diff --git a/src/common/logger.py b/src/common/logger.py index a8fcd6603..29be8c756 100644 --- a/src/common/logger.py +++ b/src/common/logger.py @@ -81,13 +81,15 @@ MEMORY_STYLE_CONFIG = { "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"), }, "simple": { - "console_format": ("{time:MM-DD HH:mm} | 海马体 | {message}"), + "console_format": ( + "{time:MM-DD HH:mm} | 海马体 | {message}" + ), "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 海马体 | {message}"), }, } -#MOOD +# MOOD MOOD_STYLE_CONFIG = { "advanced": { "console_format": ( @@ -152,7 +154,9 @@ HEARTFLOW_STYLE_CONFIG = { "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦大脑袋 | {message}"), }, "simple": { - "console_format": ("{time:MM-DD HH:mm} | 麦麦大脑袋 | {message}"), # noqa: E501 + "console_format": ( + "{time:MM-DD HH:mm} | 麦麦大脑袋 | {message}" + ), # noqa: E501 "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦大脑袋 | {message}"), }, } @@ -223,7 +227,9 @@ CHAT_STYLE_CONFIG = { "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 见闻 | {message}"), }, "simple": { - "console_format": ("{time:MM-DD HH:mm} | 见闻 | {message}"), # noqa: E501 + "console_format": ( + "{time:MM-DD HH:mm} | 见闻 | {message}" + ), # noqa: E501 "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 见闻 | {message}"), }, } @@ -240,7 +246,9 @@ SUB_HEARTFLOW_STYLE_CONFIG = { "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"), }, "simple": { - "console_format": ("{time:MM-DD HH:mm} | 麦麦小脑袋 | {message}"), # noqa: E501 + "console_format": ( + "{time:MM-DD HH:mm} | 麦麦小脑袋 | {message}" + ), # noqa: E501 "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 麦麦小脑袋 | {message}"), }, } @@ -257,17 +265,17 @@ WILLING_STYLE_CONFIG = { "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"), }, "simple": { - "console_format": ("{time:MM-DD HH:mm} | 意愿 | {message}"), # noqa: E501 + "console_format": ( + "{time:MM-DD HH:mm} | 意愿 | {message}" + ), # noqa: E501 "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"), }, } - - # 根据SIMPLE_OUTPUT选择配置 MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"] -TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_STYLE_CONFIG["advanced"] +TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_STYLE_CONFIG["advanced"] SENDER_STYLE_CONFIG = SENDER_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SENDER_STYLE_CONFIG["advanced"] LLM_STYLE_CONFIG = LLM_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else LLM_STYLE_CONFIG["advanced"] CHAT_STYLE_CONFIG = CHAT_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CHAT_STYLE_CONFIG["advanced"] @@ -275,7 +283,9 @@ MOOD_STYLE_CONFIG = MOOD_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MOOD_STYLE RELATION_STYLE_CONFIG = RELATION_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else RELATION_STYLE_CONFIG["advanced"] SCHEDULE_STYLE_CONFIG = SCHEDULE_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SCHEDULE_STYLE_CONFIG["advanced"] HEARTFLOW_STYLE_CONFIG = HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else HEARTFLOW_STYLE_CONFIG["advanced"] -SUB_HEARTFLOW_STYLE_CONFIG = SUB_HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SUB_HEARTFLOW_STYLE_CONFIG["advanced"] # noqa: E501 +SUB_HEARTFLOW_STYLE_CONFIG = ( + SUB_HEARTFLOW_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SUB_HEARTFLOW_STYLE_CONFIG["advanced"] +) # noqa: E501 WILLING_STYLE_CONFIG = WILLING_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else WILLING_STYLE_CONFIG["advanced"] diff --git a/src/gui/reasoning_gui.py b/src/gui/reasoning_gui.py index 9a35e8142..d018216a2 100644 --- a/src/gui/reasoning_gui.py +++ b/src/gui/reasoning_gui.py @@ -6,8 +6,9 @@ import time from datetime import datetime from typing import Dict, List from typing import Optional -sys.path.insert(0, sys.path[0]+"/../") -sys.path.insert(0, sys.path[0]+"/../") + +sys.path.insert(0, sys.path[0] + "/../") +sys.path.insert(0, sys.path[0] + "/../") from src.common.logger import get_module_logger import customtkinter as ctk diff --git a/src/main.py b/src/main.py index 4f0361998..1395273d4 100644 --- a/src/main.py +++ b/src/main.py @@ -90,8 +90,8 @@ class MainSystem: # 启动心流系统 asyncio.create_task(heartflow.heartflow_start_working()) logger.success("心流系统启动成功") - - init_time = int(1000*(time.time()- init_start_time)) + + init_time = int(1000 * (time.time() - init_start_time)) logger.success(f"初始化完成，神经元放电{init_time}次") except Exception as e: logger.error(f"启动大脑和外部世界失败: {e}") diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py index d043204e0..a94b88fda 100644 --- a/src/plugins/chat/bot.py +++ b/src/plugins/chat/bot.py @@ -56,7 +56,7 @@ class ChatBot: 5. 更新关系 6. 