在tool_api_multi_round.py中可以进行多轮对话
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ChenXL97
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@ -32,6 +32,7 @@ functions = get_tools()
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def run_conversation(query: str, stream=False, max_retry=5):
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params = dict(model="chatglm3", messages=[{"role": "user", "content": query}], stream=stream)
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params["functions"] = functions
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print(params)
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response = get_response(**params)
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for _ in range(max_retry):
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@ -0,0 +1,78 @@
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import json
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import openai
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from colorama import init, Fore
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from loguru import logger
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import json
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from robowaiter.llm_client.tool_register import get_tools, dispatch_tool
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import requests
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import json
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import urllib3
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init(autoreset=True)
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########################################
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# 该文件实现了与大模型的通信以及工具调用
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########################################
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# 忽略https的安全性警告
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
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def get_response(**kwargs):
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data = kwargs
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response = requests.post(f"{base_url}/v1/chat/completions", json=data, stream=data["stream"], verify=False)
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decoded_line = response.json()
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return decoded_line
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functions = get_tools()
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if __name__ == "__main__":
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question = input("\n顾客：")
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data_memory = [{
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"role": "system",
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"content": "你是RoboWaiter,一个由HPCL团队开发的机器人服务员，你在咖啡厅工作。接受顾客的指令并调用工具函数来完成各种服务任务。",
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},]
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n = 1
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max_retry = 5
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params = dict(model="RoboWaiter",messages=data_memory, stream=False)
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params["functions"] = functions
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while question != 'end':
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user_dict = {"role": "user", "content": question}
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params["messages"].append(user_dict)
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# print(data_memory)
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response = get_response(**params)
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for _ in range(max_retry):
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if response["choices"][0]["message"].get("function_call"):
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function_call = response["choices"][0]["message"]["function_call"]
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logger.info(f"Function Call Response: {function_call}")
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function_args = json.loads(function_call["arguments"])
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tool_response = dispatch_tool(function_call["name"], function_args)
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logger.info(f"Tool Call Response: {tool_response}")
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return_message = response["choices"][0]["message"]
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params["messages"].append(return_message)
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t = {
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"role": "function",
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"name": function_call["name"],
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"content": tool_response, # 调用函数返回结果
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}
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params["messages"].append(t)
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response = get_response(**params)
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else:
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return_message = response["choices"][0]["message"]
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reply = return_message["content"]
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params["messages"].append(return_message)
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logger.info(f"Final Reply: \n{reply}")
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break
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question = input("\n顾客：")
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@ -0,0 +1,74 @@
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import json
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import openai
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from colorama import init, Fore
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from loguru import logger
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import json
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from robowaiter.llm_client.tool_register import get_tools, dispatch_tool
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import requests
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import json
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import urllib3
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init(autoreset=True)
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########################################
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# 该文件实现了与大模型的通信以及工具调用
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########################################
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# 忽略https的安全性警告
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
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def get_response(**kwargs):
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data = kwargs
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response = requests.post(f"{base_url}/v1/chat/completions", json=data, stream=data["stream"], verify=False)
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decoded_line = response.json()
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return decoded_line
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functions = get_tools()
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def run_conversation(query: str, stream=False, max_retry=5):
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params = dict(model="chatglm3", messages=[{"role": "user", "content": query}], stream=stream)
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params["functions"] = functions
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response = get_response(**params)
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for _ in range(max_retry):
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if response["choices"][0]["message"].get("function_call"):
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function_call = response["choices"][0]["message"]["function_call"]
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logger.info(f"Function Call Response: {function_call}")
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if "sub_task" in function_call["name"]:
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return {
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"Answer": "好的",
