修改工具调用

robowaiter/llm_client/tool_api.py

@@ -38,6 +38,12 @@ def run_conversation(query: str, stream=False, functions=None, max_retry=5):
     for _ in range(max_retry):
         if response["choices"][0]["message"].get("function_call"):
             function_call = response["choices"][0]["message"]["function_call"]
+            if "sub_task" in function_call["name"]:
+                return {
+                    "Answer": "好的",
+                    "Goal": function_call["arguments"]
+                }
+
             logger.info(f"Function Call Response: {function_call}")
             function_args = json.loads(function_call["arguments"])
             tool_response = dispatch_tool(function_call["name"], function_args)
@@ -53,31 +59,17 @@ def run_conversation(query: str, stream=False, functions=None, max_retry=5):
             )
         else:
             reply = response["choices"][0]["message"]["content"]
+            return {
+                "Answer": reply,
+                "Goal": None
+            }
             logger.info(f"Final Reply: \n{reply}")
             return
 
         response = get_response(**params)
 
 
+
 if __name__ == "__main__":
-
-    # chat_messages = [
-    #     {
-    #         "role": "system",
-    #         "content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.",
-    #     },
-    #     {
-    #         "role": "user",
-    #         "content": "你好，给我讲一个故事，大概100字"
-    #     }
-    # ]
-    # create_chat_completion("chatglm3-6b", chat_messages, use_stream=False)
-
-
-    # query = "你是谁"
-    # run_conversation(query, stream=False)
-    #
-    # logger.info("\n=========== next conversation ===========")
-
-    query = "洗手间在哪儿"
-    run_conversation(query, functions=functions, stream=False)
+    query = "给我一杯咖啡"
+    print(run_conversation(query, functions=functions, stream=False))

robowaiter/llm_client/tool_register.py

@@ -126,25 +126,25 @@ def get_tools() -> dict:
 
 @register_tool
 def create_sub_task(
-        goal: Annotated[str, '用于子任务的目标状态集合', True]
+        goal: Annotated[str, '子任务需要达到的目标条件集合', True]
 ) -> str:
     """
-    当需要完成具身任务（如做咖啡，拿放物体，扫地，前往某位置）时，调用该函数，根据用户的提示进行意图理解，生成子任务的目标状态集合，以一阶逻辑的形式来表示，例如：前往桌子的目标状态为{At(Robot,Table)}，做咖啡的目标状态为{On(Coffee,Bar)}等
+    当需要完成具身任务（如做咖啡，拿放物体，扫地，前往某位置）时，调用该函数，根据用户的提示进行意图理解，生成子任务的目标状态集合 `goal`（以一阶逻辑的形式表示）。
     """
 
     return goal
 
 @register_tool
 def get_object_info(
-        object: Annotated[str, '需要判断所在位置的物体', True]
+        obj: Annotated[str, '需要获取信息的物体名称', True]
 ) -> str:
     """
-    在场景中找到相邻的物体，并说出 `object` 在输出物体的附近
+    获取场景中指定物体 `object` 的信息
     """
     near_object = None
-    if object == "Table":
+    if obj == "Table":
         near_object = "Bar"
-    if object == "洗手间":
+    if obj == "洗手间":
         near_object = "大门"
 
     return near_object

robowaiter/scene/tasks/Open_tasks.py

@@ -18,7 +18,7 @@ class SceneOT(Scene):
         super().__init__(robot)
         # 在这里加入场景中发生的事件
         self.event_list = [
-            (5,self.create_chat_event("给我做一杯咖啡"))  # (事件发生的时间，事件函数)
+            (5,self.create_chat_event("给我一杯咖啡"))  # (事件发生的时间，事件函数)
         ]
 
     def _reset(self):

run_robowaiter.py

@@ -1,7 +1,7 @@
 import os
 from robowaiter import Robot, task_map
 
-TASK_NAME = 'VLN'
+TASK_NAME = 'OT'
 
 # create robot
 project_path = "./robowaiter"