From 20652dda10fd88857fa47999b18e2c60cc3b9938 Mon Sep 17 00:00:00 2001 From: "yuqi.zq" Date: Fri, 20 Dec 2024 18:17:12 +0800 Subject: [PATCH] =?UTF-8?q?=E5=90=8C=E6=AD=A51220?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 21 +- docker-compose.yaml | 2 +- examples/ekg_examples/who_is_spy_game.py | 68 +- muagent/connector/memory_manager.py | 28 +- .../graph_db_handler/nebula_handler.py | 13 +- muagent/schemas/ekg/ekg_reason.py | 324 ++++ .../geabase_handler/geabase_handlerplus.py | 108 +- .../src/graph_search/geabase_search_plus.py | 820 +-------- .../src/graph_search/graph_search_main.py | 424 ++++- .../src/graph_search/task_node_agent.py | 1487 +++++++++++++++++ .../src/graph_search/task_node_prompt.py | 114 ++ .../intention_recognition_tool.py | 112 +- .../src/memory_handler/ekg_memory_handler.py | 383 ++++- .../src/question_answer/qa_function.py | 215 +-- .../ekg_reasoning/src/utils/__init__.py | 0 .../ekg_reasoning/src/utils/call_llm.py | 50 +- requirements.txt | 3 +- .../config/application-default.properties | 5 +- .../src/main/resources/log4j2-spring.xml | 8 +- .../main/resources/static/avatar/hangang.png | Bin 0 -> 171121 bytes .../main/resources/static/avatar/hezixuan.png | Bin 0 -> 149417 bytes .../main/resources/static/avatar/liangjun.png | Bin 0 -> 152035 bytes .../resources/static/avatar/shenqiang.png | Bin 0 -> 158411 bytes .../main/resources/static/avatar/zhoujie.png | Bin 0 -> 193195 bytes .../resources/static/avatar/zhouxinyi.png | Bin 0 -> 150712 bytes .../main/resources/static/avatar/zhuli.png | Bin 0 -> 171621 bytes .../resources/tools/system.llm_query.json | 49 + .../resources/tools/system.select_tool.json | 2 +- .../tools/undercover.dispatch_keyword.json | 4 +- .../tools/undercover.dispatch_position.json | 4 +- .../resources/tools/undercover.judge.json | 4 +- .../resources/tools/undercover.summary.json | 4 +- .../tools/werewolf.dispatch_keyword.json | 50 + .../tools/werewolf.dispatch_position.json | 49 + .../resources/tools/werewolf.hangang.json | 49 + .../resources/tools/werewolf.hezixuan.json | 49 + .../main/resources/tools/werewolf.judge.json | 49 + .../resources/tools/werewolf.liangjun.json | 49 + .../resources/tools/werewolf.shenqiang.json | 49 + .../resources/tools/werewolf.summary.json | 49 + .../resources/tools/werewolf.zhoujie.json | 49 + .../resources/tools/werewolf.zhouxinyi.json | 49 + .../main/resources/tools/werewolf.zhuli.json | 49 + .../chat/content/RoleResponseContent.java | 56 + .../exception/EkgToolNotFindException.java | 7 + .../model/scheduler/SubmitTaskRequest.java | 2 + .../service/chat/impl/EkgChatServiceImpl.java | 212 ++- .../service/sheduler/impl/BaseScheduler.java | 24 +- .../com/alipay/muagent/util/StringUtils.java | 4 + .../muagent/web/interceptor/WebConfigure.java | 2 +- tests/service/test_main_sswd_long.py | 153 +- 51 files changed, 4099 insertions(+), 1153 deletions(-) create mode 100644 muagent/schemas/ekg/ekg_reason.py create mode 100644 muagent/service/ekg_reasoning/src/graph_search/task_node_agent.py create mode 100644 muagent/service/ekg_reasoning/src/graph_search/task_node_prompt.py create mode 100644 muagent/service/ekg_reasoning/src/utils/__init__.py create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/hangang.png create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/hezixuan.png create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/liangjun.png create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/shenqiang.png create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/zhoujie.png create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/zhouxinyi.png create mode 100644 runtime/bootstrap/src/main/resources/static/avatar/zhuli.png create mode 100644 runtime/bootstrap/src/main/resources/tools/system.llm_query.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_keyword.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_position.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.hangang.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.hezixuan.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.judge.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.liangjun.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.shenqiang.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.summary.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.zhoujie.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.zhouxinyi.json create mode 100644 runtime/bootstrap/src/main/resources/tools/werewolf.zhuli.json create mode 100644 runtime/model/src/main/java/com/alipay/muagent/model/exception/EkgToolNotFindException.java diff --git a/.gitignore b/.gitignore index fe0c959..6b59328 100644 --- a/.gitignore +++ b/.gitignore @@ -21,6 +21,8 @@ zdatafront* *log examples/mysql/db + + # frontend frontend/node_modules frontend/.env.local @@ -37,4 +39,21 @@ frontend/.mfsu frontend/.swc frontend/pnpm-lock.yaml -*.jar \ No newline at end of file +*.jar + + + + +.spyproject/ +model_config.py +model_config_example.py +config.py +**/service_onlyant +**/ekg_test +**/generalization_reasoning +ekg.yaml +*.ipynb +**/web_operation +examples/mysql/db +tests/service/test_* +tests/service/replacements.py \ No newline at end of file diff --git a/docker-compose.yaml b/docker-compose.yaml index 27f4262..84cbafd 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -149,7 +149,7 @@ services: mysql: - image: mysql:8.0.23 + image: mysql:8.4.3 container_name: mysql environment: MYSQL_ROOT_PASSWORD: 'root' diff --git a/examples/ekg_examples/who_is_spy_game.py b/examples/ekg_examples/who_is_spy_game.py index 970242d..309da56 100644 --- a/examples/ekg_examples/who_is_spy_game.py +++ b/examples/ekg_examples/who_is_spy_game.py @@ -129,36 +129,46 @@ def hash_id(nodeId, sessionId='', otherstr = None): new_nodes_2 = \ [GNode(id='剧本杀/谁是卧底', type='opsgptkg_intent', attributes={'ID': -5201231166222141228, 'teamids': '', 'gdb_timestamp': '1725088421109', 'description': '谁是卧底', 'name': '谁是卧底', 'extra': ''}), - GNode(id='剧本杀/狼人杀', type='opsgptkg_intent', attributes={'ID': 5476827419397129797, 'description': '狼人杀', 'name': '狼人杀', 'extra': '', 'teamids': '', 'gdb_timestamp': '1724815561170'}), - GNode(id='剧本杀/谁是卧底/智能交互', type='opsgptkg_schedule', attributes={'ID': 603563742932974030, 'extra': '', 'teamids': '', 'gdb_timestamp': '1725088469126', 'description': '智能交互', 'name': '智能交互', 'enable': 'True'}), - GNode(id='剧本杀/狼人杀/智能交互', type='opsgptkg_schedule', attributes={'ID': -5931163481230280444, 'extra': '', 'teamids': '', 'gdb_timestamp': '1724815624907', 'description': '智能交互', 'name': '智能交互', 'enable': 'False'}), + GNode(id='剧本杀/谁是卧底/智能交互', type='opsgptkg_schedule', attributes={'ID': 603563742932974030, 'extra': '', 'teamids': '', 'gdb_timestamp': '1725088469126', 'description': '智能交互', 'name': '智能交互', 'enable': 'True', 'envdescription': '{"存活的玩家": "张伟、王鹏、李静、人类玩家"}'}), GNode(id='剧本杀/谁是卧底/智能交互/分配座位', type='opsgptkg_task', attributes={'ID': 2011080219630105469, 'extra': '{"dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728912109030', 'executetype': '', 'description': '分配座位', 'name': '分配座位', 'accesscriteria': ''}), - GNode(id='剧本杀/狼人杀/智能交互/位置选择', type='opsgptkg_task', attributes={'ID': 2541178858602010284, 'description': '位置选择', 'name': '位置选择', 'accesscriteria': '', 'extra': '{"memory_tag": "all"}', 'teamids': '', 'gdb_timestamp': '1724849735167', 'executetype': ''}), GNode(id='剧本杀/谁是卧底/智能交互/角色分配和单词分配', type='opsgptkg_task', attributes={'ID': -1817533533893637377, 'accesscriteria': '', 'extra': '{"memory_tag": "None","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728912123682', 'executetype': '', 'description': '角色分配和单词分配', 'name': '角色分配和单词分配'}), - GNode(id='剧本杀/狼人杀/智能交互/角色选择', type='opsgptkg_task', attributes={'ID': -8695604495489305484, 'description': '角色选择', 'name': '角色选择', 'accesscriteria': '', 'extra': '{"memory_tag": "None"}', 'teamids': '', 'gdb_timestamp': '1724849085296', 'executetype': ''}), - GNode(id='剧本杀/谁是卧底/智能交互/通知身份', type='opsgptkg_task', attributes={'ID': 8901447933395410622, 'extra': '{"pattern": "react","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728912141060', 'executetype': '', 'description': '##角色##\n你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。\n目前已经完成 1)位置分配; 2)角色分配和单词分配。\n##任务##\n向所有玩家通知信息他们的 座位信息和单词信息。\n发送格式是： 【身份通知】你是{player_name}, 你的位置是{位置号}号， 你分配的单词是{单词}\n##详细步骤##\nstep1.依次向所有玩家通知信息他们的 座位信息和单词信息。发送格式是： 你是{player_name}, 你的位置是{位置号}号， 你分配的单词是{单词}\nstpe2.所有玩家信息都发送后，结束\n\n##注意##\n1. 每条信息只能发送给对应的玩家，其他人无法看到。\n2. 不要告诉玩家的角色信息，即不要高斯他是平民还是卧底角色\n3. 在将每个人的信息通知到后，本阶段任务结束\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为JSON，格式为\n[{"action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}, ...]\n\n关键词含义如下：\n_ player_name (str): 行动方的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): 行动方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n\n#example#\n如果是玩家发言，则用 {"action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{ "memory_tag":["agent_name_a","agent_name_b"],"content": "str"}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]}\n\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请输出jsonstr，不用输出markdown格式\n5. 结合已有的步骤，每次只输出下一个步骤，即一个 {"action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}', 'name': '通知身份', 'accesscriteria': ''}), - GNode(id='剧本杀/狼人杀/智能交互/向玩家通知消息', type='opsgptkg_task', attributes={'ID': -4014299322597660132, 'extra': '{"pattern": "react"}', 'teamids': '', 'gdb_timestamp': '1725092109310', 'executetype': '', 'description': '##角色##\n你正在参与狼人杀这个游戏，你的角色是[主持人]。你熟悉狼人杀游戏的完整流程，你需要完成[任务]，保证狼人杀游戏的顺利进行。\n目前已经完成位置分配和角色分配。\n##任务##\n向所有玩家通知信息他们的座位信息和角色信息。\n发送格式是： 你是{player_name}, 你的位置是{位置号}号，你的身份是{角色名}\n##注意##\n1. 每条信息只能发送给对应的玩家，其他人无法看到。\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为Python可解析的JSON，格式为\n\n[{"action": {player_name, agent_name}, "observation" or "Dungeon_Master": [{content, memory_tag}, ...]}]\n\n关键词含义如下：\n_ player_name (str): 行动方的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): 行动方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n\n##example##\n如果是玩家发言，则用 {"action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{"content": "str", "memory_tag":["agent_name_a","agent_name_b"]}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{"content": "str", memory_tag:["agent_name_a","agent_name_b"]}]}\n\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请直接输出jsonstr，不用输出markdown格式\n\n##结果##', 'name': '向玩家通知消息', 'accesscriteria': ''}), + GNode(id='剧本杀/谁是卧底/智能交互/通知身份', type='opsgptkg_task', attributes={'ID': 8901447933395410622, 'extra': '{"pattern": "react","dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1728912141060', 'executetype': '', 'description': '##角色##\n你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。\n目前已经完成 1)位置分配; 2)角色分配和单词分配。\n##任务##\n向所有玩家通知信息他们的 座位信息和单词信息。\n发送格式是： 【身份通知】你是{player_name}, 你的位置是{位置号}号， 你分配的单词是{单词}\n##详细步骤##\nstep1.依次向所有玩家通知信息他们的 座位信息和单词信息。发送格式是： 你是{player_name}, 你的位置是{位置号}号， 你分配的单词是{单词}\nstpe2.所有玩家信息都发送后，结束\n\n##注意##\n1. 每条信息只能发送给对应的玩家，其他人无法看到。\n2. 不要告诉玩家的角色信息，即不要告诉他是平民还是卧底角色\n3. 在将每个人的信息通知到后，本阶段任务结束\n4.本环节所有的对话都有主持人发起\n', 'name': '通知身份', 'accesscriteria': ''}), GNode(id='剧本杀/谁是卧底/智能交互/关键信息_1', type='opsgptkg_task', attributes={'ID': 3196717310525578616, 'gdb_timestamp': '1728913619628', 'executetype': '', 'description': '关键信息', 'name': '关键信息', 'accesscriteria': '', 'extra': '{"ignorememory":"True","dodisplay":"True"}', 'teamids': ''}), - GNode(id='剧本杀/狼人杀/智能交互/狼人时刻', type='opsgptkg_task', attributes={'ID': 8926130661368382825, 'accesscriteria': 'OR', 'extra': '{"pattern": "react"}', 'teamids': '', 'gdb_timestamp': '1725092131051', 'executetype': '', 'description': '##背景##\n在狼人杀游戏中，主持人通知当前存活的狼人玩家指认一位击杀对象，所有狼人玩家给出击杀目标，主持人确定最终结果。\n\n##任务##\n整个流程分为6个步骤：\n1. 存活狼人通知：主持人向所有的狼人玩家广播，告知他们当前存活的狼人玩家有哪些。\n2. 第一轮讨论：主持人告知所有存活的狼人玩家投票，从当前存活的非狼人玩家中，挑选一个想要击杀的玩家。\n3. 第一轮投票：按照座位顺序，每一位存活的狼人为自己想要击杀的玩家投票。\n4. 第一轮结果反馈：主持人统计所有狼人的票数分布，确定他们是否达成一致。若达成一致，告知所有狼人最终被击杀的玩家的player_name，流程结束；否则，告知他们票数的分布情况，并让所有狼人重新投票指定击杀目标，主持人需要提醒他们，若该轮还不能达成一致，则取票数最大的目标为最终击杀对象。\n5. 第二轮投票：按照座位顺序，每一位存活的狼人为自己想要击杀的玩家投票。\n6. 第二轮结果反馈：主持人统计第二轮投票中所有狼人的票数分布，取票数最大的玩家为最终击杀对象，如果存在至少两个对象的票数最大且相同，取座位号最大的作为最终击杀对象。主持人告知所有狼人玩家最终被击杀的玩家的player_name。\n\n该任务的参与者只有狼人玩家和主持人，信息可见对象是所有狼人玩家。\n\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为Python可解析的JSON，格式为\n\n[{"action": {player_name, agent_name}, "observation" or "Dungeon_Master": [{content, memory_tag}, ...]}]\n\n关键词含义如下：\n_ player_name (str): 行动方的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): 行动方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n\n##example##\n如果是玩家发言，则用 {"action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{"content": "str", "memory_tag":["agent_name_a","agent_name_b"]}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{"content": "str", memory_tag:["agent_name_a","agent_name_b"]}]}\n\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请直接输出jsonstr，不用输出markdown格式\n\n##结果##', 'name': '狼人时刻'}), - GNode(id='剧本杀/谁是卧底/智能交互/开始新一轮的讨论', type='opsgptkg_task', attributes={'ID': -6077057339616293423, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True",\n"memory_tag":"all",\n"dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913634866', 'executetype': '', 'description': '###以上为本局游戏记录###\n\n\n##背景##\n你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。\n\n##任务##\n以结构化的语句来模拟进行 谁是卧底的讨论环节。 在这一个环节里，所有主持人先宣布目前存活的玩家，然后每位玩家按照座位顺序发言\n\n\n##详细步骤##\nstep1. 主持人根据本局游戏历史记录，感知最开始所有的玩家 以及 在前面轮数中已经被票选死亡的玩家。注意死亡的玩家不能参与本轮游戏。得到当前存活的玩家个数以及其player_name。 并告知所有玩家当前存活的玩家个数以及其player_name。\nstep2. 主持人确定发言规则并告知所有玩家，发言规则步骤如下: 存活的玩家按照座位顺序由小到大进行发言\n（一个例子：假设总共有5个玩家，如果3号位置处玩家死亡，则发言顺序为：1_>2_>4_>5）\nstep3. 存活的的玩家按照顺序依次发言\nstpe4. 在每一位存活的玩家都发言后，结束\n\n \n \n##注意##\n1.之前的游戏轮数可能已经投票选中了某位/某些玩家，被票选中的玩家会立即死亡，不再视为存活玩家,死亡的玩家不能参与本轮游戏 \n2.你要让所有存活玩家都参与发言，不能遗漏任何存活玩家。在本轮所有玩家只发言一次\n3.该任务的参与者为主持人和所有存活的玩家，信息可见对象为所有玩家。\n4.不仅要模拟主持人的发言，还需要模拟玩家的发言\n5.每一位存活的玩家均发完言后，本阶段结束\n\n\n\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为JSON，格式为\n[ {"thought": str, "action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}] }, ...]\n\n\n\n\n关键词含义如下：\n_ thought (str): 主持人执行行动的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断等。 \n_ player_name (str): 行动方的 player_name，若行动方为主持人，为空 ;否则为玩家的 player_name；\n_ agent_name (str): 行动方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为本条信息的可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n\n##example##\n如果是玩家发言，则用 {"thought": "str", "action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{ "memory_tag":["agent_name_a","agent_name_b"],"content": "str"}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"thought": "str", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]}\n\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请输出jsonstr，不用输出markdown格式\n5. 结合已有的步骤，每次只输出下一个步骤，即一个 {"thought": str, "action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}\n6. 如果是人类玩家发言， 一定要选择类似 agent_人类玩家 这样的agent_name', 'name': '开始新一轮的讨论'}), - GNode(id='剧本杀/狼人杀/智能交互/天亮讨论', type='opsgptkg_task', attributes={'ID': 274796810216558717, 'gdb_timestamp': '1725106348469', 'executetype': '', 'description': '##角色##\n你正在参与狼人杀这个游戏，你的角色是[主持人]。你熟悉狼人杀游戏的完整流程，你需要完成[任务]，保证狼人杀游戏的顺利进行。\n##任务##\n你的任务如下: \n1. 告诉玩家昨晚发生的情况: 首先告诉玩家天亮了，然后你需要根据过往信息，告诉所有玩家，昨晚是否有玩家死亡。如果有，则向所有人宣布死亡玩家的名字，你只能宣布死亡玩家是谁如："昨晚xx玩家死了"，不要透露任何其他信息。如果没有，则宣布昨晚是平安夜。\n2. 确定发言规则并告诉所有玩家:\n确定发言规则步骤如下: \n第一步：确定第一个发言玩家，第一个发言的玩家为死者的座位号加1位置处的玩家(注意：最后一个位置+1的位置号为1号座位），如无人死亡，则从1号玩家开始。\n第二步：告诉所有玩家从第一个发言玩家开始发言，除了死亡玩家，每个人都需要按座位号依次讨论，只讨论一轮，所有人发言完毕后结束。注意不能遗忘指挥任何存活玩家发言！\n以下是一个例子：\n```\n总共有5个玩家，如果3号位置处玩家死亡，则第一个发言玩家为4号位置处玩家，因此从他开始发言，发言顺序为：4_>5_>1_>2\n```\n3. 依次指定存活玩家依次发言\n4. 被指定的玩家依次发言\n##注意##\n1. 你必须根据规则确定第一个发言玩家是谁，然后根据第一个发言玩家的座位号，确定所有人的发言顺序并将具体发言顺序并告知所有玩家，不要做任何多余解释\n2. 你要让所有存活玩家都参与发言，不能遗漏任何存活玩家\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为Python可解析的JSON，格式为\n\n[{"action": {player_name, agent_name}, "observation" or "Dungeon_Master": [{content, memory_tag}, ...]}]\n\n关键词含义如下：\n_ player_name (str): 行动方的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): 行动方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n\n##example##\n如果是玩家发言，则用 {"action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{"content": "str", "memory_tag":["agent_name_a","agent_name_b"]}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{"content": "str", memory_tag:["agent_name_a","agent_name_b"]}]}\n\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请直接输出jsonstr，不用输出markdown格式\n\n##结果（请直接在后面输出，如果后面已经有部分结果，请续写。一定要保持续写后的内容结合前者能构成一个合法的 jsonstr）##', 'name': '天亮讨论', 'accesscriteria': '', 'extra': '{"pattern": "react"}', 'teamids': ''}), + GNode(id='剧本杀/谁是卧底/智能交互/开始新一轮的讨论', type='opsgptkg_task', attributes={'ID': -6077057339616293423, 'accesscriteria': 'OR','action':'plan', 'extra': '{"pattern": "react", "endcheck": "True",\n"memory_tag":"all",\n"dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913634866', 'executetype': '', 'description': '###以上为本局游戏记录###\n\n\n##背景##\n你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。\n\n##任务##\n以结构化的语句来模拟进行 谁是卧底的讨论环节。 在这一个环节里，所有主持人先宣布目前存活的玩家，然后每位玩家按照座位顺序发言\n\n\n##详细步骤##\nstep1. 主持人根据本局游戏历史记录，感知最开始所有的玩家 以及 在前面轮数中已经被票选死亡的玩家。注意死亡的玩家不能参与本轮游戏。得到当前存活的玩家个数以及其player_name。 并告知所有玩家当前存活的玩家个数以及其player_name。\nstep2. 主持人确定发言规则并告知所有玩家，发言规则步骤如下: 存活的玩家按照座位顺序由小到大进行发言\n（一个例子：假设总共有5个玩家，如果3号位置处玩家死亡，则发言顺序为：1_>2_>4_>5）\nstep3. 存活的的玩家按照顺序依次发言\nstpe4. 在每一位存活的玩家都发言后，结束\n\n \n \n##注意##\n1.之前的游戏轮数可能已经投票选中了某位/某些玩家，被票选中的玩家会立即死亡，不再视为存活玩家, 存活玩家为：#$#存活的玩家#$#，死亡的玩家不能参与本轮游戏 \n2.你要让所有存活玩家都参与发言，不能遗漏任何存活玩家。在本轮所有玩家只发言一次\n3.该任务的参与者为主持人和所有存活的玩家，信息可见对象为所有玩家。\n4.不仅要模拟主持人的发言，还需要模拟玩家的发言\n5.每一位存活的玩家均发完言后，本阶段结束\n\n\n\n', 'name': '开始新一轮的讨论'}), GNode(id='剧本杀/谁是卧底/智能交互/关键信息_2', type='opsgptkg_task', attributes={'ID': -8309123437761850283, 'description': '关键信息', 'name': '关键信息', 'accesscriteria': '', 'extra': '{"ignorememory":"True","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913648645', 'executetype': ''}), - GNode(id='剧本杀/狼人杀/智能交互/票选凶手', type='opsgptkg_task', attributes={'ID': 1492108834523573937, 'accesscriteria': '', 'extra': '{"pattern": "react"}', 'teamids': '', 'gdb_timestamp': '1725106389716', 'executetype': '', 'description': '##角色##\n你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。\n\n##任务##\n你的任务如下:\n1. 告诉玩家投票规则，规则步骤如下: \nstep1: 确定讨论阶段第一个发言的玩家A\nstep2: 从A玩家开始，按座位号依次投票，每个玩家只能对一个玩家进行投票，投票这个玩家表示认为该玩家是“卧底”。每个玩家只能投一次票。\nstep3: 将完整投票规则告诉所有玩家\n2. 指挥存活玩家依次投票。\n3. 被指定的玩家进行投票\n4. 主持人统计投票结果，并告知所有玩家，投出的角色是谁。\n\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为Python可解析的JSON，格式为\n```\n{"action": {player_name, agent_name}, "observation" or "Dungeon_Master": [{content, memory_tag}, ...]}\n```\n关键词含义如下：\n_ player_name (str): 行动方的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): 行动方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n\n##example##\n如果是玩家发言，则用 {"action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{"content": "str", "memory_tag":["agent_name_a","agent_name_b"]}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{"content": "str", memory_tag:["agent_name_a","agent_name_b"]}]}\n\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请直接输出jsonstr，不用输出markdown格式\n\n##结果##\n', 'name': '票选凶手'}), - GNode(id='剧本杀/谁是卧底/智能交互/票选卧底_1', type='opsgptkg_task', attributes={'ID': 267468674566989196, 'teamids': '', 'gdb_timestamp': '1728913670477', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n##角色##\n你是一个统计票数大师，你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。 现在是投票阶段。\n\n##任务##\n以结构化的语句来模拟进行 谁是卧底的投票环节， 也仅仅只模拟投票环节，投票环节结束后就本阶段就停止了，由后续的阶段继续进行游戏。 在这一个环节里，由主持人先告知大家投票规则，然后组织每位存活玩家按照座位顺序发言投票, 所有人投票后，本阶段结束。 \n##详细步骤##\n你的任务如下:\nstep1. 向所有玩家通知现在进入了票选环节，在这个环节，每个人都一定要投票指定某一个玩家为卧底\nstep2. 主持人确定投票顺序并告知所有玩家。 投票顺序基于如下规则: 1: 存活的玩家按照座位顺序由小到大进行投票（一个例子：假设总共有5个玩家，如果3号位置处玩家死亡，则投票顺序为：1_>2_>4_>5）2: 按座位号依次投票，每个玩家只能对一个玩家进行投票。每个玩家只能投一次票。3：票数最多的玩家会立即死亡\n\nstep3. 存活的的玩家按照顺序进行投票\nstep4. 所有存活玩家发言完毕,主持人宣布投票环节结束\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n##注意##\n\n1.之前的游戏轮数可能已经投票选中了某位/某些玩家，被票选中的玩家会立即死亡，不再视为存活玩家 \n2.你要让所有存活玩家都参与投票，不能遗漏任何存活玩家。在本轮每一位玩家只投票一个人\n3.该任务的参与者为主持人和所有存活的玩家，信息可见对象为所有玩家。\n4.不仅要模拟主持人的发言，还需要模拟玩家的发言\n5.不允许玩家自己投自己，如果出现了这种情况，主持人会提醒玩家重新投票。\n\n\n\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为JSON，格式为\n["thought": str, {"action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}, ...]\n关键词含义如下：\n_ thought (str): 主持人执行行动的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断。 \n_ player_name (str): ***的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): ***的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n##example##\n如果是玩家发言，则用 {"thought": "str", "action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{ "memory_tag":["agent_name_a","agent_name_b"],"content": "str"}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"thought": "str", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]}\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请输出jsonstr，不用输出markdown格式\n5. 结合已有的步骤，每次只输出下一个步骤，即一个 {"thought": str, "action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}\n6. 如果是人类玩家发言， 一定要选择类似 人类agent 这样的agent_name', 'name': '票选卧底', 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "memory_tag":"all","dodisplay":"True"}'}), + GNode(id='剧本杀/谁是卧底/智能交互/票选卧底_1', type='opsgptkg_task', attributes={'ID': 267468674566989196, 'teamids': '', 'action':'parallel','gdb_timestamp': '1728913670477', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n##角色##\n你是一个统计票数大师，你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。 现在是投票阶段。\n\n##任务##\n以结构化的语句来模拟进行 谁是卧底的投票环节， 也仅仅只模拟投票环节，投票环节结束后就本阶段就停止了，由后续的阶段继续进行游戏。 在这一个环节里，由主持人先告知大家投票规则，然后组织每位存活玩家按照座位顺序发言投票, 所有人投票后，本阶段结束。 \n##详细步骤##\n你的任务如下:\nstep1. 向所有玩家通知现在进入了票选环节，在这个环节，每个人都一定要投票指定某一个玩家为卧底\nstep2. 主持人确定投票顺序并告知所有玩家。 投票顺序基于如下规则: 1: 存活的玩家按照座位顺序由小到大进行投票（一个例子：假设总共有5个玩家，如果3号位置处玩家死亡，则投票顺序为：1_>2_>4_>5）2: 按座位号依次投票，每个玩家只能对一个玩家进行投票。每个玩家只能投一次票。3：票数最多的玩家会立即死亡\n\nstep3. 存活的的玩家按照顺序进行投票\nstep4. 所有存活玩家发言完毕,主持人宣布投票环节结束\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n##注意##\n\n1.之前的游戏轮数可能已经投票选中了某位/某些玩家，被票选中的玩家会立即死亡，不再视为存活玩家, 存活玩家为：#$#存活的玩家#$#。\n2.你要让所有存活玩家都参与投票，不能遗漏任何存活玩家。在本轮每一位玩家只投票一个人\n3.该任务的参与者为主持人和所有存活的玩家，信息可见对象为所有玩家。\n4.不仅要模拟主持人的发言，还需要模拟玩家的发言\n5.不允许玩家自己投自己，如果出现了这种情况，主持人会提醒玩家重新投票。\n\n\n\n', 'name': '票选卧底', 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "memory_tag":"all","dodisplay":"True"}'}), GNode(id='剧本杀/谁是卧底/智能交互/关键信息_4', type='opsgptkg_task', attributes={'ID': -4669093152651945828, 'extra': '{"ignorememory":"True","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913685959', 'executetype': '', 'description': '关键信息_4', 'name': '关键信息_4', 'accesscriteria': ''}), - GNode(id='剧本杀/谁是卧底/智能交互/统计票数', type='opsgptkg_task', attributes={'ID': -6836070348442528830, 'teamids': '', 'gdb_timestamp': '1728913701092', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n##角色##\n你是一个统计票数大师，你非常擅长计数以及统计信息。你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。 现在是票数统计阶段\n\n##任务##\n以结构化的语句来模拟进行 谁是卧底的票数统计阶段， 也仅仅只票数统计阶段环节，票数统计阶段结束后就本阶段就停止了，由后续的阶段继续进行游戏。 在这一个环节里，由主持人根据上一轮存活的玩家投票结果统计票数。 \n##详细步骤##\n你的任务如下:\nstep1. 主持人感知上一轮投票环节每位玩家的发言, 统计投票结果，格式为[{"player_name":票数}]. \nstep2 然后，主持人宣布死亡的玩家，以最大票数为本轮被投票的目标，如果票数相同，则取座位号高的角色死亡。并告知所有玩家本轮被投票玩家的player_name。（格式为【重要通知】本轮死亡的玩家为XXX）同时向所有玩家宣布，被投票中的角色会视为立即死亡（即不再视为存活角色）\nstep3. 在宣布死亡玩家后，本阶段流程结束，由后续阶段继续推进游戏\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n##注意##\n1.如果有2个或者两个以上的被玩家被投的票数相同，则取座位号高的玩家死亡。并告知大家原因：票数相同，取座位号高的玩家死亡\n2.在统计票数时，首先确认存活玩家的数量，再先仔细回忆，谁被投了。 最后统计每位玩家被投的次数。 由于每位玩家只有一票，所以被投次数的总和等于存活玩家的数量 \n3.通知完死亡玩家是谁后，本阶段才结束，由后续阶段继续推进游戏。输出 {"action": "taskend"}即可\n4.主持人只有当通知本轮死亡的玩家时，才使用【重要通知】的前缀，其他情况下不要使用【重要通知】前缀\n5.只统计上一轮投票环节的情况\n##example##\n{"thought": "在上一轮中, 存活玩家有 小北,李光,赵鹤,张良 四个人。 其中 小北投了李光, 赵鹤投了小北, 张良投了李光, 李光投了张良。总结被投票数为： 李光:2票; 小北:1票,张良:1票. Check一下，一共有四个人投票了，被投的票是2（李光）+1（小北）+1（张良）=4，总结被投票数没有问题。 因此李光的票最多", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["all"], "content": "李光:2票; 小北:1票,张良:1票 .因此李光的票最多.【重要通知】本轮死亡玩家是李光",}]}\n\n##example##\n{"thought": "在上一轮中, 存活玩家有 小北,人类玩家,赵鹤,张良 四个人。 其中 小北投了人类玩家, 赵鹤投了小北, 张良投了小北, 人类玩家投了张良。总结被投票数为：小北:2票,人类玩家:1票,张良:0票 .Check一下，一共有四个人投票了，被投的票是2（小北）+1（人类玩家）+张良（0）=3，总结被投票数有问题。 更正总结被投票数为：小北:2票,人类玩家:1票,张良:1票。因此小北的票最多", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["all"], "content": "小北:2票,人类玩家:1票,张良:1票 .因此小北的票最多.【重要通知】本轮死亡玩家是小北",}]}\n\n\n##输出##\n请以列表的形式，给出参与者的所有行动。每个行动表示为JSON，格式为\n["thought": str, {"action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}, ...]\n关键词含义如下：\n_ thought (str): 主持人执行行动的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断。 \n_ player_name (str): ***的 player_name，若行动方为主持人，为空，否则为玩家的 player_name；\n_ agent_name (str): ***的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。\n_ content (str): 行动方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。\n_ memory_tag (List[str]): 无论行动方是主持人还是玩家，memory_tag 固定为**所有**信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"]\n##example##\n如果是玩家发言，则用 {"thought": "str", "action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{ "memory_tag":["agent_name_a","agent_name_b"],"content": "str"}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description\n如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"thought": "str", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]}\n##注意事项##\n1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。\n2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。\n3. 输出列表的最后一个元素一定是{"action": "taskend"}。\n4. 整个list是一个jsonstr，请输出jsonstr，不用输出markdown格式\n5. 结合已有的步骤，每次只输出下一个步骤，即一个 {"thought": str, "action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}\n6. 如果是人类玩家发言， 一定要选择类似 人类agent 这样的agent_name', 'name': '统计票数', 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "memory_tag":"all","model_name":"gpt_4","dodisplay":"True"}'}), + GNode(id='剧本杀/谁是卧底/智能交互/统计票数', type='opsgptkg_task', attributes={'ID': -6836070348442528830, 'teamids': '', 'action':'react','gdb_timestamp': '1728913701092', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n##角色##\n你是一个统计票数大师，你非常擅长计数以及统计信息。你正在参与“谁是卧底”这个游戏，你的角色是[主持人]。你熟悉“谁是卧底”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。 现在是票数统计阶段\n\n##任务##\n以结构化的语句来模拟进行 谁是卧底的票数统计阶段， 也仅仅只票数统计阶段环节，票数统计阶段结束后就本阶段就停止了，由后续的阶段继续进行游戏。 在这一个环节里，由主持人根据上一轮存活的玩家投票结果统计票数。 \n##详细步骤##\n你的任务如下:\nstep1. 主持人感知上一轮投票环节每位玩家的发言, 统计投票结果，格式为[{"player_name":票数}]. \nstep2 然后，主持人宣布死亡的玩家，以最大票数为本轮被投票的目标，如果票数相同，则取座位号高的角色死亡。并告知所有玩家本轮被投票玩家的player_name。（格式为【重要通知】本轮死亡的玩家为XXX）同时向所有玩家宣布，被投票中的角色会视为立即死亡（即不再视为存活角色）\nstep3. 在宣布死亡玩家后，本阶段流程结束，由后续阶段继续推进游戏\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n##注意##\n1.如果有2个或者两个以上的被玩家被投的票数相同，则取座位号高的玩家死亡。并告知大家原因：票数相同，取座位号高的玩家死亡\n2.在统计票数时，首先确认存活玩家的数量，再先仔细回忆，谁被投了。 最后统计每位玩家被投的次数。 由于每位玩家只有一票，所以被投次数的总和等于存活玩家的数量 \n3.通知完死亡玩家是谁后，本阶段才结束，由后续阶段继续推进游戏。输出 {"action": "taskend"}即可\n4.主持人只有当通知本轮死亡的玩家时，才使用【重要通知】的前缀，其他情况下不要使用【重要通知】前缀\n5.只统计上一轮投票环节的情况\n##example##\n{"thought": "在上一轮中, 存活玩家有 小北,李光,赵鹤,张良 四个人。 其中 小北投了李光, 赵鹤投了小北, 张良投了李光, 李光投了张良。总结被投票数为： 李光:2票; 小北:1票,张良:1票. Check一下，一共有四个人投票了，被投的票是2（李光）+1（小北）+1（张良）=4，总结被投票数没有问题。 因此李光的票最多", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["all"], "content": "李光:2票; 小北:1票,张良:1票 .因此李光的票最多.【重要通知】本轮死亡玩家是李光",}]}\n\n##example##\n{"thought": "在上一轮中, 存活玩家有 小北,人类玩家,赵鹤,张良 四个人。 其中 小北投了人类玩家, 赵鹤投了小北, 张良投了小北, 人类玩家投了张良。总结被投票数为：小北:2票,人类玩家:1票,张良:0票 .Check一下，一共有四个人投票了，被投的票是2（小北）+1（人类玩家）+张良（0）=3，总结被投票数有问题。 更正总结被投票数为：小北:2票,人类玩家:1票,张良:1票。因此小北的票最多", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["all"], "content": "小北:2票,人类玩家:1票,张良:1票 .因此小北的票最多.【重要通知】本轮死亡玩家是小北",}]}\n\n\n', 'name': '统计票数', 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "memory_tag":"all","model_name":"gpt_4","dodisplay":"True"}','updaterule':'{"存活的玩家":""}'}), GNode(id='剧本杀/谁是卧底/智能交互/关键信息_3', type='opsgptkg_task', attributes={'ID': -4800215480474522940, 'accesscriteria': '', 'extra': '{"ignorememory":"True","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913715255', 'executetype': '', 'description': '关键信息', 'name': '关键信息'}), GNode(id='剧本杀/谁是卧底/智能交互/判断游戏是否结束', type='opsgptkg_task', attributes={'ID': -5959590132883379159, 'description': '判断游戏是否结束', 'name': '判断游戏是否结束', 'accesscriteria': '', 'extra': '{"memory_tag": "None","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913728308', 'executetype': ''}), GNode(id='剧本杀/谁是卧底/智能交互/事实_1', type='opsgptkg_phenomenon', attributes={'ID': -525629912140732688, 'description': '是', 'name': '是', 'extra': '', 'teamids': '', 'gdb_timestamp': '1725089138724'}), GNode(id='剧本杀/谁是卧底/智能交互/事实_2', type='opsgptkg_phenomenon', attributes={'ID': 4216433814773851843, 'teamids': '', 'gdb_timestamp': '1725089593085', 'description': '否', 'name': '否', 'extra': ''}), GNode(id='剧本杀/谁是卧底/智能交互/给出每个人的单词以及最终胜利者', type='opsgptkg_task', attributes={'ID': 8878899410716129093, 'extra': '{"dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1728913745186', 'executetype': '', 'description': '给出每个人的单词以及最终胜利者', 'name': '给出每个人的单词以及最终胜利者', 'accesscriteria': ''}), - GNode(id='剧本杀/狼人杀/智能交互/判断游戏是否结束', type='opsgptkg_task', attributes={'ID': -2316854558435035646, 'description': '判断游戏是否结束 ', 'name': '判断游戏是否结束 ', 'accesscriteria': '', 'extra': '{"memory_tag": "None"}', 'teamids': '', 'gdb_timestamp': '1725092210244', 'executetype': ''}), - GNode(id='剧本杀/狼人杀/智能交互/事实_2', type='opsgptkg_phenomenon', attributes={'ID': -6298561983042120406, 'extra': '', 'teamids': '', 'gdb_timestamp': '1724816562165', 'description': '否', 'name': '否'}), - GNode(id='剧本杀/狼人杀/智能交互/事实_1', type='opsgptkg_phenomenon', attributes={'ID': 6987562967613654408, 'gdb_timestamp': '1724816495297', 'description': '是', 'name': '是', 'extra': '', 'teamids': ''}), - GNode(id='剧本杀/l狼人杀/智能交互/宣布游戏胜利者', type='opsgptkg_task', attributes={'ID': -758955621725402723, 'extra': '', 'teamids': '', 'gdb_timestamp': '1725097362872', 'executetype': '', 'description': '判断游戏是否结束', 'name': '判断游戏是否结束', 'accesscriteria': ''}), GNode(id='剧本杀', type='opsgptkg_intent', attributes={'ID': -3388526698926684245, 'description': '文本游戏相关（如狼人杀等）', 'name': '剧本杀', 'extra': '', 'teamids': '', 'gdb_timestamp': '1724815537102'})] +new_nodes_2 = new_nodes_2+ \ +[ +GNode(id='剧本杀/狼人杀', type='opsgptkg_intent', attributes={'ID': 5476827419397129797, 'description': '狼人杀', 'name': '狼人杀', 'extra': '', 'teamids': '', 'gdb_timestamp': '1724815561170'}), +GNode(id='剧本杀/狼人杀/智能交互', type='opsgptkg_schedule', attributes={'ID': -5931163481230280444, 'extra': '', 'teamids': '', 'gdb_timestamp': '1724815624907', 'description': '智能交互', 'name': '智能交互', 'enable': 'False', 'envdescription': '{"存活的玩家": "梁军、朱丽、周欣怡、贺子轩、沈强、韩刚、周杰、人类玩家", "剩余毒药的数量": "1", "剩余解药的数量": "1"}'}), +GNode(id='剧本杀/狼人杀/智能交互/狼人杀分配座位', type='opsgptkg_task', attributes={'ID': 2541178858602010284, 'description': '狼人杀分配座位', 'name': '狼人杀分配座位', 'accesscriteria': '', 'extra': '{"dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1724849735167', 'executetype': ''}), +GNode(id='剧本杀/狼人杀/智能交互/狼人杀角色分配和单词分配', type='opsgptkg_task', attributes={'ID': -8695604495489305484, 'description': '狼人杀角色分配和单词分配', 'name': '狼人杀角色分配和单词分配', 'accesscriteria': '', 'extra': '{"memory_tag": "None","dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1724849085296', 'executetype': ''}), +GNode(id='剧本杀/狼人杀/智能交互/向玩家通知消息', type='opsgptkg_task', attributes={'ID': -4014299322597660132, 'extra': '{"pattern": "react","dodisplay":"True"}', 'teamids': '', 'action':'react', 'gdb_timestamp': '1725092109310', 'executetype': '', 'description': '##角色##\n你正在参与狼人杀这个游戏，你的角色是[主持人]。你熟悉狼人杀游戏的完整流程，你需要完成[任务]，保证狼人杀游戏的顺利进行。\n目前已经完成位置分配和角色分配。\n##任务##\n向所有玩家通知信息他们的座位信息和角色信息。\n发送格式是：你是{player_name}, 你的位置是{位置号}号，你的身份是{角色名}。\n##详细步骤##\nstep1.依次向所有玩家通知信息他们的座位信息和角色信息。发送格式是：你是{player_name}, 你的位置是{位置号}号， 你的身份是{角色名}。\nstpe2.所有玩家信息都发送后，结束。\n##注意##\n1. 每条信息只能发送给对应的玩家，其他人无法看到。\n2. 在将每个人的信息通知到后，本阶段任务结束。3.本环节所有的对话都有主持人发起。\n', 'name': '向玩家通知消息', 'accesscriteria': ''}), +GNode(id='剧本杀/狼人杀/智能交互/狼人时刻_投票', type='opsgptkg_task', attributes={'ID': 8926130661368382825, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1725092131050', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##背景##\n在狼人杀游戏中，主持人通知当前存活的狼人玩家击杀一位其他存活玩家，所有狼人玩家投票给出击杀目标。\n\n##任务##\n以结构化的语句来模拟进行狼人杀的狼人_投票环节。\n整个流程分为2个步骤：\n1. 存活狼人通知：主持人向所有的狼人玩家广播，告知他们当前存活的狼人玩家有哪些。\n2. 狼人投票：按照座位顺序，每一位存活的狼人明确选择要击杀一位目前存活的玩家。\n\n##example##\n{"thought": "第一晚没有什么有用信息，随机击杀一个除了狼人队友以外的玩家即可。", "action": {"agent_name": "agent_梁军", "player_name": "梁军"}, "observation": [{"memory_tag": ["agent_梁军", "agent_周欣怡", "agent_人类玩家"], "content": "我是3号梁军，我决定今晚击杀2号玩家周杰。"}]}\n\n{"thought": "第一晚没有什么有用信息，随机击杀一个除了狼人队友以外的玩家即可。", "action": {"agent_name": "agent_周欣怡", "player_name": "周欣怡"}, "observation": [{"memory_tag": ["agent_梁军", "agent_周欣怡", "agent_人类玩家"], "content": "我是8号周欣怡，我决定今晚击杀2号玩家周杰。"}]}\n\n##注意##\n1.之前的游戏轮数可能已经投票选中了某位/某些狼人玩家或者被女巫毒死，这些狼人玩家会立即死亡，不再视为存活的狼人玩家, 死亡的狼人玩家不能参与本轮游戏。\n2.你要让所有存活的狼人玩家都参与发言，不能遗漏任何存活的狼人玩家。在本轮所有的狼人玩家只发言一次。\n3.该任务的参与者为主持人和所有存活的狼人玩家，信息可见对象为所有狼人玩家。\n4.不仅要模拟主持人的发言，还需要模拟狼人玩家的发言。\n5.狼人发言不要分析前几轮游戏没有发生过的内容。\n6.每一位存活的狼人玩家均发完言后，主持人宣布狼人时刻_投票环节结束。\n7.不可击杀其他狼人玩家。\n\n该任务的参与者只有狼人玩家和主持人，信息可见对象是所有狼人玩家, 其他角色玩家不可见。', 'name': '狼人时刻_投票'}), +GNode(id='剧本杀/狼人杀/智能交互/狼人时刻_统计票数', type='opsgptkg_task', attributes={'ID': 8926130661368382925, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1725092131051', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##背景##\n你是一个统计票数大师，你正在参与“狼人杀”这个游戏，你的角色是[主持人]。\n在狼人杀游戏中，主持人通知当前存活的狼人玩家投票给出击杀目标，主持人确定最终结果。\n\n##任务##\nstep1. 主持人感知上一轮投票环节每位狼人玩家的发言，统计投票结果，格式为[{"player_name":票数}]. \nstep2. 然后，主持人宣布死亡的玩家，以最大票数为本轮被投票的目标，如果票数相同，则取座位号高的角色死亡。并告知所有狼人玩家本轮被投票玩家的player_name。（输出格式为【重要通知】本轮死亡的玩家为XXX）同时向所有玩家宣布，被投票中的角色会视为立即死亡（即不再视为存活角色）。\nstep3. 在宣布死亡玩家后，本阶段流程结束，由后续阶段继续推进游戏。\n\n##注意##\n1.如果有2个或者两个以上的被玩家被投的票数相同，则取座位号高的玩家死亡。并告知大家原因：票数相同，取座位号高的玩家死亡。\n2.在统计票数时，首先确认存活玩家的数量，再先仔细回忆，谁被投了。最后统计玩家被投的次数。由于每位存活的狼人玩家只有一票，所以被投次数的总和等于存活狼人玩家的数量。\n3.通知完死亡玩家是谁后，本阶段才结束，由后续阶段继续推进游戏。\n4.主持人只有当通知本轮死亡的玩家时，才使用【重要通知】的前缀，其他情况下不要使用【重要通知】前缀。\n5.只统计上一轮狼人投票环节的情况。\n6.输出格式为“【重要通知】本轮死亡的玩家为XXX”，不要输出票数统计情况（[{"player_name":票数}]）等其他内容。\n\n##example##\n{"thought": "在上一轮中, 存活的狼人玩家有8号朱丽、6号韩刚、5号人类玩家。其中朱丽投了2号周欣怡，韩刚投了3号梁军，人类玩家也投了2号周欣怡。总结被投票数为：2号周欣怡:2票; 3号梁军:1票. Check一下，一共有三个人投票了，被投的票是2（周欣怡）+1（梁军）=3，总结被投票数没有问题。因此2号周欣怡的票最多。", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{"memory_tag":["agent_朱丽","agent_韩刚","agent_人类玩家"], "content": "【重要通知】本轮死亡的玩家为周欣怡"}]}\n\n##example##\n{"thought": "在上一轮中, 存活的狼人玩家有1号朱丽、2号韩刚和6号人类玩家。其中朱丽投了3号周欣怡，韩刚投了7号周杰，人类玩家也投了7号周杰。总结被投票数为：3号周欣怡:1票; 7号周杰:2票. Check一下，一共有三个人投票了，被投的票是1（周欣怡）+2（周杰）=3，总结被投票数没有问题。因此7号周杰的票最多。", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{"memory_tag":["agent_朱丽","agent_韩刚","agent_人类玩家"], "content": "【重要通知】本轮死亡的玩家为周杰"}]}\n\n该任务的参与者为主持人和所有存活的狼人玩家，信息可见对象是所有狼人，其他角色不可见', 'name': '狼人时刻_统计票数', 'updaterule':'{"存活的玩家":""}'}), +GNode(id='剧本杀/狼人杀/智能交互/预言家时刻', type='opsgptkg_task', attributes={'ID': 8926130661368382826, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1725092131051', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##背景##\n在狼人杀游戏中，主持人询问“预言家”查验哪一位存活玩家的身份，“预言家”挑选自己想要查验的玩家，主持人说明被挑选玩家是“好人”还是“狼人”。\n\n##存活的玩家##\n存活的玩家：#$#存活的玩家#$#\n\n##任务##\n整个流程分为4个步骤：\n1. 主持人判断预言家是否存活，若预言家已经死亡，则直接结束这个阶段。\n2. 