更新情绪 """ - + message = MessageRecv(message_data) groupinfo = message.message_info.group_info userinfo = message.message_info.user_info @@ -68,7 +68,7 @@ class ChatBot: chat = await chat_manager.get_or_create_stream( platform=messageinfo.platform, user_info=userinfo, - group_info=groupinfo, + group_info=groupinfo, ) message.update_chat_stream(chat) @@ -81,15 +81,12 @@ class ChatBot: logger.debug(f"2消息处理时间: {timer2 - timer1}秒") # 过滤词/正则表达式过滤 - if ( - self._check_ban_words(message.processed_plain_text, chat, userinfo) - or self._check_ban_regex(message.raw_message, chat, userinfo) + if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex( + message.raw_message, chat, userinfo ): return - await self.storage.store_message(message, chat) - timer1 = time.time() interested_rate = 0 @@ -99,7 +96,6 @@ class ChatBot: timer2 = time.time() logger.debug(f"3记忆激活时间: {timer2 - timer1}秒") - is_mentioned = is_mentioned_bot_in_message(message) if global_config.enable_think_flow: @@ -124,17 +120,17 @@ class ChatBot: timer2 = time.time() logger.debug(f"4计算意愿激活时间: {timer2 - timer1}秒") - #神秘的消息流数据结构处理 + # 神秘的消息流数据结构处理 if chat.group_info: if chat.group_info.group_name: mes_name_dict = chat.group_info.group_name - mes_name = mes_name_dict.get('group_name', '无名群聊') + mes_name = mes_name_dict.get("group_name", "无名群聊") else: - mes_name = '群聊' + mes_name = "群聊" else: - mes_name = '私聊' - - #打印收到的信息的信息 + mes_name = "私聊" + + # 打印收到的信息的信息 current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time)) logger.info( f"[{current_time}][{mes_name}]" @@ -145,48 +141,47 @@ class ChatBot: if message.message_info.additional_config: if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys(): reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"] - - + # 开始组织语言 if random() < reply_probability: timer1 = time.time() response_set, thinking_id = await self._generate_response_from_message(message, chat, userinfo, messageinfo) timer2 = time.time() logger.info(f"5生成回复时间: {timer2 - timer1}秒") - + if not response_set: logger.info("为什么生成回复失败？") return - + # 发送消息 timer1 = time.time() await self._send_response_messages(message, chat, response_set, thinking_id) timer2 = time.time() logger.info(f"7发送消息时间: {timer2 - timer1}秒") - + # 处理表情包 timer1 = time.time() await self._handle_emoji(message, chat, response_set) timer2 = time.time() logger.debug(f"8处理表情包时间: {timer2 - timer1}秒") - + timer1 = time.time() await self._update_using_response(message, chat, response_set) timer2 = time.time() logger.info(f"6更新htfl时间: {timer2 - timer1}秒") - + # 更新情绪和关系 # await self._update_emotion_and_relationship(message, chat, response_set) async def _generate_response_from_message(self, message, chat, userinfo, messageinfo): """生成回复内容 - + Args: message: 接收到的消息 chat: 聊天流对象 userinfo: 用户信息对象 messageinfo: 消息信息对象 - + Returns: tuple: (response, raw_content) 回复内容和原始内容 """ @@ -195,7 +190,7 @@ class ChatBot: user_nickname=global_config.BOT_NICKNAME, platform=messageinfo.platform, ) - + thinking_time_point = round(time.time(), 2) thinking_id = "mt" + str(thinking_time_point) thinking_message = MessageThinking( @@ -208,9 +203,9 @@ class ChatBot: message_manager.add_message(thinking_message) willing_manager.change_reply_willing_sent(chat) - + response_set = await self.gpt.generate_response(message) - + return response_set, thinking_id async def _update_using_response(self, message, response_set): @@ -221,14 +216,13 @@ class ChatBot: chat_talking_prompt = get_recent_group_detailed_plain_text( stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True ) - - heartflow.get_subheartflow(stream_id).do_after_reply(response_set, chat_talking_prompt) + heartflow.get_subheartflow(stream_id).do_after_reply(response_set, chat_talking_prompt) async def _send_response_messages(self, message, chat, response_set, thinking_id): container = message_manager.get_container(chat.stream_id) thinking_message = None - + # logger.info(f"开始发送消息准备") for msg in container.messages: if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: @@ -243,7 +237,7 @@ class ChatBot: # logger.info(f"开始发送消息") thinking_start_time = thinking_message.thinking_start_time message_set = MessageSet(chat, thinking_id) - + mark_head = False for msg in response_set: message_segment = Seg(type="text", data=msg) @@ -270,7 +264,7 @@ class ChatBot: async def _handle_emoji(self, message, chat, response): """处理表情包 - + Args: message: 接收到的消息 chat: 聊天流对象 @@ -281,10 +275,10 @@ class ChatBot: if emoji_raw: emoji_path, description = emoji_raw emoji_cq = image_path_to_base64(emoji_path) - + thinking_time_point = round(message.message_info.time, 2) bot_response_time = thinking_time_point + (1 if random() < 0.5 else -1) - + message_segment = Seg(type="emoji", data=emoji_cq) bot_message = MessageSending( message_id="mt" + str(thinking_time_point), @@ -304,7 +298,7 @@ class ChatBot: async def _update_emotion_and_relationship(self, message, chat, response, raw_content): """更新情绪和关系 - + Args: message: 接收到的消息 chat: 聊天流对象 @@ -313,27 +307,24 @@ class ChatBot: """ stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text) logger.debug(f"为 '{response}' 立场为：{stance} 