Merge 15160646fd into a30945312a

api/core/agent/fc_agent_runner.py

@@ -32,7 +32,9 @@ logger = logging.getLogger(__name__)
 
 
 class FunctionCallAgentRunner(BaseAgentRunner):
-    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
+    def run(
+        self, message: Message, query: str, **kwargs: Any
+    ) -> Union[Generator[LLMResultChunk, None, None], LLMResult]:
         """
         Run FunctionCall agent application
         """
@@ -72,6 +74,19 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
         model_instance = self.model_instance
 
+        final_prompt_messages = None
+        final_system_fingerprint = None
+
+        def response_generator() -> Generator[LLMResultChunk, None, None]:
+            nonlocal \
+                function_call_state, \
+                function_call_state, \
+                iteration_step, \
+                prompt_messages_tools, \
+                final_answer, \
+                final_prompt_messages, \
+                final_system_fingerprint
+
             while function_call_state and iteration_step <= max_iteration_steps:
                 function_call_state = False
 
@@ -114,7 +129,8 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                     for chunk in chunks:
                         if is_first_chunk:
                             self.queue_manager.publish(
-                            QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                                QueueAgentThoughtEvent(agent_thought_id=agent_thought.id),
+                                PublishFrom.APPLICATION_MANAGER,
                             )
                             is_first_chunk = False
                         # check if there is any tool call
@@ -130,6 +146,8 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                                 # ensure ascii to avoid encoding error
                                 tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
 
+                        final_prompt_messages = chunk.prompt_messages
+                        final_system_fingerprint = chunk.system_fingerprint
                         if chunk.delta.message and chunk.delta.message.content:
                             if isinstance(chunk.delta.message.content, list):
                                 for content in chunk.delta.message.content:
@@ -161,6 +179,8 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                         increase_usage(llm_usage, result.usage)
                         current_llm_usage = result.usage
 
+                    final_prompt_messages = result.prompt_messages
+                    final_system_fingerprint = result.system_fingerprint
                     if result.message and result.message.content:
                         if isinstance(result.message.content, list):
                             for content in result.message.content:
@@ -219,7 +239,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                     QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
                 )
 
-            final_answer += response + "\n"
+                final_answer += response
 
                 # call tools
                 tool_responses = []
@@ -230,7 +250,9 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                             "tool_call_id": tool_call_id,
                             "tool_call_name": tool_call_name,
                             "tool_response": f"there is not a tool named {tool_call_name}",
-                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
+                            "meta": ToolInvokeMeta.error_instance(
+                                f"there is not a tool named {tool_call_name}"
+                            ).to_dict(),
                         }
                     else:
                         # invoke tool
@@ -314,6 +336,20 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                 PublishFrom.APPLICATION_MANAGER,
             )
 
+        chunk_generator = response_generator()
+
+        if app_generate_entity.stream:
+            return chunk_generator
+        else:
+            list(chunk_generator)
+            return LLMResult(
+                model=model_instance.model,
+                prompt_messages=final_prompt_messages or [],
+                message=AssistantPromptMessage(content=final_answer),
+                usage=llm_usage["usage"] or LLMUsage.empty_usage(),
+                system_fingerprint=final_system_fingerprint or "",
+            )
+
     def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
         """
         Check if there is any tool call in llm result chunk

api/core/app/apps/agent_chat/app_generator.py

@@ -82,9 +82,6 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         """
-        if not streaming:
-            raise ValueError("Agent Chat App does not support blocking mode")
-
         if not args.get("query"):
             raise ValueError("query is required")