主持人询问“预言家”要查验哪一位存活玩家的身份。\n3. “预言家”挑选出一个自己想要查验的玩家，注意每次只能查验一个玩家。\n4. 主持人告知“预言家”被查验玩家的身份属性：若该玩家是狼人，则身份属性为“狼人”；否则，身份属性为“好人”，不能让预言家再次查验。\n\n##注意##\n1. 若预言家已经死亡，则直接结束这个阶段。\n2. 预言家发言后，一定要告知预言家被查验玩家的身份属性，只可告诉预言家被查验玩家是“狼人”还是“好人”，不可透露具体“好人”角色。告知身份属性后，本阶段结束。\n\n##example##\n{"thought": "根据5号韩刚预言家的选择，我需要告诉他8号周杰的身份。", "action": {"agent_name": "主持人", "player_name": ""}, "Dungeon_Master": [{"memory_tag": ["agent_韩刚"], "content": "8号周杰的身份是狼人。"}]}\n\n##example##\n{"thought": "根据8号周杰预言家的选择，我需要告诉他2号周欣怡的身份。", "action": {"agent_name": "主持人", "player_name": ""}, "Dungeon_Master": [{"memory_tag": ["agent_周杰"], "content": "2号欣怡的身份是好人。"}]}\n\n该任务的参与者只有“预言家”和主持人，信息可见对象是“预言家”，其他角色玩家不可见。', 'name': '预言家时刻'}), +GNode(id='剧本杀/狼人杀/智能交互/女巫时刻', type='opsgptkg_task', attributes={'ID': 8926130661368382827, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1725092131053', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##背景##\n在狼人杀游戏中，主持人告知“女巫”使用身上的药剂，“女巫”明确是否使用药剂以及使用的对象。\n\n##存活的玩家##\n存活的玩家：#$#存活的玩家#$#\n\n##剩余毒药和解药的数量##\n剩余毒药的数量：#$#剩余毒药的数量#$#\n剩余解药的数量：#$#剩余解药的数量#$#\n\n##任务##\n整个流程分为8个步骤：\n1. 若女巫之前已经死亡或者刚被狼人击杀则本阶段结束；否则主持人根据剩余毒药和解药的数量告知“女巫”：当前毒药和解药是否已经使用，询问该轮是否需要使用药剂。\n2. “女巫”根据这两瓶药剂的使用情况，告知主持人自己是否想要使用。\n3. 若“女巫”明确不使用药剂，流程结束；否则，如果“女巫”的解药还未使用，主持人告知“女巫”今晚哪位玩家被狼人击杀了，询问她是否要使用解药；如果“女巫”的解药已使用，进入步骤5。\n4. “女巫”告知主持人是否要使用身上的解药，来复活被狼人击杀的玩家。\n5. 如果“女巫”的毒药还未使用，主持人询问“女巫”是否要使用身上的毒药；否则，若毒药已使用，流程结束。\n6. “女巫”告知主持人：自己是否要使用毒药。\n7. 若“女巫”明确要使用毒药，主持人询问“女巫”要将毒药用在哪位玩家身上，从而击杀该玩家；否则，流程结束。\n8. “女巫”告知主持人：要将毒药用在哪位目前还存活的玩家身上。\n\n##注意##\n1. 若女巫已经死亡，则直接结束这个阶段。\n2. 若女巫仍然存活，一定要严格按照任务中的8个步骤执行。8个步骤顺利执行后，本阶段结束。\n\n该任务的参与者只有“女巫”和主持人，信息可见对象是“女巫”，其他角色玩家不可见。', 'name': '女巫时刻','updaterule':'{"存活的玩家":"", "剩余解药的数量":"", "剩余毒药的数量":""}'}), +# GNode(id='剧本杀/狼人杀/智能交互/猎人时刻_1', type='opsgptkg_task', attributes={'ID': 8926130661368382828, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1725092131054', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##背景##\n在狼人杀游戏中，“猎人”玩家发现自己被击杀出局，可以选择亮出身份并指定击杀一名玩家。\n\n##任务##\n整个过程如下：\n1. 主持人判断“猎人”是否死亡，如果猎人存活则本轮流程直接结束。\n2. 如果“猎人”死亡，“猎人”玩家考虑是否激活自己的能力，猎人玩家若激活自己的能力，则选择要击杀的玩家，注意只能选择目前存活的玩家。\n\n该任务的参与者只有“猎人”和主持人，信息可见对象是“猎人”，其他角色玩家不可见。', 'name': '猎人时刻_1'}), +GNode(id='剧本杀/狼人杀/智能交互/天亮讨论', type='opsgptkg_task', attributes={'ID': 274796810216558717, 'action':'react','gdb_timestamp': '1725106348469', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##角色##\n你正在参与狼人杀这个游戏，你的角色是[主持人]。你熟悉狼人杀游戏的完整流程，你需要完成[任务]，保证狼人杀游戏的顺利进行。\n\n##目前存活的玩家##\n#$#存活的玩家#$#\n\n##任务##\n以结构化的语句来模拟进行狼人杀的讨论环节。在这一个环节里，所有主持人先宣布目前存活的玩家，然后每位玩家按照座位顺序发言。\n\n##详细步骤##\n1. 主持人根据本局游戏历史记录，感知最开始所有的玩家以及在前面轮数中已经被票选死亡的玩家、被狼人杀死、被女巫毒死、被狼人杀死但是被女巫救活了的玩家(该玩家视为存活的玩家）。注意死亡的玩家不能参与本轮游戏。得到当前存活的玩家个数以及其player_name。 并告知所有玩家当前存活的玩家个数以及其player_name。 若一个人先被狼人杀害了，然后被女巫用解药就回了，则该玩家是存活的，即不能宣布该玩家死亡了。\n2. 告诉玩家昨晚发生的情况: 首先告诉玩家天亮了，然后你需要根据过往信息，告诉所有玩家，昨晚是否有玩家死亡。如果有，则向所有人宣布死亡玩家的名字，你只能宣布死亡玩家是谁如："昨晚xx玩家死了。"，或者有多位玩家死亡则宣布"昨晚xx和xx玩家死了。"，不要透露任何其他信息，不能透露是被狼人杀死还是被女巫毒死或者救回。如果没有，则宣布昨晚是平安夜。\n3. 确定发言规则并告诉所有玩家:\n确定发言规则步骤如下: \n第一步：所有存活玩家按照座位号从小到大开始发言。\n第二步：告诉所有玩家从第一个发言玩家开始发言，存活的玩家按照发言顺序依次讨论，只讨论一轮，所有人发言完毕后结束。注意不能遗忘指挥任何存活玩家发言！\n4. 依次指定存活玩家依次发言。\n5. 被指定的玩家依次发言，在每一位存活的玩家都发言后，本阶段结束。\n\n##注意##\n1. 你必须根据规则确定第一个发言玩家是谁，然后根据第一个发言玩家的座位号，确定所有人的发言顺序并将具体发言顺序并告知所有玩家，不要做任何多余解释。\n2. 不能透露任何玩家的角色信息。\n3. 你要让所有存活玩家都参与发言，不能遗漏任何存活玩家。所有玩家发言后，本阶段结束。', 'name': '天亮讨论', 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "memory_tag":"all", "dodisplay":"True"}', 'teamids': ''}), +GNode(id='剧本杀/狼人杀/智能交互/票选凶手', type='opsgptkg_task', attributes={'ID': 1492108834523573937, 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react','teamids': '', 'gdb_timestamp': '1725106389716', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##角色##\n你是一个统计票数大师，你正在参与“狼人杀”这个游戏，你的角色是[主持人]。你熟悉“狼人杀”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。 现在是投票阶段。\n\n##目前存活的玩家##\n#$#存活的玩家#$#\n\n##任务##\n以结构化的语句来模拟进行“狼人杀”的投票环节， 也仅仅只模拟投票环节，投票环节结束后就本阶段就停止了，由后续的阶段继续进行游戏。 在这一个环节里，由主持人先告知大家投票规则，然后组织每位存活玩家按照座位顺序发言投票, 所有人投票后，本阶段结束。 \n\n##详细步骤##\n你的任务如下:\nstep1. 向所有玩家通知现在进入了票选环节，在这个环节，每个人都一定要投票指定某一个玩家为狼人。\nstep2. 主持人确定投票顺序并告知所有玩家。 1:投票顺序与讨论环节一致。2: 按座位号依次投票，每个玩家只能对一个玩家进行投票。每个玩家只能投一次票。3：票数最多的玩家会立即死亡。\nstep3. 存活的的玩家按照依次顺序进行投票。\nstep4. 所有存活玩家发言完毕,主持人宣布投票环节结束。\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n\n##example##\n{"thought": "我认为我们应该更加关注那些行为模式突然改变的玩家，比如3号沈强，他的态度从最初的谨慎变得越来越自信，这让我有些怀疑。", "action": {"agent_name": "agent_贺子轩", "player_name": "贺子轩"}, "observation": [{"memory_tag": ["all"], "content": "我投给3号沈强。"}]}\n\n##注意##\n1.之前的游戏轮数可能已经投票选中了某位/某些玩家、被狼人杀死、被女巫毒死的玩家，这些玩家不再视为存活玩家。\n2.你要让所有存活玩家都参与投票，不能遗漏任何存活玩家。在本轮每一位玩家只投票一个人，并且只能投存活玩家。\n3.该任务的参与者为主持人和所有存活的玩家，信息可见对象为所有玩家。\n4.不仅要模拟主持人的发言，还需要模拟玩家的发言\n5.不允许玩家自己投自己，如果出现了这种情况，主持人会提醒玩家重新投票。\n6.若票数相同，则座位号大的死亡，无需重复投票。', 'name': '票选凶手'}), +GNode(id='剧本杀/狼人杀/智能交互/统计票数', type='opsgptkg_task', attributes={'ID': 1492108834523573938, 'accesscriteria': '', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react','teamids': '', 'gdb_timestamp': '1725106389717', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##角色##\n你是一个统计票数大师，你非常擅长计数以及统计信息。你正在参与“狼人杀”这个游戏，你的角色是[主持人]。你熟悉“狼人杀”游戏的完整流程，你需要完成[任务]，保证游戏的顺利进行。现在是票数统计阶段\n\n##任务##\n以结构化的语句来模拟进行“狼人杀”的票数统计阶段，也仅仅只票数统计阶段环节，票数统计阶段结束后就本阶段就停止了，由后续的阶段继续进行游戏。在这一个环节里，由主持人根据上一轮存活的玩家投票结果统计票数。 \n\n##详细步骤##\n你的任务如下:\nstep1. 主持人感知上一轮投票环节每位玩家的发言, 统计投票结果，格式为[{"player_name":票数}]. \nstep2 然后，主持人宣布死亡的玩家，以最大票数为本轮被投票的目标，如果票数相同，则取座位号高的角色死亡。并告知所有玩家本轮被投票玩家的player_name。（格式为【重要通知】本轮死亡的玩家为XXX）同时向所有玩家宣布，被投票中的角色会视为立即死亡（即不再视为存活角色）。\nstep3. 在宣布死亡玩家后，本阶段流程结束，由后续阶段继续推进游戏。\n该任务的参与者为主持人和所有存活的玩家，信息可见对象是所有玩家。\n\n##注意##\n1.如果有2个或者两个以上的被玩家被投的票数相同，则取座位号高的玩家死亡。并告知大家原因：票数相同，取座位号高的玩家死亡\n2.在统计票数时，首先确认存活玩家的数量，再先仔细回忆，谁被投了。最后统计每位玩家被投的次数。由于每位玩家只有一票，所以被投次数的总和等于存活玩家的数量 \n3.通知完死亡玩家是谁后，本阶段才结束，由后续阶段继续推进游戏。\n4.主持人只有当通知本轮死亡的玩家时，才使用【重要通知】的前缀，其他情况下不要使用【重要通知】前缀\n5.只统计上一轮投票环节的情况\n\n##example##\n{"thought": "根据上一轮发言，7号、1号、2号、3号投票给4号周欣怡，而5号和8号投票给7号梁军。", "action": {"agent_name": "主持人", "player_name": ""}, "Dungeon_Master": [{"memory_tag": ["all"], "content": "【重要通知】经过整理，投票结果如下：4号周欣怡:4票；7号梁军:2票。因此周欣怡的票最多。本轮死亡玩家是4号周欣怡。"}]}', 'name': '统计票数','updaterule':'{"存活的玩家":""}'}), +# GNode(id='剧本杀/狼人杀/智能交互/猎人时刻_2', type='opsgptkg_task', attributes={'ID': 8926130661368382829, 'accesscriteria': 'OR', 'extra': '{"pattern": "react", "endcheck": "True", "dodisplay":"True"}', 'action':'react', 'teamids': '', 'gdb_timestamp': '1725092131055', 'executetype': '', 'description': '##以上为本局游戏历史记录##\n\n##背景##\n在狼人杀游戏中，“猎人”玩家发现自己被投票出局，可以选择亮出身份并指定击杀一名玩家。\n\n##任务##\n整个过程如下：\n1. 主持人判断“猎人”是否死亡，如果猎人存活则本轮流程直接结束。\n2. 主持人判断“猎人”是否是刚被投票出局的，若之前已经死亡，则本轮流程直接结束。\n3. 如果“猎人”死亡，“猎人”玩家考虑是否激活自己的能力，“猎人”玩家若激活自己的能力，则选择要击杀的玩家，注意只能选择目前存活的玩家。\n4. 主持人告知所有玩家：被“猎人”击杀的玩家死亡。（输出格式为【重要通知】本轮猎人击杀的玩家为XXX）同时向所有玩家宣布，被投票中的角色会视为立即死亡（即不再视为存活角色）。\n5. 在宣布死亡玩家后，本阶段流程结束，由后续阶段继续推进游戏。\n\n##注意##\n1.通知完死亡玩家是谁后，本阶段才结束，由后续阶段继续推进游戏。在下一轮输出 {"action": "taskend"}即可。\n2.主持人只有当通知本轮死亡的玩家时，才使用【重要通知】的前缀，其他情况下不要使用【重要通知】前缀。\n\n##example##\n{"thought": "根据周杰的选择，他决定使用猎人的能力，并击杀1号贺子轩。现在需要告知所有玩家这一结果，并继续游戏。", "action": {"agent_name": "主持人", "player_name": ""}, "Dungeon_Master": [{"memory_tag": ["all"], "content": "【重要通知】本轮猎人击杀的玩家为1号贺子轩。"}]}\n\n该任务的参与者只有“猎人”和主持人，信息可见对象是“猎人”，其他角色玩家不可见。', 'name': '猎人时刻_2'}), +GNode(id='剧本杀/狼人杀/智能交互/狼人杀判断游戏是否结束', type='opsgptkg_task', attributes={'ID': -2316854558435035646, 'description': '狼人杀判断游戏是否结束', 'name': '狼人杀判断游戏是否结束', 'accesscriteria': '', 'extra': '{"memory_tag": "None"}', 'teamids': '', 'gdb_timestamp': '1725092210244', 'executetype': ''}), +GNode(id='剧本杀/狼人杀/智能交互/事实_2', type='opsgptkg_phenomenon', attributes={'ID': -6298561983042120406, 'extra': '', 'teamids': '', 'gdb_timestamp': '1724816562165', 'description': '否', 'name': '否'}), +GNode(id='剧本杀/狼人杀/智能交互/事实_1', type='opsgptkg_phenomenon', attributes={'ID': 6987562967613654408, 'gdb_timestamp': '1724816495297', 'description': '是', 'name': '是', 'extra': '', 'teamids': ''}), +GNode(id='剧本杀/狼人杀/智能交互/狼人杀给出每个人的单词以及最终胜利者', type='opsgptkg_task', attributes={'ID': -758955621725402723, 'extra': '{"dodisplay":"True"}', 'teamids': '', 'gdb_timestamp': '1725097362872', 'executetype': '', 'description': '狼人杀给出每个人的单词以及最终胜利者', 'name': '狼人杀给出每个人的单词以及最终胜利者', 'accesscriteria': ''}), +GNode(id='剧本杀', type='opsgptkg_intent', attributes={'ID': -3388526698926684245, 'description': '文本游戏相关（如狼人杀等）', 'name': '剧本杀', 'extra': '', 'teamids': '', 'gdb_timestamp': '1724815537102'}) +] new_edges_2 = \ [GEdge(start_id='剧本杀', end_id='剧本杀/谁是卧底', type='opsgptkg_intent_route_opsgptkg_intent', attributes={'SRCID': -3388526698926684245, 'DSTID': -5201231166222141228, 'gdb_timestamp': '1725088433347', 'extra': ''}), @@ -166,15 +176,18 @@ def hash_id(nodeId, sessionId='', otherstr = None): GEdge(start_id='剧本杀/谁是卧底', end_id='剧本杀/谁是卧底/智能交互', type='opsgptkg_intent_route_opsgptkg_schedule', attributes={'SRCID': -5201231166222141228, 'DSTID': 603563742932974030, 'gdb_timestamp': '1725088478251', 'extra': ''}), GEdge(start_id='剧本杀/狼人杀', end_id='剧本杀/狼人杀/智能交互', type='opsgptkg_intent_route_opsgptkg_schedule', attributes={'SRCID': 5476827419397129797, 'DSTID': -5931163481230280444, 'gdb_timestamp': '1724815633494', 'extra': ''}), GEdge(start_id='剧本杀/谁是卧底/智能交互', end_id='剧本杀/谁是卧底/智能交互/分配座位', type='opsgptkg_schedule_route_opsgptkg_task', attributes={'SRCID': 603563742932974030, 'DSTID': 2011080219630105469, 'gdb_timestamp': '1725088659469', 'extra': ''}), - GEdge(start_id='剧本杀/狼人杀/智能交互', end_id='剧本杀/狼人杀/智能交互/位置选择', type='opsgptkg_schedule_route_opsgptkg_task', attributes={'SRCID': -5931163481230280444, 'DSTID': 2541178858602010284, 'gdb_timestamp': '1724815720186', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互', end_id='剧本杀/狼人杀/智能交互/狼人杀分配座位', type='opsgptkg_schedule_route_opsgptkg_task', attributes={'SRCID': -5931163481230280444, 'DSTID': 2541178858602010284, 'gdb_timestamp': '1724815720186', 'extra': ''}), GEdge(start_id='剧本杀/谁是卧底/智能交互/分配座位', end_id='剧本杀/谁是卧底/智能交互/角色分配和单词分配', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 2011080219630105469, 'DSTID': -1817533533893637377, 'gdb_timestamp': '1725088761379', 'extra': ''}), - GEdge(start_id='剧本杀/狼人杀/智能交互/位置选择', end_id='剧本杀/狼人杀/智能交互/角色选择', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 2541178858602010284, 'DSTID': -8695604495489305484, 'extra': '', 'gdb_timestamp': '1724815828424'}), + GEdge(start_id='剧本杀/狼人杀/智能交互/狼人杀分配座位', end_id='剧本杀/狼人杀/智能交互/狼人杀角色分配和单词分配', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 2541178858602010284, 'DSTID': -8695604495489305484, 'extra': '', 'gdb_timestamp': '1724815828424'}), GEdge(start_id='剧本杀/谁是卧底/智能交互/角色分配和单词分配', end_id='剧本杀/谁是卧底/智能交互/通知身份', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -1817533533893637377, 'DSTID': 8901447933395410622, 'gdb_timestamp': '1725088813780', 'extra': ''}), - GEdge(start_id='剧本杀/狼人杀/智能交互/角色选择', end_id='剧本杀/狼人杀/智能交互/向玩家通知消息', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -8695604495489305484, 'DSTID': -4014299322597660132, 'gdb_timestamp': '1724815943792', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/狼人杀角色分配和单词分配', end_id='剧本杀/狼人杀/智能交互/向玩家通知消息', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -8695604495489305484, 'DSTID': -4014299322597660132, 'gdb_timestamp': '1724815943792', 'extra': ''}), GEdge(start_id='剧本杀/谁是卧底/智能交互/通知身份', end_id='剧本杀/谁是卧底/智能交互/关键信息_1', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 8901447933395410622, 'DSTID': 3196717310525578616, 'extra': '', 'gdb_timestamp': '1725364881808'}), - GEdge(start_id='剧本杀/狼人杀/智能交互/向玩家通知消息', end_id='剧本杀/狼人杀/智能交互/狼人时刻', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -4014299322597660132, 'DSTID': 8926130661368382825, 'gdb_timestamp': '1724815952503', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/向玩家通知消息', end_id='剧本杀/狼人杀/智能交互/狼人时刻_投票', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -4014299322597660132, 'DSTID': 8926130661368382825, 'gdb_timestamp': '1724815952503', 'extra': ''}), GEdge(start_id='剧本杀/谁是卧底/智能交互/关键信息_1', end_id='剧本杀/谁是卧底/智能交互/开始新一轮的讨论', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 3196717310525578616, 'DSTID': -6077057339616293423, 'extra': '', 'gdb_timestamp': '1725364891197'}), - GEdge(start_id='剧本杀/狼人杀/智能交互/狼人时刻', end_id='剧本杀/狼人杀/智能交互/天亮讨论', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 8926130661368382825, 'DSTID': 274796810216558717, 'gdb_timestamp': '1724911515908', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/狼人时刻_投票', end_id='剧本杀/狼人杀/智能交互/狼人时刻_统计票数', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 8926130661368382825, 'DSTID': 8926130661368382925, 'gdb_timestamp': '1724911515907', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/狼人时刻_统计票数', end_id='剧本杀/狼人杀/智能交互/预言家时刻', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 8926130661368382925, 'DSTID': 8926130661368382826, 'gdb_timestamp': '1724911515908', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/预言家时刻', end_id='剧本杀/狼人杀/智能交互/女巫时刻', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 8926130661368382826, 'DSTID': 8926130661368382827, 'gdb_timestamp': '1724911515909', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/女巫时刻', end_id='剧本杀/狼人杀/智能交互/天亮讨论', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 8926130661368382827, 'DSTID': 274796810216558717, 'gdb_timestamp': '1724911515911', 'extra': ''}), GEdge(start_id='剧本杀/谁是卧底/智能交互/开始新一轮的讨论', end_id='剧本杀/谁是卧底/智能交互/关键信息_2', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -6077057339616293423, 'DSTID': -8309123437761850283, 'extra': '', 'gdb_timestamp': '1725364966817'}), GEdge(start_id='剧本杀/狼人杀/智能交互/天亮讨论', end_id='剧本杀/狼人杀/智能交互/票选凶手', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 274796810216558717, 'DSTID': 1492108834523573937, 'extra': '', 'gdb_timestamp': '1724816423574'}), GEdge(start_id='剧本杀/谁是卧底/智能交互/关键信息_2', end_id='剧本杀/谁是卧底/智能交互/票选卧底_1', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': -8309123437761850283, 'DSTID': 267468674566989196, 'gdb_timestamp': '1725507894066', 'extra': ''}), @@ -186,11 +199,12 @@ def hash_id(nodeId, sessionId='', otherstr = None): GEdge(start_id='剧本杀/谁是卧底/智能交互/判断游戏是否结束', end_id='剧本杀/谁是卧底/智能交互/事实_2', type='opsgptkg_task_route_opsgptkg_phenomenon', attributes={'SRCID': -5959590132883379159, 'DSTID': 4216433814773851843, 'extra': '', 'gdb_timestamp': '1725089603500'}), GEdge(start_id='剧本杀/谁是卧底/智能交互/事实_1', end_id='剧本杀/谁是卧底/智能交互/给出每个人的单词以及最终胜利者', type='opsgptkg_phenomenon_route_opsgptkg_task', attributes={'SRCID': -525629912140732688, 'DSTID': 8878899410716129093, 'gdb_timestamp': '1725089654391', 'extra': ''}), GEdge(start_id='剧本杀/谁是卧底/智能交互/事实_2', end_id='剧本杀/谁是卧底/智能交互/开始新一轮的讨论', type='opsgptkg_phenomenon_route_opsgptkg_task', attributes={'SRCID': 4216433814773851843, 'DSTID': -6077057339616293423, 'extra': '', 'gdb_timestamp': '1725089612866'}), - GEdge(start_id='剧本杀/狼人杀/智能交互/票选凶手', end_id='剧本杀/狼人杀/智能交互/判断游戏是否结束', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 1492108834523573937, 'DSTID': -2316854558435035646, 'extra': '', 'gdb_timestamp': '1724816464917'}), - GEdge(start_id='剧本杀/狼人杀/智能交互/判断游戏是否结束', end_id='剧本杀/狼人杀/智能交互/事实_2', type='opsgptkg_task_route_opsgptkg_phenomenon', attributes={'SRCID': -2316854558435035646, 'DSTID': -6298561983042120406, 'gdb_timestamp': '1724816570641', 'extra': ''}), - GEdge(start_id='剧本杀/狼人杀/智能交互/判断游戏是否结束', end_id='剧本杀/狼人杀/智能交互/事实_1', type='opsgptkg_task_route_opsgptkg_phenomenon', attributes={'SRCID': -2316854558435035646, 'DSTID': 6987562967613654408, 'gdb_timestamp': '1724816506031', 'extra': ''}), - GEdge(start_id='剧本杀/狼人杀/智能交互/事实_2', end_id='剧本杀/狼人杀/智能交互/狼人时刻', type='opsgptkg_phenomenon_route_opsgptkg_task', attributes={'SRCID': -6298561983042120406, 'DSTID': 8926130661368382825, 'extra': '', 'gdb_timestamp': '1724816585403'}), - GEdge(start_id='剧本杀/狼人杀/智能交互/事实_1', end_id='剧本杀/l狼人杀/智能交互/宣布游戏胜利者', type='opsgptkg_phenomenon_route_opsgptkg_task', attributes={'SRCID': 6987562967613654408, 'DSTID': -758955621725402723, 'gdb_timestamp': '1724911404270', 'extra': ''})] + GEdge(start_id='剧本杀/狼人杀/智能交互/票选凶手', end_id='剧本杀/狼人杀/智能交互/统计票数', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 1492108834523573937, 'DSTID': 8926130661368382838, 'extra': '', 'gdb_timestamp': '1724816464915'}), + GEdge(start_id='剧本杀/狼人杀/智能交互/统计票数', end_id='剧本杀/狼人杀/智能交互/狼人杀判断游戏是否结束', type='opsgptkg_task_route_opsgptkg_task', attributes={'SRCID': 1492108834523573938, 'DSTID': -2316854558435035646, 'extra': '', 'gdb_timestamp': '1724816464917'}), + GEdge(start_id='剧本杀/狼人杀/智能交互/狼人杀判断游戏是否结束', end_id='剧本杀/狼人杀/智能交互/事实_2', type='opsgptkg_task_route_opsgptkg_phenomenon', attributes={'SRCID': -2316854558435035646, 'DSTID': -6298561983042120406, 'gdb_timestamp': '1724816570641', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/狼人杀判断游戏是否结束', end_id='剧本杀/狼人杀/智能交互/事实_1', type='opsgptkg_task_route_opsgptkg_phenomenon', attributes={'SRCID': -2316854558435035646, 'DSTID': 6987562967613654408, 'gdb_timestamp': '1724816506031', 'extra': ''}), + GEdge(start_id='剧本杀/狼人杀/智能交互/事实_2', end_id='剧本杀/狼人杀/智能交互/狼人时刻_投票', type='opsgptkg_phenomenon_route_opsgptkg_task', attributes={'SRCID': -6298561983042120406, 'DSTID': 8926130661368382825, 'extra': '', 'gdb_timestamp': '1724816585403'}), + GEdge(start_id='剧本杀/狼人杀/智能交互/事实_1', end_id='剧本杀/狼人杀/智能交互/狼人杀给出每个人的单词以及最终胜利者', type='opsgptkg_phenomenon_route_opsgptkg_task', attributes={'SRCID': 6987562967613654408, 'DSTID': -758955621725402723, 'gdb_timestamp': '1724911404270', 'extra': ''})] new_edges_3 = [GEdge(start_id='ekg_team_default', end_id='剧本杀', type='opsgptkg_intent_route_opsgptkg_intent', attributes={'SRCID': 9015207174144, 'DSTID': -3388526698926684245, 'gdb_timestamp': '1724816506031', 'extra': ''})] diff --git a/muagent/connector/memory_manager.py b/muagent/connector/memory_manager.py index 5c5bcd0..e536466 100644 --- a/muagent/connector/memory_manager.py +++ b/muagent/connector/memory_manager.py @@ -5,6 +5,7 @@ from collections import Counter from loguru import logger import numpy as np +import logging from langchain_community.docstore.document import Document @@ -877,7 +878,7 @@ def get_msg_by_role_name(self, chat_index: str, role_name: str) -> Optional[Mess return msg return None - def get_msg_content_by_role_name(self, chat_index: str, role_name: str) -> Optional[str]: + def get_msg_content_by_rule_name(self, chat_index: str, role_name: str) -> Optional[str]: message = self.get_msg_by_role_name(chat_index, role_name) if message == None: return None @@ -889,16 +890,33 @@ def update_msg_content_by_rule(self, chat_index: str, role_name: str, new_conten if message == None: return False - - prompt = f"{new_content}\n{role_name}:{message.role_content}\n{update_rule}" - + if update_rule == '': + prompt = '任务：请根据游戏内容，更新变量，变量名为：' + role_name + '，变量更新前的内容为：' + message.role_content + '。本节点游戏记录：' + new_content + '。请根据游戏内容，输出更新后的变量内容，不要包含其他信息，不要重复变量名，只输出变量更新后的内容即可。。' + else: + prompt = '任务：请根据游戏内容，更新变量，变量名为：' + role_name + '，变量更新前的内容为：' + message.role_content + '。本节点游戏记录：' + new_content + '。变量更新规则为：' + update_rule + '。请根据游戏内容和变量更新规则，输出更新后的变量内容，不要包含其他信息，不要重复变量名，只输出变量更新后的内容即可。' + logging.info(f'变量更新的prompt:{prompt}') model = getChatModelFromConfig(self.llm_config) new_role_content = model.predict(prompt) - + logging.info(f'变量更新的输出结果:{new_role_content}') if new_role_content is not None: message.role_content = new_role_content self.append(message) + logging.info(f'输出结果：{self.get_msg_content_by_rule_name(chat_index, role_name)}') + + return True + else: + return False + + def update_global_msg_content(self, chat_index: str, role_name: str, new_content: str) -> bool: + message = self.get_msg_by_role_name(chat_index, role_name) + print(f' message if {message}') + if message == None: + return False + + if new_content is not None: + message.role_content = new_content + self.append(message) return True else: return False \ No newline at end of file diff --git a/muagent/db_handler/graph_db_handler/nebula_handler.py b/muagent/db_handler/graph_db_handler/nebula_handler.py index cdbd825..9d94180 100644 --- a/muagent/db_handler/graph_db_handler/nebula_handler.py +++ b/muagent/db_handler/graph_db_handler/nebula_handler.py @@ -63,11 +63,13 @@ def __init__(self,gb_config : GBConfig = None): self.nb_pw = '' or 'nebula' self.space_name = "client" else: + logger.info('NebulaGraph容器启动中，请等待') if self.nebula_started(gb_config): self.connection_pool.init([(gb_config.extra_kwargs.get("host"), gb_config.extra_kwargs.get("port"))], config) + self.username = gb_config.extra_kwargs.get("username") self.nb_pw = gb_config.extra_kwargs.get("password") self.space_name = gb_config.extra_kwargs.get("space") @@ -317,16 +319,7 @@ def add_node(self, node: GNode) -> GbaseExecStatus: if prop_name in {'extra', 'description', 'envdescription','updaterule'}: # 转义换行符和双引号 value = value.replace("\n", "\\n").replace("\"", "\\\"") - cypher += f'"{value}",' - elif prop_name == 'description': - value = value.replace("\n", "\\n").replace("\"", "\\\"") - cypher += f'"{value}",' - elif prop_name == 'envdescription': - value = value.replace("\n", "\\n").replace("\"", "\\\"") - cypher += f'"{value}",' - else: - cypher += f'"{value}",' - #cypher += f'"{value}",' + cypher += f'"{value}",' else: cypher += f'{value},' cypher = cypher.rstrip(',') diff --git a/muagent/schemas/ekg/ekg_reason.py b/muagent/schemas/ekg/ekg_reason.py new file mode 100644 index 0000000..11af385 --- /dev/null +++ b/muagent/schemas/ekg/ekg_reason.py @@ -0,0 +1,324 @@ +from pydantic import BaseModel, Field +from typing import List, Dict, Optional, Union +from enum import Enum +import copy +import json + + + + +##################################################################### +############################ LingSiResponse ############################# +##################################################################### +class LingSiResponse(BaseModel): + ''' + lingsi的输出值， 算法的输入值 + ''' + currentNodeId: Optional[str]=None + observation: Optional[Union[str,Dict]] # jsonstr + scene: str + sessionId: str + startRootNodeId: Optional[str] = None + intentionRule: Optional[Union[List,str] ]= None + intentionData: Optional[Union[List,str] ] = None + startFromRoot: Optional[str] = None + type: Optional[str] = None + userAnswer: Optional[str]=None + agentName:Optional[str]=None + usingRootNode:Optional[bool]=False + + + + + + + +##################################################################### +############################ #定义PlanningRunning 和 parallel 模式下大模型返回格式 ############################# +##################################################################### + +class ActionOneStep(BaseModel): + ''' + 指定下一步的动作由哪一个agent / player 完成 + example {"player_name":str, "agent_name":str} + ''' + agent_name: str + player_name:str + +class ActionPlan(BaseModel): + ''' + 指定后续的动作由哪些agent / player 完成 + + [{"player_name":str, "agent_name":str}, {"player_name":str, "agent_name":str}, ... ] + ''' + data: List[ActionOneStep] + def get_player_name_by_agent_name(self, agent_name:str)->str: + ''' + 根据agent_name 返回 player_name + + ''' + for i in range(len(self.data)): + if self.data[i].agent_name == agent_name: + return self.data[i].player_name + return None #没找到合适的匹配 + + + + +class ObservationItem(BaseModel): + ''' + 假设 agent 说话时返回的数据格式，除了content外，还有memory_tag, 指定这个信息有谁可见 + ''' + memory_tag: List[str] + content: str + + + + + +class PlanningRunningAgentReply(BaseModel): + ''' + 示例数据 + sss = { + "thought": "思考内容", + "action_plan": [ + {"player_name": "player1", "agent_name": "agent1"}, + {"player_name": "player2", "agent_name": "agent2"} + ], + "Dungeon_Master": [ + {"memory_tag": ["agent_name_a", "agent_name_b"], "content": "DM 内容1"}, + {"memory_tag": ["agent_name_c"], "content": "DM 内容2"} + ] + } + ''' + thought: str = "None" + action_plan: ActionPlan=[] + observation: List[ObservationItem]=[] + + def __init__(self, **kwargs): + # 处理 action_plan + action_steps = [ActionOneStep(**step) for step in kwargs.get('action_plan', [])] + action_plan = ActionPlan(data=action_steps) + + # 处理 observation + observations = [ObservationItem(**item) for item in kwargs.get('Dungeon_Master', [])] + + # 调用父类的初始化方法 + super().__init__( + thought=kwargs.get('thought', "None"), + action_plan=action_plan, + observation=observations + ) + + + +##################################################################### +############################ #定义输出plan格式 ############################# +##################################################################### + +class QuestionContent(BaseModel): + ''' + {'question': '请玩家根据当前情况发言', 'candidate': None } + ''' + question:str + candidate:Optional[str]=None + +class QuestionDescription(BaseModel): + ''' + {'questionType': 'essayQuestion', + 'questionContent': {'question': '请玩家根据当前情况发言','candidate': None }} + ''' + questionType:str + questionContent:QuestionContent + +class ToolPlanOneStep(BaseModel): + ''' + tool_plan_one_step = {'toolDescription': '请用户回答', + 'currentNodeId': nodeId, + 'memory': None, + 'type': 'userProblem', + 'questionDescription': {'questionType': 'essayQuestion', + 'questionContent': {'question': '请玩家根据当前情况发言', + 'candidate': None }}} + ''' + toolDescription:str + currentNodeId: Optional[str] = None + currentNodeInfo:Optional[str] = None + memory:Optional[str] = None + type:str + questionDescription:Optional[QuestionDescription]=None + +##################################################################### +############################ ResToLingsi ############################# +##################################################################### +class ResToLingsi(BaseModel): + ''' + lingsi的输入值， 算法的输出值 + +{'intentionRecognitionSituation': 'None', + 'sessionId': 'c122401123504af09dbf80f94be0854d', + 'type': 'onlyTool', + 'summary': None, + 'toolPlan': [{'toolDescription': 'agent_李静', + 'currentNodeId': '26921eb05153216c5a1f585f9d318c77%%@@#agent_李静', + 'currentNodeInfo': 'agent_李静', + 'memory': '["{\\"content\\": \\"开始玩谁是卧底的游戏\\"}", "分配座位", "\\n\\n| 座位 | 玩家 |\\n|---|---|\\n| 1 | **李静** |\\n| 2 | **张伟** |\\n| 3 | **人类玩家** |\\n| 4 | **王鹏** |", "通知身份", "主持人 : 【身份通知】你是李静, 你的位置是1号， 你分配的单词是包子", "开始新一轮的讨论", "主持人 : 各位玩家请注意，现在所有玩家均存活，我们将按照座位顺序进行发言。发言顺序为1号李静、2号张伟、3号人类玩家、4号王鹏。现在，请1号李静开始发言。"]', + 'type': 'reactExecution', + 'questionDescription': None}], + 'userInteraction': '开始新一轮的讨论

**主持人:**
各位玩家请注意，现在所有玩家均存活，我们将按照座位顺序进行发言。发言顺序为1号李静、2号张伟、3号人类玩家、4号王鹏。现在，请1号李静开始发言。'} + + ''' + intentionRecognitionSituation: Optional[str]=None + sessionId: str + type: Optional[str] = None + summary:Optional[str] = None + toolPlan:Optional[List[ToolPlanOneStep]] = None + userInteraction:Optional[str]=None + +if __name__ == '__main__': + # response_data = LingSiResponse( + # currentNodeId="node_1", + # observation='{"key": "value"}', + # scene="example_scene", + # sessionId="session_123", + # startRootNodeId="root_node_1", + # type="example_type", + # userAnswer="user_answer_example" + + # ) + + # print( response_data.type ) + # 示例数据 + # sss = { + # "thought": "思考内容", + # "action_plan": [ + # {"player_name": "player1", "agent_name": "agent1"}, + # {"player_name": "player2", "agent_name": "agent2"} + # ], + # "Dungeon_Master": [ + # {"memory_tag": ["agent_name_a", "agent_name_b"], "content": "DM 内容1"}, + # {"memory_tag": ["agent_name_c"], "content": "DM 内容2"} + # ] + # } + + # # 直接使用 sss 创建 PlanningRunningAgentReply 对象 + # reply = PlanningRunningAgentReply(**sss) + + + # #测试ToolPlanOneStep + # # 定义 Sss + # nodeId = 'somenodeid' + # Sss = { + # 'toolDescription': '请用户回答', + # 'currentNodeId': nodeId, + # 'memory': None, + # 'type': 'userProblem', + # 'questionDescription': { + # 'questionType': 'essayQuestion', + # 'questionContent': { + # 'question': '请玩家根据当前情况发言', + # 'candidate': None + # } + # } + # } + + # # 将 Sss 转换为 ToolPlanOneStep 对象 + # tool_plan_one_step = ToolPlanOneStep(**Sss) + + + ###测试 LingSiResponse + sessionId = '349579720439752097847_25' + #谁是卧底 通用 + params_string = \ + { + + "scene": "NEXA", + "startRootNodeId": '4bf08f20487bfb3d34048ddf455bf5dd', #hash_id('剧本杀' ), + "intentionRule": ["nlp"], + "intentionData": "执行谁是卧底进程", + "observation": "{\"content\":\"开始游戏\"}", + "sessionId": sessionId + } + + lingsi_response = LingSiResponse(**params_string) + print(lingsi_response) + + + + # ###测试 LingSiResponse + # nodeId = 's' + # execute_agent_name = 'n' + + # tool_one_step= ToolPlanOneStep( + # **{'toolDescription': '请用户回答', + # 'currentNodeId': nodeId + '%%@@#' + execute_agent_name, + # 'currentNodeInfo':execute_agent_name, + # 'memory': None, + # 'type': 'userProblem', + # 'questionDescription': {'questionType': 'essayQuestion', + # 'questionContent': {'question': '请玩家根据当前情况发言', + # 'candidate': None }}} + # ) + # print(tool_one_step) + + + + ###测试 ActionPlan + + + + # ccc = {'data': [{'agent_name': 'agent_2', 'player_name': 'player_1'}, + # {'agent_name': '人类agent_a', 'player_name': '李四(人类玩家)'}, + # {'agent_name': 'agent_3', 'player_name': 'player_2'}, + # {'agent_name': 'agent_1', 'player_name': 'player_3'}]} + + # ccc = [{"player_name": "主持人", "agent_name": "主持人"}, + # {"player_name": "player_1", "agent_name": "agent_2"}, + # {"player_name": "李四(人类玩家)", "agent_name": "人类agent_a"}, + # {"player_name": "player_2", "agent_name": "agent_3"}, + # {"player_name": "player_3", "agent_name": "agent_1"}] + # ccc = {'data':ccc} + + # action_plan =ActionPlan(**ccc) + # print(action_plan) + + # agent_name= action_plan.get_player_name_by_agent_name('人类agent_a') + # print(agent_name) + + + ###测试 ResToLingsi ### + nodeId = 'somenodeid' + # sss = { + # 'toolDescription': '请用户回答', + # 'currentNodeId': nodeId, + # 'memory': None, + # 'type': 'userProblem', + # 'questionDescription': { + # 'questionType': 'essayQuestion', + # 'questionContent': { + # 'question': '请玩家根据当前情况发言', + # 'candidate': None + # } + # } + # } + # sss = { + # "toolDescription": "toolDescriptionA", + # "currentNodeId": "INT_1", + # "memory": '', + # "type":"onlyTool", + # } + # # 将 Sss 转换为 ToolPlanOneStep 对象 + # tool_plan_one_step = ToolPlanOneStep(**sss) + # bbb = {'intentionRecognitionSituation': 'None', + # 'sessionId': 'c122401123504af09dbf80f94be0854d', + # 'type': 'onlyTool', + # 'summary': None, + # 'toolPlan': [tool_plan_one_step], + # 'userInteraction': '开始新一轮的讨论

