_filter if provided
+        document_ids_filter = kwargs.get("document_ids_filter")
+        if document_ids_filter:
+            filter_sql = psql.SQL("meta->>'document_id' IN ({})").format(
+                psql.SQL(", ").join(psql.Literal(id) for id in document_ids_filter)
+            )
+            query = query.where(filter_sql)
+
+        results = query.fetchall()
+        return self._process_vector_results(results, score_threshold)
+
+    def _process_vector_results(self, results: list, score_threshold: float) -> list[Document]:
+        """Process vector search results into Document objects."""
+        docs = []
+        for row in results:
+            # row format: (distance, id, text, meta)
+            # distance is first because search_vector() adds the computed column before selected columns
+            distance = row[0]
+            text = row[2]
+            meta = row[3]
+
+            if isinstance(meta, str):
+                meta = json.loads(meta)
+
+            # Convert distance to similarity score (consistent with pgvector)
+            score = 1 - distance
+            meta["score"] = score
+
+            if score >= score_threshold:
+                docs.append(Document(page_content=text, metadata=meta))
+
+        return docs
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        """Search for documents by full-text search."""
+        if not self._client.check_table_exist(self.table_name):
+            return []
+
+        top_k = kwargs.get("top_k", 4)
+
+        table = self._client.open_table(self.table_name)
+        search_query = table.search_text(
+            column="text",
+            expression=query,
+            return_score=True,
+            return_score_name="score",
+            return_all_columns=True,
+        ).limit(top_k)
+
+        # Apply document_ids_filter if provided
+        document_ids_filter = kwargs.get("document_ids_filter")
+        if document_ids_filter:
+            filter_sql = psql.SQL("meta->>'document_id' IN ({})").format(
+                psql.SQL(", ").join(psql.Literal(id) for id in document_ids_filter)
+            )
+            search_query = search_query.where(filter_sql)
+
+        results = search_query.fetchall()
+        return self._process_full_text_results(results)
+
+    def _process_full_text_results(self, results: list) -> list[Document]:
+        """Process full-text search results into Document objects."""
+        docs = []
+        for row in results:
+            # row format: (id, text, meta, embedding, score)
+            text = row[1]
+            meta = row[2]
+            score = row[-1]  # score is the last column from return_score
+
+            if isinstance(meta, str):
+                meta = json.loads(meta)
+
+            meta["score"] = score
+            docs.append(Document(page_content=text, metadata=meta))
+
+        return docs
+
+    def delete(self):
+        """Delete the entire collection table."""
+        if self._client.check_table_exist(self.table_name):
+            self._client.drop_table(self.table_name)
+
+    def _create_collection(self, dimension: int):
+        """Create the collection table with vector and full-text indexes."""
+        lock_name = f"vector_indexing_lock_{self._collection_name}"
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
+            if redis_client.get(collection_exist_cache_key):
+                return
+
+            if not self._client.check_table_exist(self.table_name):
+                # Create table via SQL with CHECK constraint for vector dimension
+                create_table_sql = psql.SQL("""
+                    CREATE TABLE IF NOT EXISTS {} (
+                        id TEXT PRIMARY KEY,
+                        text TEXT NOT NULL,
+                        meta JSONB NOT NULL,
+                        embedding float4[] NOT NULL
+                            CHECK (array_ndims(embedding) = 1
+                                   AND array_length(embedding, 1) = {})
+                    );
+                """).format(psql.Identifier(self.table_name), psql.Literal(dimension))
+                self._client.execute(create_table_sql)
+
+                # Wait for table to be fully ready before creating indexes
+                max_wait_seconds = 30
+                poll_interval = 2
+                for _ in range(max_wait_seconds // poll_interval):
+                    if self._client.check_table_exist(self.table_name):
+                        break
+                    time.sleep(poll_interval)
+                else:
+                    raise RuntimeError(f"Table {self.table_name} was not ready after {max_wait_seconds}s")
+
+                # Open table and set vector index
+                table = self._client.open_table(self.table_name)
+                table.set_vector_index(
+                    column="embedding",
+                    distance_method=self._config.distance_method,
+                    base_quantization_type=self._config.base_quantization_type,
+                    max_degree=self._config.max_degree,
+                    ef_construction=self._config.ef_construction,
+                    use_reorder=self._config.base_quantization_type == "rabitq",
+                )
+
+                # Create full-text search index
+                table.create_text_index(
+                    index_name=f"ft_idx_{self._collection_name}",
+                    column="text",
+                    tokenizer=self._config.tokenizer,
+                )
+
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+
+class HologresVectorFactory(AbstractVectorFactory):
+    """Factory class for creating HologresVector instances."""
+
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> HologresVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            collection_name = class_prefix
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id)
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.HOLOGRES, collection_name))
+
+        return HologresVector(
+            collection_name=collection_name,
+            config=HologresVectorConfig(
+                host=dify_config.HOLOGRES_HOST or "",
+                port=dify_config.HOLOGRES_PORT,
+                database=dify_config.HOLOGRES_DATABASE or "",
+                access_key_id=dify_config.HOLOGRES_ACCESS_KEY_ID or "",
+                access_key_secret=dify_config.HOLOGRES_ACCESS_KEY_SECRET or "",
+                schema_name=dify_config.HOLOGRES_SCHEMA,
+                tokenizer=dify_config.HOLOGRES_TOKENIZER,
+                distance_method=dify_config.HOLOGRES_DISTANCE_METHOD,
+                base_quantization_type=dify_config.HOLOGRES_BASE_QUANTIZATION_TYPE,
+                max_degree=dify_config.HOLOGRES_MAX_DEGREE,
+                ef_construction=dify_config.HOLOGRES_EF_CONSTRUCTION,
+            ),
+        )

api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py

@@ -135,8 +135,8 @@ class PGVectoRS(BaseVector):
     def get_ids_by_metadata_field(self, key: str, value: str):
         result = None
         with Session(self._client) as session:
-            select_statement = sql_text(f"SELECT id FROM {self._collection_name} WHERE meta->>'{key}' = '{value}'; ")
-            result = session.execute(select_statement).fetchall()
+            select_statement = sql_text(f"SELECT id FROM {self._collection_name} WHERE meta->>:key = :value")
+            result = session.execute(select_statement, {"key": key, "value": value}).fetchall()
         if result:
             return [item[0] for item in result]
         else:
@@ -172,9 +172,9 @@ class PGVectoRS(BaseVector):
     def text_exists(self, id: str) -> bool:
         with Session(self._client) as session:
             select_statement = sql_text(
-                f"SELECT id FROM {self._collection_name} WHERE meta->>'doc_id' = '{id}' limit 1; "
+                f"SELECT id FROM {self._collection_name} WHERE meta->>'doc_id' = :doc_id limit 1"
             )
-            result = session.execute(select_statement).fetchall()
+            result = session.execute(select_statement, {"doc_id": id}).fetchall()
         return len(result) > 0
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:

api/core/rag/datasource/vdb/relyt/relyt_vector.py

@@ -154,10 +154,8 @@ class RelytVector(BaseVector):
     def get_ids_by_metadata_field(self, key: str, value: str):
         result = None
         with Session(self.client) as session:
-            select_statement = sql_text(
-                f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>'{key}' = '{value}'; """
-            )
-            result = session.execute(select_statement).fetchall()
+            select_statement = sql_text(f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>:key = :value""")
+            result = session.execute(select_statement, {"key": key, "value": value}).fetchall()
         if result:
             return [item[0] for item in result]
         else:
@@ -201,11 +199,10 @@ class RelytVector(BaseVector):
 
     def delete_by_ids(self, ids: list[str]):
         with Session(self.client) as session:
-            ids_str = ",".join(f"'{doc_id}'" for doc_id in ids)
             select_statement = sql_text(
-                f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>'doc_id' in ({ids_str}); """
+                f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>'doc_id' = ANY(:doc_ids)"""
             )
-            result = session.execute(select_statement).fetchall()
+            result = session.execute(select_statement, {"doc_ids": ids}).fetchall()
         if result:
             ids = [item[0] for item in result]
             self.delete_by_uuids(ids)
@@ -218,9 +215,9 @@ class RelytVector(BaseVector):
     def text_exists(self, id: str) -> bool:
         with Session(self.client) as session:
             select_statement = sql_text(
-                f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>'doc_id' = '{id}' limit 1; """
+                f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>'doc_id' = :doc_id limit 1"""
             )
-            result = session.execute(select_statement).fetchall()
+            result = session.execute(select_statement, {"doc_id": id}).fetchall()
         return len(result) > 0
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:

api/core/rag/datasource/vdb/vector_factory.py

@@ -38,7 +38,7 @@ class AbstractVectorFactory(ABC):
 class Vector:
     def __init__(self, dataset: Dataset, attributes: list | None = None):
         if attributes is None:
-            attributes = ["doc_id", "dataset_id", "document_id", "doc_hash"]
+            attributes = ["doc_id", "dataset_id", "document_id", "doc_hash", "doc_type"]
         self._dataset = dataset
         self._embeddings = self._get_embeddings()
         self._attributes = attributes
@@ -191,6 +191,10 @@ class Vector:
                 from core.rag.datasource.vdb.iris.iris_vector import IrisVectorFactory
 
                 return IrisVectorFactory
+            case VectorType.HOLOGRES:
+                from core.rag.datasource.vdb.hologres.hologres_vector import HologresVectorFactory
+
+                return HologresVectorFactory
             case _:
                 raise ValueError(f"Vector store {vector_type} is not supported.")
 

