fix: require function declarations to be present for setting gemini tool config

Co-authored-by: Jin Peng <jinjpeng@Jins-MacBook-Pro.local>

letta/llm_api/google_vertex_client.py

@@ -239,7 +239,7 @@ class GoogleVertexClient(LLMClientBase):
             request_data["config"]["response_mime_type"] = "application/json"
             request_data["config"]["response_schema"] = self.get_function_call_response_schema(tools[0])
             del request_data["config"]["tools"]
-        else:
+        elif tools:
             tool_config = ToolConfig(
                 function_calling_config=FunctionCallingConfig(
                     # ANY mode forces the model to predict only function calls

letta/services/summarizer/summarizer.py

@@ -348,12 +348,9 @@ async def simple_summary(messages: List[Message], llm_config: LLMConfig, actor:
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": summary_transcript},
         ]
-    print("messages going to summarizer:", input_messages)
     input_messages_obj = [simple_message_wrapper(msg) for msg in input_messages]
-    print("messages going to summarizer (objs):", input_messages_obj)
-
     request_data = llm_client.build_request_data(input_messages_obj, llm_config, tools=[])
-    print("request data:", request_data)
+
     # NOTE: we should disable the inner_thoughts_in_kwargs here, because we don't use it
     # I'm leaving it commented it out for now for safety but is fine assuming the var here is a copy not a reference
     # llm_config.put_inner_thoughts_in_kwargs = False