**主持人:**
各位玩家请注意，现在所有玩家均存活，我们将按照座位顺序进行发言。发言顺序为1号李静、2号张伟、3号人类玩家、4号王鹏。现在，请1号李静开始发言。'} + + # ResToLingsi_one = ResToLingsi(**bbb) + # print(ResToLingsi_one.dict()) + + + + \ No newline at end of file diff --git a/muagent/service/ekg_reasoning/src/geabase_handler/geabase_handlerplus.py b/muagent/service/ekg_reasoning/src/geabase_handler/geabase_handlerplus.py index 68c9000..2edcc68 100644 --- a/muagent/service/ekg_reasoning/src/geabase_handler/geabase_handlerplus.py +++ b/muagent/service/ekg_reasoning/src/geabase_handler/geabase_handlerplus.py @@ -7,6 +7,7 @@ #路径增加 import sys import os +from typing import List, Dict, Optional, Union src_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) ) @@ -63,14 +64,14 @@ def geabase_is_react_node(self, start_nodeid, start_nodetype): extra = json.loads(extra) if 'pattern' in extra.keys(): - if extra['pattern'] == 'one-tool': + if extra['pattern'] == 'one-tool' or extra['pattern'] == 'single': return False - elif extra['pattern'] == 'react': + elif extra['pattern'] == 'react' or extra['pattern'] == 'parallel' or extra['pattern'] == 'plan': return True else: return False - else: #默认为 one-tool + else: #默认为 single return False @@ -95,6 +96,76 @@ def geabase_search_return_all_nodeandedge_(self, start_nodeid = 'None', for i in range(len(t.edges)): edge_in_subtree.append({ "startNodeId": t.edges[i].start_id, "endNodeId": t.edges[i].end_id }) return nodeid_in_subtree, edge_in_subtree + + + def geabase_search_reture_nodeslist(self, start_nodeid:str,start_nodetype:str, block_search_nodetype:List=[] ): + + ''' + #返回start_nodeid后续子树上的所有节点id , start_nodeid 是一个意图节点的末端节点， 可以设置停止探索的类型，比如 + #block_search_nodetype = ['opsgptkg_schedule', 'opsgptkg_task'] 时，代表这两种类型的节点不往后继续探索 + #同时返回边 + + 不直接使用 get_hop_infos + + 一个意图节点（非叶子意图节点）下可能有多个叶子节点，每个叶子节点也可能有多个意图节点。 在并行的时候，需要将某个意图节点单独成一个 nodeid_in_subtree + 如果直接调用 geabase_search_return_all_nodeandedge ，会把某个节点下所有的内容全放到一个list里 + ''' + + nodeid_in_subtree = [{'nodeId':start_nodeid, 'nodeType':start_nodetype, 'nodeDescription':None, 'nodeName':None}] + edge_in_subtree = [] + nodeid_in_search = [{'nodeId':start_nodeid, 'nodeType':start_nodetype}] + count = 0 + + reslist = [] + + + while len(nodeid_in_search)!= 0: + # print(f'count is {count}') + nodedict_now = nodeid_in_search.pop() + nodeid_now = nodedict_now['nodeId'] + nodetype_now = nodedict_now['nodeType'] + + + neighborNodes = self.geabase_handler.get_neighbor_nodes(attributes= {"id": nodeid_now,} + , node_type= nodetype_now ) + + for i in range(len(neighborNodes)): + + nodeid_new = neighborNodes[i].id + nodetype_new = neighborNodes[i].type + nodedescription_new = neighborNodes[i].attributes['description'] + nodename_new = neighborNodes[i].attributes['name'] + if nodetype_new in block_search_nodetype: #遇到阻塞的节点类型就终止， 不继续探索，也不纳入到结果中 + continue + if nodetype_new == 'opsgptkg_schedule': + + one_subtree, _ = self.geabase_search_return_all_nodeandedge(start_nodeid = nodeid_new, + start_nodetype = nodetype_new, block_search_nodetype = []) + + one_subtree.append({'nodeId':nodeid_now, 'nodeType':nodetype_now, 'nodeDescription':None, 'nodeName':None}) + reslist.append(one_subtree) + + + if { "startNodeId": nodeid_now, "endNodeId": nodeid_new } not in edge_in_subtree:#避免重复导入相同的元素 + nodeid_in_subtree.append({'nodeId':nodeid_new, 'nodeType':nodetype_new, + 'nodeDescription':nodedescription_new, + 'nodeName':nodename_new}) + edge_in_subtree.append({ "startNodeId": nodeid_now, "endNodeId": nodeid_new }) + nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + else: + continue + count = count +1 + + #去重复 + unique_set = set(tuple(sorted(d.items())) for d in nodeid_in_subtree) + # 将去重后的元组转换回字典形式，得到去重后的list + nodeid_in_subtree = [dict(t) for t in unique_set] + + unique_set = set(tuple(sorted(d.items())) for d in edge_in_subtree) + # 将去重后的元组转换回字典形式，得到去重后的list + edge_in_subtree = [dict(t) for t in unique_set] + + return reslist def geabase_search_return_all_nodeandedge(self, start_nodeid = 'None', start_nodetype = 'opsgptkg_intent', block_search_nodetype = []): @@ -443,6 +514,8 @@ def geabase_getnodetype(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_tas else: return 'onlyTool' + + def get_extra_tag(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_task', key = 'ignorememory'): # print(f'rootNodeId is {rootNodeId}, rootNodeType is {rootNodeType}') try: @@ -463,8 +536,14 @@ def get_extra_tag(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_task', key return None else: return extra[key] + + + def get_tag_(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_task', key = 'ignorememory')->str: + ''' + 得到一个节点的属性值, 只判断属性 + ''' - def get_tag(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_task', key = 'ignorememory'): + # print(f'rootNodeId is {rootNodeId}, rootNodeType is {rootNodeType}') try: oneNode = self.geabase_handler.get_current_node(attributes={"id": rootNodeId,}, @@ -482,8 +561,23 @@ def get_tag(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_task', key = 'ig return oneNode.attributes[key] - - + def get_tag(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_task', key = 'ignorememory')->str: + ''' + 得到一个节点的属性值。 优先判断属性值；如果属性值为None，则判断extra中是否有值。都没有值返回None + ''' + if key == 'action': + key_ = 'pattern' + else: + key_ = key + get_tag_result = self.get_tag_(rootNodeId , rootNodeType , key ) + get_extra_tag_result = self.get_extra_tag(rootNodeId , rootNodeType , key_ ) + logging.info(f'get_tag_result is {get_tag_result}, get_extra_tag_result is {get_extra_tag_result}') + if get_tag_result != None: + return get_tag_result + elif get_tag_result==None and get_extra_tag_result!=None: + return get_extra_tag_result + else: + return None @@ -496,6 +590,8 @@ def user_input_memory_tag(self, rootNodeId = 'None', rootNodeType = 'opsgptkg_t logging.info('user_input_memory_tag 没有找到合适的数据， 可能原因是当前查找对象不是 opsgptkg_task 类型的节点' ) return None # print(oneNode) + if oneNode.attributes['extra'] == None: + return None if oneNode.attributes['extra'] == '': return None extra = oneNode.attributes['extra'] diff --git a/muagent/service/ekg_reasoning/src/graph_search/geabase_search_plus.py b/muagent/service/ekg_reasoning/src/graph_search/geabase_search_plus.py index 12706b1..d55c8e7 100644 --- a/muagent/service/ekg_reasoning/src/graph_search/geabase_search_plus.py +++ b/muagent/service/ekg_reasoning/src/graph_search/geabase_search_plus.py @@ -4,6 +4,7 @@ #路径增加 import sys import os +import re src_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) ) @@ -45,68 +46,18 @@ from muagent.schemas.db import GBConfig from muagent.service.ekg_construct import EKGConstructService from muagent.service.ekg_inference import IntentionRouter +from muagent.schemas.ekg.ekg_reason import LingSiResponse # from loguru import logger as logging from src.utils.call_llm import call_llm, extract_final_result , robust_call_llm from src.geabase_handler.geabase_handlerplus import GB_handler from src.utils.normalize import hash_id from src.memory_handler.ekg_memory_handler import memory_handler_ekg +from src.graph_search.task_node_agent import TaskNodeAgent -def is_valid_json(string): - try: - json.loads(string) - return True - except ValueError: - return False -def robust_json_loads(llm_result): - ''' - 将llm的输出转换为json， 有一定的概率这个json 在格式上是不完备的。不然少了 } 或者] - ''' - - try: - llm_result_json = json.loads(llm_result) - except: - # for _ in range(2): - try: - logging.info('大模型的输出转换json报错， 现在再用大模型修正一遍') - input_query = f''' - ##输入## - {llm_result} - ##任务## - 上述**输入**可能是一个不完整的dict，如果是这种情况，请将上述转换**输入**为完整的 dict。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 - 上述**输入**也可能是包含多个dict，如果是这种情况，只保留第一个dict即可。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 - ##直接输出结果## - ''' + '以{开头，任何其他内容都是不允许的！' - ouput_result = robust_call_llm(input_query, 'gpt_4') - logging.info(f'修正后的输出为{ouput_result}') - llm_result_json = json.loads(ouput_result) - - - except: - logging.info('大模型的输出转换json报错， 现在再用大模型修正一遍') - input_query = f''' - ##输入## - {llm_result} - ##任务## - 上述**输入**可能是一个不完整的dict，如果是这种情况，请将上述转换**输入**为完整的 dict。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 - 上述**输入**也可能是包含多个dict，如果是这种情况，只保留第一个dict即可。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 - ##直接输出结果## - ''' + '以{开头，任何其他内容都是不允许的！' - ouput_result = robust_call_llm(input_query, 'gpt_4',temperature = 0.1) - logging.info(f'修正后的输出为{ouput_result}') - llm_result_json = json.loads(ouput_result) - return llm_result_json - -def agent_respond_extract_output(input_str): - if 'output' not in input_str: - return input_str - input_str = input_str.split('output')[-1] #取output后面的值 - input_str = input_str.replace('"', '').replace(':', '').replace('}', '').replace('{', '') #去除掉可能的符号 - return input_str - class graph_search_tool(): @@ -120,675 +71,9 @@ def __init__(self, geabase_handler, memory_manager, llm_config=None): self.gb_handler = GB_handler(self.geabase_handler) #gb_handler 以 geabase_handler 为基础，封装了一些处理逻辑 self.memory_handler = memory_handler_ekg(memory_manager, geabase_handler) self.llm_config = llm_config - + self.task_node_agent = TaskNodeAgent(self.geabase_handler, self.memory_manager, self.llm_config) - def robust_call_llm_with_llmname(self, input_query, rootNodeId, stop = None, temperature = 0, presence_penalty=0): - - #logging.info('using a gpt_4') - res = call_llm(input_content = input_query, llm_model = 'gpt_4', stop = stop,temperature=temperature, presence_penalty=presence_penalty, - llm_config=self.llm_config) - return res - - - model_name = self.gb_handler.get_extra_tag( rootNodeId = rootNodeId, rootNodeType = 'opsgptkg_task', key = 'model_name') - logging.info(f'model_name is {model_name}') - if model_name == None: - model_name = 'gpt_4' - if model_name == 'gpt_4': - try: - logging.info('using a gpt_4') - res = call_llm(input_content = input_query, llm_model = 'gpt_4', stop = stop,temperature=temperature, presence_penalty=presence_penalty) - except: - logging.info('using Qwen2_72B_Instruct_OpsGPT') - res = call_llm(input_content = input_query, llm_model = 'Qwen2_72B_Instruct_OpsGPT',stop = stop, temperature=temperature,presence_penalty=presence_penalty) - return res - else: - try: - logging.info('using Qwen2_72B_Instruct_OpsGPT') - res = call_llm(input_content = input_query, llm_model = 'Qwen2_72B_Instruct_OpsGPT',stop = stop, temperature=temperature,presence_penalty=presence_penalty) - except: - logging.info('using a gpt_4') - res = call_llm(input_content = input_query, llm_model = 'gpt_4', stop = stop,temperature=temperature, presence_penalty=presence_penalty) - return res - - def stop_at_observation(self, historytext, llm_text): - - - # 检查llm_text是否完全不包含"observation" - if "observation" not in llm_text: - return llm_text - - # 检查historytext中是否有"observation" - if "observation" not in historytext: - return llm_text.split("observation", 1)[0] - - # 统计historytext中的"observation"数量 - n_hist = historytext.count("observation") - - # 统计llm_text中的"observation"数量 - n_llm = llm_text.count("observation") - - # 如果两者数量相等，返回第n个observation之后的文本 - if n_hist == n_llm: - parts = llm_text.rsplit("observation", n_hist) - return parts[-1] # 返回最后一个部分（即最后一个"observation"之后的文本） - - # 如果上述条件都不满足，找到第n个和第n+1个"observation"之间的文本 - else: - parts = llm_text.split("observation", n_hist + 1) # 分割出n+1个部分 - if len(parts) > n_hist + 1: # 确保有足够多的部分来获取所需范围 - return parts[n_hist] # 返回第n个和第n+1个observation之间的文本 - else: - return "" # 如果没有找到合适的范围，则返回空字符串或其他适当处理 - - - - def sort_messages_by_time(self, messages): - # 使用 sorted() 对列表进行排序，key 参数指定排序依据 - return sorted(messages, key=lambda msg: msg.start_datetime) - - def endcheck(self, nodeId, nodeType, oneNodeName='None', oneNodeDescription='None', current_node_history_json='None'): - ''' - 借助gpt4 来帮忙判断本阶段任务是否结束 - ''' - oneNode = self.geabase_handler.get_current_node(attributes={"id": nodeId,}, - node_type=nodeType) - extra = oneNode.attributes['extra'] - print(extra) - try: - - extra_json = json.loads(extra) - if extra_json['endcheck'] == 'True': - endcheck_flag = True - else: - endcheck_flag = False - except: - endcheck_flag= False - - if endcheck_flag == False: - return True #endcheck 通过 - - else: - endcheck_llm_input = oneNodeName + '\n' +oneNodeDescription+ '\n##已有步骤##\n' + json.dumps(current_node_history_json,ensure_ascii=False) + \ - '\n##请结合已有步骤，判断本阶段任务是否结束，只返回中文的 是 或者 否即可，不要输出其他内容:##\n' - - logging.info('=============endcheck_llm_result==================') - logging.info(endcheck_llm_input) - llm_result = self.robust_call_llm_with_llmname(endcheck_llm_input, nodeId) - logging.info('=============endcheck_llm_result==================') - logging.info(llm_result) - - if '是' in llm_result: - return False - else: - return True - - - def react_running(self, sessionId, nodeId, nodeType, agent_respond = ''): - ''' - react 模块 运行 - - ''' - if agent_respond == None: - agent_respond = '' - if type(agent_respond) == str: - agent_respond = agent_respond.replace('"', '').replace("'", "") #需要去除agent返回中的 " 和 ' - agent_respond = agent_respond_extract_output(agent_respond) # 去除 agent_respond 中的 thought 和 output - #stpe1 判断当前状态 - get_messages_res = self.memory_handler.nodecount_get( sessionId, nodeId) - - if get_messages_res == [] : - logging.info('当前这个{sessionId} react节点 是第一次运行') - first_run_react_flag = True - else: - if json.loads(get_messages_res[0].role_content)['nodestage'] == 'end' :#在上一轮已经结束了，这一轮还未开始 - logging.info('当前这个{sessionId} react节点在上一轮已经结束了，这一轮还未开始，在这一轮也算是第一次执行') - first_run_react_flag = True - else: - logging.info('当前这个{sessionId} react节点 不是第一次执行') - first_run_react_flag = False - - if first_run_react_flag == True: - # 当react的状态是 end 或者为空的时候调用此函数，进行初始化 或者 chapter + 1 - self.memory_handler.init_react_count(sessionId, nodeId) - - #step2 获取节点名字 + 节点描述 - oneNode = self.geabase_handler.get_current_node(attributes={"id": nodeId,}, - node_type=nodeType) - - oneNodeName = oneNode.attributes['name'] - oneNodeDescription = oneNode.attributes['description'] - - #step3.1 获取memory， 构成给大模型的输入 - #获取memory， 主持人能看到的memory， 和获取tool的memory类似 - - # tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) - # get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #此时是主持人，所以需要看到所有的memory，无需加tag。 对于在我这一侧需要运行的llm，一定是看到所有信息，因为我就是主持人 - # assembled_memory = self.assemble_memory(nodeId, nodeType, get_messages_res_sorted) - assembled_memory = self.get_memory_for_dm(sessionId, nodeId) - assembled_memory = json.dumps(assembled_memory, ensure_ascii=False) - logging.info(f'assembled_memory is {assembled_memory}') - - #step3.2 获取当前节点的历史运行情况。如果是第一次运行，需要将react node 的name 存入到 memory中 - if first_run_react_flag == True: - current_node_history = '' - #第一次运行，对于react模块，只将标题放置在memory里， 因为对于react模块，description太多了，循环的情况下，很罗嗦且超过上下文 - self.memory_handler.react_nodedescription_save(sessionId, nodeId, oneNodeName) - - else: - #不是第一次运行。那么肯定历史history进来 - logging.info(f'#不是第一次运行。那么肯定历史history进来{sessionId}, {nodeId}') - current_node_history = self.memory_handler.react_current_history_get(sessionId, nodeId) - # llm_result_truncation + '": [{"content": ' + user_responds - - ''' - history 里存储 observation的截断，不包含observation， - llm_output 输出整个完整的流程（如果是gpt_4, 不能有停用词，因为每次都是从头开始录的）， - self.stop_at_observation，需要接受 current_node_history ，先分析里面有几个observation（N个）， 然后我可以往后扩展一个observation， 不包含observation - - jsonstring 就转全量的， 但是录入到memory中的时候，注意只录入 N+1 个 observation的信息。 - - - - ''' - #step4 执行 llm， - if first_run_react_flag == True: - llm_input = assembled_memory + '\n' + oneNodeName + '\n' + oneNodeDescription + '\n##已有步骤##\n无' + '\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' - logging.info('=============llm_input==================') - logging.info(llm_input) - llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) - current_node_history_json = [] - logging.info('=============llm_result==================') - logging.info(llm_result) - llm_result_json = robust_json_loads(llm_result) - if type(llm_result_json)!=dict: - llm_result_json = llm_result_json[0] - logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') - # raise ValueError(f'llm的输出应该是一个dict才对 ') - current_node_history_json.append(llm_result_json) - else: - # current_node_history[-1]["observation"]['content'] = agent_respond - # llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n' + current_node_history + '": [{"content":" ' + agent_respond + '"' - # llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n' + current_node_history + '": [{"content":" ' + agent_respond + '", "memory_tag:' \ - # + '\n ' - - current_node_history_json = json.loads(current_node_history) #历史记录里可能包含虚假信息 - logging.info(f'current_node_history_json is {current_node_history_json}') - if current_node_history_json[-1]['action']['agent_name'] != '主持人': - current_node_history_json[-1]["observation"][0]['content'] = agent_respond #将历史中最后一次主持人幻觉的输出，转换为用户补充的输入 - try: - current_node_history_json[-1]["thought"] = '' #在非主持人环节时，应该将thought 设置为'' - except: - pass - llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n##已有步骤##\n' + json.dumps(current_node_history_json,ensure_ascii=False) + \ - '\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' - logging.info('=============llm_input==================') - logging.info(llm_input) - llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) - logging.info('=============llm_result==================') - logging.info(llm_result) - llm_result_json = robust_json_loads(llm_result) - if type(llm_result_json)!=dict: - llm_result_json = llm_result_json[0] - logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') - # raise ValueError(f'llm的输出应该是一个dict才对 ') - current_node_history_json.append(llm_result_json) - - retry_llm = 0 - while(( retry_llm <= 8) and ("taskend" not in llm_result) and (llm_result_json['action']['agent_name'] == '主持人' )): - logging.info('由于是主持人发言，情况继续') - - endcheck_res = self.endcheck( nodeId, nodeType, oneNodeName, oneNodeDescription, current_node_history_json) - if endcheck_res== False: - logging.info('endchek没有通过，主持人发言终止, 强行将 llm_result == {"action": "taskend"}') - llm_result = json.dumps({"action": "taskend"}) - llm_result_json = robust_json_loads(llm_result) - current_node_history_json.append(llm_result_json) - break - - - - llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n##已有步骤##\n' + json.dumps(current_node_history_json,ensure_ascii=False) + \ - '\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' - logging.info('=============llm_input==================') - logging.info(llm_input) - llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) - logging.info('=============llm_result==================') - logging.info(llm_result) - llm_result_json = robust_json_loads(llm_result) - current_node_history_json.append(llm_result_json) - if type(llm_result_json)!=dict: - llm_result_json = llm_result_json[0] - logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') - raise ValueError(f'llm的输出应该是一个dict才对 ') - retry_llm = retry_llm + 1 - - logging.info('大模型调用结束') - - - - #step5 分析 llm_result 执行结果 - #llm_result 为最后一次llm的输出 - if 'taskend' in llm_result: - - - react_flag = 'end' - logging.info(f'当前为{react_flag}, 将本次节点的count设置为end ') - self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'end') - elif 'observation' in llm_result: - react_flag = 'waiting_other_agent' - - logging.info(f'当前为{react_flag}, 尝试补充字符使得llm_result_truncation能转为json格式 ') - # llm_result_truncation_json = json.loads(current_node_history + llm_result_truncation + '":[]}]') - # llm_result_truncation_json = json.loads(current_node_history + llm_result_truncation + '":[]}]') - - #提取此时应该执行的agent_name - execute_agent_name = current_node_history_json[-1]['action']['agent_name'] - execute_player_name = current_node_history_json[-1]['action']['player_name'] - - #将该节点的count 设置为 runninng - self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'running') - - - - - #step6 存储 history # for DM - logging.info(f'存储 history # for DM') - if react_flag == 'waiting_other_agent' and first_run_react_flag == True: - #step6.1 存储 llm_result_truncation - - self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False) ) - - elif react_flag == 'waiting_other_agent' and first_run_react_flag == False: - self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False)) - - elif react_flag == 'end' and first_run_react_flag == True: #第一次运行就运行到结尾了 - - self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False)) - - elif react_flag == 'end' and first_run_react_flag == False: #第N次运行 运行到结尾了 - self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False)) - - - #step7 存储 memory # for other agent - logging.info(f'存储 memory # for other agent') - if react_flag == 'waiting_other_agent' and first_run_react_flag == True: - logging.info('#第一次运行 等待agent返回') - self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) - if react_flag == 'waiting_other_agent' and first_run_react_flag == False: - logging.info('#第N次运行 等待agent返回') - self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) - - elif react_flag == 'end' and first_run_react_flag == True: #第一次运行就运行到结尾了: - logging.info('#第一次运行就运行到结尾了:') - self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) - - elif react_flag == 'end' and first_run_react_flag == False: #第N次运行 运行到结尾了 - logging.info('#第N次运行 运行到结尾了') - self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) - - - #step8 返回 question_plan - if react_flag == 'end': - question_plan = [] - elif react_flag == 'waiting_other_agent': - question_plan = self.react_get_question_plan(sessionId, nodeId, execute_agent_name) - else: - question_plan = [] - return react_flag, question_plan - - - - def get_memory_for_tool(self,sessionId, nodeId): - ''' - react 节点中 对于一个 主持人， 构建memory的函数。 - 只需要将祖先节点弄好即可，不需要加自己，因为自己有 history进行维护 - ''' - nodeType = 'opsgptkg_task' #假设一定是task节点 - tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) - get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #对于tool,假设都是主持人的工具，所以需要看到所有的memory，无需加tag。 - assembled_memory = self.assemble_memory_for_tool(nodeId, nodeType, get_messages_res_sorted) # tool 的memory需要兼顾以前的格式 - return assembled_memory - - - def get_memory_for_dm(self,sessionId, nodeId): - ''' - react 节点中 对于一个 主持人， 构建memory的函数。 - 只需要将祖先节点弄好即可，不需要加自己，因为自己有 history进行维护 - ''' - nodeType = 'opsgptkg_task' #假设一定是task节点 - tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) - get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #此时是主持人，所以需要看到所有的memory，无需加tag。 对于在我这一侧需要运行的llm，一定是看到所有信息，因为我就是主持人 - assembled_memory = self.assemble_memory_for_reactagent(nodeId, nodeType, get_messages_res_sorted) - return assembled_memory - - def get_memory_for_computer_agent(self,sessionId, nodeId, execute_agent_name): - ''' - react 节点中 对于一个 agent_x (电脑agent)， 构建memory的函数 - ''' - nodeType = 'opsgptkg_task' #假设一定是task节点 - tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) - - if nodeId not in [nodeinancestor['nodeId'] for nodeinancestor in tool_ancestor]: - tool_ancestor = tool_ancestor + [{'nodeId': nodeId, 'nodeType':nodeType}] - - #需要将自己也加上，方便在下一步memory检索的时候把本节点的历史也得到，由于在生成str的时候，第一时间就save本届点的memory，所以这样做是可以的 - #需要注意的是，给agent和给主持人看到的输入是不一样的。 主持人看到的是 memory + node_text + currentnodehistory, currentnodehistory 是文本，因为主持人需要维持一个 结构化的输出。 - #agent看到的是 memory，agent只需要说出一句话即可 - get_messages_res_sorted = self.get_memory_from_ancestor(tool_ancestor, sessionId, execute_agent_name) #此时是调用外部agent，所以需要加tag - assembled_memory = self.assemble_memory_for_reactagent(nodeId, nodeType, get_messages_res_sorted) - return assembled_memory - - def react_get_question_plan(self, sessionId, nodeId, execute_agent_name): - ''' - 如果react模块 react_flag==waiting_other_agent， 则需要返回 question_plan - 可能需要区分人来回答还是大模型来回答 - ''' - if '人类' in execute_agent_name: #需要提交给人 - ''' - example: {'toolDescription': '请用户回答', - 'currentNodeId': 'INT_3', - 'memory': None, - 'type': 'userProblem', - 'questionDescription': {'questionType': 'essayQuestion', - 'questionContent': {'question': '请输入', - 'candidate': None }}} - - 一定是一个问答题， 无需提问，这里question变成一个固定值了。 最重要的是把memory 也是空， 因为历史信息在对话里已经显示了。 - ''' - toolPlan = [ {'toolDescription': '请用户回答', - 'currentNodeId': nodeId, - 'memory': None, - 'type': 'userProblem', - 'questionDescription': {'questionType': 'essayQuestion', - 'questionContent': {'question': '请玩家根据当前情况发言', - 'candidate': None }}} ] - - return toolPlan - else: #需要执行agent - ''' - example :[{ - "toolDescription": "toolDescriptionA", - "currentNodeId": "INT_1", - "memory": JsonStr, - "type":"onlyTool", - }] - ''' - - - assembled_memory = self.get_memory_for_computer_agent(sessionId, nodeId, execute_agent_name) - react_memory = assembled_memory - if type(react_memory)!= str: - react_memory = json.dumps(react_memory, ensure_ascii=False) - - # logging.info(f'react_memory is {react_memory}') - toolPlan = [{ - "toolDescription": execute_agent_name, - "currentNodeId": nodeId, - "memory": react_memory, - "type":"reactExecution", - }] - return toolPlan - - - - def get_tool_ancestor(self, sessionId, start_nodeid = '为什么余额宝没收到收益_complaint', start_nodetype = 'opsgptkg_task'): - - #1 对每个nodeid，得到其memory， 首先需要遍历其所有的祖先task节点，将相关信息记录下来 - tool_ancestor = [] - nodeid_in_search = [{'nodeId':start_nodeid, 'nodeType':start_nodetype}] - nodeid_in_search_all = [{'nodeId':start_nodeid, 'nodeType':start_nodetype}] - - while len(nodeid_in_search)!= 0: - nodedict_now = nodeid_in_search.pop() - nodeid_now = nodedict_now['nodeId'] - nodetype_now = nodedict_now['nodeType'] - - - #查祖先节点 reverse=True - neighborNodes = self.geabase_handler.get_neighbor_nodes(attributes={"id": nodeid_now,}, node_type=nodetype_now, reverse=True) - #print(nodeid_now, nodetype_now, neighborNodes, '=========') - - - for i in range(len(neighborNodes) ): - # if res['resultSet']['rows'][i]['columns'][0] == {}: - # continue - - # else: - nodeid_new = neighborNodes[i].id - nodetype_new = neighborNodes[i].type - if nodeid_new in [kk['nodeId'] for kk in nodeid_in_search]: #已经探索过了，不再探索 - continue - - elif nodetype_new == 'opsgptkg_task': #如果是task节点，则加入到tool_plan中，同时继续往前延展。 - - #查询该任务节点有没有收到过response，直接查询count，不用在意count的个数 - message_res = self.memory_handler.nodecount_get( sessionId, nodeid_new) #查看这个节点的count计数 - - if len(message_res) == 0: #这个task节点没有memory 或者没有收到response，则不再往前延展，减少geabase查询个数 - print(f'#这个task节点{nodeid_new}没有memory 或者没有收到response，则不再往前延展，减少geabase查询个数') - continue - else: - print('#如果是task节点，则加入到tool_plan中，同时继续往前延展。 get_tool_ancestor') - tool_ancestor.insert(0, {'nodeId':nodeid_new, 'nodeType':nodetype_new}) # 倒叙插入到图谱中 - if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : - nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - - - - - elif nodetype_now != 'opsgptkg_intent' and nodetype_new == 'opsgptkg_intent': - #第一次出现意图节点，需要尝试 - #print('#第一次出现意图节点，需要尝试') - tool_ancestor.insert(0, {'nodeId':nodeid_new, 'nodeType':nodetype_new}) # 倒叙插入到图谱中 - if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : - nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - elif nodetype_now == 'opsgptkg_intent' and nodetype_new == 'opsgptkg_intent': - #从意图节点再次碰到意图节点，终止 - #print('#从意图节点再次碰到意图节点，终止') - pass - elif nodetype_new == 'opsgptkg_phenomenon': - #如果是事实节点，则继续 - tool_ancestor.insert(0, {'nodeId':nodeid_new, 'nodeType':nodetype_new}) # 倒叙插入到图谱中 - if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : - nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - - else:##如果是不是task节点，也不是意图节点，不加入到tool_plan中，继续延展 - #print('#如果是不是task节点，也不是意图节点，不加入到tool_plan中，继续延展') - if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : - nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - - tool_ancestor_new = [] - for i in range(len(tool_ancestor)): - item_i = tool_ancestor[i] - if item_i not in tool_ancestor_new: - tool_ancestor_new.append(item_i) - logging.info(f'geabase_getmemory tool_ancestor_new 的个数为{len(tool_ancestor_new)}') - logging.info(f'geabase_getmemory tool_ancestor 的个数为{len(tool_ancestor)}') - #print('tool_ancestor_new', tool_ancestor_new) - tool_ancestor = tool_ancestor_new - return tool_ancestor - - def get_memory_from_ancestor(self, tool_ancestor, sessionId, role_tags = None): - ''' - 给定了一堆祖先节点 + 当前节点，从中提取出memory - 祖先节点 - 对于祖先tool 而言，提取出 nodedescription、tool responds - 对于祖先react 而言，提取出 nodedescription（name）， 每一条message - 将这个list 按照时间顺序排好 - 当前节点 - 对于tool 而言，没有当前节点的memory - 对于react节点而言， 有运行到中间状态的memory - 将这个list 按照时间顺序排好 - 按照固定格式输出 memory_list_output - - role_tags 是一系列list, 如果指定为空，则没有约束 - - ''' - if role_tags == None: - role_tags = None - else: - role_tags = ['all'] + [role_tags] - # print(role_tags) - message_res_list = [] - for i in range(len(tool_ancestor)): - logging.info(f'geabase_getmemory 查询第{i}个祖先节点') - # logging.info(tool_ancestor[i]) - nodeId = tool_ancestor[i]['nodeId'] - nodeType = tool_ancestor[i]['nodeType'] - logging.info(f'【查询】memory message_index {nodeId}; sessionId {sessionId} ') - # if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: - #当前节点为tool or react 节点，一次性获得该节点所有的 chapter的memory数据 - if nodeType == 'opsgptkg_task': - memory_res = self.memory_manager.get_memory_pool_by_all({ - # "message_index": hash_id(nodeId, sessionId), #nodeId.replace(":", "_").replace("-", "_"), - 'user_name': hash_id(nodeId), - "chat_index": sessionId, - "role_tags": role_tags, - } - ) - message_res = memory_res.get_messages() - message_res_list = message_res_list + message_res - - - - elif nodeType == 'opsgptkg_intent': - #如果祖先节点是意图节点, 意图节点的memory 暂时不分 tag - memory_res = self.memory_manager.get_memory_pool_by_all({ - "message_index": hash_id(nodeId, sessionId), #nodeId.replace(":", "_").replace("-", "_"), - "chat_index": sessionId, - "role_type": "user"}) - - message_res = memory_res.get_messages() - message_res_list = message_res_list + message_res - - #根据时间排序 - # message_res_list = message_res_list[::-1] #倒转message， 因为发现tbase存数据是类似堆栈的格式存的。 后来者在上; 似乎不对 - get_messages_res_sorted = self.sort_messages_by_time(message_res_list) - return get_messages_res_sorted - - - def assemble_memory_for_reactagent(self, nodeId, nodeType, get_messages_res_sorted): - ''' - 假设 祖先节点已经选择好了，而且 节点中相关的message也选择好了， 也经过时间排序了 - react 节点中 对于一个 agent_x (电脑agent)， 组装memory的函数 - ''' - - if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: - raise ValueError(f'当前应该是 react 节点才对 ') - else: #react 节点 - memory_list = [] - for i in range(len( get_messages_res_sorted ) ): - if get_messages_res_sorted[i].role_name in ['firstUserInput', 'function_caller', 'user' ]: - # # 第一次输入， tool返回， tool描述， - # memory_list.append({ - # 'role_type': get_messages_res_sorted[i].role_type, - # 'role_name': get_messages_res_sorted[i].role_name, - # 'role_content': get_messages_res_sorted[i].role_content} - # )#此处存疑，需要实验后才知道效果如何，注释后，相当于主持人和agent只能看到tool的标题和执行结果，且以list的形式呈现 - memory_list.append(get_messages_res_sorted[i].role_content) - elif get_messages_res_sorted[i].role_type in ['react_memory_save']: - # react 模块各个角色说的话， - memory_list.append(get_messages_res_sorted[i].role_content) - return memory_list - - def assemble_memory_for_tool(self, nodeId, nodeType, get_messages_res_sorted): - if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: - ''' - ''' - memory_list = [] - for i in range(len( get_messages_res_sorted ) ): - if get_messages_res_sorted[i].role_name == 'firstUserInput': - memory_list.append({ - 'role_type': 'user', - 'role_name': 'firstUserInput', - 'role_content': get_messages_res_sorted[i].role_content} - ) - if get_messages_res_sorted[i].role_name == 'user': - memory_list.append({ - 'role_type': 'user', - 'role_name': 'None', - 'role_content': get_messages_res_sorted[i].role_content} - ) - if get_messages_res_sorted[i].role_name == 'function_caller': - memory_list.append({ - 'role_type': 'observation', - 'role_name': 'function_caller', - 'role_content': get_messages_res_sorted[i].role_content} - ) - if get_messages_res_sorted[i].role_type in ['react_memory_save']: - # react 模块各个角色说的话， - memory_list.append({ - 'role_type': get_messages_res_sorted[i].role_type, - 'role_name': get_messages_res_sorted[i].role_name, - 'role_content': get_messages_res_sorted[i].role_content} - ) - else: #react 节点 - raise ValueError(f'当前应该是 tool task 节点才对 ') - - return memory_list - - def assemble_memory(self, nodeId, nodeType, get_messages_res_sorted): - ''' - 组装memory - get_messages_res_sorted 已经根据时间排序好了。 但是对于tool 和 react模块的memory拼装做法有所不同 - ''' - if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: - ''' - - ''' - memory_list = [] - for i in range(len( get_messages_res_sorted ) ): - if get_messages_res_sorted[i].role_name == 'firstUserInput': - memory_list.append({ - 'role_type': 'user', - 'role_name': 'firstUserInput', - 'role_content': get_messages_res_sorted[i].role_content} - ) - if get_messages_res_sorted[i].role_name == 'user': - memory_list.append({ - 'role_type': 'user', - 'role_name': 'None', - 'role_content': get_messages_res_sorted[i].role_content} - ) - if get_messages_res_sorted[i].role_name == 'function_caller': - memory_list.append({ - 'role_type': 'observation', - 'role_name': 'function_caller', - 'role_content': get_messages_res_sorted[i].role_content} - ) - if get_messages_res_sorted[i].role_type in ['react_memory_save']: - # react 模块各个角色说的话， - memory_list.append({ - 'role_type': get_messages_res_sorted[i].role_type, - 'role_name': get_messages_res_sorted[i].role_name, - 'role_content': get_messages_res_sorted[i].role_content} - ) - else: #react 节点 - memory_list = [] - for i in range(len( get_messages_res_sorted ) ): - if get_messages_res_sorted[i].role_name in ['firstUserInput', 'function_caller', 'user' ]: - # 第一次输入， tool返回， tool描述， - memory_list.append({ - 'role_type': get_messages_res_sorted[i].role_type, - 'role_name': get_messages_res_sorted[i].role_name, - 'role_content': get_messages_res_sorted[i].role_content} - ) - elif get_messages_res_sorted[i].role_type in ['react_memory_save']: - # react 模块各个角色说的话， - memory_list.append({ - 'role_type': get_messages_res_sorted[i].role_type, - 'role_name': get_messages_res_sorted[i].role_name, - 'role_content': get_messages_res_sorted[i].role_content} - ) - return memory_list + def search_node_type(self, nodeid_in_subtree, nodeId ): ''' #返回nodeid_in_subtree 中 nodeId 的nodeType @@ -829,6 +114,7 @@ def geabase_judgeNodeReachability(self, sessionId, # else: nodeid_new = neighborNodes[i].id nodetype_new = neighborNodes[i].type + logging.info(f'======nodeid_new is {nodeid_new} , nodetype_new is {nodetype_new}=====') if nodetype_new == 'opsgptkg_task' or nodetype_new == 'opsgptkg_phenomenon': #如果是task 或者 phenomenon节点，则加入到tool_plan中，但是不往后续延展了。 tool_parent.append({'nodeId':nodeid_new, 'nodeType': nodetype_new}) elif nodetype_new == 'opsgptkg_intent': @@ -837,19 +123,23 @@ def geabase_judgeNodeReachability(self, sessionId, else:##如果是不是task节点，也不是意图节点，不加入到tool_plan中，往后续延展 nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - + logging.info(f'===tool_parent is {tool_parent} ===') #tool_parent = list(set(tool_parent)) # 将字典转化为元组，然后放入set中去重 unique_set = set(tuple(d.items()) for d in tool_parent) # 再将元组转回字典 tool_parent = [{k: v for k, v in t} for t in unique_set] + if len(tool_parent) == 0: + logging.info(f'======geabase_judgeNodeReachability 没有有效的父节点 则一定为可达=====') + return True node_description = self.gb_handler.geabase_getDescription( rootNodeId = start_nodeid, rootNodeType = start_nodetype) if gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = start_nodeid, rootNodeType = start_nodetype,key = 'accesscriteria') == 'AND' \ or gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = start_nodeid, rootNodeType = start_nodetype,key = 'accesscriteria') == '{"type":"AND"}' \ or gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = start_nodeid, rootNodeType = start_nodetype,key = 'accesscriteria') == None or \ - gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = start_nodeid, rootNodeType = start_nodetype,key = 'accesscriteria') == '': + gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = start_nodeid, rootNodeType = start_nodetype,key = 'accesscriteria') == '' or \ + gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = start_nodeid, rootNodeType = start_nodetype,key = 'accesscriteria') == '{}': logging.info('#此节点为and处理逻辑') #此节点为and处理逻辑 logging.info(f'{start_nodeid}的tool_parent为{tool_parent}') @@ -974,7 +264,7 @@ def fact_branch_judgment(self, current_task, neighbor_node_id_list, next_node_de thought(尽量不要超过40个字)： ''' logging.info(prompt_temp) - response = call_llm(input_content = prompt_temp, llm_model = 'Qwen2_72B_Instruct_OpsGPT',llm_config=self.llm_config)# qwen_chat_14b #Qwen_72B_Chat_vLLM + response = call_llm(input_content = prompt_temp, llm_model = 'Qwen2_72B_Instruct_OpsGPT' , llm_config=self.llm_config)# qwen_chat_14b #Qwen_72B_Chat_vLLM logging.info(f'大模型的结果为：{response}') #final_choice = extract_final_result(json.loads(response.text)['data'], special_str = "最终结果为：" ) final_choice = extract_final_result(response, special_str = "最终结果为：" ) @@ -1055,7 +345,27 @@ def write_analysis_memory(self, sessionId, neighbor_node_id_list, chosen_nodeid) - + def toolResponseError_check(self, lingsi_response:LingSiResponse)->bool: + #判断当前tool 执行是否成功， 主要根据 observation中是否有toolResponse字段来判断 + print('=======print(lingsi_response)===========') + print(lingsi_response.dict()) + try: + lingsi_response.observation = json.loads(lingsi_response.observation) + except: + pass + if lingsi_response.type == None: + return False #第一次输入，不涉及tool 的返回 + elif lingsi_response.observation == None: + return True + elif 'toolResponse' not in list(lingsi_response.observation.keys()): + return True + elif 'toolKey' in list(lingsi_response.observation.keys()) and lingsi_response.observation['toolKey'] == 'COMMON_QA_QWEN:MAIN_SITE:HttpClient.execute': + #灵思兜底逻辑 + logging.info(f'当前为灵思兜底逻辑，toolResponseError_check == True') + return True + else: + return False + #raise ValueError('toolResponseError_check 出现未知情况') def geabase_summary_check(self, sessionId, nodeid_in_subtree): logging.info('geabase_summary_check start 判断当前状态下是不是已经走到了需要进行summary的情况') @@ -1182,7 +492,8 @@ def geabase_summary_check(self, sessionId, nodeid_in_subtree): def geabase_nodediffusion_plus(self, sessionId, - start_nodeid = '为什么余额宝没收到收益_complaint', start_nodetype = 'opsgptkg_intent', agent_respond = None): + start_nodeid = 'complaint', start_nodetype = 'opsgptkg_intent', + agent_respond = None, lingsi_response = None): ''' 增加了react模块的逻辑 进行单步的向后扩散， 得到下一步应该执行的计划 @@ -1192,6 +503,7 @@ def geabase_nodediffusion_plus(self, sessionId, logging.info('================geabase_nodediffusion_plus start===================') logging.info('===================================') logging.info('===================================') + logging.info(f'agent_respond is {agent_respond}; lingsi_response is {lingsi_response}') # 0 创建 GB_handler的实例 gb_handler = self.gb_handler self.memory_handler = memory_handler_ekg(self.memory_manager, self.geabase_handler) @@ -1207,8 +519,8 @@ def geabase_nodediffusion_plus(self, sessionId, if node_completed_flag == False: #没有执行完，需要继续执行 # - logging.info(f'react节点{start_nodeid}没有执行完，需要继续执行') - runningFlag, reactPlan = self.react_running( sessionId, start_nodeid, start_nodetype, agent_respond ) + logging.info(f'类react节点{start_nodeid}没有执行完，需要继续执行') + runningFlag, reactPlan = self.task_node_agent.task_running(sessionId, start_nodeid, start_nodetype, lingsi_response ) #继续执行还是没有执行完 if runningFlag == 'waiting_other_agent': @@ -1238,6 +550,7 @@ def geabase_nodediffusion_plus(self, sessionId, nodetype_now = nodedict_now['nodeType'] neighborNodes = self.geabase_handler.get_neighbor_nodes(attributes={"id": nodeid_now,}, node_type=nodetype_now, reverse=False) + logging.info(f'neighborNodes is {neighborNodes}') if self.gb_handler.all_nodetype_check(rootNodeId = nodeid_now, rootNodeType = nodetype_now, neighborNodeType = 'opsgptkg_phenomenon') == True: @@ -1287,7 +600,10 @@ def geabase_nodediffusion_plus(self, sessionId, # logging.info(f'{nodeid_new}#是task 节点中的 react, 节点尝试执行') #是task 节点中的 react, 节点尝试执行 # logging.info(f'sessionId {sessionId}, nodeid_new {nodeid_new}, nodetype_new {nodetype_new}') - runningFlag, reactPlan = self.react_running( sessionId, nodeid_new, nodetype_new, None )#这种时候是react节点第一次运行，一定是主持人，一定要看到全局信息 + + runningFlag, reactPlan = self.task_node_agent.task_running( sessionId, nodeid_new, nodetype_new, None ) + logging.info(f'前面节点执行完毕后，在向后探索的过程中，执行了task_running,执行结果为runningFlag is {runningFlag}, reactPlan is {reactPlan}' ) + #这种时候是react节点第一次运行，一定是主持人，一定要看到全局信息 # logging.info(f'{nodeid_new}#继续执行还是没有执行完, 需要留下 reactPlan，且不往后面扩展') #继续执行还是没有执行完, 需要留下 reactPlan，且不往后面扩展 @@ -1300,7 +616,11 @@ def geabase_nodediffusion_plus(self, sessionId, #这个react 执行了一下执行完了. 