api/core/rag/datasource/vdb/vector_type.py

@@ -34,3 +34,4 @@ class VectorType(StrEnum):
     MATRIXONE = "matrixone"
     CLICKZETTA = "clickzetta"
     IRIS = "iris"
+    HOLOGRES = "hologres"

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -196,6 +196,7 @@ class WeaviateVector(BaseVector):
                             ),
                             wc.Property(name="document_id", data_type=wc.DataType.TEXT),
                             wc.Property(name="doc_id", data_type=wc.DataType.TEXT),
+                            wc.Property(name="doc_type", data_type=wc.DataType.TEXT),
                             wc.Property(name="chunk_index", data_type=wc.DataType.INT),
                         ],
                         vector_config=wc.Configure.Vectors.self_provided(),
@@ -225,6 +226,8 @@ class WeaviateVector(BaseVector):
             to_add.append(wc.Property(name="document_id", data_type=wc.DataType.TEXT))
         if "doc_id" not in existing:
             to_add.append(wc.Property(name="doc_id", data_type=wc.DataType.TEXT))
+        if "doc_type" not in existing:
+            to_add.append(wc.Property(name="doc_type", data_type=wc.DataType.TEXT))
         if "chunk_index" not in existing:
             to_add.append(wc.Property(name="chunk_index", data_type=wc.DataType.INT))
 

api/core/rag/index_processor/index_processor.py

@@ -9,8 +9,8 @@ from flask import current_app
 from sqlalchemy import delete, func, select
 
 from core.db.session_factory import session_factory
-from dify_graph.nodes.knowledge_index.exc import KnowledgeIndexNodeError
-from dify_graph.repositories.index_processor_protocol import Preview, PreviewItem, QaPreview
+from core.workflow.nodes.knowledge_index.exc import KnowledgeIndexNodeError
+from core.workflow.nodes.knowledge_index.protocols import Preview, PreviewItem, QaPreview
 from models.dataset import Dataset, Document, DocumentSegment
 
 from .index_processor_factory import IndexProcessorFactory

api/core/rag/index_processor/index_processor_base.py

@@ -294,7 +294,7 @@ class BaseIndexProcessor(ABC):
             logging.warning("Error downloading image from %s: %s", image_url, str(e))
             return None
         except Exception:
-            logging.exception("Unexpected error downloading image from %s", image_url)
+            logging.warning("Unexpected error downloading image from %s", image_url, exc_info=True)
             return None
 
     def _download_tool_file(self, tool_file_id: str, current_user: Account) -> str | None:

api/core/rag/retrieval/dataset_retrieval.py

@@ -56,18 +56,18 @@ from core.rag.retrieval.template_prompts import (
 )
 from core.tools.signature import sign_upload_file
 from core.tools.utils.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
-from dify_graph.file import File, FileTransferMethod, FileType
-from dify_graph.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMUsage
-from dify_graph.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
-from dify_graph.model_runtime.entities.model_entities import ModelFeature, ModelType
-from dify_graph.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from dify_graph.nodes.knowledge_retrieval import exc
-from dify_graph.repositories.rag_retrieval_protocol import (
+from core.workflow.nodes.knowledge_retrieval import exc
+from core.workflow.nodes.knowledge_retrieval.retrieval import (
     KnowledgeRetrievalRequest,
     Source,
     SourceChildChunk,
     SourceMetadata,
 )
+from dify_graph.file import File, FileTransferMethod, FileType
+from dify_graph.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMUsage
+from dify_graph.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
+from dify_graph.model_runtime.entities.model_entities import ModelFeature, ModelType
+from dify_graph.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 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

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -18,7 +18,7 @@ from tenacity import before_sleep_log, retry, retry_if_exception, stop_after_att
 
 from configs import dify_config
 from dify_graph.entities import WorkflowNodeExecution
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from dify_graph.enums import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from dify_graph.model_runtime.utils.encoders import jsonable_encoder
 from dify_graph.repositories.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
 from dify_graph.workflow_type_encoder import WorkflowRuntimeTypeConverter
@@ -146,7 +146,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             index=db_model.index,
             predecessor_node_id=db_model.predecessor_node_id,
             node_id=db_model.node_id,
-            node_type=NodeType(db_model.node_type),
+            node_type=db_model.node_type,
             title=db_model.title,
             inputs=inputs,
             process_data=process_data,

api/core/tools/utils/configuration.py

@@ -116,6 +116,7 @@ class ToolParameterConfigurationManager:
 
         return a deep copy of parameters with decrypted values
         """
+        parameters = self._deep_copy(parameters)
 
         cache = ToolParameterCache(
             tenant_id=self.tenant_id,

api/core/tools/utils/workflow_configuration_sync.py

@@ -3,7 +3,7 @@ from typing import Any
 
 from core.tools.entities.tool_entities import WorkflowToolParameterConfiguration
 from core.tools.errors import WorkflowToolHumanInputNotSupportedError
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.nodes.base.entities import OutputVariableEntity
 from dify_graph.variables.input_entities import VariableEntity
 
@@ -51,7 +51,7 @@ class WorkflowToolConfigurationUtils:
     def ensure_no_human_input_nodes(cls, graph: Mapping[str, Any]) -> None:
         nodes = graph.get("nodes", [])
         for node in nodes:
-            if node.get("data", {}).get("type") == NodeType.HUMAN_INPUT:
+            if node.get("data", {}).get("type") == BuiltinNodeTypes.HUMAN_INPUT:
                 raise WorkflowToolHumanInputNotSupportedError()
 
     @classmethod

api/core/trigger/constants.py

@@ -0,0 +1,18 @@
+from typing import Final
+
+TRIGGER_WEBHOOK_NODE_TYPE: Final[str] = "trigger-webhook"
+TRIGGER_SCHEDULE_NODE_TYPE: Final[str] = "trigger-schedule"
+TRIGGER_PLUGIN_NODE_TYPE: Final[str] = "trigger-plugin"
+TRIGGER_INFO_METADATA_KEY: Final[str] = "trigger_info"
+
+TRIGGER_NODE_TYPES: Final[frozenset[str]] = frozenset(
+    {
+        TRIGGER_WEBHOOK_NODE_TYPE,
+        TRIGGER_SCHEDULE_NODE_TYPE,
+        TRIGGER_PLUGIN_NODE_TYPE,
+    }
+)
+
+
+def is_trigger_node_type(node_type: str) -> bool:
+    return node_type in TRIGGER_NODE_TYPES

api/core/trigger/debug/event_selectors.py

@@ -11,6 +11,11 @@ from typing import Any
 from pydantic import BaseModel
 
 from core.plugin.entities.request import TriggerInvokeEventResponse
+from core.trigger.constants import (
+    TRIGGER_PLUGIN_NODE_TYPE,
+    TRIGGER_SCHEDULE_NODE_TYPE,
+    TRIGGER_WEBHOOK_NODE_TYPE,
+)
 from core.trigger.debug.event_bus import TriggerDebugEventBus
 from core.trigger.debug.events import (
     PluginTriggerDebugEvent,
@@ -19,10 +24,9 @@ from core.trigger.debug.events import (
     build_plugin_pool_key,
     build_webhook_pool_key,
 )
+from core.workflow.nodes.trigger_plugin.entities import TriggerEventNodeData
+from core.workflow.nodes.trigger_schedule.entities import ScheduleConfig
 from dify_graph.entities.graph_config import NodeConfigDict
-from dify_graph.enums import NodeType
-from dify_graph.nodes.trigger_plugin.entities import TriggerEventNodeData
-from dify_graph.nodes.trigger_schedule.entities import ScheduleConfig
 from extensions.ext_redis import redis_client
 from libs.datetime_utils import ensure_naive_utc, naive_utc_now
 from libs.schedule_utils import calculate_next_run_at
@@ -206,21 +210,19 @@ def create_event_poller(
     if not node_config:
         raise ValueError("Node data not found for node %s", node_id)
     node_type = draft_workflow.get_node_type_from_node_config(node_config)
-    match node_type:
-        case NodeType.TRIGGER_PLUGIN:
-            return PluginTriggerDebugEventPoller(
-                tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
-            )
-        case NodeType.TRIGGER_WEBHOOK:
-            return WebhookTriggerDebugEventPoller(
-                tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
-            )
-        case NodeType.TRIGGER_SCHEDULE:
-            return ScheduleTriggerDebugEventPoller(
-                tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
-            )
-        case _:
-            raise ValueError("unable to create event poller for node type %s", node_type)
+    if node_type == TRIGGER_PLUGIN_NODE_TYPE:
+        return PluginTriggerDebugEventPoller(
+            tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
+        )
+    if node_type == TRIGGER_WEBHOOK_NODE_TYPE:
+        return WebhookTriggerDebugEventPoller(
+            tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
+        )
+    if node_type == TRIGGER_SCHEDULE_NODE_TYPE:
+        return ScheduleTriggerDebugEventPoller(
+            tenant_id=tenant_id, user_id=user_id, app_id=app_id, node_config=node_config, node_id=node_id
+        )
+    raise ValueError("unable to create event poller for node type %s", node_type)
 
 
 def select_trigger_debug_events(

api/core/workflow/__init__.py

@@ -1,4 +1 @@
-from .node_factory import DifyNodeFactory
-from .workflow_entry import WorkflowEntry
-
-__all__ = ["DifyNodeFactory", "WorkflowEntry"]
+"""Core workflow package."""

