Merge branch 'main' into feat/pull-a-variable

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import json
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, cast
@@ -167,7 +169,7 @@ class AgentNode(Node[AgentNodeData]):
         variable_pool: VariablePool,
         node_data: AgentNodeData,
         for_log: bool = False,
-        strategy: "PluginAgentStrategy",
+        strategy: PluginAgentStrategy,
     ) -> dict[str, Any]:
         """
         Generate parameters based on the given tool parameters, variable pool, and node data.
@@ -328,7 +330,7 @@ class AgentNode(Node[AgentNodeData]):
     def _generate_credentials(
         self,
         parameters: dict[str, Any],
-    ) -> "InvokeCredentials":
+    ) -> InvokeCredentials:
         """
         Generate credentials based on the given agent parameters.
         """
@@ -442,9 +444,7 @@ class AgentNode(Node[AgentNodeData]):
                     model_schema.features.remove(feature)
         return model_schema
 
-    def _filter_mcp_type_tool(
-        self, strategy: "PluginAgentStrategy", tools: list[dict[str, Any]]
-    ) -> list[dict[str, Any]]:
+    def _filter_mcp_type_tool(self, strategy: PluginAgentStrategy, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
         """
         Filter MCP type tool
         :param strategy: plugin agent strategy

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import json
 from abc import ABC
 from builtins import type as type_
@@ -111,7 +113,7 @@ class DefaultValue(BaseModel):
             raise DefaultValueTypeError(f"Cannot convert to number: {value}")
 
     @model_validator(mode="after")
-    def validate_value_type(self) -> "DefaultValue":
+    def validate_value_type(self) -> DefaultValue:
         # Type validation configuration
         type_validators = {
             DefaultValueType.STRING: {

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import importlib
 import logging
 import operator
@@ -59,7 +61,7 @@ logger = logging.getLogger(__name__)
 
 
 class Node(Generic[NodeDataT]):
-    node_type: ClassVar["NodeType"]
+    node_type: ClassVar[NodeType]
     execution_type: NodeExecutionType = NodeExecutionType.EXECUTABLE
     _node_data_type: ClassVar[type[BaseNodeData]] = BaseNodeData
 
@@ -198,14 +200,14 @@ class Node(Generic[NodeDataT]):
         return None
 
     # Global registry populated via __init_subclass__
-    _registry: ClassVar[dict["NodeType", dict[str, type["Node"]]]] = {}
+    _registry: ClassVar[dict[NodeType, dict[str, type[Node]]]] = {}
 
     def __init__(
         self,
         id: str,
         config: Mapping[str, Any],
-        graph_init_params: "GraphInitParams",
-        graph_runtime_state: "GraphRuntimeState",
+        graph_init_params: GraphInitParams,
+        graph_runtime_state: GraphRuntimeState,
     ) -> None:
         self._graph_init_params = graph_init_params
         self.id = id
@@ -241,7 +243,7 @@ class Node(Generic[NodeDataT]):
         return
 
     @property
-    def graph_init_params(self) -> "GraphInitParams":
+    def graph_init_params(self) -> GraphInitParams:
         return self._graph_init_params
 
     @property
@@ -457,7 +459,7 @@ class Node(Generic[NodeDataT]):
         raise NotImplementedError("subclasses of BaseNode must implement `version` method.")
 
     @classmethod
-    def get_node_type_classes_mapping(cls) -> Mapping["NodeType", Mapping[str, type["Node"]]]:
+    def get_node_type_classes_mapping(cls) -> Mapping[NodeType, Mapping[str, type[Node]]]:
         """Return mapping of NodeType -> {version -> Node subclass} using __init_subclass__ registry.
 
         Import all modules under core.workflow.nodes so subclasses register themselves on import.

api/core/workflow/nodes/base/template.py

@@ -4,6 +4,8 @@ This module provides a unified template structure for both Answer and End nodes,
 similar to SegmentGroup but focused on template representation without values.
 """
 
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
 from dataclasses import dataclass
@@ -58,7 +60,7 @@ class Template:
     segments: list[TemplateSegmentUnion]
 
     @classmethod
-    def from_answer_template(cls, template_str: str) -> "Template":
+    def from_answer_template(cls, template_str: str) -> Template:
         """Create a Template from an Answer node template string.
 
