feat: mypy for all type check (#10921)

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

@@ -88,8 +88,8 @@ class LLMNode(BaseNode[LLMNodeData]):
     _node_data_cls = LLMNodeData
     _node_type = NodeType.LLM
 
-    def _run(self) -> NodeRunResult | Generator[NodeEvent | InNodeEvent, None, None]:
-        node_inputs = None
+    def _run(self) -> Generator[NodeEvent | InNodeEvent, None, None]:
+        node_inputs: Optional[dict[str, Any]] = None
         process_data = None
 
         try:
@@ -196,7 +196,6 @@ class LLMNode(BaseNode[LLMNodeData]):
                     error_type=type(e).__name__,
                 )
             )
-            return
         except Exception as e:
             yield RunCompletedEvent(
                 run_result=NodeRunResult(
@@ -206,7 +205,6 @@ class LLMNode(BaseNode[LLMNodeData]):
                     process_data=process_data,
                 )
             )
-            return
 
         outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
 
@@ -302,7 +300,7 @@ class LLMNode(BaseNode[LLMNodeData]):
         return messages
 
     def _fetch_jinja_inputs(self, node_data: LLMNodeData) -> dict[str, str]:
-        variables = {}
+        variables: dict[str, Any] = {}
 
         if not node_data.prompt_config:
             return variables
@@ -319,7 +317,7 @@ class LLMNode(BaseNode[LLMNodeData]):
                 """
                 # check if it's a context structure
                 if "metadata" in input_dict and "_source" in input_dict["metadata"] and "content" in input_dict:
-                    return input_dict["content"]
+                    return str(input_dict["content"])
 
                 # else, parse the dict
                 try:
@@ -557,7 +555,8 @@ class LLMNode(BaseNode[LLMNodeData]):
         variable_pool: VariablePool,
         jinja2_variables: Sequence[VariableSelector],
     ) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
-        prompt_messages = []
+        # FIXME: fix the type error cause prompt_messages is type quick a few times
+        prompt_messages: list[Any] = []
 
         if isinstance(prompt_template, list):
             # For chat model
@@ -783,7 +782,7 @@ class LLMNode(BaseNode[LLMNodeData]):
         else:
             raise InvalidVariableTypeError(f"Invalid prompt template type: {type(prompt_template)}")
 
-        variable_mapping = {}
+        variable_mapping: dict[str, Any] = {}
         for variable_selector in variable_selectors:
             variable_mapping[variable_selector.variable] = variable_selector.value_selector
 
@@ -981,7 +980,7 @@ def _handle_memory_chat_mode(
     memory_config: MemoryConfig | None,
     model_config: ModelConfigWithCredentialsEntity,
 ) -> Sequence[PromptMessage]:
-    memory_messages = []
+    memory_messages: Sequence[PromptMessage] = []
     # Get messages from memory for chat model
     if memory and memory_config:
         rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)