继续， tool_plan中不标记这个节点，往后探索即可。 nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - + # + elif nodetype_new == 'opsgptkg_analysis': #如果是opsgptkg_analysis节点， 将其标注为已到达 + self.write_analysis_memory( sessionId, [nodeid_new], None) + + else:##如果是不是task节点，不加入到tool_plan中，往后续延展 nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) @@ -1308,19 +628,20 @@ def geabase_nodediffusion_plus(self, sessionId, # unique_set = set(tuple(sorted(d.items())) for d in tool_plan) #暂时去掉去重，但不知有何问题 # 将去重后的元组转换回字典形式，得到去重后的list # tool_plan = [dict(t) for t in unique_set] #暂时去掉去重，但不知有何问题 - # logging.info('====tool_plan===在去重之后===') - # logging.info(f'tool_plan {tool_plan}') - # logging.info('====tool_plan======') + logging.info('====tool_plan===在去重之后===') + logging.info(f'tool_plan {tool_plan}') + logging.info('====tool_plan======') #2 判断这些tool的父节点是否都已经有observation, 即判断可达性， 进行筛选 - tool_plan_2 = [] + tool_plan_reachable = [] for i in range(len(tool_plan)): nodeId = tool_plan[i]['nodeId'] nodeType = tool_plan[i]['nodeType'] if self.geabase_judgeNodeReachability( sessionId, nodeId, nodeType) == True: - tool_plan_2.append({'nodeId':nodeId, 'nodeType':nodeType}) - # logging.info(f'tool_plan_2 经过可达性判断删选后的 {tool_plan_2}') + tool_plan_reachable.append( tool_plan[i] ) + # logging.info(f'tool_plan_reachable 经过可达性判断删选后的 {tool_plan_reachable}') + #3 获取每个tool的memory tool_plan_return = [] @@ -1331,10 +652,12 @@ def geabase_nodediffusion_plus(self, sessionId, # } - for i in range(len(tool_plan_2)): - nodeId = tool_plan[i]['nodeId'] - nodeType = tool_plan[i]['nodeType'] - if 'reactFlag' not in tool_plan[i].keys(): + for i in range(len(tool_plan_reachable)): + nodeId = tool_plan_reachable[i]['nodeId'] + nodeType = tool_plan_reachable[i]['nodeType'] + + if 'reactFlag' not in tool_plan_reachable[i].keys(): + # 这里表示是tool 执行 # memory = self.geabase_getmemory( sessionId, nodeId, nodeType) #获取memory， 这个是task tool的memory， # tool_ancestor = self.get_tool_ancestor( sessionId, nodeId, nodeType) @@ -1342,7 +665,7 @@ def geabase_nodediffusion_plus(self, sessionId, # get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #此时是nodeId是一个tool，假设tool一定是主持人的工具，能看到所有tag的memory # memory = self.assemble_memory( nodeId, nodeType, get_messages_res_sorted) - memory = self.get_memory_for_tool(sessionId, nodeId) + memory = self.task_node_agent.get_memory_for_tool(sessionId, nodeId) # logging.info(f'{nodeId}的 memory is {memory}') toolDescription = self.gb_handler.geabase_getDescription( rootNodeId = nodeId, rootNodeType = nodeType) @@ -1358,12 +681,19 @@ def geabase_nodediffusion_plus(self, sessionId, ) else: #对于react 模块的 memory，另有方法提取，在制定plan的时候就提取了。 + #这里也包括 parallel 和 plan类型节点 + # + for m in range(len(tool_plan_reachable[i]['reactPlan'])) :#tool_plan_reachable[i]['reactPlan'] 本身就是一个list, 必须先取元素，否则格式会出错 + tool_plan_return.append( tool_plan_reachable[i]['reactPlan'][m] ) - tool_plan_return.append( tool_plan[i]['reactPlan'][0] ) #tool_plan[i]['reactPlan'] 本身就是一个list, 必须先取元素，否则格式会出错 - - - - return tool_plan, tool_plan_return + logging.info('===================================') + logging.info('===================================') + logging.info('================geabase_nodediffusion_plus end===================') + logging.info('===================================') + logging.info('===================================') + logging.info(f'tool_plan_reachable is {tool_plan_reachable}, tool_plan_return is {tool_plan_return}') + + return tool_plan_reachable, tool_plan_return if __name__ == "__main__": diff --git a/muagent/service/ekg_reasoning/src/graph_search/graph_search_main.py b/muagent/service/ekg_reasoning/src/graph_search/graph_search_main.py index 92b9696..2051bcc 100644 --- a/muagent/service/ekg_reasoning/src/graph_search/graph_search_main.py +++ b/muagent/service/ekg_reasoning/src/graph_search/graph_search_main.py @@ -44,6 +44,11 @@ from muagent.schemas.db import GBConfig from muagent.service.ekg_construct import EKGConstructService from muagent.service.ekg_inference import IntentionRouter +from muagent.schemas.ekg.ekg_reason import LingSiResponse, ResToLingsi +if os.environ['operation_mode'] == 'antcode': # 'open_source' or 'antcode' + from muagent.service.web_operation.web_act_ant import WebAgent + + #内部其他函数 from src.utils.call_llm import call_llm, extract_final_result @@ -54,11 +59,13 @@ if os.environ['operation_mode'] == 'antcode': # 'open_source' or 'antcode' #内部的意图识别接口调用函数 from src.intention_recognition.intention_recognition_tool import intention_recognition_ekgfunc, intention_recognition_querypatternfunc, intention_recognition_querytypefunc + from src.generalization_reasoning.generalization_reason import GeneralizationReason from src.question_answer.qa_function import qa_class from src.memory_handler.ekg_memory_handler import memory_handler_ekg from src.graph_search.call_old_fuction import call_old_fuction + # 配置logging模块 logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s - %(lineno)d', level=logging.INFO) @@ -69,7 +76,7 @@ class graph_search_process(): ''' 图谱推理主流程class ''' - def __init__(self, geabase_handler, memory_manager, intention_router, scene , sessionId, currentNodeId, + def __init__(self, geabase_handler, memory_manager, intention_router, lingsi_response, scene , sessionId, currentNodeId, observation, userAnswer, inputType, startRootNodeId, intentionRule, intentionData, startFromRoot = True, index_name = 'ekg_migration_new', unique_name="EKG", @@ -77,6 +84,7 @@ def __init__(self, geabase_handler, memory_manager, intention_router, scene , s ): self.memory_manager = memory_manager #memory_init(index_name = 'ekg_migration', unique_name="EKG") + self.lingsi_response = lingsi_response self.scene = scene self.sessionId = sessionId self.currentNodeId = currentNodeId @@ -111,8 +119,11 @@ def __init__(self, geabase_handler, memory_manager, intention_router, scene , s #计算起始时间 self.start_datetime = int(time.time()*1000) - - def state_judgement(self): + # def scene_judgement(self, scene:str)->str: + # if scene == 'generalizationReasoning': + # return 'generalizationReasoning' + + def state_judgement(self, inputType:str)->str: ''' 根据当前情况，判断当前算法输入所处的状态 { @@ -123,24 +134,24 @@ def state_judgement(self): "tool_QUESTION_RETURN_ANSWER" #task阶段，不执行tool，而是向用户问填空题/选择题 } ''' - if self.inputType == None: - self.algorithm_State = 'FIRST_INPUT' + if inputType == None: + #self.algorithm_State = 'FIRST_INPUT' return 'FIRST_INPUT' - elif self.inputType == 'intentQuestion':#INTENT_QUESTION - self.algorithm_State = 'INTENT_QUESTION_RETURN_ANSWER' + elif inputType == 'intentQuestion':#INTENT_QUESTION + #self.algorithm_State = 'INTENT_QUESTION_RETURN_ANSWER' return "INTENT_QUESTION_RETURN_ANSWER" - elif self.inputType == 'onlyTool': - self.algorithm_State = "TOOL_EXECUTION_RESULT" + elif inputType == 'onlyTool': + #self.algorithm_State = "TOOL_EXECUTION_RESULT" return "TOOL_EXECUTION_RESULT" - elif self.inputType == 'reactExecution':#REACT_EXECUTION_RESULT - self.algorithm_State = "REACT_EXECUTION_RESULT" + elif inputType == 'reactExecution':#REACT_EXECUTION_RESULT + #self.algorithm_State = "REACT_EXECUTION_RESULT" return "REACT_EXECUTION_RESULT" - elif self.inputType == 'userProblem': - self.algorithm_State = "TOOL_QUESTION_RETURN_ANSWER" + elif inputType == 'userProblem': + #self.algorithm_State = "TOOL_QUESTION_RETURN_ANSWER" return "TOOL_QUESTION_RETURN_ANSWER" def intentionRecongnition(self): @@ -214,13 +225,17 @@ def intentionRecongnitionProcess(self): if os.environ['operation_mode'] == 'antcode': node_id = '剧本杀/狼人杀' Designated_intent = [node_id] + self.queryPattern = 'executePattern' + self.intentionRecognitionSituation = 'success' + self.intention_recognition_path = Designated_intent + self.currentNodeId = '剧本杀/狼人杀' else: node_id = hash_id('剧本杀/狼人杀') Designated_intent = [node_id] - self.queryPattern = 'executePattern' - self.intentionRecognitionSituation = 'success' - self.intention_recognition_path = Designated_intent - self.currentNodeId = hash_id('剧本杀/狼人杀') + self.queryPattern = 'executePattern' + self.intentionRecognitionSituation = 'success' + self.intention_recognition_path = Designated_intent + self.currentNodeId = hash_id('剧本杀/狼人杀') return 1 #直接结束 if self.scene == 'UNDERCOVER' and self.algorithm_State == "FIRST_INPUT": logging.info(f'现在直接到谁是卧底模式') @@ -228,14 +243,31 @@ def intentionRecongnitionProcess(self): if os.environ['operation_mode'] == 'antcode': node_id = '剧本杀/谁是卧底' Designated_intent = [node_id] + logging.info(f'意图识别结果为:{Designated_intent}') + self.queryPattern = 'executePattern' + self.intentionRecognitionSituation = 'success' + self.intention_recognition_path = Designated_intent + self.currentNodeId = '剧本杀/谁是卧底' else: node_id = hash_id('剧本杀/谁是卧底') Designated_intent = [node_id] + logging.info(f'意图识别结果为:{Designated_intent}') + self.queryPattern = 'executePattern' + self.intentionRecognitionSituation = 'success' + self.intention_recognition_path = Designated_intent + self.currentNodeId = hash_id('剧本杀/谁是卧底') + return 1 #直接结束 + if self.lingsi_response.usingRootNode == True and self.algorithm_State == "FIRST_INPUT": + logging.info(f'现在直接到fatherIntention {self.startRootNodeId}') + + node_id = self.startRootNodeId + Designated_intent = [node_id] logging.info(f'意图识别结果为:{Designated_intent}') self.queryPattern = 'executePattern' self.intentionRecognitionSituation = 'success' self.intention_recognition_path = Designated_intent - self.currentNodeId = hash_id('剧本杀/谁是卧底') + self.currentNodeId = node_id + return 1 #直接结束 @@ -246,8 +278,13 @@ def intentionRecongnitionProcess(self): #step 2 调用 ekg func 主函数， self.intentionRecognitionSituation , self.intentionRecognitionRes #step 3 写入memory #不是第一次输入， 接受的是意图识别问题用户给的答案 + + self.queryPattern 有两种 'executePattern' or 'qaPattern' + self.queryType 整体计划查询； 'allPlan'， 下一步任务查询；'nextStep'， 闲聊; 'justChat' + + ''' - #step 1 判断是执行式还是答疑式输入 + #step 1.1 判断是执行式还是答疑式输入 if self.intentionRule != ['nlp']: self.queryPattern = 'executePattern' else: @@ -261,11 +298,13 @@ def intentionRecongnitionProcess(self): self.queryPattern = 'executePattern' else: self.queryPattern = 'qaPattern' - if os.environ['operation_mode'] == 'antcode' : - self.queryType = intention_recognition_querytypefunc( self.intentionData ) - elif os.environ['operation_mode'] == 'open_source' : - self.queryType = self.intention_router.get_intention_consult_which( self.intentionData ) - + + #step 1.2 判断是 整体计划查询； 'allPlan'， 下一步任务查询；'nextStep'， 闲聊; 'justChat' + if os.environ['operation_mode'] == 'antcode' : + self.queryType = intention_recognition_querytypefunc( self.intentionData ) + elif os.environ['operation_mode'] == 'open_source' : + self.queryType = self.intention_router.get_intention_consult_which( self.intentionData ) + logging.info(f'意图分析的结果为 queryType is {self.queryType}, self.queryPattern is {self.queryPattern}') @@ -302,6 +341,8 @@ def intentionRecongnitionProcess(self): #raise ValueError('意图识别noMatch ，退出') elif intentionRecognitionRes['intentionRecognitionSituation'] == 'toChoose': self.intentionRecognitionSituation = 'toChoose' + #raise ValueError('反问逻辑尚未构建，退出') + return 'intention_error' else: return 'intention_error' #raise ValueError('意图识别得到了意料之外的状态值，退出') @@ -407,6 +448,49 @@ def summary_check(self, nodeid_in_subtree): return False return True #所有task节点都有observation，则需要summary + + def initialize_replacements(self, nodeId: str, nodeType: str) -> bool: + """ + 初始化变量，调用self.memory_manager.init_global_msg实现 + """ + # nodeId, nodeType = "剧本杀/谁是卧底/智能交互", "opsgptkg_schedule" + #cur_node = self.geabase_handler.get_current_node(attributes={"id": nodeId}, node_type=nodeType) + #cur_node_envdescription = json.loads(cur_node.attributes['envdescription']) + + try: + node_envdescription = self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = nodeType, key = 'envdescription') + cur_node_envdescription = json.loads(node_envdescription) + except Exception as e: + logging.info(f"发生了一个错误：{e}") + logging.info(f"变量初始化失败，略过") + #logging.info(f"node_envdescription：{node_envdescription}") + return False + + #raise ValueError(f"node_envdescription is {node_envdescription}") + + + # cur_node_envdescription = json.loads('{"witch_poision": "当前女巫的毒药个数为1"}') + + try: + node_envdescription = self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = nodeType, key = 'envdescription') + cur_node_envdescription = json.loads(node_envdescription) + except Exception as e: + logging.info(f"发生了一个错误：{e}") + logging.info(f"输入不是json格式或者为空，变量初始化失败，略过") + logging.info(f"node_envdescription: {node_envdescription}") + return False + + + init_flag = False + try: + for role_name, role_content in cur_node_envdescription.items(): + if role_name and role_content: + init_flag = self.memory_manager.init_global_msg(self.sessionId, role_name, role_content) + except Exception as e: + logging.info(f"变量初始化错误！{e}") + return init_flag + + def first_user_memory_write(self): #如果当前是第一次输入，memory如何填写 @@ -428,8 +512,23 @@ def first_user_memory_write(self): # logging.info(f'type(geabase_search_return_all_node) is {gsran}') nodeid_in_subtree, _ = self.gb_handler.geabase_search_return_all_nodeandedge( last_intention_nodeid, 'opsgptkg_intent') - logging.info(f'整条链路上的节点个数是 len(nodeid_in_subtree) is {len(nodeid_in_subtree)}') nodeid_in_subtree_str = json.dumps(nodeid_in_subtree, ensure_ascii=False) + logging.info(f'整条链路上的节点个数是 len(nodeid_in_subtree) is {len(nodeid_in_subtree)}') + logging.info(f'整条连路上的节点信息是：{nodeid_in_subtree}') + + #nodeid_in_subtree_list = self.gb_handler.geabase_search_reture_nodeslist( last_intention_nodeid, 'opsgptkg_intent') + #nodeid_in_subtree_list_str = json.dumps(nodeid_in_subtree_list, ensure_ascii=False) + + # 获取 `nodeType` 为 `opsgptkg_schedule` 的 `nodeId` + try: + nodeId = [item['nodeId'] for item in nodeid_in_subtree if item['nodeType'] == 'opsgptkg_schedule'][0] + except Exception as e: + logging.info("不存在opsgptkg_schedule节点") + init_flag = self.initialize_replacements(nodeId, nodeType='opsgptkg_schedule') + if init_flag: + logging.info('变量初始化完成！') + else: + logging.info('变量初始化失败！') # logging.info(f'=============================================') # logging.info(f'nodeid_in_subtree_str {nodeid_in_subtree_str}') # logging.info(f'=============================================') @@ -502,8 +601,10 @@ def memorywrite(self): def get_summary(self): #后续待优化，当前只输出所有激活的summary节点 summary_list = [] - nodeid_in_subtree_memory = self.memory_manager.get_memory_pool_by_all({ "chat_index": self.sessionId, "role_type": "nodeid_in_subtree"}) - nodeid_in_subtree = json.loads( nodeid_in_subtree_memory.get_messages()[0].role_content ) + #nodeid_in_subtree_memory = self.memory_manager.get_memory_pool_by_all({ "chat_index": self.sessionId, "role_type": "nodeid_in_subtree"}) + #nodeid_in_subtree = json.loads( nodeid_in_subtree_memory.get_messages()[0].role_content ) + + nodeid_in_subtree = self.get_nodeid_in_subtree(self.sessionId, self.currentNodeId) for i in range(len(nodeid_in_subtree)): if nodeid_in_subtree[i]['nodeType'] == 'opsgptkg_analysis': #从nodeid_in_subtree中找到analysis的节点 nodeId = nodeid_in_subtree[i]['nodeId'] @@ -555,17 +656,35 @@ def get_summary(self): return summary_str - def get_nodeid_in_subtree(self): - logging.info(f'self.sessionId is {self.sessionId}') - # nodeid_in_subtree_memory= self.memory_manager.get_memory_pool_by_all({ "chat_index": self.sessionId, "role_type": "nodeid_in_subtree"}) - nodeid_in_subtree_memory= self.memory_manager.get_memory_pool_by_all({ "chat_index": self.sessionId, "role_type": "nodeid_in_subtree"}) + def get_nodeid_in_subtree(self, sessionId, nodeId): + #logging.info(f' sessionId is {self.sessionId}, nodeId is {nodeId}') + nodeid_in_subtree_memory = self.memory_manager.get_memory_pool_by_all({ "chat_index": self.sessionId, "role_type": "nodeid_in_subtree"}) + nodeid_in_subtree = json.loads( nodeid_in_subtree_memory.get_messages()[0].role_content ) + return nodeid_in_subtree + - logging.info(f'nodeid_in_subtree_memory is {nodeid_in_subtree_memory}') + # if nodeId == None: + # logging.info(f' nodeId == None, 为第一次输入，调用 geabase_search_return_all_nodeandedge 取 nodeid_in_subtree的值' ) + # nodeid_in_subtree, _ = self.gb_handler.geabase_search_return_all_nodeandedge( last_intention_nodeid, 'opsgptkg_intent') + # return nodeid_in_subtree + + + # # nodeid_in_subtree_memory= self.memory_manager.get_memory_pool_by_all({ "chat_index": self.sessionId, "role_type": "nodeid_in_subtree"}) + # nodeid_in_subtree_memory= self.memory_manager.get_memory_pool_by_all({ "chat_index": sessionId, "role_type": "nodeid_in_subtree"}) - nodeid_in_subtree = json.loads( nodeid_in_subtree_memory.get_messages()[0].role_content ) - # logging.info(f'nodeid_in_subtree is {nodeid_in_subtree}') - return nodeid_in_subtree, nodeid_in_subtree_memory + + # logging.info(f'nodeid_in_subtree_memory is {nodeid_in_subtree_memory}') + + # nodeid_in_subtree_list = json.loads( nodeid_in_subtree_memory.get_messages()[0].role_content ) + # if len(nodeid_in_subtree_list) == 0: + # return nodeid_in_subtree_list[0] + # for nodeid_in_subtree in nodeid_in_subtree_list: + # for one_node_info in nodeid_in_subtree: + # if one_node_info['nodeId'] == nodeId: + # return nodeid_in_subtree + + # raise ValueError('len(nodeid_in_subtree_list)>0 但是当前节点不在nodeid_in_subtree_list 中') def qaProcess(self, nodeid_in_subtree): ''' @@ -592,6 +711,53 @@ def qaProcess(self, nodeid_in_subtree): res = '输入为闲聊，暂不做回复' return res + + def grProcess(self, scene:str, sessionId:str, currentNodeId:str, algorithm_State:bool, + lingsi_response:LingSiResponse, + geabase_handler, + memory_handler, llm_config)->dict: + ''' + 调用 泛化推理 + ''' + logging.info(f'当前scene为{scene}') + if scene == 'generalizationReasoning_nonretrieval': + generalizaiton_reasoning = GeneralizationReason( sessionId = sessionId, + currentNodeId = currentNodeId, + memory_handler = memory_handler, + geabase_handler = geabase_handler, + llm_config=llm_config, + retrieval_flag = False) + elif scene == 'generalizationReasoning_test': + generalizaiton_reasoning = GeneralizationReason( sessionId = sessionId, + currentNodeId = currentNodeId, + memory_handler = memory_handler, + geabase_handler = geabase_handler, + llm_config=llm_config, + retrieval_flag = True) + elif scene == 'generalizationReasoning' or scene =='NEXA': + generalizaiton_reasoning = GeneralizationReason( sessionId = sessionId, + currentNodeId = currentNodeId, + memory_handler = memory_handler, + geabase_handler = geabase_handler, + llm_config=llm_config, + retrieval_flag = True) + else: + raise ValueError(f'scene if {scene}') + if algorithm_State == 'FIRST_INPUT': + if type(lingsi_response.observation) == str: + lingsi_response.observation = json.loads(lingsi_response.observation) + input_str_gr = lingsi_response.observation['content'] + else: + if type(lingsi_response.observation) == str: + lingsi_response.observation = json.loads(lingsi_response.observation) + input_str_gr = lingsi_response.observation['toolResponse'] + + res_to_lingsi = generalizaiton_reasoning.process( + sessionId = sessionId, + currentNodeId = 'generalization_reason', + input_str = input_str_gr) + return res_to_lingsi + def outputFuc(self): try: tool_plan = self.tool_plan @@ -608,7 +774,7 @@ def outputFuc(self): if self.gb_handler.get_extra_tag(rootNodeId = self.currentNodeId, rootNodeType = currentNodeType, key = 'dodisplay') == 'True' \ or self.gb_handler.get_extra_tag(rootNodeId = self.currentNodeId, rootNodeType = currentNodeType, key = 'dodisplay') == 'Ture' \ or self.gb_handler.get_tag(rootNodeId = self.currentNodeId, rootNodeType = currentNodeType, key = 'dodisplay') == 'True' : - outputinfo_str = self.memory_handler.get_output(self.sessionId, self.start_datetime, self.end_datetime) + outputinfo_str = self.memory_handler.get_output(self.sessionId, self.start_datetime, self.end_datetime, self.lingsi_response.agentName) else: dodisplaystr = self.gb_handler.get_tag(rootNodeId = self.currentNodeId, rootNodeType = currentNodeType, key = 'dodisplay') logging.info(f" 查询dodisplay字段结果为空, 或者为{dodisplaystr}，本次不对外输出") @@ -696,6 +862,16 @@ def outputFuc(self): "toolPlan": None , "userInteraction": outputinfo_str, } + elif self.toolResponseError_flag == True: + logging.info("出现了tool 执行错误，终止， 当前能返回什么summary就返回什么summary") + res_to_lingsi = { + 'intentionRecognitionSituation': self.intentionRecognitionSituation, + "sessionId": self.sessionId, + "type": "summary", + "summary" : self.get_summary(), + "toolPlan": None , + "userInteraction": outputinfo_str, + } else: raise ValueError('图谱扩散得到了预料之外的情况，退出') @@ -705,26 +881,115 @@ def outputFuc(self): def process(self): #step1 根据当前情况，判断当前算法输入所处的状态 logging.info(f'#step1 根据当前情况，判断当前算法输入所处的状态 self.inputType is {self.inputType}') - self.state_judgement() + self.algorithm_State = self.state_judgement(self.inputType) logging.info(f'#step1 over，当前算法状态为{self.algorithm_State}') + #step1.1 其他情况， 泛化推理 或者 WebAgent + html = None + task_query = None + + gr_flag = self.memory_handler.message_get( + sessionId = self.sessionId, + nodeId = 'gr', + hashpostfix='gr', + role_name='gr', + role_type='gr') + logging.info(f'gr_flag is {gr_flag}') + + if self.scene == 'WebAgent': + logging.info(f'当前scene为{self.scene}') + + if type(self.lingsi_response.observation) == str: + self.lingsi_response.observation = json.loads(self.lingsi_response.observation) + + if self.algorithm_State == 'FIRST_INPUT': + task_query = self.lingsi_response.observation['content'] + html = self.lingsi_response.observation['toolResponse'] + + else: + html = self.lingsi_response.observation['toolResponse'] + #html = html[0:50000] + + agent = WebAgent(memory_manager=self.memory_manager, llm_model_name = 'Qwen2_72B_Instruct_OpsGPT') + logging.info(f'self.sessionId is {self.sessionId}') + res_to_lingsi = agent.web_action(chat_index=self.sessionId , + html = html, task_query = task_query) + + return res_to_lingsi + # example :[{ + # "toolDescription": "toolDescriptionA", + # "currentNodeId": "INT_1", + # "memory": JsonStr, + # "type":"onlyTool", + # }] + + + + elif self.scene == 'generalizationReasoning_test' or \ + self.scene == 'generalizationReasoning_nonretrieval' or \ + self.scene == 'generalizationReasoning' or \ + gr_flag == 'gr': + #print(f'scene is {self.scene}') + #根据scene 直接触发 grProcess + + self.memory_handler.message_save( + sessionId = self.sessionId, + nodeId = 'gr', + role_content='gr', + hashpostfix='gr', + user_name='gr', + role_name='gr', + role_type='gr') + + res_to_lingsi = self.grProcess(scene = self.scene, sessionId = self.sessionId, + currentNodeId = self.currentNodeId, algorithm_State = self.algorithm_State, + lingsi_response = self.lingsi_response, + geabase_handler = self.geabase_handler, + memory_handler = self.memory_handler, + llm_config = self.llm_config) + + return res_to_lingsi + + + #step2 意图识别 logging.info('#step2 意图识别') intention_error_flag = self.intentionRecongnitionProcess() if intention_error_flag == 'intention_error': - - - logging.info("所有分支到达终点，summary") - res_to_lingsi = { - 'intentionRecognitionSituation': self.intentionRecognitionSituation, - "sessionId": self.sessionId, - "type": "summary", - "summary" : '意图识别未检验到相关数据，终止', - "toolPlan": None , - "userInteraction": None, - } - return res_to_lingsi + # 意图识别查询失败 + + if self.queryType != 'justChat' and os.environ['operation_mode'] == 'antcode': + logging.info(f'意图识别查询失败, 表示没有数据，同时现在不是闲聊 需要泛化推理') + + self.memory_handler.message_save( + sessionId = self.sessionId, + nodeId = 'gr', + role_content='gr', + hashpostfix='gr', + user_name='gr', + role_name='gr', + role_type='gr') + + res_to_lingsi = self.grProcess(scene = self.scene, sessionId = self.sessionId, + currentNodeId = self.currentNodeId, algorithm_State = self.algorithm_State, + lingsi_response= self.lingsi_response, + geabase_handler = self.geabase_handler, + memory_handler = self.memory_handler, llm_config = self.llm_config) + return res_to_lingsi + else: + logging.info('意图识别查询失败, 表示没有数据，且现在是闲聊。直接终止') + res_to_lingsi = { + 'intentionRecognitionSituation': self.intentionRecognitionSituation, + "sessionId": self.sessionId, + "type": "summary", + "summary" : '意图识别未检验到相关数据，且提问和已沉淀知识无关，终止', + "toolPlan": None , + "userInteraction": None, + } + return res_to_lingsi + + logging.info(f'#step2 意图识别 over，') #step3 memory 写入 @@ -734,7 +999,7 @@ def process(self): #step4 #get_nodeid_in_subtree logging.info('#step4 get_nodeid_in_subtree') - nodeid_in_subtree, nodeid_in_subtree_memory = self.get_nodeid_in_subtree() + nodeid_in_subtree = self.get_nodeid_in_subtree(self.sessionId, self.currentNodeId) self.nodeid_in_subtree = nodeid_in_subtree logging.info('#step4 get_nodeid_in_subtree') @@ -742,6 +1007,11 @@ def process(self): logging.info(f'step5 #summary_flag 判断') self.summary_flag = self.gst.geabase_summary_check(self.sessionId, nodeid_in_subtree) logging.info(f'step5 over, summary_flag is {self.summary_flag}') + + #step5.1 #toolResponseError_flag 判断 + logging.info(f'step5 #toolResponseError_flag 判断') + self.toolResponseError_flag = self.gst.toolResponseError_check(self.lingsi_response) + logging.info(f'step5 over, toolResponseError_flag is {self.summary_flag}') #step 6 self.currentNodeId 更新 得到 start_nodetype logging.info(f'step 6 self.currentNodeId 更新 得到 start_nodetype') @@ -783,7 +1053,7 @@ def process(self): logging.info(f'图谱扩散的输入 self.sessionId {self.sessionId}; currentNodeId {currentNodeId}; start_nodetype {start_nodetype}') tool_plan, tool_plan_3 = self.gst.geabase_nodediffusion_plus(self.sessionId, -currentNodeId, start_nodetype, agent_respond ) +currentNodeId, start_nodetype, agent_respond , self.lingsi_response) self.tool_plan = tool_plan self.tool_plan_3 = tool_plan_3 logging.info(f'step 8 图谱扩散 over') @@ -813,12 +1083,24 @@ def main(params_string, memory_manager, geabase_handler, intention_router = No params = params_string if type(params) == str: params = json.loads(params) + logging.info(f'=======开始新一轮输入=============') + logging.info(f'=======开始新一轮输入=============') + logging.info(f'=======开始新一轮输入=============') + logging.info(f'=======开始新一轮输入=============') + logging.info(f'=======开始新一轮输入=============') logging.info(f'params={params}') + logging.info(f'llm_config={llm_config}') + lingsi_response = LingSiResponse(**params) + lingsi_response = lingsi_response_process(lingsi_response) # process，currentnodeid 和 agentname都放到currentnodeid里，需要分割开来 + logging.info(f'lingsi_response is {lingsi_response}') + #params = lingsi_response.dict() + scene = params.get('scene', None) sessionId = params.get('sessionId', None) # - currentNodeId = params.get('currentNodeId', None) # + #currentNodeId = params.get('currentNodeId', None) # + currentNodeId = lingsi_response.currentNodeId observation = params.get('observation', None) # userAnswer = params.get('userAnswer', None) # inputType = params.get('type', None) # @@ -827,9 +1109,13 @@ def main(params_string, memory_manager, geabase_handler, intention_router = No intentionData = params.get('intentionData', None) # startFromRoot = params.get('startFromRoot', True) # + + - if scene in ['WEREWOLF' , 'NEXA', 'UNDERCOVER']: + + if scene in ['WEREWOLF' , 'NEXA', 'UNDERCOVER', + 'generalizationReasoning_nonretrieval', 'generalizationReasoning', 'WebAgent']: #标注这个sessionId属于新逻辑 message = Message( @@ -901,10 +1187,11 @@ def main(params_string, memory_manager, geabase_handler, intention_router = No else: logging.info(f'当前不为graphStructureSearch模式， 正常EKG') - state, last_res_to_lingsi = abnormal_and_retry(inputType, observation, sessionId, memory_manager) - if state == 'retry_now': - logging.info('现在进行重试，返回上一次在memory存储的结果') - return last_res_to_lingsi + # #重试逻辑，如果返回值 observation中没有toolresponse， 则进行重试 + # state, last_res_to_lingsi = abnormal_and_retry(inputType, observation, sessionId, memory_manager) + # if state == 'retry_now': + # logging.info('现在进行重试，返回上一次在memory存储的结果') + # return last_res_to_lingsi @@ -912,6 +1199,7 @@ def main(params_string, memory_manager, geabase_handler, intention_router = No geabase_handler = geabase_handler, memory_manager=memory_manager, intention_router = intention_router, + lingsi_response = lingsi_response, scene= scene, sessionId=sessionId, currentNodeId = currentNodeId, observation = observation, userAnswer = userAnswer, inputType = inputType, @@ -928,6 +1216,28 @@ def main(params_string, memory_manager, geabase_handler, intention_router = No return res_to_lingsi + + +def lingsi_response_process(lingsi_response:LingSiResponse)->LingSiResponse: + ''' + # currentnodeid 和 agentname都放到currentnodeid里，需要分割开来 + # 是在agentname字段未上线的临时方案 + # 后续可以去除 + ''' + + currentNodeId_add_agentName = lingsi_response.currentNodeId + if currentNodeId_add_agentName == None: + #不做任何处理 + return lingsi_response + elif '%%@@#' not in currentNodeId_add_agentName: + #lingsi_response.currentNodeId = lingsi_response.currentNodeId + #lingsi_response.agentName = None + return lingsi_response + else: + currentNodeId, agentName = currentNodeId_add_agentName.split('%%@@#') + lingsi_response.currentNodeId = currentNodeId + lingsi_response.agentName = agentName + return lingsi_response def save_res_to_memory(res_to_lingsi, sessionId, memory_manager): ''' diff --git a/muagent/service/ekg_reasoning/src/graph_search/task_node_agent.py b/muagent/service/ekg_reasoning/src/graph_search/task_node_agent.py new file mode 100644 index 0000000..4578604 --- /dev/null +++ b/muagent/service/ekg_reasoning/src/graph_search/task_node_agent.py @@ -0,0 +1,1487 @@ +# -*- coding: utf-8 -*- +#此代码为在aistudio上运行的代码 + +#路径增加 +import sys +import os +import re +from typing import List, Dict, Optional, Tuple, Literal,Union +src_dir = os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +) +# print(src_dir) +sys.path.append(src_dir) + + +src_dir = os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))) +) +sys.path.append(src_dir) +# print(src_dir) + + + + + + + +#一般依赖包 +import json +import requests +import time +import logging +import copy +import sys +import os, base64 +from loguru import logger +import uuid + + + +#muagent 依赖包 +from muagent.connector.schema import Message +from muagent.schemas.db import TBConfig +from muagent.db_handler import * +from muagent.connector.memory_manager import TbaseMemoryManager +from muagent.llm_models.llm_config import EmbedConfig, LLMConfig +from muagent.schemas.db import GBConfig +from muagent.service.ekg_construct import EKGConstructService +from muagent.service.ekg_inference import IntentionRouter +from muagent.schemas.ekg.ekg_reason import LingSiResponse, ToolPlanOneStep, PlanningRunningAgentReply, ActionOneStep, ActionPlan + +from src.graph_search.task_node_prompt import REACT_RUNNING_PROMPT, PLANNING_RUNNING_PROMPT, \ + PLANNING_RUNNING_PROMPT_SUFFIX_1,PLANNING_RUNNING_PROMPT_SUFFIX_2, PLANNING_RUNNING_AGENT_REPLY_TEMPLATE, \ + PLANNING_RUNNING_PROMPT_DM_SPEECH,PARALLEL_RUNNING_PROMPT +# from loguru import logger as logging +from src.utils.call_llm import call_llm, extract_final_result , robust_call_llm +from src.geabase_handler.geabase_handlerplus import GB_handler +from src.utils.normalize import hash_id +from src.memory_handler.ekg_memory_handler import memory_handler_ekg + + + + + +def is_valid_json(string): + try: + json.loads(string) + return True + except ValueError: + return False + +def remove_duplicate_keys(data): + if not isinstance(data, dict): + return data + + result = {} + for key, value in data.items(): + if key not in result: + result[key] = remove_duplicate_keys(value) + else: + result[key] = value + + return result + +def robust_json_loads(llm_result): + ''' + 将llm的输出转换为json， 有一定的概率这个json 在格式上是不完备的。不然少了 } 或者] + ''' + + try: + llm_result_json = json.loads(llm_result) + except: + # for _ in range(2): + try: + logging.info('大模型的输出转换json报错， 现在再用大模型修正一遍') + input_query = f''' + ##输入## + {llm_result} + ##任务## + 上述**输入**可能是一个不完整的dict，如果是这种情况，请将上述转换**输入**为完整的 dict。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 + 上述**输入**也可能是包含多个dict，如果是这种情况，只保留第一个dict即可。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 + ##直接输出结果## + ''' + '以{开头，任何其他内容都是不允许的！' + ouput_result = call_llm(input_query ) + logging.info(f'修正后的输出为{ouput_result}') + llm_result_json = json.loads(ouput_result) + + + except: + logging.info('大模型的输出转换json报错， 现在再用大模型修正一遍') + input_query = f''' + ##输入## + {llm_result} + ##任务## + 上述**输入**可能是一个不完整的dict，如果是这种情况，请将上述转换**输入**为完整的 dict。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 + 上述**输入**也可能是包含多个dict，如果是这种情况，只保留第一个dict即可。 不要新增任何内容，只将格式补全/修正为一个完整dict格式即可 + ##直接输出结果## + ''' + '以{开头，任何其他内容都是不允许的！' + ouput_result = call_llm(input_query, temperature = 0.1) + logging.info(f'修正后的输出为{ouput_result}') + llm_result_json = json.loads(ouput_result) + if type(llm_result_json)!