api/core/workflow/node_factory.py

@@ -1,4 +1,7 @@
-from collections.abc import Callable, Mapping
+import importlib
+import pkgutil
+from collections.abc import Callable, Iterator, Mapping, MutableMapping
+from functools import lru_cache
 from typing import TYPE_CHECKING, Any, TypeAlias, cast, final
 
 from sqlalchemy import select
@@ -8,7 +11,6 @@ from typing_extensions import override
 from configs import dify_config
 from core.app.entities.app_invoke_entities import DifyRunContext
 from core.app.llm.model_access import build_dify_model_access
-from core.datasource.datasource_manager import DatasourceManager
 from core.helper.code_executor.code_executor import (
     CodeExecutionError,
     CodeExecutor,
@@ -17,12 +19,9 @@ from core.helper.ssrf_proxy import ssrf_proxy
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
-from core.rag.index_processor.index_processor import IndexProcessor
-from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
-from core.rag.summary_index.summary_index import SummaryIndex
 from core.repositories.human_input_repository import HumanInputFormRepositoryImpl
 from core.tools.tool_file_manager import ToolFileManager
-from core.workflow.node_resolution import resolve_workflow_node_class
+from core.trigger.constants import TRIGGER_NODE_TYPES
 from core.workflow.nodes.agent.message_transformer import AgentMessageTransformer
 from core.workflow.nodes.agent.plugin_strategy_adapter import (
     PluginAgentStrategyPresentationProvider,
@@ -32,7 +31,7 @@ from core.workflow.nodes.agent.runtime_support import AgentRuntimeSupport
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.entities.graph_config import NodeConfigDict, NodeConfigDictAdapter
 from dify_graph.entities.graph_init_params import DIFY_RUN_CONTEXT_KEY
-from dify_graph.enums import NodeType, SystemVariableKey
+from dify_graph.enums import BuiltinNodeTypes, NodeType, SystemVariableKey
 from dify_graph.file.file_manager import file_manager
 from dify_graph.graph.graph import NodeFactory
 from dify_graph.model_runtime.entities.model_entities import ModelType
@@ -46,6 +45,7 @@ from dify_graph.nodes.document_extractor import UnstructuredApiConfig
 from dify_graph.nodes.http_request import build_http_request_config
 from dify_graph.nodes.llm.entities import LLMNodeData
 from dify_graph.nodes.llm.exc import LLMModeRequiredError, ModelNotExistError
+from dify_graph.nodes.llm.protocols import TemplateRenderer
 from dify_graph.nodes.parameter_extractor.entities import ParameterExtractorNodeData
 from dify_graph.nodes.question_classifier.entities import QuestionClassifierNodeData
 from dify_graph.nodes.template_transform.template_renderer import (
@@ -59,6 +59,135 @@ if TYPE_CHECKING:
     from dify_graph.entities import GraphInitParams
     from dify_graph.runtime import GraphRuntimeState
 
+LATEST_VERSION = "latest"
+_START_NODE_TYPES: frozenset[NodeType] = frozenset(
+    (BuiltinNodeTypes.START, BuiltinNodeTypes.DATASOURCE, *TRIGGER_NODE_TYPES)
+)
+
+
+def _import_node_package(package_name: str, *, excluded_modules: frozenset[str] = frozenset()) -> None:
+    package = importlib.import_module(package_name)
+    for _, module_name, _ in pkgutil.walk_packages(package.__path__, package.__name__ + "."):
+        if module_name in excluded_modules:
+            continue
+        importlib.import_module(module_name)
+
+
+@lru_cache(maxsize=1)
+def register_nodes() -> None:
+    """Import production node modules so they self-register with ``Node``."""
+    _import_node_package("dify_graph.nodes")
+    _import_node_package("core.workflow.nodes")
+
+
+def get_node_type_classes_mapping() -> Mapping[NodeType, Mapping[str, type[Node]]]:
+    """Return a read-only snapshot of the current production node registry.
+
+    The workflow layer owns node bootstrap because it must compose built-in
+    `dify_graph.nodes.*` implementations with workflow-local nodes under
+    `core.workflow.nodes.*`. Keeping this import side effect here avoids
+    reintroducing registry bootstrapping into lower-level graph primitives.
+    """
+    register_nodes()
+    return Node.get_node_type_classes_mapping()
+
+
+def resolve_workflow_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:
+    node_mapping = get_node_type_classes_mapping().get(node_type)
+    if not node_mapping:
+        raise ValueError(f"No class mapping found for node type: {node_type}")
+
+    latest_node_class = node_mapping.get(LATEST_VERSION)
+    matched_node_class = node_mapping.get(node_version)
+    node_class = matched_node_class or latest_node_class
+    if not node_class:
+        raise ValueError(f"No latest version class found for node type: {node_type}")
+    return node_class
+
+
+def is_start_node_type(node_type: NodeType) -> bool:
+    """Return True when the node type can serve as a workflow entry point."""
+    return node_type in _START_NODE_TYPES
+
+
+def get_default_root_node_id(graph_config: Mapping[str, Any]) -> str:
+    """Resolve the default entry node for a persisted top-level workflow graph.
+
+    This workflow-layer helper depends on start-node semantics defined by
+    `is_start_node_type`, so it intentionally lives next to the node registry
+    instead of in the raw `dify_graph.entities.graph_config` schema module.
+    """
+    nodes = graph_config.get("nodes")
+    if not isinstance(nodes, list):
+        raise ValueError("nodes in workflow graph must be a list")
+
+    for node in nodes:
+        if not isinstance(node, Mapping):
+            continue
+
+        if node.get("type") == "custom-note":
+            continue
+
+        node_id = node.get("id")
+        data = node.get("data")
+        if not isinstance(node_id, str) or not isinstance(data, Mapping):
+            continue
+
+        node_type = data.get("type")
+        if isinstance(node_type, str) and is_start_node_type(node_type):
+            return node_id
+
+    raise ValueError("Unable to determine default root node ID from workflow graph")
+
+
+class _LazyNodeTypeClassesMapping(MutableMapping[NodeType, Mapping[str, type[Node]]]):
+    """Mutable dict-like view over the current node registry."""
+
+    def __init__(self) -> None:
+        self._cached_snapshot: dict[NodeType, Mapping[str, type[Node]]] = {}
+        self._cached_version = -1
+        self._deleted: set[NodeType] = set()
+        self._overrides: dict[NodeType, Mapping[str, type[Node]]] = {}
+
+    def _snapshot(self) -> dict[NodeType, Mapping[str, type[Node]]]:
+        current_version = Node.get_registry_version()
+        if self._cached_version != current_version:
+            self._cached_snapshot = dict(get_node_type_classes_mapping())
+            self._cached_version = current_version
+        if not self._deleted and not self._overrides:
+            return self._cached_snapshot
+
+        snapshot = {key: value for key, value in self._cached_snapshot.items() if key not in self._deleted}
+        snapshot.update(self._overrides)
+        return snapshot
+
+    def __getitem__(self, key: NodeType) -> Mapping[str, type[Node]]:
+        return self._snapshot()[key]
+
+    def __setitem__(self, key: NodeType, value: Mapping[str, type[Node]]) -> None:
+        self._deleted.discard(key)
+        self._overrides[key] = value
+
+    def __delitem__(self, key: NodeType) -> None:
+        if key in self._overrides:
+            del self._overrides[key]
+            return
+        if key in self._cached_snapshot:
+            self._deleted.add(key)
+            return
+        raise KeyError(key)
+
+    def __iter__(self) -> Iterator[NodeType]:
+        return iter(self._snapshot())
+
+    def __len__(self) -> int:
+        return len(self._snapshot())
+
+
+# Keep the canonical node-class mapping in the workflow layer that also bootstraps
+# legacy `core.workflow.nodes.*` registrations.
+NODE_TYPE_CLASSES_MAPPING: MutableMapping[NodeType, Mapping[str, type[Node]]] = _LazyNodeTypeClassesMapping()
+
 
 LLMCompatibleNodeData: TypeAlias = LLMNodeData | QuestionClassifierNodeData | ParameterExtractorNodeData
 
@@ -100,6 +229,16 @@ class DefaultWorkflowCodeExecutor:
         return isinstance(error, CodeExecutionError)
 
