Fix: surface workflow container LLM usage (#27021)

api/core/rag/retrieval/dataset_retrieval.py

@@ -72,6 +72,19 @@ default_retrieval_model: dict[str, Any] = {
 class DatasetRetrieval:
     def __init__(self, application_generate_entity=None):
         self.application_generate_entity = application_generate_entity
+        self._llm_usage = LLMUsage.empty_usage()
+
+    @property
+    def llm_usage(self) -> LLMUsage:
+        return self._llm_usage.model_copy()
+
+    def _record_usage(self, usage: LLMUsage | None) -> None:
+        if usage is None or usage.total_tokens <= 0:
+            return
+        if self._llm_usage.total_tokens == 0:
+            self._llm_usage = usage
+        else:
+            self._llm_usage = self._llm_usage.plus(usage)
 
     def retrieve(
         self,
@@ -312,15 +325,18 @@ class DatasetRetrieval:
             )
             tools.append(message_tool)
         dataset_id = None
+        router_usage = LLMUsage.empty_usage()
         if planning_strategy == PlanningStrategy.REACT_ROUTER:
             react_multi_dataset_router = ReactMultiDatasetRouter()
-            dataset_id = react_multi_dataset_router.invoke(
+            dataset_id, router_usage = react_multi_dataset_router.invoke(
                 query, tools, model_config, model_instance, user_id, tenant_id
             )
 
         elif planning_strategy == PlanningStrategy.ROUTER:
             function_call_router = FunctionCallMultiDatasetRouter()
-            dataset_id = function_call_router.invoke(query, tools, model_config, model_instance)
+            dataset_id, router_usage = function_call_router.invoke(query, tools, model_config, model_instance)
+
+        self._record_usage(router_usage)
 
         if dataset_id:
             # get retrieval model config
@@ -983,7 +999,8 @@ class DatasetRetrieval:
             )
 
             # handle invoke result
-            result_text, _ = self._handle_invoke_result(invoke_result=invoke_result)
+            result_text, usage = self._handle_invoke_result(invoke_result=invoke_result)
+            self._record_usage(usage)
 
             result_text_json = parse_and_check_json_markdown(result_text, [])
             automatic_metadata_filters = []

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

@@ -2,7 +2,7 @@ from typing import Union
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.model_manager import ModelInstance
-from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.entities.message_entities import PromptMessageTool, SystemPromptMessage, UserPromptMessage
 
 
@@ -13,15 +13,15 @@ class FunctionCallMultiDatasetRouter:
         dataset_tools: list[PromptMessageTool],
         model_config: ModelConfigWithCredentialsEntity,
         model_instance: ModelInstance,
-    ) -> Union[str, None]:
+    ) -> tuple[Union[str, None], LLMUsage]:
         """Given input, decided what to do.
         Returns:
             Action specifying what tool to use.
         """
         if len(dataset_tools) == 0:
-            return None
+            return None, LLMUsage.empty_usage()
         elif len(dataset_tools) == 1:
-            return dataset_tools[0].name
+            return dataset_tools[0].name, LLMUsage.empty_usage()
 
         try:
             prompt_messages = [
@@ -34,9 +34,10 @@ class FunctionCallMultiDatasetRouter:
                 stream=False,
                 model_parameters={"temperature": 0.2, "top_p": 0.3, "max_tokens": 1500},
             )
+            usage = result.usage or LLMUsage.empty_usage()
             if result.message.tool_calls:
                 # get retrieval model config
-                return result.message.tool_calls[0].function.name
-            return None
+                return result.message.tool_calls[0].function.name, usage
+            return None, usage
         except Exception:
-            return None
+            return None, LLMUsage.empty_usage()

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

@@ -58,15 +58,15 @@ class ReactMultiDatasetRouter:
         model_instance: ModelInstance,
         user_id: str,
         tenant_id: str,
-    ) -> Union[str, None]:
+    ) -> tuple[Union[str, None], LLMUsage]:
         """Given input, decided what to do.
         Returns:
             Action specifying what tool to use.
         """
         if len(dataset_tools) == 0:
-            return None
+            return None, LLMUsage.empty_usage()
         elif len(dataset_tools) == 1:
-            return dataset_tools[0].name
+            return dataset_tools[0].name, LLMUsage.empty_usage()
 
         try:
             return self._react_invoke(
@@ -78,7 +78,7 @@ class ReactMultiDatasetRouter:
                 tenant_id=tenant_id,
             )
         except Exception:
-            return None
+            return None, LLMUsage.empty_usage()
 
     def _react_invoke(
         self,
@@ -91,7 +91,7 @@ class ReactMultiDatasetRouter:
         prefix: str = PREFIX,
         suffix: str = SUFFIX,
         format_instructions: str = FORMAT_INSTRUCTIONS,
-    ) -> Union[str, None]:
+    ) -> tuple[Union[str, None], LLMUsage]:
         prompt: Union[list[ChatModelMessage], CompletionModelPromptTemplate]
         if model_config.mode == "chat":
             prompt = self.create_chat_prompt(
@@ -120,7 +120,7 @@ class ReactMultiDatasetRouter:
             memory=None,
             model_config=model_config,
         )
-        result_text, _ = self._invoke_llm(
+        result_text, usage = self._invoke_llm(
             completion_param=model_config.parameters,
             model_instance=model_instance,
             prompt_messages=prompt_messages,
@@ -131,8 +131,8 @@ class ReactMultiDatasetRouter:
         output_parser = StructuredChatOutputParser()
         react_decision = output_parser.parse(result_text)
         if isinstance(react_decision, ReactAction):
-            return react_decision.tool
-        return None
+            return react_decision.tool, usage
+        return None, usage
 
     def _invoke_llm(
         self,