chore: apply ty checks on api code with script and ci action (#24653)

api/core/rag/retrieval/router/multi_dataset_function_call_router.py

@@ -1,4 +1,4 @@
-from typing import Union, cast
+from typing import Union
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.model_manager import ModelInstance
@@ -28,14 +28,11 @@ class FunctionCallMultiDatasetRouter:
                 SystemPromptMessage(content="You are a helpful AI assistant."),
                 UserPromptMessage(content=query),
             ]
-            result = cast(
-                LLMResult,
-                model_instance.invoke_llm(
-                    prompt_messages=prompt_messages,
-                    tools=dataset_tools,
-                    stream=False,
-                    model_parameters={"temperature": 0.2, "top_p": 0.3, "max_tokens": 1500},
-                ),
+            result: LLMResult = model_instance.invoke_llm(
+                prompt_messages=prompt_messages,
+                tools=dataset_tools,
+                stream=False,
+                model_parameters={"temperature": 0.2, "top_p": 0.3, "max_tokens": 1500},
             )
             if result.message.tool_calls:
                 # get retrieval model config

api/core/rag/retrieval/router/multi_dataset_react_route.py

@@ -1,5 +1,5 @@
 from collections.abc import Generator, Sequence
-from typing import Union, cast
+from typing import Union
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.model_manager import ModelInstance
@@ -150,15 +150,12 @@ class ReactMultiDatasetRouter:
         :param stop: stop
         :return:
         """
-        invoke_result = cast(
-            Generator[LLMResult, None, None],
-            model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=completion_param,
-                stop=stop,
-                stream=True,
-                user=user_id,
-            ),
+        invoke_result: Generator[LLMResult, None, None] = model_instance.invoke_llm(
+            prompt_messages=prompt_messages,
+            model_parameters=completion_param,
+            stop=stop,
+            stream=True,
+            user=user_id,
         )
 
         # handle invoke result