 
+class DefaultLLMTemplateRenderer(TemplateRenderer):
+    def render_jinja2(self, *, template: str, inputs: Mapping[str, Any]) -> str:
+        result = CodeExecutor.execute_workflow_code_template(
+            language=CodeLanguage.JINJA2,
+            code=template,
+            inputs=inputs,
+        )
+        return str(result.get("result", ""))
+
+
 @final
 class DifyNodeFactory(NodeFactory):
     """
@@ -126,11 +265,11 @@ class DifyNodeFactory(NodeFactory):
             max_object_array_length=dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH,
         )
         self._template_renderer = CodeExecutorJinja2TemplateRenderer(code_executor=self._code_executor)
+        self._llm_template_renderer: TemplateRenderer = DefaultLLMTemplateRenderer()
         self._template_transform_max_output_length = dify_config.TEMPLATE_TRANSFORM_MAX_LENGTH
         self._http_request_http_client = ssrf_proxy
         self._http_request_tool_file_manager_factory = ToolFileManager
         self._http_request_file_manager = file_manager
-        self._rag_retrieval = DatasetRetrieval()
         self._document_extractor_unstructured_api_config = UnstructuredApiConfig(
             api_url=dify_config.UNSTRUCTURED_API_URL,
             api_key=dify_config.UNSTRUCTURED_API_KEY or "",
@@ -177,56 +316,46 @@ class DifyNodeFactory(NodeFactory):
         node_class = self._resolve_node_class(node_type=node_data.type, node_version=str(node_data.version))
         node_type = node_data.type
         node_init_kwargs_factories: Mapping[NodeType, Callable[[], dict[str, object]]] = {
-            NodeType.CODE: lambda: {
+            BuiltinNodeTypes.CODE: lambda: {
                 "code_executor": self._code_executor,
                 "code_limits": self._code_limits,
             },
-            NodeType.TEMPLATE_TRANSFORM: lambda: {
+            BuiltinNodeTypes.TEMPLATE_TRANSFORM: lambda: {
                 "template_renderer": self._template_renderer,
                 "max_output_length": self._template_transform_max_output_length,
             },
-            NodeType.HTTP_REQUEST: lambda: {
+            BuiltinNodeTypes.HTTP_REQUEST: lambda: {
                 "http_request_config": self._http_request_config,
                 "http_client": self._http_request_http_client,
                 "tool_file_manager_factory": self._http_request_tool_file_manager_factory,
                 "file_manager": self._http_request_file_manager,
             },
-            NodeType.HUMAN_INPUT: lambda: {
+            BuiltinNodeTypes.HUMAN_INPUT: lambda: {
                 "form_repository": HumanInputFormRepositoryImpl(tenant_id=self._dify_context.tenant_id),
             },
-            NodeType.KNOWLEDGE_INDEX: lambda: {
-                "index_processor": IndexProcessor(),
-                "summary_index_service": SummaryIndex(),
-            },
-            NodeType.LLM: lambda: self._build_llm_compatible_node_init_kwargs(
+            BuiltinNodeTypes.LLM: lambda: self._build_llm_compatible_node_init_kwargs(
                 node_class=node_class,
                 node_data=node_data,
                 include_http_client=True,
             ),
-            NodeType.DATASOURCE: lambda: {
-                "datasource_manager": DatasourceManager,
-            },
-            NodeType.KNOWLEDGE_RETRIEVAL: lambda: {
-                "rag_retrieval": self._rag_retrieval,
-            },
-            NodeType.DOCUMENT_EXTRACTOR: lambda: {
+            BuiltinNodeTypes.DOCUMENT_EXTRACTOR: lambda: {
                 "unstructured_api_config": self._document_extractor_unstructured_api_config,
                 "http_client": self._http_request_http_client,
             },
-            NodeType.QUESTION_CLASSIFIER: lambda: self._build_llm_compatible_node_init_kwargs(
+            BuiltinNodeTypes.QUESTION_CLASSIFIER: lambda: self._build_llm_compatible_node_init_kwargs(
                 node_class=node_class,
                 node_data=node_data,
                 include_http_client=True,
             ),
-            NodeType.PARAMETER_EXTRACTOR: lambda: self._build_llm_compatible_node_init_kwargs(
+            BuiltinNodeTypes.PARAMETER_EXTRACTOR: lambda: self._build_llm_compatible_node_init_kwargs(
                 node_class=node_class,
                 node_data=node_data,
                 include_http_client=False,
             ),
-            NodeType.TOOL: lambda: {
+            BuiltinNodeTypes.TOOL: lambda: {
                 "tool_file_manager_factory": self._http_request_tool_file_manager_factory(),
             },
-            NodeType.AGENT: lambda: {
+            BuiltinNodeTypes.AGENT: lambda: {
                 "strategy_resolver": self._agent_strategy_resolver,
                 "presentation_provider": self._agent_strategy_presentation_provider,
                 "runtime_support": self._agent_runtime_support,
@@ -274,6 +403,8 @@ class DifyNodeFactory(NodeFactory):
                 model_instance=model_instance,
             ),
         }
+        if validated_node_data.type in {BuiltinNodeTypes.LLM, BuiltinNodeTypes.QUESTION_CLASSIFIER}:
+            node_init_kwargs["template_renderer"] = self._llm_template_renderer
         if include_http_client:
             node_init_kwargs["http_client"] = self._http_request_http_client
         return node_init_kwargs

api/core/workflow/node_resolution.py

@@ -1,42 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Mapping
-from importlib import import_module
-
-from dify_graph.enums import NodeType
-from dify_graph.nodes.base.node import Node
-from dify_graph.nodes.node_mapping import LATEST_VERSION, get_node_type_classes_mapping
-
-_WORKFLOW_NODE_MODULES = ("core.workflow.nodes.agent",)
-_workflow_nodes_registered = False
-
-
-def ensure_workflow_nodes_registered() -> None:
-    """Import workflow-local node modules so they can register with `Node.__init_subclass__`."""
-    global _workflow_nodes_registered
-
-    if _workflow_nodes_registered:
-        return
-
-    for module_name in _WORKFLOW_NODE_MODULES:
-        import_module(module_name)
-
-    _workflow_nodes_registered = True
-
-
-def get_workflow_node_type_classes_mapping() -> Mapping[NodeType, Mapping[str, type[Node]]]:
-    ensure_workflow_nodes_registered()
-    return get_node_type_classes_mapping()
-
-
-def resolve_workflow_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:
-    node_mapping = get_workflow_node_type_classes_mapping().get(node_type)
-    if not node_mapping:
-        raise ValueError(f"No class mapping found for node type: {node_type}")
-
-    latest_node_class = node_mapping.get(LATEST_VERSION)
-    matched_node_class = node_mapping.get(node_version)
-    node_class = matched_node_class or latest_node_class
-    if not node_class:
-        raise ValueError(f"No latest version class found for node type: {node_type}")
-    return node_class

api/core/workflow/nodes/__init__.py

@@ -0,0 +1 @@
+"""Workflow node implementations that remain under the legacy core.workflow namespace."""

api/core/workflow/nodes/agent/agent_node.py

@@ -4,7 +4,7 @@ from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 from dify_graph.entities.graph_config import NodeConfigDict
-from dify_graph.enums import NodeType, SystemVariableKey, WorkflowNodeExecutionStatus
+from dify_graph.enums import BuiltinNodeTypes, SystemVariableKey, WorkflowNodeExecutionStatus
 from dify_graph.node_events import NodeEventBase, NodeRunResult, StreamCompletedEvent
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.base.variable_template_parser import VariableTemplateParser
@@ -24,7 +24,7 @@ if TYPE_CHECKING:
 
 
 class AgentNode(Node[AgentNodeData]):
-    node_type = NodeType.AGENT
+    node_type = BuiltinNodeTypes.AGENT
 
     _strategy_resolver: AgentStrategyResolver
     _presentation_provider: AgentStrategyPresentationProvider

api/core/workflow/nodes/agent/entities.py

@@ -6,11 +6,11 @@ from pydantic import BaseModel
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.tools.entities.tool_entities import ToolSelector
 from dify_graph.entities.base_node_data import BaseNodeData
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 
 
 class AgentNodeData(BaseNodeData):
-    type: NodeType = NodeType.AGENT
+    type: NodeType = BuiltinNodeTypes.AGENT
     agent_strategy_provider_name: str
     agent_strategy_name: str
     agent_strategy_label: str

api/core/workflow/nodes/agent/message_transformer.py

@@ -8,7 +8,7 @@ from sqlalchemy.orm import Session
 
 from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.utils.message_transformer import ToolFileMessageTransformer
-from dify_graph.enums import NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from dify_graph.enums import BuiltinNodeTypes, NodeType, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from dify_graph.file import File, FileTransferMethod
 from dify_graph.model_runtime.entities.llm_entities import LLMUsage, LLMUsageMetadata
 from dify_graph.model_runtime.utils.encoders import jsonable_encoder
@@ -123,7 +123,7 @@ class AgentMessageTransformer:
                 )
             elif message.type == ToolInvokeMessage.MessageType.JSON:
                 assert isinstance(message.message, ToolInvokeMessage.JsonMessage)
-                if node_type == NodeType.AGENT:
+                if node_type == BuiltinNodeTypes.AGENT:
                     if isinstance(message.message.json_object, dict):
                         msg_metadata: dict[str, Any] = message.message.json_object.pop("execution_metadata", {})
                         llm_usage = LLMUsage.from_metadata(cast(LLMUsageMetadata, msg_metadata))

api/core/workflow/nodes/datasource/__init__.py

@@ -0,0 +1 @@
+"""Datasource workflow node package."""

api/core/workflow/nodes/datasource/datasource_node.py

@@ -1,22 +1,17 @@
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
+from core.datasource.datasource_manager import DatasourceManager
 from core.datasource.entities.datasource_entities import DatasourceProviderType
 from core.plugin.impl.exc import PluginDaemonClientSideError
 from dify_graph.entities.graph_config import NodeConfigDict
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType, SystemVariableKey
+from dify_graph.enums import BuiltinNodeTypes, NodeExecutionType, SystemVariableKey, WorkflowNodeExecutionMetadataKey
 from dify_graph.node_events import NodeRunResult, StreamCompletedEvent
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.base.variable_template_parser import VariableTemplateParser
-from dify_graph.repositories.datasource_manager_protocol import (
-    DatasourceManagerProtocol,
-    DatasourceParameter,
-    OnlineDriveDownloadFileParam,
-)
 
-from ...entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
-from .entities import DatasourceNodeData
+from .entities import DatasourceNodeData, DatasourceParameter, OnlineDriveDownloadFileParam
 from .exc import DatasourceNodeError
 
 if TYPE_CHECKING:
@@ -29,7 +24,7 @@ class DatasourceNode(Node[DatasourceNodeData]):
     Datasource Node
     """
 