=dict: + llm_result_json = llm_result_json[0] + logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') + raise ValueError(f'llm的输出应该是一个dict才对 ,经过修正仍然报错') + return llm_result_json + +def agent_respond_extract_output(input_str): + if input_str == None: + return None + if 'output' not in input_str: + return input_str + input_str = input_str.split('output')[-1] #取output后面的值 + input_str = input_str.replace('"', '').replace(':', '').replace('}', '').replace('{', '') #去除掉可能的符号 + return input_str + + + +class TaskNodeAgent(): + ''' + 在图谱推理过程中task node 的运行agent + 需要同时 使用memory and geabase + ''' + def __init__(self, geabase_handler, memory_manager, llm_config=None): + self.geabase_handler = geabase_handler + self.memory_manager = memory_manager + self.gb_handler = GB_handler(self.geabase_handler) #gb_handler 以 geabase_handler 为基础，封装了一些处理逻辑 + self.memory_handler = memory_handler_ekg(memory_manager, geabase_handler) + self.llm_config = llm_config + + + + + def robust_call_llm_with_llmname(self, input_query, rootNodeId, stop = [], temperature = 0, presence_penalty=0): + + #logging.info('using a gpt_4') + res = call_llm(input_content = input_query, + llm_model = None , + stop = stop,temperature=temperature, presence_penalty=presence_penalty, + llm_config=self.llm_config) + return res + + + # model_name = self.gb_handler.get_extra_tag( rootNodeId = rootNodeId, rootNodeType = 'opsgptkg_task', key = 'model_name') + # logging.info(f'model_name is {model_name}') + # if model_name == None: + # model_name = 'gpt_4' + # if model_name == 'gpt_4': + # try: + # logging.info('using a gpt_4') + # res = call_llm(input_content = input_query, llm_model = 'gpt_4', stop = stop,temperature=temperature, presence_penalty=presence_penalty) + # except: + # logging.info('using Qwen2_72B_Instruct_OpsGPT') + # res = call_llm(input_content = input_query, llm_model = 'Qwen2_72B_Instruct_OpsGPT',stop = stop, temperature=temperature,presence_penalty=presence_penalty) + # return res + # else: + # try: + # logging.info('using Qwen2_72B_Instruct_OpsGPT') + # res = call_llm(input_content = input_query, llm_model = 'Qwen2_72B_Instruct_OpsGPT',stop = stop, temperature=temperature,presence_penalty=presence_penalty) + # except: + # logging.info('using a gpt_4') + # res = call_llm(input_content = input_query, llm_model = 'gpt_4', stop = stop,temperature=temperature, presence_penalty=presence_penalty) + # return res + + def stop_at_observation(self, historytext, llm_text): + + + # 检查llm_text是否完全不包含"observation" + if "observation" not in llm_text: + return llm_text + + # 检查historytext中是否有"observation" + if "observation" not in historytext: + return llm_text.split("observation", 1)[0] + + # 统计historytext中的"observation"数量 + n_hist = historytext.count("observation") + + # 统计llm_text中的"observation"数量 + n_llm = llm_text.count("observation") + + # 如果两者数量相等，返回第n个observation之后的文本 + if n_hist == n_llm: + parts = llm_text.rsplit("observation", n_hist) + return parts[-1] # 返回最后一个部分（即最后一个"observation"之后的文本） + + # 如果上述条件都不满足，找到第n个和第n+1个"observation"之间的文本 + else: + parts = llm_text.split("observation", n_hist + 1) # 分割出n+1个部分 + if len(parts) > n_hist + 1: # 确保有足够多的部分来获取所需范围 + return parts[n_hist] # 返回第n个和第n+1个observation之间的文本 + else: + return "" # 如果没有找到合适的范围，则返回空字符串或其他适当处理 + + + + def sort_messages_by_time(self, messages): + # 使用 sorted() 对列表进行排序，key 参数指定排序依据 + return sorted(messages, key=lambda msg: msg.start_datetime) + + + + def endcheck(self, nodeId, nodeType, oneNodeName='None', oneNodeDescription='None', current_node_history_json='None'): + ''' + 借助gpt4 来帮忙判断本阶段任务是否结束 + ''' + logging.info(f'endcheck nodeId is {nodeId} nodeType is {nodeType}') + oneNode = self.geabase_handler.get_current_node(attributes={"id": nodeId,}, + node_type=nodeType) + extra = oneNode.attributes['extra'] + print(extra) + try: + + extra_json = json.loads(extra) + if extra_json['endcheck'] == 'True': + endcheck_flag = True + else: + endcheck_flag = False + except: + endcheck_flag= False + + if endcheck_flag == False: + return True #endcheck 通过 + + else: + endcheck_llm_input = oneNodeName + '\n' +oneNodeDescription+ '\n##已有步骤##\n' + json.dumps(current_node_history_json,ensure_ascii=False) + \ + '\n##请结合已有步骤，判断本阶段任务是否结束，只返回中文的 是 或者 否即可，不要输出其他内容:##\n' + + logging.info('=============endcheck_llm_result==================') + logging.info(endcheck_llm_input) + llm_result = self.robust_call_llm_with_llmname(endcheck_llm_input, nodeId) + logging.info('=============endcheck_llm_result==================') + logging.info(llm_result) + + if '是' in llm_result: + return False + else: + return True + + + def naive_agent_input_prompt(self, histroy:str, node_name:str, node_decription:str, running_type_prompt:str, + suffix_1:str = '', suffix_2:str = '') -> str: + llm_input = histroy + '\n' + node_name + '\n' + node_decription + '\n' + running_type_prompt\ + + suffix_1 + suffix_2 + return llm_input + + + def task_running(self, sessionId : str, nodeId: str, nodeType:str, lingsi_respond: LingSiResponse): + ''' + 通用的节点task_running 模块， 根据情况，当前分为 react， parallel，plan + ''' + + if self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = nodeType, key = 'action') == 'react': + logging.info(f'======对于节点{nodeId},当前节点为 react 模式======') + return self.react_running( sessionId ,nodeId , nodeType , lingsi_respond ) + elif self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = nodeType, key = 'action') == 'parallel': + logging.info(f'======对于节点{nodeId}, 当前节点为 parallel 模式======') + return self.parallel_running( sessionId ,nodeId , nodeType , lingsi_respond ) + elif self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = nodeType, key = 'action') == 'plan': + logging.info(f'======对于节点{nodeId}, 当前节点为 plan 模式======') + return self.planning_running( sessionId ,nodeId , nodeType , lingsi_respond ) + else: + action = self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = nodeType, key = 'action') + logging.info(f'action当前字段为{action}') + raise ValueError('action 字段格式不合法，当前仅支持 react parallel plan 三种模式') + + + def judge_running_state(self, sessionId: str, nodeId: str)-> bool : + ''' + 判断当前节点是否是第一次运行 + ''' + get_messages_res = self.memory_handler.nodecount_get( sessionId, nodeId) + + if get_messages_res == [] : + logging.info('当前这个{sessionId} react节点 是第一次运行') + first_run_flag = True + else: + if json.loads(get_messages_res[0].role_content)['nodestage'] == 'end' :#在上一轮已经结束了，这一轮还未开始 + logging.info('当前这个{sessionId} react节点在上一轮已经结束了，这一轮还未开始，在这一轮也算是第一次执行') + first_run_flag = True + else: + logging.info('当前这个{sessionId} react节点 不是第一次执行') + first_run_flag = False + return first_run_flag + + + def parallel_running(self, sessionId : str, nodeId: str, nodeType:str, lingsi_respond: LingSiResponse): + ''' + parallel 模块 + + ''' + #step0 根据返回值，得到实际agent执行内容 + + logging.info(f'react_running 接受到的 lingsi_respond is {lingsi_respond}') + try: + if lingsi_respond == None:#第一次该该节点被调用是，输入式将lingsi_respond设置为None + agent_respond = None + else: + if type(lingsi_respond.observation) ==str: + agent_respond = json.loads(lingsi_respond.observation)['toolResponse'] + else: + agent_respond = lingsi_respond.observation['toolResponse'] + agent_respond = agent_respond_extract_output(agent_respond) + except Exception as e: + logging.info(f'lingsi_respond is {lingsi_respond}' ) + raise ValueError(f'从lingsi_respond中提取 agent_respond报错, 报错信息：{e}') + + + + #stpe1 判断当前状态, 看是否是第一次运行， 如果是，则需要将节点初始化 + first_run_flag = self.judge_running_state( sessionId,nodeId) + if first_run_flag == True: + # 当react的状态是 end 或者为空的时候调用此函数，进行初始化 或者 chapter + 1 + self.memory_handler.init_react_count(sessionId, nodeId) + + + #step2 获取节点名字 + 节点描述 + # 变量替换 + oneNode = self.geabase_handler.get_current_node(attributes={"id": nodeId,}, + node_type=nodeType) + oneNodeName = oneNode.attributes['name'] + oneNodeDescription = oneNode.attributes['description'] + oneNodeDescription = self.fill_replacements(oneNodeDescription, sessionId) + + + + + #step3.1 获取memory， 构成给大模型的输入 + #获取memory， 主持人能看到的memory， 和获取tool的memory类似 + assembled_memory = self.get_memory_for_dm(sessionId, nodeId) + assembled_memory = json.dumps(assembled_memory, ensure_ascii=False) + logging.info(f'assembled_memory is {assembled_memory}') + + #step3.2 获取当前节点的历史运行情况。如果是第一次运行，需要将react node 的name 存入到 memory中 + #如果不是第一次运行，肯定有历史history 和 action plan + if first_run_flag == True: + current_node_history = '' + current_node_history_json = [] + #第一次运行，对于react模块，只将标题放置在memory里， 因为对于react模块，description太多了，循环的情况下，很罗嗦且超过上下文 + self.memory_handler.react_nodedescription_save(sessionId, nodeId, oneNodeName) + else: + #不是第一次运行。那么肯定历史history进来 + logging.info(f'#不是第一次运行。那么肯定存储了plan') + current_node_history = self.memory_handler.react_current_history_get(sessionId, nodeId) + current_node_history_json = json.loads(current_node_history) + + action_plan = json.loads( self.memory_handler.current_plan_get(sessionId, nodeId ) ) + #action_plan = json.loads(json.dumps(action_plan, ensure_ascii=False)) #去除乱码 + logging.info(f'对于parallel， 之前存储的action_plan is {action_plan} ，type(action_plan) is {type(action_plan) } ') + action_plan =ActionPlan(**action_plan) + + + #step4 执行 llm(如果是第一次调用)，生成action_plan + #如不是第一次调用，将已运行的agent_name 存下来 + if first_run_flag == True: + + llm_input = self.naive_agent_input_prompt(assembled_memory, oneNodeName, oneNodeDescription ,PARALLEL_RUNNING_PROMPT + , PLANNING_RUNNING_PROMPT_SUFFIX_1 , PLANNING_RUNNING_PROMPT_SUFFIX_2) + + logging.info('=============llm_input==================') + logging.info(llm_input) + + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + + logging.info('=============llm_result==================') + logging.info(llm_result) + llm_result_json = robust_json_loads(llm_result) + llm_result_PRAR = PlanningRunningAgentReply(**llm_result_json) + + current_node_history_json.append(llm_result_json) #将当前记录放到current_node_history_json里 + + logging.info('planning_running 第一次大模型调用结束') + + + + #此时的action_plan 是一个string + #action_plan = llm_result_json["action_plan"] + # 将 sss 中的字典转换为 ActionOneStep 对象 + #action_steps = [ActionOneStep(**step) for step in sss] + + # 创建 ActionPlan 对象并设置 data 属性 + action_plan = llm_result_PRAR.action_plan + + + + else: + logging.info('不是第一次调用，无需执行大模型, 将已运行的agent_name 存下来') + self.memory_handler.processed_agentname_save(sessionId, nodeId, lingsi_respond.agentName ) + + #if lingsi_respond.agentName == '主持人' + + + + #step5 分析 llm_result 执行结果，如果第一次运行，则提取execute_agent_names from action_plan + #如果不是第一次运行，则判断 remain_action_plan，看是否节点任务结束 + + if first_run_flag == True and len(action_plan.data) >0 : + react_flag = 'waiting_other_agent' + + logging.info(f'当前为{react_flag}, 因为初次运行且plan不为空') + execute_agent_names = [action_plan.data[i].agent_name for i in range(len(action_plan.data))] + #将该节点的count 设置为 runninng + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'running') + + + else: + processed_agentname = self.memory_handler.processed_agentname_get(sessionId, nodeId)#list + logging.info(f'已经处理的agent_name 为 {processed_agentname}') + if processed_agentname == []: + raise ValueError(f'此时不是第一次运行，在memory中应该已经有processed_agentname才对，但是processed_agentname is {processed_agentname} ') + + remain_action_plan = [] + for i in range(len(action_plan.data)): + if action_plan.data[i].agent_name not in processed_agentname: + remain_action_plan.append(action_plan.data[i]) + + logging.info(f'剩余待处理的的agent_name 为 {remain_action_plan}') + if len(remain_action_plan) == 0: + execute_agent_names = [] + react_flag = 'end' + logging.info(f'当前为{react_flag}, 将本次节点的count设置为end, 因为remain_action_plan 为空 ') + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'end') + else: + react_flag = 'waiting_other_agent' + logging.info(f'当前为{react_flag}, 因为第一次大模型生成的plan还没有执行成完,还需等待其他步骤执行完毕 ') + execute_agent_names = [] #对于parallel 模式，中途的结果不给下一步指示，因为tool_plan在第一次就并行执行了 + + #将该节点的count 设置为 runninng + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'running') + + + #step6 分析 llm_result 执行结果, 并保存 + + if first_run_flag == True: + action_plan_json = llm_result_json["action_plan"] + # 将 sss 中的字典转换为 ActionOneStep 对象 + action_steps = [ActionOneStep(**step) for step in action_plan_json] + + # 创建 ActionPlan 对象并设置 data 属性 + action_plan = ActionPlan(data=action_steps) + action_plan_json_str = json.dumps(action_plan.dict() ,ensure_ascii=False) + + logging.info(f'将要存储action_plan, action_plan_json_str is {action_plan_json_str}') + #存plan + self.memory_handler.current_plan_save(sessionId, nodeId, json.dumps(action_plan.dict() ,ensure_ascii=False) ) + #存对话 + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False) ) + + else: + #存agent返回结果的对话 + #agent_respond_template = self.plan_running_agent_responds_build() + + + agent_respond_template = copy.deepcopy(PLANNING_RUNNING_AGENT_REPLY_TEMPLATE) + + agent_respond_template['action']['agent_name'] = lingsi_respond.agentName + agent_respond_template['action']['player_name'] = action_plan.get_player_name_by_agent_name(lingsi_respond.agentName) + #agent_respond_template['observation'] = [{ "memory_tag":[nodeId],"content": agent_respond}] # + first_memory_tag = current_node_history_json[0]['Dungeon_Master'][0]['memory_tag'] #取大模型第一次选中的memory_tag,在这一轮里沿用 + agent_respond_template['observation'] = [{ "memory_tag":first_memory_tag,"content": agent_respond}] + + ## TODO: "memory_tag":["all"] 这是一个临时解决方案，需要在后续版本中替换为更优的算法,具体为 无论是planning还是parallel，可见范围都最好需要时立即能知道。所以不能等到玩家发言后，再到前端显示 + ## 当前设置为all了，但是这是不对的， 有一个解决方案是，将主持人最开始的memory_tag作为后续所有的memory_tag，有一个风险是需要区分 信息的可见范围以及 plan的区别 + ## 可以看见这些memory不一定表示 要参与行动。 + + + current_node_history_json.append(agent_respond_template) + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False) ) + logging.info(f'current_node_history_json is {current_node_history_json}##### lingsi_respond is {lingsi_respond}; ##### agent_respond is {agent_respond} ##### agent_respond_template is {agent_respond_template}' ) + + + #step7 存储 memory # for other agent + self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) + + update_flag = self.update_replacement(sessionId, nodeId) + if react_flag == 'end': + if update_flag: + logging.info("变量更新成功！") + else: + logging.info("无变量更新或变量更新失败") + #step8 返回 question_plan + if react_flag == 'end': + question_plan = [] + elif react_flag == 'waiting_other_agent': + question_plan = self.react_get_question_plan(sessionId, nodeId, execute_agent_names) + logging.info(f'parallel question_plan is {question_plan}') + else: + question_plan = [] + return react_flag, question_plan + + + def planning_running(self, sessionId : str, nodeId: str, nodeType:str, lingsi_respond: LingSiResponse): + + ''' + planning 模块 + + ''' + #step0 根据返回值，得到实际agent执行内容 + + logging.info(f'planning_running 接受到的 lingsi_respond is {lingsi_respond}') + try: + if lingsi_respond == None:#第一次该该节点被调用是，输入式将lingsi_respond设置为None + agent_respond = None + else: + if type(lingsi_respond.observation) ==str: + agent_respond = json.loads(lingsi_respond.observation)['toolResponse'] + else: + agent_respond = lingsi_respond.observation['toolResponse'] + agent_respond = agent_respond_extract_output(agent_respond) + except Exception as e: + logging.info(f'lingsi_respond is {lingsi_respond}' ) + raise ValueError(f'从lingsi_respond中提取 agent_respond报错, 报错信息：{e}') + + + + + #stpe1 判断当前状态, 看是否是第一次运行 + first_run_flag = self.judge_running_state( sessionId,nodeId) + if first_run_flag == True: + # 当react的状态是 end 或者为空的时候调用此函数，进行初始化 或者 chapter + 1 + self.memory_handler.init_react_count(sessionId, nodeId) + + + #step2 获取节点名字 + 节点描述 + oneNode = self.geabase_handler.get_current_node(attributes={"id": nodeId,}, + node_type=nodeType) + oneNodeName = oneNode.attributes['name'] + oneNodeDescription = oneNode.attributes['description'] + + + oneNodeDescription = self.fill_replacements(oneNodeDescription, sessionId) + + # 变量替换 + + + #step3.1 获取memory， 构成给大模型的输入 + #获取memory， 主持人能看到的memory， 和获取tool的memory类似 + assembled_memory = self.get_memory_for_dm(sessionId, nodeId) + assembled_memory = json.dumps(assembled_memory, ensure_ascii=False) + logging.info(f'assembled_memory is {assembled_memory}') + + #step3.2 获取当前节点的历史运行情况。如果是第一次运行，需要将react node 的name 存入到 memory中 + if first_run_flag == True: + current_node_history = '' + current_node_history_json = [] + #第一次运行，对于react模块，只将标题放置在memory里， 因为对于react模块，description太多了，循环的情况下，很罗嗦且超过上下文 + self.memory_handler.react_nodedescription_save(sessionId, nodeId, oneNodeName) + else: + #不是第一次运行。那么肯定历史history进来 + logging.info(f'#不是第一次运行。那么肯定存储了plan') + current_node_history = self.memory_handler.react_current_history_get(sessionId, nodeId) + current_node_history_json = json.loads(current_node_history) + + + + #step4 执行 llm(如果是第一次调用) 获取action plan； 如果不是第一次调用，无需执行大模型，将已运行的agent_name 存下来 + if first_run_flag == True: + + llm_input = self.naive_agent_input_prompt(assembled_memory, oneNodeName, oneNodeDescription ,PLANNING_RUNNING_PROMPT + , PLANNING_RUNNING_PROMPT_SUFFIX_1 , PLANNING_RUNNING_PROMPT_SUFFIX_2) + + logging.info('=============llm_input==================') + logging.info(llm_input) + + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + + logging.info('=============llm_result==================') + logging.info(llm_result) + llm_result_json = robust_json_loads(llm_result) + + current_node_history_json.append(llm_result_json) #将当前记录放到current_node_history_json里 + + logging.info('planning_running 第一次大模型调用结束') + + + + action_plan = llm_result_json["action_plan"] + action_plan =ActionPlan(**{'data':action_plan}) + + + + else: + #存已有的agentName + logging.info('不是第一次调用，无需执行大模型, 将已运行的agent_name 存下来') + self.memory_handler.processed_agentname_save(sessionId, nodeId, lingsi_respond.agentName ) + + #从memory中获取 action plan + action_plan = json.loads( self.memory_handler.current_plan_get(sessionId, nodeId ) ) + logging.info(f'对于 planning 之前存储的action_plan is {action_plan} ，type(action_plan) is {type(action_plan) } ') + action_plan =ActionPlan(**action_plan) + + + #组装agent返回的格式 agent_respond_template + agent_respond_template = copy.deepcopy(PLANNING_RUNNING_AGENT_REPLY_TEMPLATE) + + agent_respond_template['action']['agent_name'] = lingsi_respond.agentName + #agent_respond_template['action']['player_name'] = self.get_player_name_from_action_plan(action_plan, lingsi_respond.agentName) + first_memory_tag = current_node_history_json[0]['Dungeon_Master'][0]['memory_tag'] #取大模型第一次选中的memory_tag,在这一轮里沿用 + agent_respond_template['action']['player_name'] = action_plan.get_player_name_by_agent_name(lingsi_respond.agentName) + agent_respond_template['observation'] = [{ "memory_tag":first_memory_tag,"content": agent_respond}]# TODO + current_node_history_json.append(agent_respond_template) + + + #step4.5 如果需要执行 主持人。 修改 action_plan 和 current_node_history_json + if first_run_flag == True: + while action_plan.data[0].agent_name == '主持人': + + llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription + PLANNING_RUNNING_PROMPT_DM_SPEECH + '\n##已有步骤##\n' + \ + json.dumps(current_node_history_json,ensure_ascii=False) + PLANNING_RUNNING_PROMPT_SUFFIX_2 + + logging.info('=============llm_input 主持人==================') + logging.info(llm_input) + + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + + logging.info('=============llm_result 主持人==================') + logging.info(llm_result) + dm_result_json = robust_json_loads(llm_result) + + + dm_respond_template = copy.deepcopy(PLANNING_RUNNING_AGENT_REPLY_TEMPLATE) + dm_respond_template['action']['agent_name'] = '主持人' + dm_respond_template['action']['player_name'] = '主持人' + first_memory_tag = current_node_history_json[0]['Dungeon_Master'][0]['memory_tag'] #取大模型第一次选中的memory_tag,在这一轮里沿用 + dm_respond_template['Dungeon_Master'] = [{ "memory_tag":first_memory_tag, "content": dm_result_json['content']}]# TODO + current_node_history_json.append(dm_respond_template) #将当前记录放到current_node_history_json里 + action_plan.data.pop(0) #从action_plan.data里 抛弃第一个元素 + logging.info(f'经过pop 后 action_plan.data is {action_plan.data}') + else: + + processed_agentname = self.memory_handler.processed_agentname_get(sessionId, nodeId)#list + logging.info(f'已经处理的agent_name 为 {processed_agentname}') + if processed_agentname == []: + raise ValueError(f'此时不是第一次运行，在memory中应该已经有processed_agentname才对，但是processed_agentname is {processed_agentname} ') + + remain_action_plan = [] + for i in range(len(action_plan.data)): + if action_plan.data[i].agent_name not in processed_agentname: + remain_action_plan.append(action_plan.data[i]) + + while len(remain_action_plan)>=1 and remain_action_plan[0].agent_name == '主持人': + + llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription + PLANNING_RUNNING_PROMPT_DM_SPEECH + '\n##已有步骤##\n' + \ + json.dumps(current_node_history_json,ensure_ascii=False) + PLANNING_RUNNING_PROMPT_SUFFIX_2 + + logging.info('=============llm_input 主持人==================') + logging.info(llm_input) + + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + + logging.info('=============llm_result 主持人==================') + logging.info(llm_result) + dm_result_json = robust_json_loads(llm_result) + + + dm_respond_template = copy.deepcopy(PLANNING_RUNNING_AGENT_REPLY_TEMPLATE) + dm_respond_template['action']['agent_name'] = '主持人' + dm_respond_template['action']['player_name'] = '主持人' + first_memory_tag = current_node_history_json[0]['Dungeon_Master'][0]['memory_tag'] #取大模型第一次选中的memory_tag,在这一轮里沿用 + dm_respond_template['Dungeon_Master'] = [{ "memory_tag":first_memory_tag, "content": dm_result_json['content']}]# TODO + current_node_history_json.append(dm_respond_template) #将当前记录放到current_node_history_json里 + remain_action_plan.pop(0) #remain_action_plan.data里 抛弃第一个元素 + logging.info(f'经过pop 后 action_plan.data is {action_plan.data}') + + + #step5 分析 llm_result 执行结果 + if first_run_flag == True and len(action_plan.data) >0 : + react_flag = 'waiting_other_agent' + + logging.info(f'当前为{react_flag}, 因为初次运行且plan不为空') + # llm_result_truncation_json = json.loads(current_node_history + llm_result_truncation + '":[]}]') + # llm_result_truncation_json = json.loads(current_node_history + llm_result_truncation + '":[]}]') + + #提取此时应该执行的agent_name + execute_agent_name = action_plan.data[0].agent_name + execute_player_name = action_plan.data[0].player_name + logging.info(f'execute_agent_name is {execute_agent_name}, execute_player_name is {execute_player_name}') + + #将该节点的count 设置为 runninng + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'running') + + + else: + + logging.info(f'剩余待处理的的agent_name 为 {remain_action_plan}') + if len(remain_action_plan) == 0: + + react_flag = 'end' + logging.info(f'当前为{react_flag}, 将本次节点的count设置为end, 因为remain_action_plan 为空 ') + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'end') + else: + react_flag = 'waiting_other_agent' + logging.info(f'当前为{react_flag}, 因为第一次大模型生成的plan还没有执行成完 ') + + #提取此时应该执行的agent_name + execute_agent_name = remain_action_plan[0].agent_name + execute_player_name = remain_action_plan[0].player_name + logging.info(f'execute_agent_name is {execute_agent_name}, execute_player_name is {execute_player_name}') + + #将该节点的count 设置为 runninng + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'running') + + + #step6 分析 llm_result 执行结果, 并保存 + + if first_run_flag == True: + self.memory_handler.current_plan_save(sessionId, nodeId, json.dumps(action_plan.dict() ,ensure_ascii=False) ) + + + + #step7 存储 memory # for other agent + logging.info(f'current_node_history_json if {current_node_history_json}') + + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False) ) + self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) + + if react_flag == 'end': + update_flag = self.update_replacement(sessionId, nodeId) + if update_flag: + logging.info("变量更新成功！") + else: + logging.info("无变量更新或变量更新失败") + #step8 返回 question_plan + if react_flag == 'end': + question_plan = [] + elif react_flag == 'waiting_other_agent': + question_plan = self.react_get_question_plan(sessionId, nodeId, execute_agent_name) + else: + question_plan = [] + return react_flag, question_plan + + def get_player_name_from_action_plan(self, action_plan, agent_name): + ''' + 从action_plan中获取agent_name + ''' + for i in range(len(action_plan)): + if action_plan[i]['agent_name'] == agent_name: + return action_plan[i]['player_name'] + raise ValueError(f'action plan {action_plan }中未找到 对应的 agent name {agent_name}') + + def fill_replacements(self, node_description: str, sessionId: str) -> str: + """ + 构建替换后的 prompt 字符串。 + :param sessionId: 一个函数，接收占位符名称并返回对应的值 + :return prompt: 替换后的 prompt 字符串 + :return placeholders 涉及到的变量 + """ + prompt = node_description + logging.info(f'prompt:{prompt}') + placeholders = re.findall(r"#\$\#(.*?)#\$\#", prompt) + logging.info("开始变量替换") + logging.info(f'需要替换的变量{placeholders}') + try: + for placeholder in placeholders: + logging.info(f'placeholder为{placeholder}') + value = self.memory_manager.get_msg_content_by_rule_name(sessionId, placeholder) + logging.info(f'value为{value}') + if value != None: + prompt = prompt.replace(f'#$#{placeholder}#$#', value) + logging.info(f'替换后的prompt{prompt}') + except Exception as e: + logging.info(f"变量替换出现错误！{e}") + + return prompt + + + def update_replacement(self, sessionId: str, nodeId: str) -> bool: + """ + 更新变量名， + :param sessionId: 对话id + :param nodeId: 节点id + """ + cur_node_memory = self.get_cur_node_memory(sessionId, nodeId) + logging.info(f'当前节点游戏记录:{cur_node_memory}') + cur_node_memory = "当前节点游戏记录:" + ''.join(cur_node_memory) + + try: + updaterule = self.gb_handler.get_tag(rootNodeId = nodeId, rootNodeType = "opsgptkg_task", key = 'updaterule') + cur_node_updaterule = json.loads(updaterule) + except Exception as e: + logging.info(f"出现错误: {e}") + logging.info(f"输入不是json格式或者为空，变量更新失败，略过.") + logging.info(f"node_envdescription: {updaterule}") + return False + + update_flag = False + try: + for placeholder, update_role in cur_node_updaterule.items(): + logging.info(f'更新的变量为:{placeholder}') + logging.info(f'当前节点游戏记录为:{cur_node_memory}') + update_flag = self.memory_manager.update_msg_content_by_rule(sessionId, placeholder, cur_node_memory, update_role) + logging.info(f'update_flag:{update_flag}') + except Exception as e: + logging.info(f"变量更新出现错误！{e}") + return update_flag + + + def react_running(self, sessionId : str, nodeId: str, nodeType:str, lingsi_respond: LingSiResponse): + ''' + react 模块 运行 + ''' + + + logging.info(f'react_running 接受到的 lingsi_respond is {lingsi_respond}') + try: + if lingsi_respond == None:#第一次该该节点被调用是，输入式将lingsi_respond设置为None + agent_respond = None + else: + if type(lingsi_respond.observation) ==str: + agent_respond = json.loads(lingsi_respond.observation)['toolResponse'] + else: + agent_respond = lingsi_respond.observation['toolResponse'] + except Exception as e: + logging.info(f'lingsi_respond is {lingsi_respond}' ) + raise ValueError(f'从lingsi_respond中提取 agent_respond报错, 报错信息：{e}') + + + + + if agent_respond == None: + agent_respond = '' + if type(agent_respond) == str: + agent_respond = agent_respond.replace('"', '').replace("'", "") #需要去除agent返回中的 " 和 ' + agent_respond = agent_respond_extract_output(agent_respond) # 去除 agent_respond 中的 thought 和 output + #stpe1 判断当前状态 + get_messages_res = self.memory_handler.nodecount_get( sessionId, nodeId) + + if get_messages_res == [] : + logging.info('当前这个{sessionId} react节点 是第一次运行') + first_run_react_flag = True + else: + if json.loads(get_messages_res[0].role_content)['nodestage'] == 'end' :#在上一轮已经结束了，这一轮还未开始 + logging.info('当前这个{sessionId} react节点在上一轮已经结束了，这一轮还未开始，在这一轮也算是第一次执行') + first_run_react_flag = True + else: + logging.info('当前这个{sessionId} react节点 不是第一次执行') + first_run_react_flag = False + + if first_run_react_flag == True: + # 当react的状态是 end 或者为空的时候调用此函数，进行初始化 或者 chapter + 1 + self.memory_handler.init_react_count(sessionId, nodeId) + + #step2 获取节点名字 + 节点描述 + oneNode = self.geabase_handler.get_current_node(attributes={"id": nodeId,}, + node_type=nodeType) + + oneNodeName = oneNode.attributes['name'] + oneNodeDescription = oneNode.attributes['description'] + + #step3.1 获取memory， 构成给大模型的输入 + #获取memory， 主持人能看到的memory， 和获取tool的memory类似 + + # tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) + # get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #此时是主持人，所以需要看到所有的memory，无需加tag。 对于在我这一侧需要运行的llm，一定是看到所有信息，因为我就是主持人 + # assembled_memory = self.assemble_memory(nodeId, nodeType, get_messages_res_sorted) + assembled_memory = self.get_memory_for_dm(sessionId, nodeId) + assembled_memory = json.dumps(assembled_memory, ensure_ascii=False) + logging.info(f'assembled_memory is {assembled_memory}') + + #step3.2 获取当前节点的历史运行情况。如果是第一次运行，需要将react node 的name 存入到 memory中 + if first_run_react_flag == True: + current_node_history = '' + #第一次运行，对于react模块，只将标题放置在memory里， 因为对于react模块，description太多了，循环的情况下，很罗嗦且超过上下文 + self.memory_handler.react_nodedescription_save(sessionId, nodeId, oneNodeName) + + else: + #不是第一次运行。那么肯定历史history进来 + logging.info(f'#不是第一次运行。那么肯定历史history进来{sessionId}, {nodeId}') + current_node_history = self.memory_handler.react_current_history_get(sessionId, nodeId) + # llm_result_truncation + '": [{"content": ' + user_responds + + ''' + history 里存储 observation的截断，不包含observation， + llm_output 输出整个完整的流程（如果是gpt_4, 不能有停用词，因为每次都是从头开始录的）， + self.stop_at_observation，需要接受 current_node_history ，先分析里面有几个observation（N个）， 然后我可以往后扩展一个observation， 不包含observation + + jsonstring 就转全量的， 但是录入到memory中的时候，注意只录入 N+1 个 observation的信息。 + + + + ''' + #step4 执行 llm， + if '##输入##' not in oneNodeDescription: + oneNodeDescription += REACT_RUNNING_PROMPT + logging.info("react 模式 prompt 分离完成") + oneNodeDescription = self.fill_replacements(oneNodeDescription, sessionId) + logging.info("变量替换完成") + if first_run_react_flag == True: + llm_input = assembled_memory + '\n' + oneNodeName + '\n' + oneNodeDescription + '\n##已有步骤##\n无' + '\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' + logging.info('=============llm_input==================') + logging.info(llm_input) + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + current_node_history_json = [] + llm_result_json = robust_json_loads(llm_result) + llm_result_json = remove_duplicate_keys(llm_result_json) + logging.info('=============llm_result==================') + logging.info(llm_result_json) + if type(llm_result_json)!=dict: + llm_result_json = llm_result_json[0] + logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') + # raise ValueError(f'llm的输出应该是一个dict才对 ') + current_node_history_json.append(llm_result_json) + else: + # current_node_history[-1]["observation"]['content'] = agent_respond + # llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n' + current_node_history + '": [{"content":" ' + agent_respond + '"' + # llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n' + current_node_history + '": [{"content":" ' + agent_respond + '", "memory_tag:' \ + # + '\n ' + + current_node_history_json = json.loads(current_node_history) #历史记录里可能包含虚假信息 + logging.info(f'current_node_history_json is {current_node_history_json}') + if current_node_history_json[-1]['action']['agent_name'] != '主持人': + current_node_history_json[-1]["observation"][0]['content'] = agent_respond #将历史中最后一次主持人幻觉的输出，转换为用户补充的输入 + try: + current_node_history_json[-1]["thought"] = '' #在非主持人环节时，应该将thought 设置为'' + except: + pass + llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n##已有步骤##\n' + json.dumps(current_node_history_json,ensure_ascii=False) + \ + '\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' + logging.info('=============llm_input==================') + logging.info(llm_input) + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + llm_result_json = robust_json_loads(llm_result) + llm_result_json = remove_duplicate_keys(llm_result_json) + logging.info('=============llm_result==================') + logging.info(llm_result_json) + if type(llm_result_json)!=dict: + llm_result_json = llm_result_json[0] + logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') + # raise ValueError(f'llm的输出应该是一个dict才对 ') + current_node_history_json.append(llm_result_json) + + retry_llm = 0 + while(( retry_llm <= 20) and ("taskend" not in llm_result) and (llm_result_json['action']['agent_name'] == '主持人' )): + logging.info('由于是主持人发言，情况继续') + + endcheck_res = self.endcheck( nodeId, nodeType, oneNodeName, oneNodeDescription, current_node_history_json) + if endcheck_res== False: + logging.info('endchek没有通过，主持人发言终止, 强行将 llm_result == {"action": "taskend"}') + llm_result = json.dumps({"action": "taskend"}) + llm_result_json = robust_json_loads(llm_result) + current_node_history_json.append(llm_result_json) + break + + + + llm_input = assembled_memory + '\n' + oneNodeName + '\n' +oneNodeDescription+ '\n##已有步骤##\n' + json.dumps(current_node_history_json,ensure_ascii=False) + \ + '\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' + logging.info('=============llm_input==================') + logging.info(llm_input) + llm_result = self.robust_call_llm_with_llmname(llm_input, nodeId) + llm_result_json = robust_json_loads(llm_result) + llm_result_json = remove_duplicate_keys(llm_result_json) + logging.info('=============llm_result==================') + logging.info(llm_result_json) + current_node_history_json.append(llm_result_json) + if type(llm_result_json)!=dict: + llm_result_json = llm_result_json[0] + logging.info('llm的输出应该是一个dict才对, 有时候出现[{one step}], 所以尝试选其中一个元素转换为dict') + raise ValueError(f'llm的输出应该是一个dict才对 ') + retry_llm = retry_llm + 1 + + logging.info('大模型调用结束') + + + + #step5 分析 llm_result 执行结果 + #llm_result 为最后一次llm的输出 + if 'taskend' in llm_result: + react_flag = 'end' + logging.info(f'当前为{react_flag}, 将本次节点的count设置为end ') + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'end') + elif 'observation' in llm_result: + react_flag = 'waiting_other_agent' + + logging.info(f'当前为{react_flag}, 尝试补充字符使得llm_result_truncation能转为json格式 ') + # llm_result_truncation_json = json.loads(current_node_history + llm_result_truncation + '":[]}]') + # llm_result_truncation_json = json.loads(current_node_history + llm_result_truncation + '":[]}]') + + #提取此时应该执行的agent_name + execute_agent_name = current_node_history_json[-1]['action']['agent_name'] + execute_player_name = current_node_history_json[-1]['action']['player_name'] + + #将该节点的count 设置为 runninng + self.memory_handler.nodecount_set_key(sessionId, nodeId, 'nodestage', 'running') + + + + + #step6 存储 history # for DM + logging.info(f'存储 history # for DM') + if react_flag == 'waiting_other_agent' and first_run_react_flag == True: + #step6.1 存储 llm_result_truncation + + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False) ) + + elif react_flag == 'waiting_other_agent' and first_run_react_flag == False: + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False)) + + elif react_flag == 'end' and first_run_react_flag == True: #第一次运行就运行到结尾了 + + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False)) + + elif react_flag == 'end' and first_run_react_flag == False: #第N次运行 运行到结尾了 + self.memory_handler.react_current_history_save(sessionId, nodeId, json.dumps(current_node_history_json ,ensure_ascii=False)) + + + #step7 存储 memory # for other agent + logging.info(f'存储 memory # for other agent') + if react_flag == 'waiting_other_agent' and first_run_react_flag == True: + logging.info('#第一次运行 等待agent返回') + self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) + if react_flag == 'waiting_other_agent' and first_run_react_flag == False: + logging.info('#第N次运行 等待agent返回') + self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) + + elif react_flag == 'end' and first_run_react_flag == True: #第一次运行就运行到结尾了: + logging.info('#第一次运行就运行到结尾了:') + self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) + + elif react_flag == 'end' and first_run_react_flag == False: #第N次运行 运行到结尾了 + logging.info('#第N次运行 运行到结尾了') + self.memory_handler.react_memory_save(sessionId, nodeId, current_node_history_json) + + if react_flag == 'end': + update_flag = self.update_replacement(sessionId, nodeId) + if update_flag: + logging.info("变量更新成功！") + else: + logging.info("无变量更新或变量更新失败") + #step8 返回 question_plan + if react_flag == 'end': + question_plan = [] + elif react_flag == 'waiting_other_agent': + question_plan = self.react_get_question_plan(sessionId, nodeId, execute_agent_name) + else: + question_plan = [] + return react_flag, question_plan + + + + def get_intentionNodes_from_subtree(self, sessionId)->list: + ''' + #抽取 nodeid_in_subtree 中的所有 intention_nodes + 返回值以这种list [{'nodeId': 'xxx=','nodeType': 'opsgptkg_intent'}] + ''' + intention_nodes = [] + try: + nodeid_in_subtree_memory = self.memory_manager.get_memory_pool_by_all( + { "chat_index": sessionId, "role_type": "nodeid_in_subtree"}) + nodeid_in_subtree = json.loads( nodeid_in_subtree_memory.get_messages()[0].role_content ) + intention_nodes = [] + for i in range(len(nodeid_in_subtree)): + onenode = nodeid_in_subtree[i] + if onenode['nodeType'] == 'opsgptkg_intent': + intention_nodes.append( {'nodeId': onenode['nodeId'], 'nodeType': onenode['nodeType']}) + except Exception as e: + logging.info(f"发生了一个错误：{e}") + return intention_nodes + + def get_memory_for_tool(self,sessionId, nodeId): + ''' + react 节点中 对于一个 主持人， 构建memory的函数。 + 只需要将祖先节点弄好即可，不需要加自己，因为自己有 history进行维护 + + 默认将这个sessionId里所有的意图识别节点，弄到 tool_ancestor里 + ''' + + intention_nodes = self.get_intentionNodes_from_subtree(sessionId) + nodeType = 'opsgptkg_task' #假设一定是task节点 + tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) + '''[{'nodeId': 'xxx=','nodeType': 'opsgptkg_intent'}]''' + tool_ancestor = tool_ancestor + intention_nodes + get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #对于tool,假设都是主持人的工具，所以需要看到所有的memory，无需加tag。 + assembled_memory = self.assemble_memory_for_tool(nodeId, nodeType, get_messages_res_sorted) # tool 的memory需要兼顾以前的格式 + return assembled_memory + + + def get_memory_for_dm(self,sessionId, nodeId): + ''' + react 节点中 对于一个 主持人， 构建memory的函数。 + 只需要将祖先节点弄好即可，不需要加自己，因为自己有 history进行维护 + ''' + intention_nodes = self.get_intentionNodes_from_subtree(sessionId) + nodeType = 'opsgptkg_task' #假设一定是task节点 + tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) + tool_ancestor = tool_ancestor + intention_nodes + get_messages_res_sorted = self.get_memory_from_ancestor( tool_ancestor, sessionId, role_tags = None) #此时是主持人，所以需要看到所有的memory，无需加tag。 对于在我这一侧需要运行的llm，一定是看到所有信息，因为我就是主持人 + assembled_memory = self.assemble_memory_for_reactagent(nodeId, nodeType, get_messages_res_sorted) + return assembled_memory + + def get_memory_for_computer_agent(self,sessionId, nodeId, execute_agent_name): + ''' + react 节点中 对于一个 agent_x (电脑agent)， 构建memory的函数 + ''' + intention_nodes = self.get_intentionNodes_from_subtree(sessionId) + nodeType = 'opsgptkg_task' #假设一定是task节点 + tool_ancestor = self.get_tool_ancestor(sessionId, nodeId, nodeType) + tool_ancestor = tool_ancestor + intention_nodes + + if nodeId not in [nodeinancestor['nodeId'] for nodeinancestor in tool_ancestor]: + tool_ancestor = tool_ancestor + [{'nodeId': nodeId, 'nodeType':nodeType}] + + #需要将自己也加上，方便在下一步memory检索的时候把本节点的历史也得到，由于在生成str的时候，第一时间就save本届点的memory，所以这样做是可以的 + #需要注意的是，给agent和给主持人看到的输入是不一样的。 主持人看到的是 memory + node_text + currentnodehistory, currentnodehistory 是文本，因为主持人需要维持一个 结构化的输出。 + #agent看到的是 memory，agent只需要说出一句话即可 + get_messages_res_sorted = self.get_memory_from_ancestor(tool_ancestor, sessionId, execute_agent_name) #此时是调用外部agent，所以需要加tag + assembled_memory = self.assemble_memory_for_reactagent(nodeId, nodeType, get_messages_res_sorted) + return assembled_memory + def get_cur_node_memory(self, sessionId, nodeId): + """ + 获取当前节点的游戏记录，不包含祖先节点。 + """ + nodeType = 'opsgptkg_task' #假设一定是task节点 + tool_ancestor = [{'nodeId':nodeId, 'nodeType':nodeType}] + logging.info(f'tool_ancestor:{tool_ancestor}') + get_messages_res_sorted = self.get_memory_from_ancestor(tool_ancestor, sessionId, role_tags = None) #此时是主持人，所以需要看到所有的memory，无需加tag。 对于在我这一侧需要运行的llm，一定是看到所有信息，因为我就是主持人 + logging.info(f'get_messages_res_sorted:{get_messages_res_sorted}') + assembled_memory = self.assemble_memory_for_reactagent(nodeId, nodeType, get_messages_res_sorted) + return assembled_memory + + def react_get_question_plan(self, sessionId:str, nodeId:str, execute_agent_names : Union[str, List[str]]): + ''' + 如果react模块 react_flag==waiting_other_agent， 则需要返回 question_plan + 可能需要区分人来回答还是大模型来回答 + ''' + logging.info(f"nodeId is {nodeId} , execute_agent_names is {execute_agent_names}") + toolPlan = [] + if type(execute_agent_names) == str: + #如果只输入一个agent_name + execute_agent_names = [execute_agent_names] + + + for execute_agent_name in execute_agent_names: + if '人类' in execute_agent_name: #需要提交给人 + ''' + example: {'toolDescription': '请用户回答', + 'currentNodeId': 'INT_3', + 'memory': None, + 'type': 'userProblem', + 'questionDescription': {'questionType': 'essayQuestion', + 'questionContent': {'question': '请输入', + 'candidate': None }}} + + 一定是一个问答题， 无需提问，这里question变成一个固定值了。 最重要的是把memory 也是空， 因为历史信息在对话里已经显示了。 + ''' + tool_one_step= ToolPlanOneStep( + **{'toolDescription': '请用户回答', + 'currentNodeId': nodeId + '%%@@#' + execute_agent_name, + 'currentNodeInfo':execute_agent_name, + 'memory': None, + 'type': 'userProblem', + 'questionDescription': {'questionType': 'essayQuestion', + 'questionContent': {'question': '请玩家根据当前情况发言', + 'candidate': None }}} + ) + + toolPlan.append( tool_one_step.dict()) + + else: #需要执行agent + ''' + example :[{ + "toolDescription": "toolDescriptionA", + "currentNodeId": "INT_1", + "memory": JsonStr, + "type":"onlyTool", + }] + ''' + + + assembled_memory = self.get_memory_for_computer_agent(sessionId, nodeId, execute_agent_name) + react_memory = assembled_memory + if type(react_memory)!= str: + react_memory = json.dumps(react_memory, ensure_ascii=False) + + # logging.info(f'react_memory is {react_memory}') + tool_one_step= ToolPlanOneStep(**{ + "toolDescription": execute_agent_name, + 'currentNodeInfo':execute_agent_name, + "currentNodeId": nodeId + '%%@@#' + execute_agent_name, + "memory": react_memory, + "type":"reactExecution", + }) + + + # toolPlan = [{ + # "toolDescription": execute_agent_name, + # "currentNodeId": nodeId, + # "memory": react_memory, + # "type":"reactExecution", + # }] + + toolPlan.append(tool_one_step.dict()) + + + return toolPlan + + + + def get_tool_ancestor(self, sessionId, + start_nodeid = '为什么余额宝没收到收益_complaint', + start_nodetype = 'opsgptkg_task'): + + #1 对每个nodeid，得到其memory， 首先需要遍历其所有的祖先task节点，将相关信息记录下来 + tool_ancestor = [] + nodeid_in_search = [{'nodeId':start_nodeid, 'nodeType':start_nodetype}] + nodeid_in_search_all = [{'nodeId':start_nodeid, 'nodeType':start_nodetype}] + + while len(nodeid_in_search)!= 0: + nodedict_now = nodeid_in_search.pop() + nodeid_now = nodedict_now['nodeId'] + nodetype_now = nodedict_now['nodeType'] + + + #查祖先节点 reverse=True + neighborNodes = self.geabase_handler.get_neighbor_nodes(attributes={"id": nodeid_now,}, node_type=nodetype_now, reverse=True) + print(nodeid_now, nodetype_now, neighborNodes, '=========') + + + for i in range(len(neighborNodes) ): + # if res['resultSet']['rows'][i]['columns'][0] == {}: + # continue + + # else: + nodeid_new = neighborNodes[i].id + nodetype_new = neighborNodes[i].type + if nodeid_new in [kk['nodeId'] for kk in nodeid_in_search]: #已经探索过了，不再探索 + continue + + elif nodetype_new == 'opsgptkg_task': #如果是task节点，则加入到tool_plan中，同时继续往前延展。 + + #查询该任务节点有没有收到过response，直接查询count，不用在意count的个数 + message_res = self.memory_handler.nodecount_get( sessionId, nodeid_new) #查看这个节点的count计数 + + if len(message_res) == 0: #这个task节点没有memory 或者没有收到response，则不再往前延展，减少geabase查询个数 + print(f'#这个task节点{nodeid_new}没有memory 或者没有收到response，则不再往前延展，减少geabase查询个数') + continue + else: + print('#如果是task节点，则加入到tool_plan中，同时继续往前延展。 get_tool_ancestor') + tool_ancestor.insert(0, {'nodeId':nodeid_new, 'nodeType':nodetype_new}) # 倒叙插入到图谱中 + if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : + nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + + + + + elif nodetype_now != 'opsgptkg_intent' and nodetype_new == 'opsgptkg_intent': + #第一次出现意图节点，需要尝试 + #print('#第一次出现意图节点，需要尝试') + tool_ancestor.insert(0, {'nodeId':nodeid_new, 'nodeType':nodetype_new}) # 倒叙插入到图谱中 + if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : + nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + elif nodetype_now == 'opsgptkg_intent' and nodetype_new == 'opsgptkg_intent': + #从意图节点再次碰到意图节点，终止 + #print('#从意图节点再次碰到意图节点，终止') + pass + elif nodetype_new == 'opsgptkg_phenomenon': + #如果是事实节点，则继续 + tool_ancestor.insert(0, {'nodeId':nodeid_new, 'nodeType':nodetype_new}) # 倒叙插入到图谱中 + if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : + nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + + else:##如果是不是task节点，也不是意图节点，不加入到tool_plan中，继续延展 + #print('#如果是不是task节点，也不是意图节点，不加入到tool_plan中，继续延展') + if {'nodeId':nodeid_new, 'nodeType':nodetype_new} not in nodeid_in_search_all : + nodeid_in_search_all.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + + tool_ancestor_new = [] + for i in range(len(tool_ancestor)): + item_i = tool_ancestor[i] + if item_i not in tool_ancestor_new: + tool_ancestor_new.append(item_i) + logging.info(f'geabase_getmemory tool_ancestor_new 的个数为{len(tool_ancestor_new)}') + logging.info(f'geabase_getmemory tool_ancestor 的个数为{len(tool_ancestor)}') + #print('tool_ancestor_new', tool_ancestor_new) + tool_ancestor = tool_ancestor_new + return tool_ancestor + + def get_memory_from_ancestor(self, tool_ancestor, sessionId, role_tags = None): + ''' + 给定了一堆祖先节点 + 当前节点，从中提取出memory + 祖先节点 + 对于祖先tool 而言，提取出 nodedescription、tool responds + 对于祖先react 而言，提取出 nodedescription（name）， 每一条message + 将这个list 按照时间顺序排好 + 当前节点 + 对于tool 而言，没有当前节点的memory + 对于react节点而言， 有运行到中间状态的memory + 将这个list 按照时间顺序排好 + 按照固定格式输出 memory_list_output + + role_tags 是一系列list, 如果指定为空，则没有约束 + + ''' + if role_tags == None: + role_tags = None + else: + role_tags = ['all'] + [role_tags] + # print(role_tags) + message_res_list = [] + for i in range(len(tool_ancestor)): + logging.info(f'geabase_getmemory 查询第{i}个祖先节点') + # logging.info(tool_ancestor[i]) + nodeId = tool_ancestor[i]['nodeId'] + nodeType = tool_ancestor[i]['nodeType'] + logging.info(f'【查询】memory message_index {nodeId}; sessionId {sessionId} ') + # if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: + #当前节点为tool or react 节点，一次性获得该节点所有的 chapter的memory数据 + if nodeType == 'opsgptkg_task': + memory_res = self.memory_manager.get_memory_pool_by_all({ + # "message_index": hash_id(nodeId, sessionId), #nodeId.replace(":", "_").replace("-", "_"), + 'user_name': hash_id(nodeId), + "chat_index": sessionId, + "role_tags": role_tags, + } + ) + logging.info(f'memory_res为:{memory_res}') + message_res = memory_res.get_messages() + message_res_list = message_res_list + message_res + + + + elif nodeType == 'opsgptkg_intent': + #如果祖先节点是意图节点, 意图节点的memory 暂时不分 tag + memory_res = self.memory_manager.get_memory_pool_by_all({ + "message_index": hash_id(nodeId, sessionId), #nodeId.replace(":", "_").replace("-", "_"), + "chat_index": sessionId, + "role_type": "user"}) + + message_res = memory_res.get_messages() + message_res_list = message_res_list + message_res + + #根据时间排序 + # message_res_list = message_res_list[::-1] #倒转message， 因为发现tbase存数据是类似堆栈的格式存的。 后来者在上; 似乎不对 + get_messages_res_sorted = self.sort_messages_by_time(message_res_list) + return get_messages_res_sorted + + + def assemble_memory_for_reactagent(self, nodeId, nodeType, get_messages_res_sorted): + ''' + 假设 祖先节点已经选择好了，而且 节点中相关的message也选择好了， 也经过时间排序了 + react 节点中 对于一个 agent_x (电脑agent)， 组装memory的函数 + ''' + + if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: + raise ValueError(f'当前应该不是 single 节点才对 ') + else: #react 节点 + memory_list = [] + for i in range(len( get_messages_res_sorted ) ): + if get_messages_res_sorted[i].role_name in ['firstUserInput', 'function_caller', 'user' ]: + # # 第一次输入， tool返回， tool描述， + # memory_list.append({ + # 'role_type': get_messages_res_sorted[i].role_type, + # 'role_name': get_messages_res_sorted[i].role_name, + # 'role_content': get_messages_res_sorted[i].role_content} + # )#此处存疑，需要实验后才知道效果如何，注释后，相当于主持人和agent只能看到tool的标题和执行结果，且以list的形式呈现 + memory_list.append(get_messages_res_sorted[i].role_content) + elif get_messages_res_sorted[i].role_type in ['react_memory_save']: + # react 模块各个角色说的话， + memory_list.append(get_messages_res_sorted[i].role_content) + return memory_list + + def assemble_memory_for_tool(self, nodeId, nodeType, get_messages_res_sorted): + if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: + ''' + ''' + memory_list = [] + for i in range(len( get_messages_res_sorted ) ): + if get_messages_res_sorted[i].role_name == 'firstUserInput': + memory_list.append({ + 'role_type': 'user', + 'role_name': 'firstUserInput', + 'role_content': get_messages_res_sorted[i].role_content} + ) + if get_messages_res_sorted[i].role_name == 'user': + memory_list.append({ + 'role_type': 'user', + 'role_name': 'None', + 'role_content': get_messages_res_sorted[i].role_content} + ) + if get_messages_res_sorted[i].role_name == 'function_caller': + memory_list.append({ + 'role_type': 'observation', + 'role_name': 'function_caller', + 'role_content': get_messages_res_sorted[i].role_content} + ) + if get_messages_res_sorted[i].role_type in ['react_memory_save']: + # react 模块各个角色说的话， + memory_list.append({ + 'role_type': get_messages_res_sorted[i].role_type, + 'role_name': get_messages_res_sorted[i].role_name, + 'role_content': get_messages_res_sorted[i].role_content} + ) + else: #react 节点 + raise ValueError(f'当前应该是 tool task 节点才对 ') + + return memory_list + + def assemble_memory(self, nodeId, nodeType, get_messages_res_sorted): + ''' + 组装memory + get_messages_res_sorted 已经根据时间排序好了。 但是对于tool 和 react模块的memory拼装做法有所不同 + 已经弃用 + ''' + if self.gb_handler.geabase_is_react_node(nodeId, nodeType) == False: + ''' + + ''' + memory_list = [] + for i in range(len( get_messages_res_sorted ) ): + if get_messages_res_sorted[i].role_name == 'firstUserInput': + memory_list.append({ + 'role_type': 'user', + 'role_name': 'firstUserInput', + 'role_content': get_messages_res_sorted[i].role_content} + ) + if get_messages_res_sorted[i].role_name == 'user': + memory_list.append({ + 'role_type': 'user', + 'role_name': 'None', + 'role_content': get_messages_res_sorted[i].role_content} + ) + if get_messages_res_sorted[i].role_name == 'function_caller': + memory_list.append({ + 'role_type': 'observation', + 'role_name': 'function_caller', + 'role_content': get_messages_res_sorted[i].role_content} + ) + if get_messages_res_sorted[i].role_type in ['react_memory_save']: + # react 模块各个角色说的话， + memory_list.append({ + 'role_type': get_messages_res_sorted[i].role_type, + 'role_name': get_messages_res_sorted[i].role_name, + 'role_content': get_messages_res_sorted[i].role_content} + ) + else: #react 节点 + memory_list = [] + for i in range(len( get_messages_res_sorted ) ): + if get_messages_res_sorted[i].role_name in ['firstUserInput', 'function_caller', 'user' ]: + # 第一次输入， tool返回， tool描述， + memory_list.append({ + 'role_type': get_messages_res_sorted[i].role_type, + 'role_name': get_messages_res_sorted[i].role_name, + 'role_content': get_messages_res_sorted[i].role_content} + ) + elif get_messages_res_sorted[i].role_type in ['react_memory_save']: + # react 模块各个角色说的话， + memory_list.append({ + 'role_type': get_messages_res_sorted[i].role_type, + 'role_name': get_messages_res_sorted[i].role_name, + 'role_content': get_messages_res_sorted[i].role_content} + ) + return memory_list + + + +if __name__ == "__main__": + + + pass + + # \ No newline at end of file diff --git a/muagent/service/ekg_reasoning/src/graph_search/task_node_prompt.py b/muagent/service/ekg_reasoning/src/graph_search/task_node_prompt.py new file mode 100644 index 0000000..3f60932 --- /dev/null +++ b/muagent/service/ekg_reasoning/src/graph_search/task_node_prompt.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- + +PARALLEL_RUNNING_PROMPT= \ +''' +##输出## +请以dict的形式，给出参与者的所有行动计划。行动计划表示为JSON，格式为 + {"thought": str, "action_plan": [{"player_name":str, "agent_name":str}, {"player_name":str, "agent_name":str}], "Dungeon_Master": [{"memory_tag":str,"content":str}] } + + +关键词含义如下： +_ thought (str): 主持人的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断等。 +_ player_name (str): 玩家的 player_name +_ agent_name (str): 玩家的 agent_name; +_ content (str): 为主持人的告知信息, +_ memory_tag (List[str]): memory_tag 固定为本条信息的可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"] + +##example## +{"thought": "str", "action_plan": [{"player_name":str, "agent_name":str}, {"player_name":str, "agent_name":str}, ... ], "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]} + +##注意事项## +1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。 +2. 整个dict是一个jsonstr，请输出jsonstr，不用输出markdown格式 +3. 结合已有的步骤，只输出一个 dict，不要包含其他信息 +''' + + + +PLANNING_RUNNING_PROMPT= \ +''' +##输出## +请以dict的形式，给出参与者的所有行动计划。行动计划表示为JSON，格式为 + {"thought": str, "action_plan": [{"player_name":str, "agent_name":str, "task_description":str}, {"player_name":str, "agent_name":str,"task_description":str}], "Dungeon_Master": [{"memory_tag":str}] } + + +关键词含义如下： +_ thought (str): 主持人的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断等。 +_ player_name (str): 行动方的 player_name, 若行动方为玩家，否则为玩家的 player_name;如果行动方是主持人，为 "主持人"， +_ agent_name (str): 行动方的 agent_name, 若行动方为玩家，否则为玩家的 agent_name;如果行动方是主持人，为 "主持人"， +_ task_description (str): 为行动方本轮的任务. 只要写要做什么事儿即可，因为现在还处于任务规划阶段，事情还没有开始做。比如行动方本轮的目的是讨论，写出讨论的话题、目的等即可，不要把讨论的内容也写出来，因为现在还没有开始讨论，只是在规划行动计划。 +_ memory_tag (List[str]): memory_tag 固定为本条信息的可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"] + +##example## +{"thought": "str", "action_plan": [{"player_name":str, "agent_name":str, "task_description":str}, {"player_name":str, "agent_name":str, "task_description":str}, ... ], "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]} + +##注意事项## +1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。 +2. 整个dict是一个jsonstr，请输出jsonstr，不用输出markdown格式 +3. 结合已有的步骤，只输出一个 dict，不要包含其他信息 +''' + + +PLANNING_RUNNING_PROMPT_DM_SPEECH= \ +''' +##输出## +本阶段行动计划已经给出了，根据行动计划，此时轮到主持人发言。主持人的发言表示为JSON，格式为 +{"thought": str, "content":str}]} + +主持人请尽量不要重复上一轮的发言 + +关键词含义如下： +_ thought (str): 主持人的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断等。 +_ content (str): 为主持人的具体发言信息, + + + + +##注意事项## +1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。 +2. 整个dict是一个jsonstr，请输出jsonstr，不用输出markdown格式 +3. 结合已有的步骤，只输出一个 dict，不要包含其他信息 +''' + + +PLANNING_RUNNING_AGENT_REPLY_TEMPLATE= \ +{"thought": "None", "action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, + "observation": [{ "memory_tag":["agent_name_a"],"content": "str"}]} + +PLANNING_RUNNING_PROMPT_SUFFIX_1 = \ +'\n##已有步骤##\n无' + + + +PLANNING_RUNNING_PROMPT_SUFFIX_2 = \ +'\n##请输出下一个步骤,切记只输出一个步骤，它应该只是一个dict ##\n' + +REACT_RUNNING_PROMPT = \ +""" +##输出## +请以列表的形式，给出参与者的所有行动。每个行动表示为JSON，格式为 +[{"thought": str, "action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]}, ...] + +关键词含义如下： +_ thought (str): 主持人执行行动的一些思考,包括分析玩家的存活状态，对历史对话信息的理解，对当前任务情况的判断。 +_ action (dict): 行动发起方的信息。 +_ player_name (str): 行动发起方的 player_name，若行动发起方为主持人，为空，否则为玩家的 player_name； +_ agent_name (str): 行动发起方的 agent_name，若为主持人，则 agent_name 为 "主持人"，否则为玩家的 agent_name。 +_ content (str): 行动发起方的具体行为，若为主持人，content 为告知信息；否则，content 为玩家的具体行动。 +_ memory_tag (List[str]): 无论行动发起方是主持人还是玩家，memory_tag 固定为所有信息可见对象的agent_name, 如果信息可见对象为所有玩家，固定为 ["all"] + + +##example## +如果是玩家发言，则用 {"thought": "str", "action": {"agent_name": "agent_name_c", "player_name":"player_name_d"}, "observation": [{ "memory_tag":["agent_name_a","agent_name_b"],"content": "str"}]} 格式表示。content是玩家发出的信息；memory_tag是这条信息可见的对象，需要填写agent名。不要填写 agent_description +如果agent_name是主持人，则无需输入player_name， 且observation变为 Dungeon_Master。即{"thought": "str", "action": {"agent_name": "主持人", "player_name":""}, "Dungeon_Master": [{ "memory_tag":["agent_name_a","agent_name_b"], "content": "str",}]} + +##注意事项## +1. 所有玩家的座位、身份、agent_name、存活状态等信息在开头部分已给出。 +2. "observation" or "Dungeon_Master"如何选择？若 agent_name 为"主持人"，则为"Dungeon_Master"，否则为 "observation"。 +3. 输出列表的最后一个元素一定是{"action": "taskend"}。 +4. 整个list是一个jsonstr，请输出jsonstr，不用输出markdown格式 +5. 结合已有的步骤，每次只输出下一个步骤，即一个 {"thought": str, "action": {"player_name":str, "agent_name":str}, "observation" or "Dungeon_Master": [{"memory_tag":str,"content":str}]} +6. 如果是人类玩家发言， 一定要选择类似 人类agent 这样的agent_name +""" + + diff --git a/muagent/service/ekg_reasoning/src/intention_recognition/intention_recognition_tool.py b/muagent/service/ekg_reasoning/src/intention_recognition/intention_recognition_tool.py index 2b81fe4..1d06723 100644 --- a/muagent/service/ekg_reasoning/src/intention_recognition/intention_recognition_tool.py +++ b/muagent/service/ekg_reasoning/src/intention_recognition/intention_recognition_tool.py @@ -7,7 +7,7 @@ logger = logging.getLogger() logging.basicConfig(level=logging.DEBUG) - +RETRY_MAX_NUM = 3 def intention_recognition_ekgfunc( root_node_id, rule, query, memory, start_from_root = True, url= os.environ['intention_url'] ): ''' @@ -45,15 +45,38 @@ def intention_recognition_ekgfunc( root_node_id, rule, query, memory, start_from } logging.info( body ) - r = requests.post(url, json=body, headers=headers) - - logging.info( str((r.json() )) ) #12:00:37 - output_1 = (r.json()) - # logging.info('============意图识别的结果是================') - # logging.info(f'意图识别结果是 {output_1}') - # logging.info('============================================') - res = json.loads(output_1['resultMap']['algorithmResult']) - return res + retry_num = 0 + while retry_num <= RETRY_MAX_NUM: + retry_num = retry_num + 1 + try: + r = requests.post(url, json=body, headers=headers) + + + logging.info( str((r.json() )) ) #12:00:37 + output_1 = (r.json()) + # logging.info('============意图识别的结果是================') + # logging.info(f'意图识别结果是 {output_1}') + # logging.info('============================================') + res = json.loads(output_1['resultMap']['algorithmResult']) + return res + + except Exception as e: + + logging.info(f'意图识别报错:{e}') + sleep(1) + raise ValueError(f'意图识别报错 超过了最大重试次数RETRY_MAX_NUM:{RETRY_MAX_NUM}') + return None + + + # r = requests.post(url, json=body, headers=headers) + + # logging.info( str((r.json() )) ) #12:00:37 + # output_1 = (r.json()) + # # logging.info('============意图识别的结果是================') + # # logging.info(f'意图识别结果是 {output_1}') + # # logging.info('============================================') + # res = json.loads(output_1['resultMap']['algorithmResult']) + # return res @@ -85,17 +108,31 @@ def intention_recognition_querypatternfunc( query, } logging.info( body ) - r = requests.post(url, json=body, headers=headers) - logging.info( str((r.json() )) ) #12:00:37 - output_1 = (r.json()) - # logging.info('============意图识别的结果是================') - # logging.info(f'意图识别结果是 {output_1}') - # logging.info('============================================') - res = json.loads(output_1['resultMap']['algorithmResult']) - if type(res) == 'str': - res = json.loads(res) - return res['output'] + retry_num = 0 + while retry_num <= RETRY_MAX_NUM: + retry_num = retry_num + 1 + try: + r = requests.post(url, json=body, headers=headers) + + + logging.info( str((r.json() )) ) #12:00:37 + output_1 = (r.json()) + # logging.info('============意图识别的结果是================') + # logging.info(f'意图识别结果是 {output_1}') + # logging.info('============================================') + res = json.loads(output_1['resultMap']['algorithmResult']) + if type(res) == 'str': + res = json.loads(res) + return res['output'] + except Exception as e: + + logging.info(f'意图识别报错:{e}') + sleep(1) + raise ValueError(f'意图识别报错 超过了最大重试次数RETRY_MAX_NUM:{RETRY_MAX_NUM}') + return None + + @@ -133,17 +170,30 @@ def intention_recognition_querytypefunc( query, } logging.info( body ) - r = requests.post(url, json=body, headers=headers) - - logging.info( str((r.json() )) ) #12:00:37 - output_1 = (r.json()) - # logging.info('============意图识别的结果是================') - # logging.info(f'意图识别结果是 {output_1}') - # logging.info('============================================') - res = json.loads(output_1['resultMap']['algorithmResult']) - if type(res) == 'str': - res = json.loads(res) - return res['output'] + + retry_num = 0 + while retry_num <= RETRY_MAX_NUM: + retry_num = retry_num + 1 + try: + r = requests.post(url, json=body, headers=headers) + + + logging.info( str((r.json() )) ) #12:00:37 + output_1 = (r.json()) + # logging.info('============意图识别的结果是================') + # logging.info(f'意图识别结果是 {output_1}') + # logging.info('============================================') + res = json.loads(output_1['resultMap']['algorithmResult']) + if type(res) == 'str': + res = json.loads(res) + return res['output'] + except Exception as e: + + logging.info(f'意图识别报错:{e}') + sleep(1) + raise ValueError(f'意图识别报错 超过了最大重试次数RETRY_MAX_NUM:{RETRY_MAX_NUM}') + return None + diff --git a/muagent/service/ekg_reasoning/src/memory_handler/ekg_memory_handler.py b/muagent/service/ekg_reasoning/src/memory_handler/ekg_memory_handler.py index aa6ae05..8f5eb51 100644 --- a/muagent/service/ekg_reasoning/src/memory_handler/ekg_memory_handler.py +++ b/muagent/service/ekg_reasoning/src/memory_handler/ekg_memory_handler.py @@ -5,6 +5,8 @@ #路径增加 import sys import os +from typing import Union, Optional +from typing import List, Dict, Optional, Union src_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) ) @@ -213,7 +215,7 @@ def nodeid_in_subtree_get(self, currentNodeId, sessionId): hashpostfix = '-nodeid_in_subtree' memory_manager_res= self.memory_manager.get_memory_pool_by_all({ "chat_index": sessionId, - # "message_index" : hash_id( currentNodeId, sessionId, hashpostfix), + "message_index" : hash_id( currentNodeId, sessionId, hashpostfix), # "role_type": "nodeid_in_subtree", }) get_messages_res = memory_manager_res.get_messages() @@ -345,6 +347,15 @@ def tool_nodecount_add_chapter(self, sessionId, currentNodeId): logging.info(f'节点{sessionId} 的 count_info现在是{count_info}') self.nodecount_set( sessionId, currentNodeId,count_info) #重写count数据， + ####### 泛化推理相关 #### + def gr_save_prompt_main(self, sessionId:str, currentNodeId:str, prompt_main:Dict, )->Dict: + ''' + 存储泛化推理的当前 prompt_main 到 memory中 + gr_count: 当前交互到第几步了 + ''' + return react_current_history_get + + def tool_nodedescription_save(self, sessionId, currentNodeId, role_content, user_input_memory_tag=None): ''' 将 tool 的 description 存入到memory中, 分成 chapter @@ -432,14 +443,20 @@ def react_nodedescription_save(self, sessionId, currentNodeId, role_content): self.memory_manager.append(message) + + # + + + def react_memory_save(self, sessionId, currentNodeId, llm_res): ''' 将一个node中 llm的返回值 解析成json，存储在memory里。 每次覆盖式存储。 存主持人说的话、其他agent说的话； 同时修改count中的计数 + 格式为 [player_name, agent_name, content, memory_tag, section] llm_str = llm_result_truncation + '":[]}]' llm_result_truncation = '[\n {"action": {"agent_name": "主持人"}, "Dungeon_Master": [{"content": "狼人时刻开始，以下玩家是狼人：player_1（狼人-1），李四（人类玩家）（狼人-2）。", "memory_tag":["agent_1", "人类agent"]}]},\n {"action": {"agent_name": "agent_1", "player_name":"player_1"}, "observation' ''' - + #1. 解析 llm的输出 if type(llm_res) == str: llm_res_json = json.loads(llm_res) @@ -450,35 +467,67 @@ def react_memory_save(self, sessionId, currentNodeId, llm_res): section = 0 memory_save_info_list = [] for i in range(len(llm_res_json)): - if llm_res_json[i]['action'] == 'taskend': - #任务执行完毕 - break - agent_name = llm_res_json[i]['action']['agent_name'] - if agent_name == '主持人': - player_name = '主持人' - for j in range(len(llm_res_json[i]['Dungeon_Master'])): - content = llm_res_json[i]['Dungeon_Master'][j]['content'] - memory_tag = llm_res_json[i]['Dungeon_Master'][j]['memory_tag'] - section += 1 - memory_save_info_list.append([player_name, agent_name, content, memory_tag, section]) - else: - logging.info(f' llm_res_json[i]是{llm_res_json[i]}') - if llm_res_json[i]['observation'] == []: + if 'action' in llm_res_json[i].keys(): + #react 类型 或者 是 parallel中电脑玩家的返回值 + + if llm_res_json[i]['action'] == 'taskend': + #任务执行完毕 + break + agent_name = llm_res_json[i]['action']['agent_name'] + if agent_name == '主持人': + player_name = '主持人' + for j in range(len(llm_res_json[i]['Dungeon_Master'])): + content = llm_res_json[i]['Dungeon_Master'][j]['content'] + memory_tag = llm_res_json[i]['Dungeon_Master'][j]['memory_tag'] + section += 1 + memory_save_info_list.append([player_name, agent_name, content, memory_tag, section]) + else: + #logging.info(f' llm_res_json[i]是{llm_res_json[i]}') + if llm_res_json[i]['observation'] == []: + continue + player_name = llm_res_json[i]['action']['player_name'] + for j in range(len(llm_res_json[i]['observation'])): + content = llm_res_json[i]['observation'][j]['content'] + memory_tag = llm_res_json[i]['observation'][j]['memory_tag'] + section += 1 + memory_save_info_list.append([player_name, agent_name, content, memory_tag, section]) + + elif 'action_plan' in llm_res_json[i].keys(): + #此时为 PlanningRunningAgentReply 格式， 可能为 plan 或者 parallel的第一次返回值 + + if self.gb_handler.get_tag(rootNodeId = currentNodeId, rootNodeType = 'opsgptkg_task', key = 'action') == 'plan': + #plan模式会略过 制定plan时说的话//现在制定plan时也不再说话了 + #parallel模式则不会略过制定action_plan是说的话 continue - player_name = llm_res_json[i]['action']['player_name'] - for j in range(len(llm_res_json[i]['observation'])): - content = llm_res_json[i]['observation'][j]['content'] - memory_tag = llm_res_json[i]['observation'][j]['memory_tag'] - section += 1 - memory_save_info_list.append([player_name, agent_name, content, memory_tag, section]) + else: + if llm_res_json[i]['action_plan'] == 'taskend': + #任务执行完毕 + break + agent_name = '主持人' #只有主持人能生成plan + player_name = '主持人' + + + for j in range(len(llm_res_json[i]['Dungeon_Master'])): + content = llm_res_json[i]['Dungeon_Master'][j]['content'] + memory_tag = llm_res_json[i]['Dungeon_Master'][j]['memory_tag'] + section += 1 + memory_save_info_list.append([player_name, agent_name, content, memory_tag, section]) + + - if 'taskend' not in json.dumps(llm_res, ensure_ascii=False) and memory_save_info_list[-1][1] != '主持人': - #现在还没有run到最后。那么 #最后一个observation，这个是幻觉，主持人替其他玩家说的话， 不能存入到memory里 - #如果run到最后了， 那么最后一个observation是本次填充的 - memory_save_info_list = memory_save_info_list[0:-1] + logging.info(f'memory_save_info_list 初步 is {memory_save_info_list}') + + + if 'taskend' not in json.dumps(llm_res, ensure_ascii=False) and memory_save_info_list!= [] and memory_save_info_list[-1][1] != '主持人' and \ + self.gb_handler.get_tag(rootNodeId = currentNodeId, rootNodeType = 'opsgptkg_task', key = 'action') == 'react': + #现在还没有run到最后。那么 #最后一个observation，这个是幻觉，主持人替其他玩家说的话， 不能存入到memory里 + #如果run到最后了， 那么最后一个observation是本次填充的 + #这个只在react模式下生效 + memory_save_info_list = memory_save_info_list[0:-1] + - logging.info(f'memory_save_info_list is {memory_save_info_list}') + logging.info(f'memory_save_info_list 最终经过删减（假如是react，如果不是则不删减） is {memory_save_info_list}') #2. 将llm的输出存入到memory中，更新，已经写入的就不更新了 hashpostfix = '_reactmemory' @@ -495,8 +544,9 @@ def react_memory_save(self, sessionId, currentNodeId, llm_res): for i in range(len(memory_save_info_list)): player_name, agent_name, content, memory_tag, section = memory_save_info_list[i] - hashpostfix_all = f'_chapter{chapter}_section{section}' + hashpostfix - + hashpostfix_all = f'_chapter{chapter}_section{section}_agentName{agent_name}' + hashpostfix + # 原来react_memory_save 的问题在于， 根据 _chapter{chapter}_section{section} 进行编码的情况会覆盖其他 并行的agent的结果 + # 另外，取结果时，由于三个agent都是同时发生的，所以原来根据时间取结果的方式也会出现问题，造成三个agent的返回值都是一个 memory_manager_res= self.memory_manager.get_memory_pool_by_all({ # "chat_index": sessionId, @@ -505,7 +555,7 @@ def react_memory_save(self, sessionId, currentNodeId, llm_res): }) get_messages_res = memory_manager_res.get_messages() if get_messages_res != []: - #这一条数据已经有了，不用重复写入，只更新没有写入的 + #这一条数据已经有了，不用重复写入，只更新没有写入的, 这里根据hashpostfix_all 中的 section和chapter 来进行判断 continue user_input_memory_tag = self.gb_handler.user_input_memory_tag(currentNodeId, 'opsgptkg_task') @@ -532,6 +582,223 @@ def react_memory_save(self, sessionId, currentNodeId, llm_res): role_content_nodeinfo['section'] = len(memory_save_info_list) self.nodecount_set( sessionId, currentNodeId, role_content_nodeinfo) + def message_get(self, sessionId:str, nodeId:str, hashpostfix:str, + role_name:str,role_type:str )-> Union[str, list]: + ''' + 得到信息, 作为一个通用函数。