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"Goal": json.loads(function_call["arguments"])["goal"]
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}
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function_args = json.loads(function_call["arguments"])
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tool_response = dispatch_tool(function_call["name"], function_args)
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logger.info(f"Tool Call Response: {tool_response}")
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params["messages"].append(response["choices"][0]["message"])
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params["messages"].append(
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{
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"role": "function",
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"name": function_call["name"],
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"content": tool_response, # 调用函数返回结果
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}
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)
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else:
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reply = response["choices"][0]["message"]["content"]
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return {
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"Answer": reply,
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"Goal": None
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}
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logger.info(f"Final Reply: \n{reply}")
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return
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response = get_response(**params)
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if __name__ == "__main__":
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query = "可以带我去吗"
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print(run_conversation(query, stream=False))
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@ -132,8 +132,13 @@ def create_sub_task(
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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），用户意图
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做一杯咖啡,则该函数的参数为 "On(Coffee,Bar)",
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前往一号桌,则该函数的参数为 "At(Robot,Table1)",
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打开空调,则该函数的参数为 "Is(AC,On)",。
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关空调,则该函数的参数为 "Is(AC,Off)",。
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前往二号桌,则该函数的参数为 "At(Robot,Table2)",
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打开空调,则该函数的参数为 "Is(AC,On)",
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关空调,则该函数的参数为 "Is(AC,Off)",
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打开窗帘,则该函数的参数为 "Is(Curtain,On)",
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关闭窗帘,则该函数的参数为 "Is(Curtain,Off)",
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拖地,则该函数的参数为 "Is(Floor,Clean)",
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打开大厅灯,则该函数的参数为 "Is(HallLight,On)",
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"""
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return goal
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@ -143,9 +148,9 @@ def get_object_info(
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obj: Annotated[str, '需要获取信息的物体名称', True]
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) -> str:
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"""
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获取场景中指定物体 `object` 在哪里，
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如果`object` 是一个地点，例如洗手间，地方，则输出。
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如果`object`是一个咖啡，则输出。
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获取场景中指定物体 `object` 在哪里，不涉及到具体的执行任务
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如果`object` 是一个地点，例如洗手间，则输出大门。
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如果`object`是咖啡，则输出桌子，咖啡在桌子上。
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如果`object` 是空桌子，则输出一号桌
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"""
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near_object = None
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@ -16,8 +16,9 @@ class SceneAT(Scene):
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super().__init__(robot)
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def _reset(self):
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self.add_walker(1085, 2630, 220)
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self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
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# self.add_walker(1085, 2630, 220)
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# self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
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pass
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def _run(self):
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@ -22,8 +22,8 @@ class SceneGQA(Scene):
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def _reset(self):
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# self.clean_walker()
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self.add_walkers()
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# self.add_walkers([[50, 500,90]])
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pass
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# self.walker_bubble("洗手间在哪里")
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# self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
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@ -19,8 +19,7 @@ class SceneOT(Scene):
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super().__init__(robot)
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# 在这里加入场景中发生的事件
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self.event_list = [
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# (5,self.create_chat_event("给我一杯咖啡")) # (事件发生的时间，事件函数)
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(5, self.create_chat_event("我有点热，能开个空调吗？")) # (事件发生的时间，事件函数)
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(5, self.create_chat_event("我有点热，能开个空调吗？")),
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]
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def _reset(self):
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@ -30,7 +29,6 @@ class SceneOT(Scene):
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print("scene.walkers:",scene.walkers)
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cont = scene.walkers[0].name+":我有点热，能开个空调吗？"
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self.control_robot_action(0,3,cont)
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# self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
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pass
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def _run(self):
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@ -18,20 +18,11 @@ class SceneOT(Scene):
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super().__init__(robot)
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# 在这里加入场景中发生的事件
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self.event_list = [
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# (5,self.create_chat_event("做一杯咖啡")),
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(5,self.create_chat_event("感觉有点冷，可以关一下空调吗")),
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(5, self.create_chat_event("给我一杯咖啡"))
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]
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def _reset(self):
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scene = self.add_walkers([[50, 300, 0]])
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# time.sleep(2.0)
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# print("我有点热，能开个空调吗？")
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print("scene.walkers:",scene.walkers)
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cont = scene.walkers[0].name+":我有点热，能开个空调吗？"
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self.control_robot_action(0,3,cont)
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# self.add_walker(1085, 2630, 220)
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# self.control_walker([self.walker_control_generator(0, False, 100, 755, 1900, 180)])
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pass
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def _run(self):
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