-    node_type = NodeType.DATASOURCE
+    node_type = BuiltinNodeTypes.DATASOURCE
     execution_type = NodeExecutionType.ROOT
 
     def __init__(
@@ -38,7 +33,6 @@ class DatasourceNode(Node[DatasourceNodeData]):
         config: NodeConfigDict,
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
-        datasource_manager: DatasourceManagerProtocol,
     ):
         super().__init__(
             id=id,
@@ -46,7 +40,7 @@ class DatasourceNode(Node[DatasourceNodeData]):
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
         )
-        self.datasource_manager = datasource_manager
+        self.datasource_manager = DatasourceManager
 
     def populate_start_event(self, event) -> None:
         event.provider_id = f"{self.node_data.plugin_id}/{self.node_data.provider_name}"

api/core/workflow/nodes/datasource/entities.py

@@ -4,7 +4,7 @@ from pydantic import BaseModel, field_validator
 from pydantic_core.core_schema import ValidationInfo
 
 from dify_graph.entities.base_node_data import BaseNodeData
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 
 
 class DatasourceEntity(BaseModel):
@@ -17,7 +17,7 @@ class DatasourceEntity(BaseModel):
 
 
 class DatasourceNodeData(BaseNodeData, DatasourceEntity):
-    type: NodeType = NodeType.DATASOURCE
+    type: NodeType = BuiltinNodeTypes.DATASOURCE
 
     class DatasourceInput(BaseModel):
         # TODO: check this type
@@ -42,3 +42,14 @@ class DatasourceNodeData(BaseNodeData, DatasourceEntity):
             return typ
 
     datasource_parameters: dict[str, DatasourceInput] | None = None
+
+
+class DatasourceParameter(BaseModel):
+    workspace_id: str
+    page_id: str
+    type: str
+
+
+class OnlineDriveDownloadFileParam(BaseModel):
+    id: str
+    bucket: str

api/core/workflow/nodes/datasource/protocols.py

@@ -1,25 +1,10 @@
 from collections.abc import Generator
 from typing import Any, Protocol
 
-from pydantic import BaseModel
-
 from dify_graph.file import File
 from dify_graph.node_events import StreamChunkEvent, StreamCompletedEvent
 
-
-class DatasourceParameter(BaseModel):
-    workspace_id: str
-    page_id: str
-    type: str
-
-
-class OnlineDriveDownloadFileParam(BaseModel):
-    id: str
-    bucket: str
-
-
-class DatasourceFinal(BaseModel):
-    data: dict[str, Any] | None = None
+from .entities import DatasourceParameter, OnlineDriveDownloadFileParam
 
 
 class DatasourceManagerProtocol(Protocol):

api/core/workflow/nodes/knowledge_index/__init__.py

@@ -0,0 +1,5 @@
+"""Knowledge index workflow node package."""
+
+KNOWLEDGE_INDEX_NODE_TYPE = "knowledge-index"
+
+__all__ = ["KNOWLEDGE_INDEX_NODE_TYPE"]

api/core/workflow/nodes/knowledge_index/entities.py

@@ -3,6 +3,7 @@ from typing import Literal, Union
 from pydantic import BaseModel
 
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
+from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
 
@@ -156,7 +157,7 @@ class KnowledgeIndexNodeData(BaseNodeData):
     Knowledge index Node Data.
     """
 
-    type: NodeType = NodeType.KNOWLEDGE_INDEX
+    type: NodeType = KNOWLEDGE_INDEX_NODE_TYPE
     chunk_structure: str
     index_chunk_variable_selector: list[str]
     indexing_technique: str | None = None

api/core/workflow/nodes/knowledge_index/knowledge_index_node.py

@@ -2,14 +2,15 @@ import logging
 from collections.abc import Mapping
 from typing import TYPE_CHECKING, Any
 
+from core.rag.index_processor.index_processor import IndexProcessor
+from core.rag.summary_index.summary_index import SummaryIndex
+from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from dify_graph.entities.graph_config import NodeConfigDict
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType, SystemVariableKey
+from dify_graph.enums import NodeExecutionType, SystemVariableKey
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.base.template import Template
-from dify_graph.repositories.index_processor_protocol import IndexProcessorProtocol
-from dify_graph.repositories.summary_index_service_protocol import SummaryIndexServiceProtocol
 
 from .entities import KnowledgeIndexNodeData
 from .exc import (
@@ -25,7 +26,7 @@ _INVOKE_FROM_DEBUGGER = "debugger"
 
 
 class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
-    node_type = NodeType.KNOWLEDGE_INDEX
+    node_type = KNOWLEDGE_INDEX_NODE_TYPE
     execution_type = NodeExecutionType.RESPONSE
 
     def __init__(
@@ -34,12 +35,10 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
         config: NodeConfigDict,
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
-        index_processor: IndexProcessorProtocol,
-        summary_index_service: SummaryIndexServiceProtocol,
     ) -> None:
         super().__init__(id, config, graph_init_params, graph_runtime_state)
-        self.index_processor = index_processor
-        self.summary_index_service = summary_index_service
+        self.index_processor = IndexProcessor()
+        self.summary_index_service = SummaryIndex()
 
     def _run(self) -> NodeRunResult:  # type: ignore
         node_data = self.node_data

api/core/workflow/nodes/knowledge_index/protocols.py

@@ -5,21 +5,21 @@ from pydantic import BaseModel, Field
 
 
 class PreviewItem(BaseModel):
-    content: str | None = Field(None)
-    child_chunks: list[str] | None = Field(None)
-    summary: str | None = Field(None)
+    content: str | None = Field(default=None)
+    child_chunks: list[str] | None = Field(default=None)
+    summary: str | None = Field(default=None)
 
 
 class QaPreview(BaseModel):
-    answer: str | None = Field(None)
-    question: str | None = Field(None)
+    answer: str | None = Field(default=None)
+    question: str | None = Field(default=None)
 
 
 class Preview(BaseModel):
     chunk_structure: str
-    parent_mode: str | None = Field(None)
-    preview: list[PreviewItem] = Field([])
-    qa_preview: list[QaPreview] = Field([])
+    parent_mode: str | None = Field(default=None)
+    preview: list[PreviewItem] = Field(default_factory=list)
+    qa_preview: list[QaPreview] = Field(default_factory=list)
     total_segments: int
 
 
@@ -39,3 +39,9 @@ class IndexProcessorProtocol(Protocol):
     def get_preview_output(
         self, chunks: Any, dataset_id: str, document_id: str, chunk_structure: str, summary_index_setting: dict | None
     ) -> Preview: ...
+
+
+class SummaryIndexServiceProtocol(Protocol):
+    def generate_and_vectorize_summary(
+        self, dataset_id: str, document_id: str, is_preview: bool, summary_index_setting: dict | None = None
+    ) -> None: ...

api/core/workflow/nodes/knowledge_retrieval/__init__.py

@@ -0,0 +1 @@
+"""Knowledge retrieval workflow node package."""

api/core/workflow/nodes/knowledge_retrieval/entities.py

@@ -4,7 +4,7 @@ from typing import Literal
 from pydantic import BaseModel, Field
 
 from dify_graph.entities.base_node_data import BaseNodeData
-from dify_graph.enums import NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 from dify_graph.nodes.llm.entities import ModelConfig, VisionConfig
 
 
@@ -114,7 +114,7 @@ class KnowledgeRetrievalNodeData(BaseNodeData):
     Knowledge retrieval Node Data.
     """
 
-    type: NodeType = NodeType.KNOWLEDGE_RETRIEVAL
+    type: NodeType = BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL
     query_variable_selector: list[str] | None | str = None
     query_attachment_selector: list[str] | None | str = None
     dataset_ids: list[str]

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -1,12 +1,19 @@
+"""Knowledge retrieval workflow node implementation.
+
+This node now lives under ``core.workflow.nodes`` and is discovered directly by
+the workflow node registry.
+"""
+
 import logging
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal
 
 from core.app.app_config.entities import DatasetRetrieveConfigEntity
+from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from dify_graph.entities import GraphInitParams
 from dify_graph.entities.graph_config import NodeConfigDict
 from dify_graph.enums import (
-    NodeType,
+    BuiltinNodeTypes,
     WorkflowNodeExecutionMetadataKey,
     WorkflowNodeExecutionStatus,
 )
@@ -15,7 +22,6 @@ from dify_graph.model_runtime.utils.encoders import jsonable_encoder
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base import LLMUsageTrackingMixin
 from dify_graph.nodes.base.node import Node
-from dify_graph.repositories.rag_retrieval_protocol import KnowledgeRetrievalRequest, RAGRetrievalProtocol, Source
 from dify_graph.variables import (
     ArrayFileSegment,
     FileSegment,
@@ -32,6 +38,7 @@ from .exc import (
     KnowledgeRetrievalNodeError,
     RateLimitExceededError,
 )
+from .retrieval import KnowledgeRetrievalRequest, Source
 
 if TYPE_CHECKING:
     from dify_graph.file.models import File
@@ -41,7 +48,7 @@ logger = logging.getLogger(__name__)
 
 
 class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeData]):
-    node_type = NodeType.KNOWLEDGE_RETRIEVAL
+    node_type = BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL
 
     # Instance attributes specific to LLMNode.
     # Output variable for file
@@ -53,7 +60,6 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
         config: NodeConfigDict,
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
-        rag_retrieval: RAGRetrievalProtocol,
     ):
         super().__init__(
             id=id,
@@ -63,7 +69,7 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
         )
         # LLM file outputs, used for MultiModal outputs.
         self._file_outputs = []
-        self._rag_retrieval = rag_retrieval
+        self._rag_retrieval = DatasetRetrieval()
 
     @classmethod
     def version(cls):

api/core/workflow/nodes/knowledge_retrieval/retrieval.py

@@ -3,9 +3,10 @@ from typing import Any, Literal, Protocol
 from pydantic import BaseModel, Field
 
 from dify_graph.model_runtime.entities import LLMUsage
-from dify_graph.nodes.knowledge_retrieval.entities import MetadataFilteringCondition
 from dify_graph.nodes.llm.entities import ModelConfig
 
