From a571b3abb277d8fc666b08aaeec4894860d44d3c Mon Sep 17 00:00:00 2001
From: Stream <Stream_2@qq.com>
Date: Wed, 28 Jan 2026 06:43:08 +0800
Subject: [PATCH] chore: fix type issues

---
 api/controllers/console/app/generator.py | 8 ++++----
 api/core/llm_generator/llm_generator.py  | 2 ++
 api/core/workflow/nodes/llm/node.py      | 7 ++++---
 3 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/api/controllers/console/app/generator.py b/api/controllers/console/app/generator.py
index 2349f16b9b..63870f8038 100644
--- a/api/controllers/console/app/generator.py
+++ b/api/controllers/console/app/generator.py
@@ -70,8 +70,8 @@ class ContextGeneratePayload(BaseModel):
     model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
     available_vars: list[AvailableVarPayload] = Field(..., description="Available variables from upstream nodes")
     parameter_info: ParameterInfoPayload = Field(..., description="Target parameter metadata from the frontend")
-    code_context: CodeContextPayload | None = Field(
-        default=None, description="Existing code node context for incremental generation"
+    code_context: CodeContextPayload = Field(
+        description="Existing code node context for incremental generation"
     )
 
 
@@ -81,8 +81,8 @@ class SuggestedQuestionsPayload(BaseModel):
     language: str = Field(
         default="English", description="Language for generated questions (e.g. English, Chinese, Japanese)"
     )
-    model_config_data: dict[str, Any] | None = Field(
-        default=None,
+    model_config_data: dict[str, Any] = Field(
+        default_factory=dict,
         alias="model_config",
         description="Model configuration (optional, uses system default if not provided)",
     )
diff --git a/api/core/llm_generator/llm_generator.py b/api/core/llm_generator/llm_generator.py
index ab24c4328e..f8741729f9 100644
--- a/api/core/llm_generator/llm_generator.py
+++ b/api/core/llm_generator/llm_generator.py
@@ -730,6 +730,8 @@ Generate {language} code to extract/transform available variables for the target
             raise ValueError("Workflow not found for the given app model.")
         last_run = workflow_service.get_node_last_run(app_model=app, workflow=workflow, node_id=node_id)
         try:
+            if not last_run:
+                raise ValueError()
             node_type = last_run.node_type
         except Exception:
             try:
diff --git a/api/core/workflow/nodes/llm/node.py b/api/core/workflow/nodes/llm/node.py
index a25e9cb1f2..924be1a2e2 100644
--- a/api/core/workflow/nodes/llm/node.py
+++ b/api/core/workflow/nodes/llm/node.py
@@ -1402,12 +1402,12 @@ class LLMNode(Node[LLMNodeData]):
         # Create typed NodeData from dict
         typed_node_data = LLMNodeData.model_validate(node_data)
 
-        prompt_template = typed_node_data.prompt_template
+        prompt_template: (Sequence[LLMNodeChatModelMessage | PromptMessageContext] |
+                          LLMNodeCompletionModelPromptTemplate) = typed_node_data.prompt_template
         variable_selectors = []
         prompt_context_selectors: list[Sequence[str]] = []
         if isinstance(prompt_template, list):
             for prompt in prompt_template:
-                prompt: LLMNodeChatModelMessage | PromptMessageContext
                 if isinstance(prompt, LLMNodeChatModelMessage) and prompt.edition_type == "jinja2":
                     variable_template_parser = VariableTemplateParser(template=prompt.text)
                     variable_selectors.extend(variable_template_parser.extract_variable_selectors())
@@ -1453,10 +1453,11 @@ class LLMNode(Node[LLMNodeData]):
 
             if isinstance(prompt_template, list):
                 for prompt in prompt_template:
-                    prompt: LLMNodeChatModelMessage | PromptMessageContext
                     if isinstance(prompt, LLMNodeChatModelMessage) and prompt.edition_type == "jinja2":
                         enable_jinja = True
                         break
+                    if isinstance(prompt, PromptMessageContext):
+                        prompt_context_selectors.append(prompt.value_selector)
             else:
                 prompt_template: LLMNodeCompletionModelPromptTemplate
                 enable_jinja = True