如果没有检索到信息，则返回空的'', 如果检索到了信息，则返回str,只返回第0个message的str + 注意对hashpostfix_chapter进行了特殊处理 + ''' + node_count_res = self.nodecount_get( sessionId, nodeId) + if node_count_res == []: + chapter = 1 + else: + chapter = json.loads(node_count_res[0].role_content)['chapter'] + + #hashpostfix = '_plan' + hashpostfix_chapter = f'_chapter{chapter}' + hashpostfix_all = hashpostfix_chapter + hashpostfix + #print(nodeId, sessionId, hashpostfix_all) + memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + # "chat_index": sessionId, + "message_index" : hash_id(nodeId, sessionId, hashpostfix_all), + "role_name" : role_name,#'plan', + "role_type" : role_type#"DM" + }) + get_messages_res = memory_manager_res.get_messages() + if len(get_messages_res) == 0: + #raise ValueError("执行memory查询没有找到") + logging.info( "执行memory查询没有找到" ) + return '' + + return get_messages_res[0].role_content + + def message_save(self, sessionId:str, nodeId:str, role_content:str, + hashpostfix:str, user_name:str, role_name:str, role_type:str)->None: + ''' + 将message 存下来，作为一个通用函数 + 注意对hashpostfix_chapter进行了特殊处理 + + for example + #hashpostfix = '_plan' + #user_name = currentNodeId + #role_name = 'plan' + #role_type = "DM" + + ''' + + + + node_count_res = self.nodecount_get( sessionId, nodeId) + if node_count_res == []: + chapter = 1 + else: + chapter = json.loads(node_count_res[0].role_content)['chapter'] + + hashpostfix_chapter = f'_chapter{chapter}' + hashpostfix_all = hashpostfix_chapter + hashpostfix + message = Message( + chat_index= sessionId, + message_index= hash_id(nodeId, sessionId, hashpostfix_all), + user_name = hash_id(user_name + str(chapter)), + role_name = role_name, + role_type = role_type, + role_content = role_content, + + ) + + self.memory_manager.append(message) + + + + def processed_agentname_get(self, sessionId:str, nodeId:str)-> str: + ''' + 得到已经处理的agentname + ''' + + + hashpostfix = '_processedAgentName' + role_name = 'PAN' #processedAgentName + role_type = "DM" + + node_count_res = self.nodecount_get( sessionId, nodeId) + if node_count_res == []: + chapter = 1 + else: + chapter = json.loads(node_count_res[0].role_content)['chapter'] + + #hashpostfix = '_plan' + #hashpostfix_chapter = f'_chapter{chapter}' + #hashpostfix_all = hashpostfix_chapter + hashpostfix + #print(nodeId, sessionId, hashpostfix_all) + memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + "chat_index": sessionId, + "user_name" : hash_id(nodeId + str(chapter)), + "role_name" : role_name,#'plan', + "role_type" : role_type#"DM" + }) + get_messages_res = memory_manager_res.get_messages() + if len(get_messages_res) == 0: + #raise ValueError("执行memory查询没有找到") + logging.info( "执行memory查询没有找到" ) + return '' + + processed_agentname_list_all = [] + for i in range(len(get_messages_res)): + processed_agentname_list_all.append( get_messages_res[i].role_content) + + + return json.dumps(processed_agentname_list_all, ensure_ascii=False) + + + + def processed_agentname_save(self, sessionId:str, nodeId:str, agentName:str)->None: + ''' + 将这次执行完的（lingsi返回的）agentname，存下来， + 注意，同名的agentname 会被覆盖 + ''' + # processed_agentname_get_str = self.processed_agentname_get(sessionId, nodeId) + # logging.info(f'已有的processed_agentname_get_str is {processed_agentname_get_str}， 当前要存入的agentName is {agentName}') + # if processed_agentname_get_str == '': #第一次有工具返回时，processed_agentname_get_str 值为’‘ + # processed_agentname_list = [] + # else: + # processed_agentname_list = json.loads( processed_agentname_get_str ) + + # processed_agentname_list.append(agentName) + # processed_agentname_list_str = json.dumps(processed_agentname_list, ensure_ascii=False) + + processed_agentname_list = agentName + hashpostfix = f'_processedAgentName_{agentName}' + user_name = nodeId + role_name = 'PAN' + role_type = "DM" + + return self.message_save( sessionId, nodeId, processed_agentname_list, + hashpostfix, user_name, role_name, role_type) + + + + def current_plan_save(self, sessionId:str, currentNodeId:str, role_content:str)->None: + ''' + 将current_plan 存下来 + + Parameters + ---------- + sessionId : str + DESCRIPTION. + currentNodeId : str + DESCRIPTION. + role_content : str + DESCRIPTION. + + Returns + ------- + None + DESCRIPTION. + + ''' + + hashpostfix = '_plan' + user_name = currentNodeId + role_name = 'plan' + role_type = "DM" + + node_count_res = self.nodecount_get( sessionId, currentNodeId) + if node_count_res == []: + chapter = 1 + else: + chapter = json.loads(node_count_res[0].role_content)['chapter'] + + hashpostfix_all = f'_chapter{chapter}' + hashpostfix + message = Message( + chat_index= sessionId, + message_index= hash_id(currentNodeId, sessionId, hashpostfix_all), + user_name = hash_id(user_name), + role_name = role_name, + role_type = role_type, + role_content = role_content, + + ) + + self.memory_manager.append(message) + + def current_plan_get(self, sessionId:str, currentNodeId:str)-> str: + ''' + + 得到当前的current_plan + Parameters + ---------- + sessionId : str + DESCRIPTION. + currentNodeId : str + DESCRIPTION. + + Returns + ------- + str + DESCRIPTION. + + ''' + node_count_res = self.nodecount_get( sessionId, currentNodeId) + if node_count_res == []: + chapter = 1 + else: + chapter = json.loads(node_count_res[0].role_content)['chapter'] + + hashpostfix = '_plan' + hashpostfix_all = f'_chapter{chapter}' + hashpostfix + print(currentNodeId, sessionId, hashpostfix_all) + memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + # "chat_index": sessionId, + "message_index" : hash_id(currentNodeId, sessionId, hashpostfix_all), + "role_name" : 'plan', + "role_type" : "DM" + }) + get_messages_res = memory_manager_res.get_messages() + return get_messages_res[0].role_content + + + + def react_current_history_save(self, sessionId, currentNodeId, role_content): hashpostfix = '_his' user_name = currentNodeId @@ -557,7 +824,7 @@ def react_current_history_save(self, sessionId, currentNodeId, role_content): self.memory_manager.append(message) - def react_current_history_get(self, sessionId, currentNodeId): + def react_current_history_get(self, sessionId:str, currentNodeId:str)->str: ''' 得到当前的current_history ''' @@ -569,7 +836,7 @@ def react_current_history_get(self, sessionId, currentNodeId): hashpostfix = '_his' hashpostfix_all = f'_chapter{chapter}' + hashpostfix - print(currentNodeId, sessionId, hashpostfix_all) + #print(currentNodeId, sessionId, hashpostfix_all) memory_manager_res= self.memory_manager.get_memory_pool_by_all({ # "chat_index": sessionId, "message_index" : hash_id(currentNodeId, sessionId, hashpostfix_all), @@ -634,19 +901,63 @@ def tool_observation_save(self, sessionId, currentNodeId, tool_information, user #logging.info(f'sessionId {sessionId} start_nodeid {start_nodeid} 的 memory 是 {memory}') - def get_output(self,sessionId, start_datetime, end_datetime): + def get_output(self,sessionId, start_datetime, end_datetime, agent_name): + + #首先提取主持人可能的返回 + get_messages_res_all = [] memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + "role_name" : '主持人', "chat_index": sessionId, 'role_tags': ['all', '人类'], "start_datetime":[start_datetime, end_datetime], }) get_messages_res = memory_manager_res.get_messages() - if get_messages_res == []: + get_messages_res_all = get_messages_res_all + get_messages_res + + #再提取 标题 user可能的返回 + get_messages_res = [] + memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + "role_name" : 'user', + "chat_index": sessionId, + 'role_tags': ['all', '人类'], + "start_datetime":[start_datetime, end_datetime], + }) + get_messages_res = memory_manager_res.get_messages() + get_messages_res_all = get_messages_res_all + get_messages_res + + + #再提取 agent 可能的返回 + if agent_name != None and type(agent_name) == str: + get_messages_res = [] + memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + "role_name" : agent_name, + "chat_index": sessionId, + 'role_tags': ['all', '人类'], + "start_datetime":[start_datetime, end_datetime], + }) + get_messages_res = memory_manager_res.get_messages() + get_messages_res_all = get_messages_res_all + get_messages_res + + + #再提取 tool 可能的返回 + get_messages_res = [] + memory_manager_res= self.memory_manager.get_memory_pool_by_all({ + "role_name" : 'function_caller', + "chat_index": sessionId, + 'role_tags': ['all', '人类'], + "start_datetime":[start_datetime, end_datetime], + }) + get_messages_res = memory_manager_res.get_messages() + get_messages_res_all = get_messages_res_all + get_messages_res + + get_messages_res_all = sorted(get_messages_res_all, key=lambda msg: msg.start_datetime)#按照时间进行排序 + + if get_messages_res_all == []: logging.info('本阶段没有能给用户看的信息') return None outputinfo = [] - for i in range(len(get_messages_res)): - outputinfo.append( get_messages_res[i].role_content ) + for i in range(len(get_messages_res_all)): + outputinfo.append( get_messages_res_all[i].role_content ) logging.info(f'outputinfo is {outputinfo}') # outputinfo_str = json.dumps(outputinfo, ensure_ascii=False) diff --git a/muagent/service/ekg_reasoning/src/question_answer/qa_function.py b/muagent/service/ekg_reasoning/src/question_answer/qa_function.py index 78d0d7c..8ebcf32 100644 --- a/muagent/service/ekg_reasoning/src/question_answer/qa_function.py +++ b/muagent/service/ekg_reasoning/src/question_answer/qa_function.py @@ -65,10 +65,9 @@ def full_link_summary(self): resstr = self.geabase_nodediffusion_qa() print(resstr) print(f'full_link_summary prompt的长度为 {len(resstr)}') - resstr_llm_summary = call_llm(input_content = resstr, llm_model = 'Qwen2_72B_Instruct_OpsGPT',llm_config=self.llm_config) - - visualization_url = self.get_visualization_url() + resstr_llm_summary = call_llm(input_content = resstr, llm_model = None,llm_config=self.llm_config) + #visualization_url = self.get_visualization_url() return resstr_llm_summary @@ -96,7 +95,7 @@ def next_step_summary(self): print(prompt) print(f'prompt的长度为 {len(prompt)}') - res = call_llm(input_content = prompt, llm_model = 'Qwen2_72B_Instruct_OpsGPT',llm_config=self.llm_config) + res = call_llm(input_content = prompt, llm_model = None, llm_config=self.llm_config) return res @@ -122,7 +121,7 @@ def get_referred_node(self): ''' print(prompt) print(f'prompt的长度为 {len(prompt)}') - res = call_llm(input_content = prompt, llm_model = 'Qwen2_72B_Instruct_OpsGPT',llm_config=self.llm_config) + res = call_llm(input_content = prompt, llm_model = None,llm_config=self.llm_config) # res = robust_call_llm(prompt) return res @@ -141,137 +140,153 @@ def geabase_nodediffusion_qa(self): ''' - #resstr = '你是一个计划总结大师，可以用比较通俗易懂的语言总结一个计划。 以下计划由一个知识图谱表示。请将其总结为自然语言的方式 \n' - + resstr = '你是一个计划总结大师，可以用比较通俗易懂的语言总结一个计划。 以下计划由一个知识图谱表示。请将其总结为自然语言的方式 \n' - # #1.假设当前节点已经运行完，得到后面的tool. 如果为事实节点，则需要采用大模型进行判断 - # tool_plan = [] - # nodeid_in_search = [{'nodeId':self.start_nodeid, 'nodeType':self.start_nodetype}] - # while len(nodeid_in_search)!= 0: - # nodedict_now = nodeid_in_search.pop() - # nodeid_now = nodedict_now['nodeId'] - # nodetype_now = nodedict_now['nodeType'] + #1.假设当前节点已经运行完，得到后面的tool. 如果为事实节点，则需要采用大模型进行判断 + tool_plan = [] + nodeid_in_search = [{'nodeId':self.start_nodeid, 'nodeType':self.start_nodetype}] + nodeid_in_search_all = [] - # if nodetype_now == 'opsgptkg_analysis': - # if self.gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = nodeid_now, rootNodeType = nodetype_now, key = 'accesscriteria') == 'or': - # #当链接到node_result_1节点的node_phenomena都满足时，才激活node_result_1 - # resstr += f'当链接到{nodeid_now}节点的node_phenomena只要满足一个时，就能激活{nodeid_now}' - # else: - # resstr += f'当链接到{nodeid_now}节点的node_phenomena都满足时，才能激活{nodeid_now}' + while len(nodeid_in_search)!= 0: + nodedict_now = nodeid_in_search.pop() + nodeid_now = nodedict_now['nodeId'] + nodetype_now = nodedict_now['nodeType'] - # #nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) - # if nodetype_now == 'opsgptkg_phenomenon': - # ''' - # node_phenomena_1 与 结论 node_result_1 相连，node_result_1的内容为 热点账户问题，无需应急。 - # node_phenomena_1 与 结论 node_result_4 相连，node_result_4的内容为 请排查其他原因 - # ''' - # neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) - # next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) - # for i in range(len(neighbor_node_id_list)): - # resstr += f'{nodeid_now}节点 与 {neighbor_node_id_list[i]} 相连，节点{neighbor_node_id_list[i]}的内容为： {next_node_description_list[i]}' - # # print('==================') - # # print(f'nodeid_in_search is {nodeid_in_search}') - # # print('=================') + if nodetype_now == 'opsgptkg_analysis': + if self.gb_handler.geabaseGetOnlyOneNodeInfoWithKey( rootNodeId = nodeid_now, rootNodeType = nodetype_now, key = 'accesscriteria') == 'or': + #当链接到node_result_1节点的node_phenomena都满足时，才激活node_result_1 + # nodeid_in_search_all.append(nodeid_now) + resstr += f'当链接到{nodeid_now}节点的node_phenomena只要满足一个时，就能激活{nodeid_now}' + + else: + # nodeid_in_search_all.append(nodeid_now) + resstr += f'当链接到{nodeid_now}节点的node_phenomena都满足时，才能激活{nodeid_now}' + + #nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + if nodetype_now == 'opsgptkg_phenomenon': + ''' + node_phenomena_1 与 结论 node_result_1 相连，node_result_1的内容为 热点账户问题，无需应急。 + node_phenomena_1 与 结论 node_result_4 相连，node_result_4的内容为 请排查其他原因 + ''' + neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) + next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) + for i in range(len(neighbor_node_id_list)): + resstr += f'{nodeid_now}节点 与 {neighbor_node_id_list[i]} 相连，节点{neighbor_node_id_list[i]}的内容为： {next_node_description_list[i]}' + # print('==================') + # print(f'nodeid_in_search is {nodeid_in_search}') + # print('=================') - # if self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opsgptkg_task') == True: - # # 后续节点都是task节点， - # neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) - # next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) - - # if nodetype_now == 'opsgptkg_schedule': #操作计划节点后第一次出现任务节点 - # if len(neighbor_node_id_list) >= 2: - # resstr += f'并行执行{len(neighbor_node_id_list)}个任务：\n' - # else: - # resstr += f'执行{len(neighbor_node_id_list)}个任务：\n ' - # for i in range(len(neighbor_node_id_list)): - # resstr += f' {neighbor_node_id_list[i]} 的内容是 : {next_node_description_list[i]} \n' - # else: - # if len(neighbor_node_id_list) >= 2: - # resstr += f'{nodeid_now}后面是并行执行{len(neighbor_node_id_list)}个任务：\n' - # else: - # resstr += f'{nodeid_now}后面是执行{len(neighbor_node_id_list)}个任务：\n ' + if self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opsgptkg_task') == True: + # 后续节点都是task节点， + neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) + next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) + + if nodetype_now == 'opsgptkg_schedule': #操作计划节点后第一次出现任务节点 + if len(neighbor_node_id_list) >= 2: + resstr += f'并行执行{len(neighbor_node_id_list)}个任务：\n' + else: + resstr += f'执行{len(neighbor_node_id_list)}个任务：\n ' + for i in range(len(neighbor_node_id_list)): + resstr += f' {neighbor_node_id_list[i]} 的内容是 : {next_node_description_list[i]} \n' + else: + if len(neighbor_node_id_list) >= 2: + resstr += f'{nodeid_now}后面是并行执行{len(neighbor_node_id_list)}个任务：\n' + else: + resstr += f'{nodeid_now}后面是执行{len(neighbor_node_id_list)}个任务：\n ' - # for i in range(len(neighbor_node_id_list)): - # resstr += f'{nodeid_now}后面是{neighbor_node_id_list[i]}, {neighbor_node_id_list[i]}的内容是:{next_node_description_list[i]} \n' + for i in range(len(neighbor_node_id_list)): + resstr += f'{nodeid_now}后面是{neighbor_node_id_list[i]}, {neighbor_node_id_list[i]}的内容是:{next_node_description_list[i]} \n' - # for i in range(len(neighbor_node_id_list)): #往后扩展 - # nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_task'}) + for i in range(len(neighbor_node_id_list)): #往后扩展 + if neighbor_node_id_list[i] not in nodeid_in_search_all: + nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_task'}) + nodeid_in_search_all.append(neighbor_node_id_list[i]) - # elif self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opsgptkg_phenomenon') == True: - # #后续所有节点都是判断节点, 则进行大模型判断，选中其中一个 phenomenon 进入后续，同时在phenomenons节点上记录memory - # neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) - # next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) + elif self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opsgptkg_phenomenon') == True: + #后续所有节点都是判断节点, 则进行大模型判断，选中其中一个 phenomenon 进入后续，同时在phenomenons节点上记录memory + neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) + next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) - # resstr += f'{nodeid_now}后面有{len(neighbor_node_id_list)}种可能 ' - # for i in range(len(neighbor_node_id_list)): #往后扩展 - # resstr += f'{neighbor_node_id_list[i]}:{next_node_description_list[i]}; ' - # resstr += '\n' + resstr += f'{nodeid_now}后面有{len(neighbor_node_id_list)}种可能 ' + for i in range(len(neighbor_node_id_list)): #往后扩展 + resstr += f'{neighbor_node_id_list[i]}:{next_node_description_list[i]}; ' + resstr += '\n' - # for i in range(len(neighbor_node_id_list)): #往后扩展 - # nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_phenomenon'}) + for i in range(len(neighbor_node_id_list)): #往后扩展 + if neighbor_node_id_list[i] not in nodeid_in_search_all: + nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_phenomenon'}) + nodeid_in_search_all.append(neighbor_node_id_list[i]) - # elif self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opsgptkg_schedule') == True: - # #后面都是操作计划节点，假设只有一个操作计划节点 - # neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) - # next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) - # # neighbor_node_id_list = get_nodeid_from_res(res) - # # next_node_description_list = get_nodedescription_from_res(res) - # for i in range(len(neighbor_node_id_list)): - # resstr = resstr + '操作计划名：' + self.gb_handler.geabase_getDescription(neighbor_node_id_list[i], 'opsgptkg_schedule') + '\n' + '最开始，' - # nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_schedule'}) + elif self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opsgptkg_schedule') == True: + #后面都是操作计划节点，假设只有一个操作计划节点 + neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) + next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) + # neighbor_node_id_list = get_nodeid_from_res(res) + # next_node_description_list = get_nodedescription_from_res(res) + for i in range(len(neighbor_node_id_list)): + resstr = resstr + '操作计划名：' + self.gb_handler.geabase_getDescription(neighbor_node_id_list[i], 'opsgptkg_schedule') + '\n' + '最开始，' + if neighbor_node_id_list[i] not in nodeid_in_search_all: + nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_schedule'}) + nodeid_in_search_all.append(neighbor_node_id_list[i]) - # elif self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opstpgkg_analysis') == True: - # neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) - # next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) + elif self.gb_handler.all_nodetype_check(nodeid_now, nodetype_now, 'opstpgkg_analysis') == True: + neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) + next_node_description_list = self.gb_handler.get_children_description( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) - # # neighbor_node_id_list = get_nodeid_from_res(res) - # # next_node_description_list = get_nodedescription_from_res(res) - # for i in range(len(neighbor_node_id_list)): + # neighbor_node_id_list = get_nodeid_from_res(res) + # next_node_description_list = get_nodedescription_from_res(res) + for i in range(len(neighbor_node_id_list)): - # resstr += f'{nodeid_now} 与 {neighbor_node_id_list[i]} 相连，{neighbor_node_id_list[i]}的内容为 {next_node_description_list[i]} ' - # nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_schedule'}) + resstr += f'{nodeid_now} 与 {neighbor_node_id_list[i]} 相连，{neighbor_node_id_list[i]}的内容为 {next_node_description_list[i]} ' + if neighbor_node_id_list[i] not in nodeid_in_search_all: + nodeid_in_search.append({'nodeId':neighbor_node_id_list[i], 'nodeType':'opsgptkg_schedule'}) + nodeid_in_search_all.append(neighbor_node_id_list[i]) - # else: - # neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) - # neighbor_node_type_list = self.gb_handler.get_children_type( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) + else: + neighbor_node_id_list = self.gb_handler.get_children_id( nodeid_now, nodetype_now) #get_nodeid_from_res(res) + neighbor_node_type_list = self.gb_handler.get_children_type( nodeid_now, nodetype_now) #get_nodedescription_from_res(res) - # for i in range(len(neighbor_node_id_list) ): + for i in range(len(neighbor_node_id_list) ): - # nodeid_new = neighbor_node_id_list[i] - # nodetype_new = neighbor_node_type_list[i] + nodeid_new = neighbor_node_id_list[i] + nodetype_new = neighbor_node_type_list[i] - # #只要后续有节点就继续扩展 - # nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + #只要后续有节点就继续扩展 + if nodeid_new not in nodeid_in_search_all: + nodeid_in_search.append({'nodeId':nodeid_new, 'nodeType':nodetype_new}) + nodeid_in_search_all.append( nodeid_new ) - full_graph_info = self.geabase_handler.get_hop_infos(attributes={"id": self.start_nodeid,}, node_type="opsgptkg_intent", hop = 15 ) - full_graph_info_str = str(full_graph_info.nodes) + '\n' + str(full_graph_info.edges) + '\n' - resstr = \ - f''' - 你是一个计划总结大师，可以用比较通俗易懂的语言总结一个计划。 - 计划由知识图谱的形式表示，即full_graph_info。 + # full_graph_info = self.geabase_handler.get_hop_infos(attributes={"id": self.start_nodeid,}, node_type="opsgptkg_intent", hop = 15 ) + # full_graph_info_str = str(full_graph_info.nodes) + '\n' + str(full_graph_info.edges) + '\n' + # resstr = \ + # f''' + # 你是一个计划总结大师，可以用比较通俗易懂的语言总结一个计划。 + # 计划由知识图谱的形式表示，即full_graph_info。 - ##注意## - full_graph_info 是一个具体的流程信息，包含了节点和边的信息 + # ##注意## + # full_graph_info 是一个具体的流程信息，包含了节点和边的信息 - full_graph_info:{full_graph_info_str} + # full_graph_info:{full_graph_info_str} - ''' + # ''' + + # resstr += ' \n \n 请总结上述排查计划，注意有些情况下可以概况总结处理.注意,请直接输出总结结果，不要输出其他的话:' + - resstr += ' \n \n 请总结上述排查计划，注意有些情况下可以概况总结处理.注意,请直接输出总结结果，不要输出其他的话:' return resstr diff --git a/muagent/service/ekg_reasoning/src/utils/__init__.py b/muagent/service/ekg_reasoning/src/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/muagent/service/ekg_reasoning/src/utils/call_llm.py b/muagent/service/ekg_reasoning/src/utils/call_llm.py index c32ac0e..3b8c4cf 100644 --- a/muagent/service/ekg_reasoning/src/utils/call_llm.py +++ b/muagent/service/ekg_reasoning/src/utils/call_llm.py @@ -21,11 +21,9 @@ from colorama import Fore from Crypto.Cipher import AES from loguru import logger +import logging + -try: - from call_antgroup_llm import call_antgroup_llm -except: - logger.warning("it's ok") MOST_RETRY_TIMES = 5 @@ -37,6 +35,11 @@ sys.path.append(src_dir) sys.path.append(src_dir + '/examples/') print(src_dir) +try: + from muagent.service.ekg_reasoning.src.utils.call_antgroup_llm import call_antgroup_llm +except: + logger.warning("加载 call_antgroup_llm 失败") + from muagent.llm_models.llm_config import LLMConfig from muagent.llm_models import getChatModelFromConfig @@ -44,8 +47,8 @@ def call_llm( input_content = '中国的首都是哪儿', - llm_model = 'qwen_chat_14b', - stop = None, + llm_model = None, + stop = [], temperature = 0.1, presence_penalty=0, llm_config=None @@ -53,7 +56,7 @@ def call_llm( if os.environ['operation_mode'] == 'open_source': # 'open_source' or 'antcode' - #开源环境，call_llm 依靠用户的配置 + logging.info('开源环境，call_llm 依靠用户的配置') try: src_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.abspath(__file__))) @@ -85,14 +88,35 @@ def call_llm( #logger.error(f"{e}") - print(f'os.environ["model_name"] is {os.environ["model_name"]}, llm_model is {llm_model}') + logger.info(f'os.environ["model_name"] is {os.environ["model_name"]}, llm_model is {llm_model}, llm_config is {llm_config}') - if llm_config is None or llm_model == 'gpt-4' or llm_model == 'gpt_4': + if ( llm_model == 'gpt-4' or llm_model == 'gpt_4'): logger.info("强制调用gpt-4 的配置") llm_config = LLMConfig( - model_name=model_name, model_engine=model_engine, api_key=api_key, api_base_url=api_base_url, + model_name=model_name, model_engine=model_engine, + api_key=api_key, api_base_url=api_base_url, temperature=llm_temperature) + elif llm_model == None: + logger.info("llm_config 未输入， 强制调用默认大模型配置") + llm_config = LLMConfig( + model_name =os.environ["model_name"], + model_engine =os.environ["model_engine"], + api_key =os.environ["OPENAI_API_KEY"], + api_base_url =os.environ["API_BASE_URL"], + temperature =os.environ["llm_temperature"] ) + # elif ( llm_model == 'qwen-72B' or llm_model == 'Qwen2_72B_Instruct_OpsGPT'): + # logger.info("强制调用 Qwen2_72B_Instruct_OpsGPT 的配置") + # llm_config = LLMConfig( + # model_name =os.environ["qwen-model_name"], + # model_engine =os.environ["qwen-model_engine"], + # api_key =os.environ["qwen-OPENAI_API_KEY"], + # api_base_url =os.environ["qwen-API_BASE_URL"], + # temperature =os.environ["qwen-llm_temperature"] ) + else: + logger.info("使用默认 llm_config 的配置") + + logger.info(f'llm_config is {llm_config}') llm_model = getChatModelFromConfig(llm_config) if llm_config else None @@ -139,9 +163,9 @@ def extract_final_result(input_string, special_str = "最终结果为：" ): #return jiequ_str -def robust_call_llm(prompt_temp, llm_model = 'useless', stop = None, temperature = 0, presence_penalty = 0): +def robust_call_llm(prompt_temp, llm_model = None, stop = None, temperature = 0, presence_penalty = 0): if os.environ['operation_mode'] != 'antcode': - res = call_llm(input_content = prompt_temp, llm_model = 'gpt_4', stop = stop,temperature=temperature, presence_penalty=presence_penalty) + res = call_llm(input_content = prompt_temp, llm_model = llm_model , stop = stop,temperature=temperature, presence_penalty=presence_penalty) return res else: try: @@ -155,5 +179,5 @@ def robust_call_llm(prompt_temp, llm_model = 'useless', stop = None, temperature if __name__ == '__main__': import test_config - res = call_llm("你的名字是什么？") + res = call_llm("你的名字是什么？", llm_model = 'Qwen2_72B_Instruct_OpsGPT') print(res) diff --git a/requirements.txt b/requirements.txt index 84f0432..115cf6a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -29,4 +29,5 @@ urllib3==1.26.6 sseclient ollama colorama -pycryptodome \ No newline at end of file +pycryptodome +dashscope \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/config/application-default.properties b/runtime/bootstrap/src/main/resources/config/application-default.properties index 77805b9..cf8ef39 100644 --- a/runtime/bootstrap/src/main/resources/config/application-default.properties +++ b/runtime/bootstrap/src/main/resources/config/application-default.properties @@ -14,4 +14,7 @@ ekg.chat.url=http://ekgservice:3737/ekg/graph/ekg_migration_reasoning ekg.candidate.tools=undercover.dispatch_keyword,undercover.dispatch_position,\ undercover.judge,undercover.lijing,undercover.summary,undercover.wangpeng,\ - undercover.zhangwei,undercover.show_key_information \ No newline at end of file + undercover.zhangwei,undercover.show_key_information,\ + werewolf.dispatch_keyword,werewolf.dispatch_position,werewolf.hangang,\ + werewolf.hezixuan,werewolf.judge,werewolf.liangjun,werewolf.shenqiang,\ + werewolf.zhoujie,werewolf.zhouxinyi,werewolf.zhuli,werewolf.summary \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/log4j2-spring.xml b/runtime/bootstrap/src/main/resources/log4j2-spring.xml index 3e56422..66b92d9 100644 --- a/runtime/bootstrap/src/main/resources/log4j2-spring.xml +++ b/runtime/bootstrap/src/main/resources/log4j2-spring.xml @@ -2,18 +2,18 @@ - + - + @@ -24,7 +24,7 @@ filePattern="${spring:logging.path}/${spring:spring.application.name}/app-default.log.%d{yyyy-MM-dd}" append="true"> diff --git a/runtime/bootstrap/src/main/resources/static/avatar/hangang.png 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{"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "llm_query", + "toolName": "llm_query", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n\n Content content = new Content()\n content.text = request\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/system.select_tool.json b/runtime/bootstrap/src/main/resources/tools/system.select_tool.json index 07eddc4..7e7e5a2 100644 --- a/runtime/bootstrap/src/main/resources/tools/system.select_tool.json +++ b/runtime/bootstrap/src/main/resources/tools/system.select_tool.json @@ -25,7 +25,7 @@ "operatorCreate": "169704", "operatorModified": "169704", "version": "3", - "owner": "你现在是一个插件选择助手，需要理解问题描述，然后从以下给出的插件中选择一个可以解决问题描述的插件\n\n##插件列表：\n###插件名称：system.select_tool\n插件描述：select_tool\n###插件名称：undercover.zhangwei\n插件描述：agent_张伟\n###插件名称：ekg-query\n插件描述：ekg-query\n\n##约束条件：\n-你必须按照以下JSON格式返回结果，并且不要给出问题分析的过程，{\"toolKey\":\"插件名称\"}\n-请尽可能从插件列表中选择合适的插件\n-如果插件列表中没有与问题描述匹配的插件请返回\"null\"\n-如果是模型不能回答的问题请返回\"null\"\n\n##问题描述：\n选择某个tool回答问题，今天是几号", + "owner": "", "deleted": null, "type": "OPEN", "status": "PROD_PUBLISHED", diff --git a/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_keyword.json b/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_keyword.json index 33ad59f..52c1b8c 100644 --- a/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_keyword.json +++ b/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_keyword.json @@ -5,8 +5,8 @@ "toolKey": "undercover.dispatch_keyword", "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, "toolId": null, - "description": "角色分配和单词分配", - "toolName": "角色分配和单词分配", + "description": "谁是卧底角色分配和单词分配", + "toolName": "谁是卧底角色分配和单词分配", "pauseStatus": "EXECUTING", "transparentInfo": null, "intention": null, diff --git a/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_position.json b/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_position.json index 4ee8cd0..5a41884 100644 --- a/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_position.json +++ b/runtime/bootstrap/src/main/resources/tools/undercover.dispatch_position.json @@ -5,8 +5,8 @@ "toolKey": "undercover.dispatch_position", "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, "toolId": null, - "description": "分配座位", - "toolName": "分配座位", + "description": "谁是卧底分配座位", + "toolName": "谁是卧底分配座位", "pauseStatus": "EXECUTING", "transparentInfo": null, "intention": null, diff --git a/runtime/bootstrap/src/main/resources/tools/undercover.judge.json b/runtime/bootstrap/src/main/resources/tools/undercover.judge.json index 156c13f..51ed0ff 100644 --- a/runtime/bootstrap/src/main/resources/tools/undercover.judge.json +++ b/runtime/bootstrap/src/main/resources/tools/undercover.judge.json @@ -5,8 +5,8 @@ "toolKey": "undercover.judge", "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, "toolId": null, - "description": "判断游戏是否结束", - "toolName": "判断游戏是否结束", + "description": "谁是卧底判断游戏是否结束", + "toolName": "谁是卧底判断游戏是否结束", "pauseStatus": "EXECUTING", "transparentInfo": null, "intention": null, diff --git a/runtime/bootstrap/src/main/resources/tools/undercover.summary.json b/runtime/bootstrap/src/main/resources/tools/undercover.summary.json index e7c5491..c71a562 100644 --- a/runtime/bootstrap/src/main/resources/tools/undercover.summary.json +++ b/runtime/bootstrap/src/main/resources/tools/undercover.summary.json @@ -5,8 +5,8 @@ "toolKey": "undercover.summary", "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, "toolId": null, - "description": "给出每个人的单词以及最终胜利者", - "toolName": "给出每个人的单词以及最终胜利者", + "description": "谁是卧底给出每个人的单词以及最终胜利者", + "toolName": "谁是卧底给出每个人的单词以及最终胜利者", "pauseStatus": "EXECUTING", "transparentInfo": null, "intention": null, diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_keyword.json b/runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_keyword.json new file mode 100644 index 0000000..ef3d76f --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_keyword.json @@ -0,0 +1,50 @@ + +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.dispatch_keyword", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀角色分配和单词分配", + "toolName": "狼人杀角色分配和单词分配", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef randRole() {\n def player = [[\"朱丽\", \"agent_朱丽\"], [ \"周杰\", \"agent_周杰\"], [\"沈强\", \"agent_沈强\"], [ \"韩刚\", \"agent_韩刚\"], [\"梁军\", \"agent_梁军\"], [ \"周欣怡\", \"agent_周欣怡\"], [\"贺子轩 \", \"agent_贺子轩\"], [\"人类玩家\", \"agent_人类玩家\"]]\n player.shuffle()\n def role = Arrays.asList(\"平民_1\", \"平民_2\", \"平民_3\", \"狼人_1\", \"狼人_2\", \"狼人_3\", \"女巫\", \"预言家\")\n role.shuffle()\n return (0..7).collect { i ->\n {\n def r = new HashMap()\n r.put(\"player_name\", player.get(i).get(0))\n r.put(\"agent_name\", player.get(i).get(1))\n r.put(\"agent_description\", role.get(i))\n return r\n }\n }\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n def roles = randRole()\n return gson.toJson(roles)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n \n int successCode;\n \n String errorMessage;\n \n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n \n if (lr.getSuccessCode() == 0) {\n return lr.getAnswer()\n }\n \n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "GROOVY", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"https://opsgptcore-pre.alipay.com/api/tool/sample/{pathVariable}"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_position.json b/runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_position.json new file mode 100644 index 0000000..e70dd88 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.dispatch_position.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.dispatch_position", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀分配座位", + "toolName": "狼人杀分配座位", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "requestGroovy": "def randPos(Integer n) {\n def player = [[\"朱丽\", \"agent_朱丽\"], [ \"周杰\", \"agent_周杰\"], [\"沈强\", \"agent_沈强\"], [ \"韩刚\", \"agent_韩刚\"], [\"梁军\", \"agent_梁军\"], [ \"周欣怡\", \"agent_周欣怡\"], [\"贺子轩 \", \"agent_贺子轩\"], [\"人类玩家\", \"agent_人类玩家\"]]\n player.shuffle()\n\n return \"\\n\\n| 座位 | 玩家 |\\n|---|---|\\n\" + (1..n).toList().collect { i ->\n {\n return \"| \" + i + \" | **\" + player.get(i-1).get(0) + \"** |\"\n }\n }.join(\"\\n\")\n}\n\ndef convertRequest(String request) {\n return randPos(8)\n}\n\nconvertRequest(request)", + "message": null, + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n \n int successCode;\n \n String errorMessage;\n \n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n \n if (lr.getSuccessCode() == 0) {\n return lr.getAnswer()\n }\n \n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "GROOVY", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"https://opsgptcore-pre.alipay.com/api/tool/sample/{pathVariable}"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.hangang.json b/runtime/bootstrap/src/main/resources/tools/werewolf.hangang.json new file mode 100644 index 0000000..1054e5a --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.hangang.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.hangang", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_韩刚", + "toolName": "agent_韩刚", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.hezixuan.json b/runtime/bootstrap/src/main/resources/tools/werewolf.hezixuan.json new file mode 100644 index 0000000..ded4942 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.hezixuan.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.hezixuan", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_贺子轩", + "toolName": "agent_贺子轩", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.judge.json b/runtime/bootstrap/src/main/resources/tools/werewolf.judge.json new file mode 100644 index 0000000..6f95558 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.judge.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.judge", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀判断游戏是否结束", + "toolName": "狼人杀判断游戏是否结束", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##本局游戏历史记录##\\n{memory}\\n\\n##背景##\\n你是一个逻辑判断大师，你正在参与“狼人杀”这个游戏，你的角色是[主持人]。你熟悉“狼人杀”游戏的完整流程，现在需要判断当前游戏是否结束。\\n\\n##任务##\\n你的任务是判断当前游戏是否结束，规则如下：\\n根据【狼人杀角色分配和单词分配】感知每一个玩家属于“好人”或者属于“狼人”，根据【本局游戏历史记录】和【重要通知】感知每一轮被投票死亡、被狼人杀死、被女巫毒死的玩家。 统计目前存活的[好人]玩家数量、[狼人]玩家数量。格式{\\\"存活的好人\\\":[player_name], \\\"存活的狼人\\\":[player_name]},判断以下条件中的一个是否满足：\\n1. 存活的“狼人”玩家数量为0。\\n2. “狼人”数量大于了“好人”数量。\\n3. “狼人”数量等于“好人”数量，“女巫”已死亡或者她的毒药已经使用。\\n若某个条件满足，游戏结束；否则游戏没有结束。\\n\\n##输出##\\n返回JSON格式，格式为：{\\\"thought\\\": str, \\\"存活的玩家信息\\\": {\\\"存活的好人\\\":[player_name], \\\"存活的狼人\\\":[player_name]}, \\\"isEnd\\\": \\\"是\\\" or \\\"否\\\"}\\n-thought **根据本局游戏历史记录** 分析 游戏最开始有哪些玩家, 他们的身份是什么, 投票导致死亡的玩家有哪些? 被狼人杀死的玩家有哪些? 被女巫毒死的玩家是谁? 析当前存活的玩家有哪些? 是否触发了游戏结束条件? 等等。\\n\\n##example##\\n{\\\"thought\\\": \\\"**游戏开始时** 有 小杭、小北、小赵、小钱、小孙、小李、小夏、小张 八位玩家, 其中 小杭、小北、小赵是[狼人], 小钱、小孙，小张是[平民], 小李是[预言家]，小夏是[女巫]，小张在第一轮被狼人杀死了，[狼人]数量大于0，且[好人]数量大于[狼人]数量, 因此游戏未结束。\\\", \\\"存活的玩家信息\\\": {\\\"存活的狼人\\\":[\\\"小杭\\\", \\\"小北\\\", \\\"小赵\\\"], \\\"存活的好人\\\":[\\\"小钱\\\", \\\"小孙\\\", \\\"小李\\\", \\\"小夏\\\"]}, \\\"isEnd\\\": \\\"否\\\" }\\n\\n##example##\\n{\\\"thought\\\": \\\"**游戏开始时** 有 小杭、小北、小赵、小钱、小孙、小李、小夏、小张 八位玩家, 其中 小杭、小北、小赵是[狼人], 小钱、小孙，小张是[平民], 小李是[预言家]，小夏是[女巫]，小张在第一轮被狼人杀死了, 小夏被第一轮投票投死了，[狼人]数量等于[好人]数量额等于[狼人]数量，女巫已死亡, 因此游戏结束。\\\", \\\"存活的玩家信息\\\": {\\\"存活的狼人\\\":[\\\"小杭\\\", \\\"小北\\\", \\\"小赵\\\"], \\\"存活的好人\\\":[\\\"小钱\\\", \\\"小孙\\\", \\\"小李\\\"]}, \\\"isEnd\\\": \\\"是\\\" }\\n\\n{\\\"thought\\\": \\\"**游戏开始时** 有 小杭、小北、小赵、小钱、小孙、小李、小夏、小张 八位玩家, 其中 小杭、小北、小赵是[狼人], 小钱、小孙，小张是[平民], 小李是[预言家]，小夏是[女巫]，小张在第一轮被狼人杀死了, 女巫毒死了小孙，小钱被投票死亡了。[狼人]数量等于3，[好人]数量等于2，[狼人]数量大于[好人]数量,因此游戏结束。\\\", \\\"存活的玩家信息\\\": {\\\"存活的狼人\\\":[\\\"小杭\\\", \\\"小北\\\", \\\"小赵\\\"], \\\"存活的好人\\\":[\\\"小李\\\", \\\"小夏\\\"]}, \\\"isEnd\\\": \\\"是\\\" }\\n\\n##注意事项##\\n1. 所有玩家的座位、身份、agent_name、存活状态、游戏进展等信息在开头部分已给出。\\n2. \\\"是\\\" or \\\"否\\\" 如何选择？若游戏结束，则为\\\"是\\\"，否则为\\\"否\\\"。\\n3. 请直接输出jsonstr，不用输出markdown格式。\\n4. 游戏可能进行了不只一轮，可能有1个或者2个玩家已经死亡，请注意感知。\\n\\n##结果##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "summaryGroovy": null, + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.liangjun.json b/runtime/bootstrap/src/main/resources/tools/werewolf.liangjun.json new file mode 100644 index 0000000..bc21249 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.liangjun.