+from .entities import MetadataFilteringCondition
+
 
 class SourceChildChunk(BaseModel):
     id: str = Field(default="", description="Child chunk ID")
@@ -28,7 +29,7 @@ class SourceMetadata(BaseModel):
     segment_id: str | None = Field(default=None, description="Segment unique identifier")
     retriever_from: str = Field(default="workflow", description="Retriever source context")
     score: float = Field(default=0.0, description="Retrieval relevance score")
-    child_chunks: list[SourceChildChunk] = Field(default=[], description="List of child chunks")
+    child_chunks: list[SourceChildChunk] = Field(default_factory=list, description="List of child chunks")
     segment_hit_count: int | None = Field(default=0, description="Number of times segment was retrieved")
     segment_word_count: int | None = Field(default=0, description="Word count of the segment")
     segment_position: int | None = Field(default=0, description="Position of segment in document")
@@ -81,28 +82,7 @@ class KnowledgeRetrievalRequest(BaseModel):
 
 
 class RAGRetrievalProtocol(Protocol):
-    """Protocol for RAG-based knowledge retrieval implementations.
-
-    Implementations of this protocol handle knowledge retrieval from datasets
-    including rate limiting, dataset filtering, and document retrieval.
-    """
-
     @property
-    def llm_usage(self) -> LLMUsage:
-        """Return accumulated LLM usage for retrieval operations."""
-        ...
+    def llm_usage(self) -> LLMUsage: ...
 
-    def knowledge_retrieval(self, request: KnowledgeRetrievalRequest) -> list[Source]:
-        """Retrieve knowledge from datasets based on the provided request.
-
-        Args:
-            request: Knowledge retrieval request with search parameters
-
-        Returns:
-            List of sources matching the search criteria
-
-        Raises:
-            RateLimitExceededError: If rate limit is exceeded
-            ModelNotExistError: If specified model doesn't exist
-        """
-        ...
+    def knowledge_retrieval(self, request: KnowledgeRetrievalRequest) -> list[Source]: ...

api/core/workflow/nodes/trigger_plugin/entities.py

@@ -3,16 +3,18 @@ from typing import Any, Literal, Union
 
 from pydantic import BaseModel, Field, ValidationInfo, field_validator
 
+from core.trigger.constants import TRIGGER_PLUGIN_NODE_TYPE
 from core.trigger.entities.entities import EventParameter
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
-from dify_graph.nodes.trigger_plugin.exc import TriggerEventParameterError
+
+from .exc import TriggerEventParameterError
 
 
 class TriggerEventNodeData(BaseNodeData):
     """Plugin trigger node data"""
 
-    type: NodeType = NodeType.TRIGGER_PLUGIN
+    type: NodeType = TRIGGER_PLUGIN_NODE_TYPE
 
     class TriggerEventInput(BaseModel):
         value: Union[Any, list[str]]

api/core/workflow/nodes/trigger_plugin/trigger_event_node.py

@@ -1,8 +1,10 @@
 from collections.abc import Mapping
+from typing import Any, cast
 
+from core.trigger.constants import TRIGGER_INFO_METADATA_KEY, TRIGGER_PLUGIN_NODE_TYPE
 from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
-from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
+from dify_graph.enums import NodeExecutionType, WorkflowNodeExecutionMetadataKey
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
 
@@ -10,7 +12,7 @@ from .entities import TriggerEventNodeData
 
 
 class TriggerEventNode(Node[TriggerEventNodeData]):
-    node_type = NodeType.TRIGGER_PLUGIN
+    node_type = TRIGGER_PLUGIN_NODE_TYPE
     execution_type = NodeExecutionType.ROOT
 
     @classmethod
@@ -44,8 +46,8 @@ class TriggerEventNode(Node[TriggerEventNodeData]):
         """
 
         # Get trigger data passed when workflow was triggered
-        metadata = {
-            WorkflowNodeExecutionMetadataKey.TRIGGER_INFO: {
+        metadata: dict[WorkflowNodeExecutionMetadataKey, Any] = {
+            cast(WorkflowNodeExecutionMetadataKey, TRIGGER_INFO_METADATA_KEY): {
                 "provider_id": self.node_data.provider_id,
                 "event_name": self.node_data.event_name,
                 "plugin_unique_identifier": self.node_data.plugin_unique_identifier,

api/core/workflow/nodes/trigger_schedule/__init__.py

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

api/core/workflow/nodes/trigger_schedule/entities.py

@@ -2,6 +2,7 @@ from typing import Literal, Union
 
 from pydantic import BaseModel, Field
 
+from core.trigger.constants import TRIGGER_SCHEDULE_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
 
@@ -11,7 +12,7 @@ class TriggerScheduleNodeData(BaseNodeData):
     Trigger Schedule Node Data
     """
 
-    type: NodeType = NodeType.TRIGGER_SCHEDULE
+    type: NodeType = TRIGGER_SCHEDULE_NODE_TYPE
     mode: str = Field(default="visual", description="Schedule mode: visual or cron")
     frequency: str | None = Field(default=None, description="Frequency for visual mode: hourly, daily, weekly, monthly")
     cron_expression: str | None = Field(default=None, description="Cron expression for cron mode")

api/core/workflow/nodes/trigger_schedule/trigger_schedule_node.py

@@ -1,15 +1,17 @@
 from collections.abc import Mapping
 
+from core.trigger.constants import TRIGGER_SCHEDULE_NODE_TYPE
 from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.enums import NodeExecutionType
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
-from dify_graph.nodes.trigger_schedule.entities import TriggerScheduleNodeData
+
+from .entities import TriggerScheduleNodeData
 
 
 class TriggerScheduleNode(Node[TriggerScheduleNodeData]):
-    node_type = NodeType.TRIGGER_SCHEDULE
+    node_type = TRIGGER_SCHEDULE_NODE_TYPE
     execution_type = NodeExecutionType.ROOT
 
     @classmethod
@@ -19,7 +21,7 @@ class TriggerScheduleNode(Node[TriggerScheduleNodeData]):
     @classmethod
     def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:
         return {
-            "type": "trigger-schedule",
+            "type": TRIGGER_SCHEDULE_NODE_TYPE,
             "config": {
                 "mode": "visual",
                 "frequency": "daily",

api/core/workflow/nodes/trigger_webhook/entities.py

@@ -3,6 +3,7 @@ from enum import StrEnum
 
 from pydantic import BaseModel, Field, field_validator
 
+from core.trigger.constants import TRIGGER_WEBHOOK_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import NodeType
 from dify_graph.variables.types import SegmentType
@@ -93,7 +94,7 @@ class WebhookData(BaseNodeData):
     class SyncMode(StrEnum):
         SYNC = "async"  # only support
 
-    type: NodeType = NodeType.TRIGGER_WEBHOOK
+    type: NodeType = TRIGGER_WEBHOOK_NODE_TYPE
     method: Method = Method.GET
     content_type: ContentType = Field(default=ContentType.JSON)
     headers: Sequence[WebhookParameter] = Field(default_factory=list)

api/core/workflow/nodes/trigger_webhook/node.py

@@ -2,9 +2,10 @@ import logging
 from collections.abc import Mapping
 from typing import Any
 
+from core.trigger.constants import TRIGGER_WEBHOOK_NODE_TYPE
 from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
 from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionStatus
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.enums import NodeExecutionType
 from dify_graph.file import FileTransferMethod
 from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
@@ -19,7 +20,7 @@ logger = logging.getLogger(__name__)
 
 
 class TriggerWebhookNode(Node[WebhookData]):
-    node_type = NodeType.TRIGGER_WEBHOOK
+    node_type = TRIGGER_WEBHOOK_NODE_TYPE
     execution_type = NodeExecutionType.ROOT
 
     @classmethod

api/core/workflow/workflow_entry.py

@@ -8,8 +8,7 @@ from core.app.apps.exc import GenerateTaskStoppedError
 from core.app.entities.app_invoke_entities import InvokeFrom, UserFrom, build_dify_run_context
 from core.app.workflow.layers.llm_quota import LLMQuotaLayer
 from core.app.workflow.layers.observability import ObservabilityLayer
-from core.workflow.node_factory import DifyNodeFactory
-from core.workflow.node_resolution import resolve_workflow_node_class
+from core.workflow.node_factory import DifyNodeFactory, resolve_workflow_node_class
 from dify_graph.constants import ENVIRONMENT_VARIABLE_NODE_ID
 from dify_graph.entities import GraphInitParams
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
@@ -22,7 +21,7 @@ from dify_graph.graph_engine.layers import DebugLoggingLayer, ExecutionLimitsLay
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
 from dify_graph.graph_engine.protocols.command_channel import CommandChannel
 from dify_graph.graph_events import GraphEngineEvent, GraphNodeEventBase, GraphRunFailedEvent
-from dify_graph.nodes import NodeType
+from dify_graph.nodes import BuiltinNodeTypes
 from dify_graph.nodes.base.node import Node
 from dify_graph.runtime import ChildGraphNotFoundError, GraphRuntimeState, VariablePool
 from dify_graph.system_variable import SystemVariable
@@ -253,7 +252,7 @@ class WorkflowEntry:
             variable_mapping=variable_mapping,
             user_inputs=user_inputs,
         )
-        if node_type != NodeType.DATASOURCE:
+        if node_type != BuiltinNodeTypes.DATASOURCE:
             cls.mapping_user_inputs_to_variable_pool(
                 variable_mapping=variable_mapping,
                 user_inputs=user_inputs,
@@ -303,7 +302,7 @@ class WorkflowEntry:
             "height": node_height,
             "type": "custom",
             "data": {
-                "type": NodeType.START,
+                "type": BuiltinNodeTypes.START,
                 "title": "Start",
                 "desc": "Start",
             },
@@ -339,8 +338,8 @@ class WorkflowEntry:
         # Create a minimal graph for single node execution
         graph_dict = cls._create_single_node_graph(node_id, node_data)
 
