116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
import py_trees as ptree
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from robowaiter.behavior_lib._base.Act import Act
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from robowaiter.llm_client.multi_rounds import ask_llm, new_history
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import random
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import spacy
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nlp = spacy.load('en_core_web_lg')
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class DealChat(Act):
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def __init__(self):
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super().__init__()
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self.chat_history = ""
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self.function_success = False
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self.func_map = {"create_sub_task": self.create_sub_task, "get_object_info": self.get_object_info, "find_location": self.find_location}
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def _update(self) -> ptree.common.Status:
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# if self.scene.status?
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name, sentence = self.scene.state['chat_list'].pop(0)
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if name == "Goal":
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self.create_sub_task(goal=sentence)
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return ptree.common.Status.RUNNING
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if name not in self.scene.state["chat_history"]:
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self.scene.state["chat_history"][name] = new_history()
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history = self.scene.state["chat_history"][name]
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self.scene.state["attention"]["customer"] = name
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self.scene.state["serve_state"] = {"last_chat_time": self.scene.time, }
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function_call, response = ask_llm(sentence, history, func_map=self.func_map)
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self.scene.chat_bubble(response) # 机器人输出对话
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return ptree.common.Status.RUNNING
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def create_sub_task(self, **args):
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try:
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goal = args['goal']
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w = goal.split(")")
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goal_set = set()
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goal_set.add(w[0] + ")")
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if len(w) > 1:
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for x in w[1:]:
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if x != "":
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goal_set.add(x[1:] + ")")
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self.function_success = True
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except:
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print("参数解析错误")
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self.scene.robot.expand_sub_task_tree(goal_set)
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def get_object_info(self, **args):
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try:
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obj = args['obj']
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self.function_success = True
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except:
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obj = None
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print("参数解析错误")
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near_object = "None"
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# 场景中现有物品
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cur_things = set()
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for item in self.status.objects:
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cur_things.add(item.name)
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# obj与现有物品进行相似度匹配
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query_token = nlp(obj)
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for w in self.all_loc_en:
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word_token = nlp(w)
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similarity = query_token.similarity(word_token)
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if similarity > max_similarity:
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max_similarity = similarity
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similar_word = w
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print("max_similarity:",max_similarity,"similar_word:",similar_word)
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if similar_word: # 存在同义词说明场景中存在该物品
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near_object = random.choices(list(cur_things), k=5) # 返回场景中的5个物品
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if obj == "洗手间":
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near_object = "大门"
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return near_object
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def find_location(self, **args):
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try:
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location = args['obj']
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self.function_success = True
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except:
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obj = None
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print("参数解析错误")
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near_location = None
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# 用户咨询的地点
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query_token = nlp(location)
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max_similarity = 0
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similar_word = None
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# 到自己维护的地点列表中找同义词
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for w in self.all_loc_en:
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word_token = nlp(w)
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similarity = query_token.similarity(word_token)
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if similarity > max_similarity:
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max_similarity = similarity
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similar_word = w
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print("similarity:", max_similarity, "similar_word:", similar_word)
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# 存在同义词说明客户咨询的地点有效
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if similar_word:
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mp = list(self.loc_map_en[similar_word])
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near_location = random.choice(mp)
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return near_location
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