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.liangjun", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_梁军", + "toolName": "agent_梁军", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.shenqiang.json b/runtime/bootstrap/src/main/resources/tools/werewolf.shenqiang.json new file mode 100644 index 0000000..f7a0e87 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.shenqiang.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.shenqiang", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_沈强", + "toolName": "agent_沈强", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.summary.json b/runtime/bootstrap/src/main/resources/tools/werewolf.summary.json new file mode 100644 index 0000000..c8d378f --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.summary.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.summary", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀给出每个人的单词以及最终胜利者", + "toolName": "狼人杀给出每个人的单词以及最终胜利者", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##本局游戏历史记录##\\n{memory}\\n\\n##背景##\\n您正在参与“狼人杀”这个游戏，角色是[主持人]。现在游戏已经结束，您需要判断胜利的一方是谁。\\n\\n##任务##\\n统计目前存活的[好人]玩家数量、[狼人]玩家数量。判断以下条件中的哪一个满足：\\n1. 存活的“狼人”玩家数量为0。\\n2. “狼人”数量超过了“好人”数量。\\n3. “狼人”数量等于“好人”数量，“女巫”已死亡或者她的毒药已经使用。\\n如果条件1满足，则[好人]胜利；如果条件2或者条件3满足，则[狼人]胜利。\\n\\n##输出##\\nPython可直接解析的jsonstr，格式如下：\\n{{\\\"原因是\\\": 获胜者为[好人]或[狼人]的原因, \\\"角色分配结果为\\\": 所有玩家的角色(根据本局游戏历史记录), \\\"获胜方为\\\": \\\"好人\\\" or \\\"狼人\\\"}}\\n以{{开头，任何其他内容都是不允许的！\\n\\n##输出示例##\\n{{\\\"原因是\\\": \\\"狼人数量为0\\\", \\\"角色分配结果为\\\": \\\"沈强：身份为狼人_1；周欣怡：身份为狼人_2；梁军：身份为狼人_3；贺子轩：身份为平民_1；人类玩家：身份为平民_2；朱丽：身份为预言家；韩刚：身份为女巫；周杰：身份为猎人。\\\", \\\"获胜方为\\\": \\\"好人\\\"}}\\n\\n##注意##\\n请输出所有玩家的角色分配结果，不要遗漏信息。\\n\\n##结果##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.zhoujie.json b/runtime/bootstrap/src/main/resources/tools/werewolf.zhoujie.json new file mode 100644 index 0000000..23ee163 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.zhoujie.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.zhoujie", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_周杰", + "toolName": "agent_周杰", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.zhouxinyi.json b/runtime/bootstrap/src/main/resources/tools/werewolf.zhouxinyi.json new file mode 100644 index 0000000..57a9d93 --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.zhouxinyi.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.zhouxinyi", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_周心怡", + "toolName": "agent_周心怡", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/bootstrap/src/main/resources/tools/werewolf.zhuli.json b/runtime/bootstrap/src/main/resources/tools/werewolf.zhuli.json new file mode 100644 index 0000000..50f1b8a --- /dev/null +++ b/runtime/bootstrap/src/main/resources/tools/werewolf.zhuli.json @@ -0,0 +1,49 @@ +{ + "id": 2, + "gmtCreate": "2024-07-17T07:20:35.000+00:00", + "gmtModified": "2024-07-17T07:20:35.000+00:00", + "toolKey": "werewolf.zhuli", + "toolDefinition": {"description":"演示tool的返回结果","name":"OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response","parameters":{"description":"演示tool的返回结果","properties":{"button":{"description":"非必填，按钮名称，默认值为「重试」","requestType":"body","type":"string"},"pathVariable":{"description":"路径参数","requestType":"path","type":"string"},"requestParam":{"description":"请求参数","requestType":"query","type":"string"}},"required":[],"type":"object"},"result":{"description":"返回信息","properties":{},"type":"object"}}, + "toolId": null, + "description": "狼人杀agent_朱丽", + "toolName": "agent_朱丽", + "pauseStatus": "EXECUTING", + "transparentInfo": null, + "intention": null, + "input": null, + "output": null, + "exeNormal": true, + "message": null, + "requestGroovy": "import com.alipay.muagent.model.connector.http.HttpParameters\nimport com.google.gson.Gson\n\nclass Content {\n String text;\n}\n\ndef getMemory() {\n return binding.hasVariable(\"stepMemory\") ? (String) binding.getVariable(\"stepMemory\") : \"[null]\"\n}\n\ndef convertRequest(String request) {\n def gson = new Gson()\n String template = \"##狼人杀游戏说明##\\n这个游戏基于文字交流, 以下是游戏规则：\\n角色:\\n主持人也是游戏的组织者，你需要正确回答他的指示。游戏中有五种角色：狼人、平民、预言家、女巫，三个狼人，一个预言家，一个女巫，三个平民。\\n好人阵营: 平民、预言家和女巫。\\n游戏阶段：游戏分为两个交替的阶段：白天和黑夜。\\n黑夜：\\n在黑夜阶段，你与主持人的交流内容是保密的，你无需担心其他玩家和主持人知道你说了什么或做了什么。\\n- 如果你是狼人，你需要和队友一起选择袭击杀死一个玩家，在投票阶段你需要明确指出击杀对象，比如【我决定今晚击杀X号玩家XX】，不可以击杀狼人队友。\\n- 如果你是女巫，你有一瓶解药，可以拯救被狼人袭击的玩家，以及一瓶毒药，可以在黑夜后毒死一个玩家。解药和毒药只能使用一次。\\n- 如果你是预言家，你可以在每个晚上检查一个玩家是否是狼人，你应该明确告诉主持人你想要查验的玩家，比如【我想要查验X号玩家XX的身份】。\\n- 如果你是村民，你在夜晚无法做任何事情。\\n白天：\\n你与存活所有玩家（包括敌人）讨论。讨论结束后，玩家投票来淘汰一个自己怀疑是狼人的玩家。玩家必须明确投票，比如【我认为X号玩家XX是狼人，我投票给X号玩家XX】，获得最多票数的玩家将被淘汰，主持人将宣布XX玩家死亡。\\n游戏目标:\\n狼人的目标是杀死所有的好人阵营中的玩家，并且不被好人阵营的玩家识别出狼人身份；\\n好人阵营的玩家，需要找出并杀死所有的狼人玩家。\\n\\n##注意##\\n你正在参与狼人杀这个游戏，你应该感知自己的名字、座位和角色。\\n1. 若你的角色为狼人，白天的发言应该尽可能隐藏身份，夜晚则要明确击杀对象，不可击杀狼人队友。\\n2. 若你的角色属于好人阵营，白天的发言应该根据游戏进展尽可能分析出谁是狼人。\\n\\n##以下为目前游戏进展##\\n{memory}\\n\\n##发言格式##\\n你的回答中需要包含你的想法并给出简洁的理由，注意请有理有据，白天的发言尽量不要与别人的发言内容重复。发言的格式应该为Python可直接解析的jsonstr，格式如下：\\n{\\\"thought\\\": 以“我是【座位号】号玩家【名字】【角色】”开头，根据主持人的通知感知自己的【名字】、【座位号】、【角色】，根据游戏进展和自己游戏角色的当前任务分析如何发言，字数不超过150字, \\\"output\\\": 您的发言应该符合目前游戏进展和自己角色的逻辑，白天投票环节不能投票给自己。}\\n##开始发言##\\n\"\n\n Content content = new Content()\n content.text = template.replace(\"{memory}\", getMemory())\n\n HttpParameters parameters = HttpParameters.builder().build()\n parameters.setRequestBody(gson.toJson(content))\n return gson.toJson(parameters)\n}\n\nconvertRequest(request)", + "responseGroovy": "import com.google.gson.Gson\n\nclass LLMResponse {\n\n int successCode;\n\n String errorMessage;\n\n String answer;\n}\n\ndef convertResponse(String response) {\n def gson = new Gson()\n LLMResponse lr = gson.fromJson(response, LLMResponse.class)\n\n if (lr.getSuccessCode() == 1) {\n return lr.getAnswer()\n }\n\n throw new RuntimeException(lr.getErrorMessage())\n}\n\nconvertResponse(response)", + "summaryGroovy": null, + "manifestSchema": {"api":{"type":"http"},"auth":{"type":"none"},"description_for_human":"演示tool的返回结果","description_for_model":"演示tool的返回结果","headers":{},"name_for_human":"ToolSampleController.queryEditableResponse_response","schema_version":"v1"}, + "toolApiPath": "OPSGPT./.POST", + "toolProtocol": "HTTP", + "serverUrl": null, + "apiSchema": {"info":{"description":"演示tool的返回结果","title":"ToolSampleController.queryEditableResponse_response","version":"0.0.1"},"openapi":"3.0.0","paths":{"http":{"method":"POST","parameters":[{"in":"body","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-0"}},{"in":"query","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-1"}},{"in":"path","schema":{"$ref":"#ToolSampleController-queryEditableResponse-request-2"}}],"path":"/api/tool/sample/{pathVariable}","responses":{"200":{"application/json":{"$ref":"#/definitions/BaseResult_ToolResponse_"}}}}},"servers":[{"url":"http://ekgservice:3737/llm/generate"}]}, + "operatorCreate": "169704", + "operatorModified": "169704", + "version": "3", + "owner": "169704", + "deleted": null, + "type": "OPEN", + "status": "PROD_PUBLISHED", + "vdbPk": "447890596018669520", + "selectSamples": "[null]", + "selectVars": "[{}]", + "invokeType": "SYNC", + "tag": "SINGLE_TOOL:OPSGPT:MAIN_SITE:ToolSampleController.queryEditableResponse_response", + "toolExtraInfo": { + "errMessage": null, + "devVdbPk": "447890596018692596", + "prodVdbPk": null, + "summaryModel": null, + "stepConfigList": null, + "nexExtraInfo": null, + "ispInfo": null, + "rpcUniqueId": null + }, + "pauseStatusList": null, + "configMap": null +} \ No newline at end of file diff --git a/runtime/model/src/main/java/com/alipay/muagent/model/chat/content/RoleResponseContent.java b/runtime/model/src/main/java/com/alipay/muagent/model/chat/content/RoleResponseContent.java index eb23f96..df7a87a 100644 --- a/runtime/model/src/main/java/com/alipay/muagent/model/chat/content/RoleResponseContent.java +++ b/runtime/model/src/main/java/com/alipay/muagent/model/chat/content/RoleResponseContent.java @@ -53,6 +53,62 @@ public static RoleResponseContent buildZhangwei(String rsp) { return rrc; } + public static RoleResponseContent buildZhuli(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("朱丽"); + rrc.setUrl("http://localhost:8080/avatar/zhuli.png"); + + return rrc; + } + + public static RoleResponseContent buildZhoujie(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("周杰"); + rrc.setUrl("http://localhost:8080/avatar/zhoujie.png"); + + return rrc; + } + + public static RoleResponseContent buildShenqiang(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("沈强"); + rrc.setUrl("http://localhost:8080/avatar/shenqiang.png"); + + return rrc; + } + + public static RoleResponseContent buildHangang(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("韩刚"); + rrc.setUrl("http://localhost:8080/avatar/hangang.png"); + + return rrc; + } + + public static RoleResponseContent buildLiangjun(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("梁军"); + rrc.setUrl("http://localhost:8080/avatar/liangjun.png"); + + return rrc; + } + + public static RoleResponseContent buildZhouxinyi(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("周心怡"); + rrc.setUrl("http://localhost:8080/avatar/zhouxinyi.png"); + + return rrc; + } + + public static RoleResponseContent buildHezixuan(String rsp) { + RoleResponseContent rrc = getRoleResponseContent(rsp); + rrc.setName("贺子轩"); + rrc.setUrl("http://localhost:8080/avatar/hezixuan.png"); + + return rrc; + } + private static RoleResponseContent getRoleResponseContent(String response) { RoleResponseContent rrc = new RoleResponseContent(); rrc.setResponse(ChatResponse.buildTextResponse(response)); diff --git a/runtime/model/src/main/java/com/alipay/muagent/model/exception/EkgToolNotFindException.java b/runtime/model/src/main/java/com/alipay/muagent/model/exception/EkgToolNotFindException.java new file mode 100644 index 0000000..d74c97d --- /dev/null +++ b/runtime/model/src/main/java/com/alipay/muagent/model/exception/EkgToolNotFindException.java @@ -0,0 +1,7 @@ +package com.alipay.muagent.model.exception; + +public class EkgToolNotFindException extends RuntimeException { + public EkgToolNotFindException(String message) { + super(message); + } +} diff --git a/runtime/model/src/main/java/com/alipay/muagent/model/scheduler/SubmitTaskRequest.java b/runtime/model/src/main/java/com/alipay/muagent/model/scheduler/SubmitTaskRequest.java index 3371192..6f18b1f 100644 --- a/runtime/model/src/main/java/com/alipay/muagent/model/scheduler/SubmitTaskRequest.java +++ b/runtime/model/src/main/java/com/alipay/muagent/model/scheduler/SubmitTaskRequest.java @@ -25,6 +25,8 @@ public class SubmitTaskRequest { private String memory; + private boolean ekgRequest; + /** * the range of tools needed to execute */ diff --git a/runtime/service/src/main/java/com/alipay/muagent/service/chat/impl/EkgChatServiceImpl.java b/runtime/service/src/main/java/com/alipay/muagent/service/chat/impl/EkgChatServiceImpl.java index a481e6f..91b2187 100644 --- a/runtime/service/src/main/java/com/alipay/muagent/service/chat/impl/EkgChatServiceImpl.java +++ b/runtime/service/src/main/java/com/alipay/muagent/service/chat/impl/EkgChatServiceImpl.java @@ -20,6 +20,7 @@ import com.alipay.muagent.model.ekg.configuration.Config; import com.alipay.muagent.model.enums.ekg.ToolPlanTypeEnum; import com.alipay.muagent.model.enums.scheduler.TaskSchedulerTypeEnum; +import com.alipay.muagent.model.exception.EkgToolNotFindException; import com.alipay.muagent.model.scheduler.SubmitTaskRequest; import com.alipay.muagent.model.scheduler.TaskExeResponse; import com.alipay.muagent.service.chat.ChatService; @@ -34,22 +35,39 @@ import io.micrometer.common.util.StringUtils; import org.slf4j.Logger; import org.slf4j.LoggerFactory; +import org.slf4j.MDC; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Service; import org.springframework.util.Assert; import org.springframework.util.CollectionUtils; import org.springframework.web.servlet.mvc.method.annotation.SseEmitter; +import java.io.IOException; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Objects; +import java.util.Optional; import java.util.UUID; - +import java.util.concurrent.ArrayBlockingQueue; +import java.util.concurrent.ExecutionException; +import java.util.concurrent.ExecutorService; +import java.util.concurrent.Future; +import java.util.concurrent.ThreadPoolExecutor; +import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicInteger; + +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildHangang; +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildHezixuan; +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildLiangjun; import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildLijing; import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildReferee; +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildShenqiang; import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildWangpeng; import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildZhangwei; +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildZhoujie; +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildZhouxinyi; +import static com.alipay.muagent.model.chat.content.RoleResponseContent.buildZhuli; import static com.alipay.muagent.model.enums.chat.ChatExtendedKeyEnum.CHAT_UNIQUE_ID; import static com.alipay.muagent.model.enums.chat.ChatExtendedKeyEnum.EKG_NODE; @@ -67,7 +85,6 @@ public class EkgChatServiceImpl implements ChatService { @Override public void chat(SseEmitter emitter, ChatRequest request) { - boolean wait = true; try { TextContent content = GsonUtils.fromString(TextContent.class, GsonUtils.toString(request.getContent())); @@ -102,68 +119,138 @@ public void chat(SseEmitter emitter, ChatRequest request) { ekgRequest.setIntentionRule(Lists.newArrayList("nlp")); ekgRequest.setScene("NEXA"); - while (wait) { - - EkgAlgorithmResult response = sendRequestToEkgPlanner(ekgRequest); + ekgHandler(emitter, ekgRequest, "1", null); + } catch (Exception e) { + LoggerUtil.error(LOGGER, e, "[EkgServiceImpl call error][{}]", request.getChatUniqueId()); + } + } - if (Objects.isNull(response)) { - emitter.send(SseEmitter.event().data(GsonUtils.toString(ChatResponse.buildTextResponses("图谱执行异常")))); - LoggerUtil.warn(LOGGER, "图谱执行异常"); - break; - } + private static final ExecutorService EKG_NODE_EXE = new ThreadPoolExecutor( + 100, + 150, + 60L, + TimeUnit.SECONDS, + new ArrayBlockingQueue<>(1), + new ThreadPoolExecutor.AbortPolicy()); - if (StringUtils.isNotBlank(response.getSummary())) { - emitter.send(SseEmitter.event().data(GsonUtils.toString(ChatResponse.buildTextResponses(response.getSummary())))); - LoggerUtil.info(LOGGER, "游戏结束"); - break; - } + private boolean ekgHandler(SseEmitter emitter, EkgQueryRequest ekgRequest, String stepNum, HashMap exContext) { + try { + MDC.put("stepNodeId", stepNum); + EkgAlgorithmResult response = sendRequestToEkgPlanner(ekgRequest); - // 需要给用户反馈信息 - if (StringUtils.isNotBlank(response.getUserInteraction())) { - RoleResponseContent rrc = buildRoleResponseContent(response.getUserInteraction()); - String userMsg = GsonUtils.toString(ChatResponse.buildRoleResponses(rrc)); - LoggerUtil.info(LOGGER, "notifyUser:{}", userMsg); - emitter.send(SseEmitter.event().data(userMsg)); - } + if (Objects.isNull(response)) { + emitter.send(SseEmitter.event().data(GsonUtils.toString(ChatResponse.buildTextResponses("图谱执行异常")))); + LoggerUtil.warn(LOGGER, "图谱执行异常"); + return false; + } - EkgNode node = response.getToolPlan().get(0); + if (StringUtils.isNotBlank(response.getSummary())) { + emitter.send(SseEmitter.event().data(GsonUtils.toString(ChatResponse.buildTextResponses(response.getSummary())))); + LoggerUtil.info(LOGGER, "游戏结束"); + return false; + } - // 需要等待用户回答，通知用户后结束轮询 - if (ToolPlanTypeEnum.USER_PROBLEM.getCode().equals(node.getType())) { - EkgQuestionDescription ques = node.getQuestionDescription(); - RoleResponseContent rrc = buildRoleResponseContent(" *请回答：* \n\n >" + ques.getQuestionContent().getQuestion()); + if (CollectionUtils.isEmpty(response.getToolPlan())) { + response.setToolPlan(Lists.newArrayList()); + } - List crs = ChatResponse.buildRoleResponses(rrc); + // 需要给用户反馈信息 + if (StringUtils.isNotBlank(response.getUserInteraction())) { + RoleResponseContent rrc = buildRoleResponseContent(response.getUserInteraction()); + List crs = ChatResponse.buildRoleResponses(rrc); + crs.get(0).setExtendContext(exContext); + String userMsg = GsonUtils.toString(crs); + LoggerUtil.info(LOGGER, "notifyUser:{}", userMsg); + emitter.send(SseEmitter.event().data(userMsg)); + } - // 将 node 和 对话id 放入上下文 - HashMap exContext = Maps.newHashMap(); - exContext.put(EKG_NODE.name(), GsonUtils.toString(node)); - exContext.put(CHAT_UNIQUE_ID.name(), ekgRequest.getSessionId()); - crs.get(0).setExtendContext(exContext); + Optional userNode = response.getToolPlan().stream().filter( + node -> ToolPlanTypeEnum.USER_PROBLEM.getCode().equals(node.getType())).findAny(); - String userMsg = GsonUtils.toString(crs); - LoggerUtil.info(LOGGER, "notifyUser:{}", userMsg); - emitter.send(SseEmitter.event().data(userMsg)); - break; - } + HashMap nodeContext = Maps.newHashMap(); + if (userNode.isPresent()) { + // 将 node 和 对话id 放入上下文 + nodeContext.put(EKG_NODE.name(), GsonUtils.toString(userNode.get())); + nodeContext.put(CHAT_UNIQUE_ID.name(), ekgRequest.getSessionId()); + } - // 执行当前 node - ExeNodeResponse enr = executeNode(node); - EkgToolResponse toolResponse = EkgToolResponse.builder() - .toolKey(enr.getToolKey()) - .toolResponse(enr.getOutput()) - .toolParam(node.getToolDescription()) - .build(); - ekgRequest.setCurrentNodeId(node.getCurrentNodeId()); - ekgRequest.setObservation(GsonUtils.toString(toolResponse)); - ekgRequest.setType(node.getType()); - ekgRequest.setIntentionData(null); - ekgRequest.setIntentionRule(null); - } - } catch (Exception e) { - LoggerUtil.error(LOGGER, e, "[EkgServiceImpl call error][{}][{}][{}][{}]", request.getChatUniqueId()); + AtomicInteger step = new AtomicInteger(1); + List> futures = response.getToolPlan().stream().map(node -> + EKG_NODE_EXE.submit(() -> { + try { + String nodeStepNum = String.format("%s.%s", stepNum, step.getAndAdd(1)); + MDC.put("stepNodeId", nodeStepNum); + LoggerUtil.info(LOGGER, "exeNode:{}", GsonUtils.toString(node)); + + EkgQueryRequest copyRequest = GsonUtils.fromString(EkgQueryRequest.class, GsonUtils.toString(ekgRequest)); + + // 需要等待用户回答，通知用户后结束任务 + if (ToolPlanTypeEnum.USER_PROBLEM.getCode().equals(node.getType())) { + EkgQuestionDescription ques = node.getQuestionDescription(); + RoleResponseContent rrc = buildRoleResponseContent( + " *请回答：* \n\n >" + ques.getQuestionContent().getQuestion()); + + List crs = ChatResponse.buildRoleResponses(rrc); + crs.get(0).setExtendContext(nodeContext); + + String userMsg = GsonUtils.toString(crs); + LoggerUtil.info(LOGGER, "notifyUser:{}", userMsg); + emitter.send(SseEmitter.event().data(userMsg)); + return false; + } + + // 执行当前 node + ExeNodeResponse enr = executeNode(node); + + EkgToolResponse toolResponse = EkgToolResponse.builder() + .toolKey(enr.getToolKey()) + .toolResponse(enr.getOutput()) + .toolParam(node.getToolDescription()) + .build(); + copyRequest.setCurrentNodeId(node.getCurrentNodeId()); + copyRequest.setObservation(GsonUtils.toString(toolResponse)); + copyRequest.setType(node.getType()); + copyRequest.setIntentionData(null); + copyRequest.setIntentionRule(null); + + return ekgHandler(emitter, copyRequest, nodeStepNum, nodeContext); + } catch (EkgToolNotFindException eetnfe) { + try { + emitter.send( + SseEmitter.event().data(GsonUtils.toString(ChatResponse.buildTextResponses(eetnfe.getMessage())))); + } catch (Exception e) { + LoggerUtil.error(LOGGER, e, "[EkgServiceImpl send exception to emitter error][{}]", + ekgRequest.getSessionId()); + } + return false; + } catch (Exception e) { + LoggerUtil.error(LOGGER, e, "exeNodeError:{}", GsonUtils.toString(node)); + return false; + } + }) + ).toList(); + + futures.parallelStream().forEach(future -> { + try { + future.get(); + } catch (InterruptedException e) { + LoggerUtil.error(LOGGER, e, "exeNodeError:InterruptedException"); + throw new RuntimeException(e); + } catch (ExecutionException e) { + LoggerUtil.error(LOGGER, e, "exeNodeError:ExecutionException"); + throw new RuntimeException(e); + } catch (Exception e) { + LoggerUtil.error(LOGGER, e, "exeNodeError:Exception"); + throw new RuntimeException(e); + } + }); + } catch (IOException e) { + LoggerUtil.error(LOGGER, e, "[EkgServiceImpl call error]"); + return false; } + + return true; } private RoleResponseContent buildRoleResponseContent(String userInteraction) { @@ -174,6 +261,20 @@ private RoleResponseContent buildRoleResponseContent(String userInteraction) { return buildLijing(userInteraction.replace("**李静:**
", "")); } else if (userInteraction.contains("**张伟:**")) { return buildZhangwei(userInteraction.replace("**张伟:**
", "")); + } else if (userInteraction.contains("**朱丽:**")) { + return buildZhuli(userInteraction.replace("**朱丽:**
", "")); + } else if (userInteraction.contains("**周杰:**")) { + return buildZhoujie(userInteraction.replace("**周杰:**
", "")); + } else if (userInteraction.contains("**沈强:**")) { + return buildShenqiang(userInteraction.replace("**沈强:**
", "")); + } else if (userInteraction.contains("**韩刚:**")) { + return buildHangang(userInteraction.replace("**韩刚:**
", "")); + } else if (userInteraction.contains("**梁军:**")) { + return buildLiangjun(userInteraction.replace("**梁军:**
", "")); + } else if (userInteraction.contains("**周欣怡:**")) { + return buildZhouxinyi(userInteraction.replace("**周欣怡:**
", "")); + } else if (userInteraction.contains("**贺子轩:**")) { + return buildHezixuan(userInteraction.replace("**贺子轩:**
", "")); } return buildReferee(userInteraction); @@ -183,10 +284,13 @@ private RoleResponseContent buildRoleResponseContent(String userInteraction) { private SchedulerManager schedulerManager; private ExeNodeResponse executeNode(EkgNode node) { + Scheduler scheduler = schedulerManager.getScheduler(TaskSchedulerTypeEnum.COMMON); + SubmitTaskRequest request = SubmitTaskRequest.builder().intention(node.getToolDescription()).build(); request.setMemory(node.getMemory()); - Scheduler scheduler = schedulerManager.getScheduler(TaskSchedulerTypeEnum.COMMON); request.setTools(config.getToolKeys()); + request.setEkgRequest(true); + TaskExeResponse toolExeResponse = scheduler.submitTask(request); ExeNodeResponse exeNodeResponse = ExeNodeResponse.builder() diff --git a/runtime/service/src/main/java/com/alipay/muagent/service/sheduler/impl/BaseScheduler.java b/runtime/service/src/main/java/com/alipay/muagent/service/sheduler/impl/BaseScheduler.java index 37eb32f..a81fded 100644 --- a/runtime/service/src/main/java/com/alipay/muagent/service/sheduler/impl/BaseScheduler.java +++ b/runtime/service/src/main/java/com/alipay/muagent/service/sheduler/impl/BaseScheduler.java @@ -5,6 +5,7 @@ package com.alipay.muagent.service.sheduler.impl; import com.alipay.muagent.model.enums.scheduler.TaskSchedulerTypeEnum; +import com.alipay.muagent.model.exception.EkgToolNotFindException; import com.alipay.muagent.model.scheduler.SubmitTaskRequest; import com.alipay.muagent.model.scheduler.TaskExeResponse; import com.alipay.muagent.model.tool.TaskExeContext; @@ -16,6 +17,7 @@ import com.alipay.muagent.service.tool.loader.ToolLoader; import com.alipay.muagent.util.GsonUtils; import com.alipay.muagent.util.LoggerUtil; +import com.alipay.muagent.util.StringUtils; import lombok.Data; import org.springframework.beans.factory.InitializingBean; import org.springframework.beans.factory.annotation.Autowired; @@ -72,13 +74,33 @@ protected Tool selectTool(TaskExeContext request) { LoggerUtil.info(LOGGER, "selectedTool:{}", GsonUtils.toString(selectResponse)); String resultStr = selectResponse.getResult(); try { + if (StringUtils.equalNull(resultStr)) { + return getTool(request); + } SelectToolResult result = GsonUtils.fromString(SelectToolResult.class, resultStr); + if (StringUtils.isEmpty(result.getToolKey()) || StringUtils.equalNull(resultStr)) { + return getTool(request); + } return toolLoader.queryToolByKey(result.getToolKey()); + } catch (EkgToolNotFindException e) { + throw e; } catch (Exception e) { throw new RuntimeException("selectToolResponseInvalid:" + resultStr, e); } } + private Tool getTool(TaskExeContext request) { + if (request.getTaskRequest().isEkgRequest()) { + // ekg 不能兜底，直接报错提示 tool 找不到 + //throw new EkgToolNotFindException("can not select a tool with intention " + request.getTaskRequest().getIntention()); + throw new EkgToolNotFindException("对不起，根据您的意图，无法找到对应的工具来帮助您，请添加相关工具后再使用图谱。"); + } else { + // 闲聊大模型兜底 + Tool tool = toolLoader.queryToolByKey("system.llm_query"); + return tool; + } + } + private String generateSelectPrompt(List tools, String intention) { StringBuilder sb = new StringBuilder("你现在是一个插件选择助手，需要理解问题描述，然后从以下给出的插件中选择一个可以解决问题描述的插件").append("\n\n"); sb.append("##插件列表：").append("\n"); @@ -112,4 +134,4 @@ class SelectToolResult { private String toolKey; } -} \ No newline at end of file +} diff --git a/runtime/util/src/main/java/com/alipay/muagent/util/StringUtils.java b/runtime/util/src/main/java/com/alipay/muagent/util/StringUtils.java index 9e9c174..4c4d4ee 100644 --- a/runtime/util/src/main/java/com/alipay/muagent/util/StringUtils.java +++ b/runtime/util/src/main/java/com/alipay/muagent/util/StringUtils.java @@ -41,6 +41,10 @@ public static String truncate(String string, int maxLength) { return string.length() > maxLength ? string.substring(0, maxLength) : string; } + public static boolean equalNull(String str) { + return "null".equalsIgnoreCase(str); + } + public static String truncate(String string, int maxLength, String truncationIndicator) { if (truncationIndicator.length() >= maxLength) { throw new IllegalArgumentException("maxLength must be greater than length of truncationIndicator"); diff --git a/runtime/web/src/main/java/com/alipay/muagent/web/interceptor/WebConfigure.java b/runtime/web/src/main/java/com/alipay/muagent/web/interceptor/WebConfigure.java index da68819..419792a 100644 --- a/runtime/web/src/main/java/com/alipay/muagent/web/interceptor/WebConfigure.java +++ b/runtime/web/src/main/java/com/alipay/muagent/web/interceptor/WebConfigure.java @@ -19,7 +19,7 @@ public class WebConfigure implements WebMvcConfigurer { public void addCorsMappings(CorsRegistry registry) { registry.addMapping("/**") - .allowedOrigins("http://30.183.177.8","http://localhost:8000") + .allowedOrigins("http://localhost:8000") .allowedMethods("GET","HEAD","PUT","POST","PATCH","DELETE","OPTIONS") .allowedHeaders("*") .allowCredentials(true); diff --git a/tests/service/test_main_sswd_long.py b/tests/service/test_main_sswd_long.py index 8c5d53e..72e211c 100644 --- a/tests/service/test_main_sswd_long.py +++ b/tests/service/test_main_sswd_long.py @@ -32,9 +32,9 @@ from loguru import logger import uuid -#geabase 依赖包 -from gdbc2.geabase_client import GeaBaseClient#, Node, Edge, MutateBatchOperation, GeaBaseUtil -from gdbc2.geabase_env import GeaBaseEnv +# #geabase 依赖包 +# from gdbc2.geabase_client import GeaBaseClient#, Node, Edge, MutateBatchOperation, GeaBaseUtil +# from gdbc2.geabase_env import GeaBaseEnv #muagent 依赖包 from muagent.connector.schema import Message @@ -72,11 +72,16 @@ if __name__ == '__main__': #1、 LLM 和 Embedding Model 配置 llm_config = LLMConfig( - model_name=os.environ["model_name"], api_key=os.environ["OPENAI_API_KEY"], - api_base_url=os.environ["API_BASE_URL"], temperature=0.3 - ) - - + model_name =os.environ["model_name"], + model_engine =os.environ["model_engine"], + api_key =os.environ["OPENAI_API_KEY"], + api_base_url =os.environ["API_BASE_URL"], + temperature =float(os.environ["llm_temperature"]), + ) + + #llm = CustomizedModel() + #llm_config_modelops = LLMConfig(llm=llm) + # 2、自定义向量模型配置接口 embeddings = None #embeddings = CustomizedEmbeddings() @@ -84,34 +89,56 @@ embed_model="default", langchain_embeddings=embeddings ) - - - # 3、tbase接口配置 - tb_config = TBConfig( - tb_type = os.environ["tb_type"], - index_name = os.environ["tb_index_name"], - host = os.environ['tb_host'], - port = int(os.environ['tb_port']), - username = os.environ['tb_username'], - password = os.environ['tb_password'], - extra_kwargs={ - 'host': os.environ['tb_host'], - 'port': int(os.environ['tb_port']), - 'username': os.environ['tb_username'], - 'password': os.environ['tb_password'], - 'definition_value': os.environ['tb_definition_value'], - 'expire_time': int(os.environ['tb_expire_time']) , - } - ) - + + # 3、tbase接口配置 + if os.environ['operation_mode'] == 'antcode': # 'open_source' or 'antcode' + tb_config = TBConfig( + tb_type = os.environ["tb_type"], + index_name = os.environ["tb_index_name"], + host = os.environ['tb_host'], + port = int(os.environ['tb_port']), + username = os.environ['tb_username'], + password = os.environ['tb_password'], + extra_kwargs={ + 'host': os.environ['tb_host'], + 'port': int(os.environ['tb_port']), + 'username': os.environ['tb_username'], + 'password': os.environ['tb_password'], + 'definition_value': os.environ['tb_definition_value'], + 'expire_time': int(os.environ['tb_expire_time']) , + } + ) + else: + # 初始化 TbaseHandler 实例 + tb_config = TBConfig( + tb_type="TbaseHandler", + index_name="shanshi_node", + host=os.environ['tb_host'], + port=os.environ['tb_port'], + username=os.environ['tb_username'], + password=os.environ['tb_password'], + extra_kwargs={ + 'host': os.environ['tb_host'], + 'port': os.environ['tb_port'], + 'username': os.environ['tb_username'] , + 'password': os.environ['tb_password'], + 'definition_value': os.environ['tb_definition_value'] + } + ) # 指定index_name index_name = os.environ["tb_index_name"] + # th = TbaseHandler(TBASE_ARGS, index_name, definition_value="message") + # th = TbaseHandler(tb_config, index_name, definition_value="message") + # th = TbaseHandler(tb_config, index_name, definition_value="message_test_new") th = TbaseHandler(tb_config, index_name, definition_value=os.environ['tb_definition_value']) - - - - + + # # drop index + # th.drop_index(index_name) + + + + # 5、memory 接口配置 # create tbase memory manager memory_manager = TbaseMemoryManager( @@ -121,27 +148,44 @@ tbase_handler=th, use_vector=False ) - - - + + + #6 geabase 接口配置 - gb_config = GBConfig( - gb_type="GeaBaseHandler", - extra_kwargs={ - 'metaserver_address': os.environ['metaserver_address'], - 'project': os.environ['project'], - 'city': os.environ['city'], - # 'lib_path': '%s/geabase/geabase-client-0.0.1/libs'%(tar_path), - 'lib_path': os.environ['lib_path'] - } - ) - + if os.environ['operation_mode'] == 'antcode': # 'open_source' or 'antcode' + gb_config = GBConfig( + gb_type="GeaBaseHandler", + extra_kwargs={ + 'metaserver_address': os.environ['metaserver_address'], + 'project': os.environ['project'], + 'city': os.environ['city'], + # 'lib_path': '%s/geabase/geabase-client-0.0.1/libs'%(tar_path), + 'lib_path': os.environ['lib_path'] + } + ) + else: + # 初始化 NebulaHandler 实例 + gb_config = GBConfig( + gb_type="NebulaHandler", + extra_kwargs={ + 'host': os.environ['nb_host'], + 'port': os.environ['nb_port'], + 'username': os.environ['nb_username'] , + 'password': os.environ['nb_password'], + "space": os.environ['nb_space'], + #'definition_value': os.environ['nb_definition_value'] + + } + ) + #7 构建ekg_construct_service + ekg_construct_service = EKGConstructService( embed_config = embed_config, llm_config = llm_config, tb_config = tb_config, gb_config = gb_config, + initialize_space = False ) intention_router = IntentionRouter( @@ -150,15 +194,21 @@ ekg_construct_service.tb, embed_config ) - + #8 获取main需要的内容 memory_manager = memory_manager - geabase_handler = GeaBaseHandler(gb_config) + #geabase_handler = GeaBaseHandler(gb_config) + geabase_handler = ekg_construct_service.gb intention_router = intention_router + + + - goc_test_sessionId = "TS_GOC_103346456601_0709001_sswd_05" + + goc_test_sessionId = "TS_GOC_103346456601_1127001_sswd_099" + #debugmode 调试，谁是卧底初次输入 @@ -238,7 +288,7 @@ time.sleep(1) ''' - {'intentionRecognitionSituation': 'None', 'sessionId': 'TS_GOC_103346456601_0709001_lrs_105', 'type': 'onlyTool', 'summary': None, 'toolPlan': [{'toolDescription': 'agent_2', 'currentNodeId': '剧本杀/谁是卧底/智能交互/开始新一轮的讨论', 'memory': '[{"role_type": "user", "role_name": "firstUserInput", "role_content": "{\\"content\\": \\"一起来玩谁是卧底\\"}"}, {"role_type": "observation", "role_name": "function_caller", "role_content": "{\\"座位分配结果\\": [{\\"player_name\\": \\"player_1\\", \\"seat_number\\": 1}, {\\"player_name\\": \\"李四(人类玩家)\\", \\"seat_number\\": 2}, {\\"player_name\\": \\"player_2\\", \\"seat_number\\": 3}, {\\"player_name\\": \\"player_3\\", \\"seat_number\\": 4}]}"}, {"role_type": "userinput", "role_name": "user", "role_content": "分配座位"}, {"role_type": "userinput", "role_name": "user", "role_content": "通知身份"}, "主持人 : 你是player_1, 你的位置是1号， 你分配的单词是汽车", {"role_type": "userinput", "role_name": "user", "role_content": "开始新一轮的讨论"}, "主持人 : 当前存活的玩家有4位，他们是player_1, 李四(人类玩家), player_2, player_3", "主持人 : 现在我们开始发言环节，按照座位顺序由小到大进行发言，首先是1号位的player_1"]', 'type': 'reactExecution'}], 'userInteraction': '["通知身份", "主持人 : 你是李四(人类玩家), 你的位置是2号， 你分配的单词是汽车", "开始新一轮的讨论", "主持人 : 现在我们开始发言环节，按照座位顺序由小到大进行发言，首先是1号位的player_1", "主持人 : 当前存活的玩家有4位，他们是player_1, 李四(人类玩家), player_2, player_3"]'} + {'intentionRecognitionSituation': 'None', 'sessionId': 'goc_test_sessionId', 'type': 'onlyTool', 'summary': None, 'toolPlan': [{'toolDescription': 'agent_2', 'currentNodeId': '剧本杀/谁是卧底/智能交互/开始新一轮的讨论', 'memory': '[{"role_type": "user", "role_name": "firstUserInput", "role_content": "{\\"content\\": \\"一起来玩谁是卧底\\"}"}, {"role_type": "observation", "role_name": "function_caller", "role_content": "{\\"座位分配结果\\": [{\\"player_name\\": \\"player_1\\", \\"seat_number\\": 1}, {\\"player_name\\": \\"李四(人类玩家)\\", \\"seat_number\\": 2}, {\\"player_name\\": \\"player_2\\", \\"seat_number\\": 3}, {\\"player_name\\": \\"player_3\\", \\"seat_number\\": 4}]}"}, {"role_type": "userinput", "role_name": "user", "role_content": "分配座位"}, {"role_type": "userinput", "role_name": "user", "role_content": "通知身份"}, "主持人 : 你是player_1, 你的位置是1号， 你分配的单词是汽车", {"role_type": "userinput", "role_name": "user", "role_content": "开始新一轮的讨论"}, "主持人 : 当前存活的玩家有4位，他们是player_1, 李四(人类玩家), player_2, player_3", "主持人 : 现在我们开始发言环节，按照座位顺序由小到大进行发言，首先是1号位的player_1"]', 'type': 'reactExecution'}], 'userInteraction': '["通知身份", "主持人 : 你是李四(人类玩家), 你的位置是2号， 你分配的单词是汽车", "开始新一轮的讨论", "主持人 : 现在我们开始发言环节，按照座位顺序由小到大进行发言，首先是1号位的player_1", "主持人 : 当前存活的玩家有4位，他们是player_1, 李四(人类玩家), player_2, player_3"]'} ''' #step 4 剧本杀/谁是卧底/智能交互/关键信息_1 params_string =\ @@ -353,6 +403,9 @@ print('UNDERCOVER', res_to_lingsi) + + + # #======================== # ## step 7-1 第二轮讨论输入 # params_string =\