-        node_type = NodeType(node_data.get("type", ""))
-        if node_type not in {NodeType.PARAMETER_EXTRACTOR, NodeType.QUESTION_CLASSIFIER}:
+        node_type = node_data.get("type", "")
+        if node_type not in {BuiltinNodeTypes.PARAMETER_EXTRACTOR, BuiltinNodeTypes.QUESTION_CLASSIFIER}:
             raise ValueError(f"Node type {node_type} not supported")
 
         node_cls = resolve_workflow_node_class(node_type=node_type, node_version="1")

api/dify_graph/README.md

@@ -113,7 +113,7 @@ The codebase enforces strict layering via import-linter:
 1. Create node class in `nodes/<node_type>/`
 1. Inherit from `BaseNode` or appropriate base class
 1. Implement `_run()` method
-1. Register in `nodes/node_mapping.py`
+1. Ensure the node module is importable under `nodes/<node_type>/`
 1. Add tests in `tests/unit_tests/dify_graph/nodes/`
 
 ### Implementing a Custom Layer

api/dify_graph/entities/base_node_data.py

@@ -121,6 +121,8 @@ class DefaultValue(BaseModel):
 class BaseNodeData(ABC, BaseModel):
     # Raw graph payloads are first validated through `NodeConfigDictAdapter`, where
     # `node["data"]` is typed as `BaseNodeData` before the concrete node class is known.
+    # `type` therefore accepts downstream string node kinds; unknown node implementations
+    # are rejected later when the node factory resolves the node registry.
     # At that boundary, node-specific fields are still "extra" relative to this shared DTO,
     # and persisted templates/workflows also carry undeclared compatibility keys such as
     # `selected`, `params`, `paramSchemas`, and `datasource_label`. Keep extras permissive

api/dify_graph/entities/workflow_node_execution.py

@@ -48,7 +48,7 @@ class WorkflowNodeExecution(BaseModel):
     index: int  # Sequence number for ordering in trace visualization
     predecessor_node_id: str | None = None  # ID of the node that executed before this one
     node_id: str  # ID of the node being executed
-    node_type: NodeType  # Type of node (e.g., start, llm, knowledge)
+    node_type: NodeType  # Type of node (e.g., start, llm, downstream response node)
     title: str  # Display title of the node
 
     # Execution data

api/dify_graph/enums.py

@@ -1,4 +1,5 @@
 from enum import StrEnum
+from typing import ClassVar, TypeAlias
 
 
 class NodeState(StrEnum):
@@ -33,56 +34,71 @@ class SystemVariableKey(StrEnum):
     INVOKE_FROM = "invoke_from"
 
 
-class NodeType(StrEnum):
-    START = "start"
-    END = "end"
-    ANSWER = "answer"
-    LLM = "llm"
-    KNOWLEDGE_RETRIEVAL = "knowledge-retrieval"
-    KNOWLEDGE_INDEX = "knowledge-index"
-    IF_ELSE = "if-else"
-    CODE = "code"
-    TEMPLATE_TRANSFORM = "template-transform"
-    QUESTION_CLASSIFIER = "question-classifier"
-    HTTP_REQUEST = "http-request"
-    TOOL = "tool"
-    DATASOURCE = "datasource"
-    VARIABLE_AGGREGATOR = "variable-aggregator"
-    LEGACY_VARIABLE_AGGREGATOR = "variable-assigner"  # TODO: Merge this into VARIABLE_AGGREGATOR in the database.
-    LOOP = "loop"
-    LOOP_START = "loop-start"
-    LOOP_END = "loop-end"
-    ITERATION = "iteration"
-    ITERATION_START = "iteration-start"  # Fake start node for iteration.
-    PARAMETER_EXTRACTOR = "parameter-extractor"
-    VARIABLE_ASSIGNER = "assigner"
-    DOCUMENT_EXTRACTOR = "document-extractor"
-    LIST_OPERATOR = "list-operator"
-    AGENT = "agent"
-    TRIGGER_WEBHOOK = "trigger-webhook"
-    TRIGGER_SCHEDULE = "trigger-schedule"
-    TRIGGER_PLUGIN = "trigger-plugin"
-    HUMAN_INPUT = "human-input"
+NodeType: TypeAlias = str
 
-    @property
-    def is_trigger_node(self) -> bool:
-        """Check if this node type is a trigger node."""
-        return self in [
-            NodeType.TRIGGER_WEBHOOK,
-            NodeType.TRIGGER_SCHEDULE,
-            NodeType.TRIGGER_PLUGIN,
-        ]
 
-    @property
-    def is_start_node(self) -> bool:
-        """Check if this node type can serve as a workflow entry point."""
-        return self in [
-            NodeType.START,
-            NodeType.DATASOURCE,
-            NodeType.TRIGGER_WEBHOOK,
-            NodeType.TRIGGER_SCHEDULE,
-            NodeType.TRIGGER_PLUGIN,
-        ]
+class BuiltinNodeTypes:
+    """Built-in node type string constants.
+
+    `node_type` values are plain strings throughout the graph runtime. This namespace
+    only exposes the built-in values shipped by `dify_graph`; downstream packages can
+    use additional strings without extending this class.
+    """
+
+    START: ClassVar[NodeType] = "start"
+    END: ClassVar[NodeType] = "end"
+    ANSWER: ClassVar[NodeType] = "answer"
+    LLM: ClassVar[NodeType] = "llm"
+    KNOWLEDGE_RETRIEVAL: ClassVar[NodeType] = "knowledge-retrieval"
+    IF_ELSE: ClassVar[NodeType] = "if-else"
+    CODE: ClassVar[NodeType] = "code"
+    TEMPLATE_TRANSFORM: ClassVar[NodeType] = "template-transform"
+    QUESTION_CLASSIFIER: ClassVar[NodeType] = "question-classifier"
+    HTTP_REQUEST: ClassVar[NodeType] = "http-request"
+    TOOL: ClassVar[NodeType] = "tool"
+    DATASOURCE: ClassVar[NodeType] = "datasource"
+    VARIABLE_AGGREGATOR: ClassVar[NodeType] = "variable-aggregator"
+    LEGACY_VARIABLE_AGGREGATOR: ClassVar[NodeType] = "variable-assigner"
+    LOOP: ClassVar[NodeType] = "loop"
+    LOOP_START: ClassVar[NodeType] = "loop-start"
+    LOOP_END: ClassVar[NodeType] = "loop-end"
+    ITERATION: ClassVar[NodeType] = "iteration"
+    ITERATION_START: ClassVar[NodeType] = "iteration-start"
+    PARAMETER_EXTRACTOR: ClassVar[NodeType] = "parameter-extractor"
+    VARIABLE_ASSIGNER: ClassVar[NodeType] = "assigner"
+    DOCUMENT_EXTRACTOR: ClassVar[NodeType] = "document-extractor"
+    LIST_OPERATOR: ClassVar[NodeType] = "list-operator"
+    AGENT: ClassVar[NodeType] = "agent"
+    HUMAN_INPUT: ClassVar[NodeType] = "human-input"
+
+
+BUILT_IN_NODE_TYPES: tuple[NodeType, ...] = (
+    BuiltinNodeTypes.START,
+    BuiltinNodeTypes.END,
+    BuiltinNodeTypes.ANSWER,
+    BuiltinNodeTypes.LLM,
+    BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL,
+    BuiltinNodeTypes.IF_ELSE,
+    BuiltinNodeTypes.CODE,
+    BuiltinNodeTypes.TEMPLATE_TRANSFORM,
+    BuiltinNodeTypes.QUESTION_CLASSIFIER,
+    BuiltinNodeTypes.HTTP_REQUEST,
+    BuiltinNodeTypes.TOOL,
+    BuiltinNodeTypes.DATASOURCE,
+    BuiltinNodeTypes.VARIABLE_AGGREGATOR,
+    BuiltinNodeTypes.LEGACY_VARIABLE_AGGREGATOR,
+    BuiltinNodeTypes.LOOP,
+    BuiltinNodeTypes.LOOP_START,
+    BuiltinNodeTypes.LOOP_END,
+    BuiltinNodeTypes.ITERATION,
+    BuiltinNodeTypes.ITERATION_START,
+    BuiltinNodeTypes.PARAMETER_EXTRACTOR,
+    BuiltinNodeTypes.VARIABLE_ASSIGNER,
+    BuiltinNodeTypes.DOCUMENT_EXTRACTOR,
+    BuiltinNodeTypes.LIST_OPERATOR,
+    BuiltinNodeTypes.AGENT,
+    BuiltinNodeTypes.HUMAN_INPUT,
+)
 
 
 class NodeExecutionType(StrEnum):
@@ -236,7 +252,6 @@ class WorkflowNodeExecutionMetadataKey(StrEnum):
     CURRENCY = "currency"
     TOOL_INFO = "tool_info"
     AGENT_LOG = "agent_log"
-    TRIGGER_INFO = "trigger_info"
     ITERATION_ID = "iteration_id"
     ITERATION_INDEX = "iteration_index"
     LOOP_ID = "loop_id"

api/dify_graph/graph/graph.py

@@ -83,50 +83,6 @@ class Graph:
 