         Example:
@@ -107,7 +109,7 @@ class Template:
         return cls(segments=segments)
 
     @classmethod
-    def from_end_outputs(cls, outputs_config: list[dict[str, Any]]) -> "Template":
+    def from_end_outputs(cls, outputs_config: list[dict[str, Any]]) -> Template:
         """Create a Template from an End node outputs configuration.
 
         End nodes are treated as templates of concatenated variables with newlines.

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import base64
 import io
 import json
@@ -114,7 +116,7 @@ class LLMNode(Node[LLMNodeData]):
 
     # Instance attributes specific to LLMNode.
     # Output variable for file
-    _file_outputs: list["File"]
+    _file_outputs: list[File]
 
     _llm_file_saver: LLMFileSaver
 
@@ -122,8 +124,8 @@ class LLMNode(Node[LLMNodeData]):
         self,
         id: str,
         config: Mapping[str, Any],
-        graph_init_params: "GraphInitParams",
-        graph_runtime_state: "GraphRuntimeState",
+        graph_init_params: GraphInitParams,
+        graph_runtime_state: GraphRuntimeState,
         *,
         llm_file_saver: LLMFileSaver | None = None,
     ):
@@ -393,7 +395,7 @@ class LLMNode(Node[LLMNodeData]):
         structured_output_enabled: bool,
         structured_output: Mapping[str, Any] | None = None,
         file_saver: LLMFileSaver,
-        file_outputs: list["File"],
+        file_outputs: list[File],
         node_id: str,
         node_type: NodeType,
         reasoning_format: Literal["separated", "tagged"] = "tagged",
@@ -447,7 +449,7 @@ class LLMNode(Node[LLMNodeData]):
         *,
         invoke_result: LLMResult | Generator[LLMResultChunk | LLMStructuredOutput, None, None],
         file_saver: LLMFileSaver,
-        file_outputs: list["File"],
+        file_outputs: list[File],
         node_id: str,
         node_type: NodeType,
         reasoning_format: Literal["separated", "tagged"] = "tagged",
@@ -557,7 +559,7 @@ class LLMNode(Node[LLMNodeData]):
         )
 
     @staticmethod
-    def _image_file_to_markdown(file: "File", /):
+    def _image_file_to_markdown(file: File, /):
         text_chunk = f"![]({file.generate_url()})"
         return text_chunk
 
@@ -822,7 +824,7 @@ class LLMNode(Node[LLMNodeData]):
     def fetch_prompt_messages(
         *,
         sys_query: str | None = None,
-        sys_files: Sequence["File"],
+        sys_files: Sequence[File],
         context: str | None = None,
         memory: BaseMemory | None = None,
         model_config: ModelConfigWithCredentialsEntity,
@@ -833,7 +835,7 @@ class LLMNode(Node[LLMNodeData]):
         variable_pool: VariablePool,
         jinja2_variables: Sequence[VariableSelector],
         tenant_id: str,
-        context_files: list["File"] | None = None,
+        context_files: list[File] | None = None,
     ) -> tuple[Sequence[PromptMessage], Sequence[str] | None]:
         prompt_messages: list[PromptMessage] = []
 
@@ -1185,7 +1187,7 @@ class LLMNode(Node[LLMNodeData]):
         *,
         invoke_result: LLMResult | LLMResultWithStructuredOutput,
         saver: LLMFileSaver,
-        file_outputs: list["File"],
+        file_outputs: list[File],
         reasoning_format: Literal["separated", "tagged"] = "tagged",
         request_latency: float | None = None,
     ) -> ModelInvokeCompletedEvent:
@@ -1227,7 +1229,7 @@ class LLMNode(Node[LLMNodeData]):
         *,
         content: ImagePromptMessageContent,
         file_saver: LLMFileSaver,
-    ) -> "File":
+    ) -> File:
         """_save_multimodal_output saves multi-modal contents generated by LLM plugins.
 
         There are two kinds of multimodal outputs:
@@ -1277,7 +1279,7 @@ class LLMNode(Node[LLMNodeData]):
         *,
         contents: str | list[PromptMessageContentUnionTypes] | None,
         file_saver: LLMFileSaver,
-        file_outputs: list["File"],
+        file_outputs: list[File],
     ) -> Generator[str, None, None]:
         """Convert intermediate prompt messages into strings and yield them to the caller.