         return node_configs_map
 
-    @classmethod
-    def _find_root_node_id(
-        cls,
-        node_configs_map: Mapping[str, NodeConfigDict],
-        edge_configs: Sequence[Mapping[str, object]],
-        root_node_id: str | None = None,
-    ) -> str:
-        """
-        Find the root node ID if not specified.
-
-        :param node_configs_map: mapping of node ID to node config
-        :param edge_configs: list of edge configurations
-        :param root_node_id: explicitly specified root node ID
-        :return: determined root node ID
-        """
-        if root_node_id:
-            if root_node_id not in node_configs_map:
-                raise ValueError(f"Root node id {root_node_id} not found in the graph")
-            return root_node_id
-
-        # Find nodes with no incoming edges
-        nodes_with_incoming: set[str] = set()
-        for edge_config in edge_configs:
-            target = edge_config.get("target")
-            if isinstance(target, str):
-                nodes_with_incoming.add(target)
-
-        root_candidates = [nid for nid in node_configs_map if nid not in nodes_with_incoming]
-
-        # Prefer START node if available
-        start_node_id = None
-        for nid in root_candidates:
-            node_data = node_configs_map[nid]["data"]
-            if node_data.type.is_start_node:
-                start_node_id = nid
-                break
-
-        root_node_id = start_node_id or (root_candidates[0] if root_candidates else None)
-
-        if not root_node_id:
-            raise ValueError("Unable to determine root node ID")
-
-        return root_node_id
-
     @classmethod
     def _build_edges(
         cls, edge_configs: list[dict[str, object]]
@@ -301,15 +257,15 @@ class Graph:
         *,
         graph_config: Mapping[str, object],
         node_factory: NodeFactory,
-        root_node_id: str | None = None,
+        root_node_id: str,
         skip_validation: bool = False,
     ) -> Graph:
         """
-        Initialize graph
+        Initialize a graph with an explicit execution entry point.
 
         :param graph_config: graph config containing nodes and edges
         :param node_factory: factory for creating node instances from config data
-        :param root_node_id: root node id
+        :param root_node_id: active root node id
         :return: graph instance
         """
         # Parse configs
@@ -327,8 +283,8 @@ class Graph:
         # Parse node configurations
         node_configs_map = cls._parse_node_configs(node_configs)
 
-        # Find root node
-        root_node_id = cls._find_root_node_id(node_configs_map, edge_configs, root_node_id)
+        if root_node_id not in node_configs_map:
+            raise ValueError(f"Root node id {root_node_id} not found in the graph")
 
         # Build edges
         edges, in_edges, out_edges = cls._build_edges(edge_configs)

api/dify_graph/graph/validation.py

@@ -4,7 +4,7 @@ from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Protocol
 
-from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.enums import BuiltinNodeTypes, NodeExecutionType, NodeType
 
 if TYPE_CHECKING:
     from .graph import Graph
@@ -71,7 +71,7 @@ class _RootNodeValidator:
     """Validates root node invariants."""
 
     invalid_root_code: str = "INVALID_ROOT"
-    container_entry_types: tuple[NodeType, ...] = (NodeType.ITERATION_START, NodeType.LOOP_START)
+    container_entry_types: tuple[NodeType, ...] = (BuiltinNodeTypes.ITERATION_START, BuiltinNodeTypes.LOOP_START)
 
     def validate(self, graph: Graph) -> Sequence[GraphValidationIssue]:
         root_node = graph.root_node
@@ -86,7 +86,7 @@ class _RootNodeValidator:
             )
             return issues
 
-        node_type = getattr(root_node, "node_type", None)
+        node_type = root_node.node_type
         if root_node.execution_type != NodeExecutionType.ROOT and node_type not in self.container_entry_types:
             issues.append(
                 GraphValidationIssue(
@@ -114,45 +114,9 @@ class GraphValidator:
             raise GraphValidationError(issues)
 
 
-@dataclass(frozen=True, slots=True)
-class _TriggerStartExclusivityValidator:
-    """Ensures trigger nodes do not coexist with UserInput (start) nodes."""
-
-    conflict_code: str = "TRIGGER_START_NODE_CONFLICT"
-
-    def validate(self, graph: Graph) -> Sequence[GraphValidationIssue]:
-        start_node_id: str | None = None
-        trigger_node_ids: list[str] = []
-
-        for node in graph.nodes.values():
-            node_type = getattr(node, "node_type", None)
-            if not isinstance(node_type, NodeType):
-                continue
-
-            if node_type == NodeType.START:
-                start_node_id = node.id
-            elif node_type.is_trigger_node:
-                trigger_node_ids.append(node.id)
-
-        if start_node_id and trigger_node_ids:
-            trigger_list = ", ".join(trigger_node_ids)
-            return [
-                GraphValidationIssue(
-                    code=self.conflict_code,
-                    message=(
-                        f"UserInput (start) node '{start_node_id}' cannot coexist with trigger nodes: {trigger_list}."
-                    ),
-                    node_id=start_node_id,
-                )
-            ]
-
-        return []
-
-
 _DEFAULT_RULES: tuple[GraphValidationRule, ...] = (
     _EdgeEndpointValidator(),
     _RootNodeValidator(),
-    _TriggerStartExclusivityValidator(),
 )
 
 

api/dify_graph/graph_engine/response_coordinator/__init__.py

@@ -6,5 +6,6 @@ of responses based on upstream node outputs and constants.
 """
 
 from .coordinator import ResponseStreamCoordinator
+from .session import RESPONSE_SESSION_NODE_TYPES
 
-__all__ = ["ResponseStreamCoordinator"]
+__all__ = ["RESPONSE_SESSION_NODE_TYPES", "ResponseStreamCoordinator"]

api/dify_graph/graph_engine/response_coordinator/session.py

@@ -3,19 +3,34 @@ Internal response session management for response coordinator.
 
 This module contains the private ResponseSession class used internally
 by ResponseStreamCoordinator to manage streaming sessions.
+
+`RESPONSE_SESSION_NODE_TYPES` is intentionally mutable so downstream applications
+can opt additional response-capable node types into session creation without
+patching the coordinator.
 """
 
 from __future__ import annotations
 
 from dataclasses import dataclass
+from typing import Protocol, cast
 
-from dify_graph.nodes.answer.answer_node import AnswerNode
+from dify_graph.enums import BuiltinNodeTypes, NodeType
 from dify_graph.nodes.base.template import Template
-from dify_graph.nodes.end.end_node import EndNode
-from dify_graph.nodes.knowledge_index import KnowledgeIndexNode
 from dify_graph.runtime.graph_runtime_state import NodeProtocol
 
 
+class _ResponseSessionNodeProtocol(NodeProtocol, Protocol):
+    """Structural contract required from nodes that can open a response session."""
+
+    def get_streaming_template(self) -> Template: ...
+
+
+RESPONSE_SESSION_NODE_TYPES: list[NodeType] = [
+    BuiltinNodeTypes.ANSWER,
+    BuiltinNodeTypes.END,
+]
+
+
 @dataclass
 class ResponseSession:
     """
@@ -33,10 +48,9 @@ class ResponseSession:
         """
         Create a ResponseSession from a response-capable node.
 
-        The parameter is typed as `NodeProtocol` because the graph is exposed behind a protocol at the runtime layer,
-        but at runtime this must be an `AnswerNode`, `EndNode`, or `KnowledgeIndexNode` that provides:
-        - `id: str`
-        - `get_streaming_template() -> Template`
+        The parameter is typed as `NodeProtocol` because the graph is exposed behind a protocol at the runtime layer.
+        At runtime this must be a node whose `node_type` is listed in `RESPONSE_SESSION_NODE_TYPES`
+        and which implements `get_streaming_template()`.
 
         Args:
             node: Node from the materialized workflow graph.
@@ -47,11 +61,22 @@ class ResponseSession:
         Raises:
             TypeError: If node is not a supported response node type.
         """
-        if not isinstance(node, AnswerNode | EndNode | KnowledgeIndexNode):
-            raise TypeError("ResponseSession.from_node only supports AnswerNode, EndNode, or KnowledgeIndexNode")
+        if node.node_type not in RESPONSE_SESSION_NODE_TYPES:
+            supported_node_types = ", ".join(RESPONSE_SESSION_NODE_TYPES)
+            raise TypeError(
+                "ResponseSession.from_node only supports node types in "
+                f"RESPONSE_SESSION_NODE_TYPES: {supported_node_types}"
+            )
+
+        response_node = cast(_ResponseSessionNodeProtocol, node)
+        try:
+            template = response_node.get_streaming_template()
+        except AttributeError as exc:
+            raise TypeError("ResponseSession.from_node requires get_streaming_template() on response nodes") from exc
+
         return cls(
             node_id=node.id,
-            template=node.get_streaming_template(),
+            template=template,
         )
 
     def is_complete(self) -> bool:

api/dify_graph/node_events/node.py

@@ -1,9 +1,9 @@
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from datetime import datetime
+from typing import Any
 
 from pydantic import Field
 
-from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from dify_graph.entities.pause_reason import PauseReason
 from dify_graph.file import File
 from dify_graph.model_runtime.entities.llm_entities import LLMUsage
@@ -13,7 +13,7 @@ from .base import NodeEventBase
 
 
 class RunRetrieverResourceEvent(NodeEventBase):
-    retriever_resources: Sequence[RetrievalSourceMetadata] = Field(..., description="retriever resources")
+    retriever_resources: Sequence[Mapping[str, Any]] = Field(..., description="retriever resources")
     context: str = Field(..., description="context")
     context_files: list[File] | None = Field(default=None, description="context files")