获取到的情感标签为：{emotion}") - await relationship_manager.calculate_update_relationship_value( - chat_stream=chat, label=emotion, stance=stance - ) + await relationship_manager.calculate_update_relationship_value(chat_stream=chat, label=emotion, stance=stance) self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor) def _check_ban_words(self, text: str, chat, userinfo) -> bool: """检查消息中是否包含过滤词 - + Args: text: 要检查的文本 chat: 聊天流对象 userinfo: 用户信息对象 - + Returns: bool: 如果包含过滤词返回True，否则返回False """ for word in global_config.ban_words: if word in text: logger.info( - f"[{chat.group_info.group_name if chat.group_info else '私聊'}]" - f"{userinfo.user_nickname}:{text}" + f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}" ) logger.info(f"[过滤词识别]消息中含有{word}，filtered") return True @@ -341,24 +332,24 @@ class ChatBot: def _check_ban_regex(self, text: str, chat, userinfo) -> bool: """检查消息是否匹配过滤正则表达式 - + Args: text: 要检查的文本 chat: 聊天流对象 userinfo: 用户信息对象 - + Returns: bool: 如果匹配过滤正则返回True，否则返回False """ for pattern in global_config.ban_msgs_regex: if re.search(pattern, text): logger.info( - f"[{chat.group_info.group_name if chat.group_info else '私聊'}]" - f"{userinfo.user_nickname}:{text}" + f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}" ) logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered") return True return False + # 创建全局ChatBot实例 chat_bot = ChatBot() diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index 7c42d4bff..18a54b1ec 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -343,7 +343,7 @@ class EmojiManager: while True: logger.info("[扫描] 开始扫描新表情包...") await self.scan_new_emojis() - await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60) + await asyncio.sleep(global_config.EMOJI_CHECK_INTERVAL * 60) def check_emoji_file_integrity(self): """检查表情包文件完整性 diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index 10d73b8f1..c5b2d197d 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -31,12 +31,9 @@ class ResponseGenerator: request_type="response", ) self.model_normal = LLM_request( - model=global_config.llm_normal, - temperature=0.7, - max_tokens=3000, - request_type="response" + model=global_config.llm_normal, temperature=0.7, max_tokens=3000, request_type="response" ) - + self.model_sum = LLM_request( model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=3000, request_type="relation" ) @@ -53,8 +50,9 @@ class ResponseGenerator: self.current_model_type = "浅浅的" current_model = self.model_normal - logger.info(f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}") # noqa: E501 - + logger.info( + f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}" + ) # noqa: E501 model_response = await self._generate_response_with_model(message, current_model) @@ -64,7 +62,6 @@ class ResponseGenerator: logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}") model_response = await self._process_response(model_response) - return model_response else: logger.info(f"{self.current_model_type}思考，失败") @@ -93,7 +90,7 @@ class ResponseGenerator: ) timer2 = time.time() logger.info(f"构建prompt时间: {timer2 - timer1}秒") - + try: content, reasoning_content, self.current_model_name = await model.generate_response(prompt) except Exception: diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index bad09d87d..ea81a14c8 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -37,7 +37,6 @@ class PromptBuilder: current_mind_info = heartflow.get_subheartflow(stream_id).current_mind - # relation_prompt = "" # for person in who_chat_in_group: # relation_prompt += relationship_manager.build_relationship_info(person) @@ -52,7 +51,7 @@ class PromptBuilder: # 心情 mood_manager = MoodManager.get_instance() mood_prompt = mood_manager.get_prompt() - + logger.info(f"心情prompt: {mood_prompt}") # 日程构建 @@ -72,13 +71,12 @@ class PromptBuilder: chat_in_group = False chat_talking_prompt = chat_talking_prompt # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}") - # 使用新的记忆获取方法 memory_prompt = "" start_time = time.time() - #调用 hippocampus 的 get_relevant_memories 方法 + # 调用 hippocampus 的 get_relevant_memories 方法 relevant_memories = await HippocampusManager.get_instance().get_memory_from_text( text=message_txt, max_memory_num=3, max_memory_length=2, max_depth=2, fast_retrieval=False ) @@ -165,11 +163,8 @@ class PromptBuilder: 请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。 {moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""" - return prompt - - def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1): current_date = time.strftime("%Y-%m-%d", time.localtime()) current_time = time.strftime("%H:%M:%S", time.localtime()) diff --git a/src/plugins/chat/storage.py b/src/plugins/chat/storage.py index 555ac997c..7275722da 100644 --- a/src/plugins/chat/storage.py +++ b/src/plugins/chat/storage.py @@ -9,9 +9,7 @@ logger = get_module_logger("message_storage") class MessageStorage: - async def store_message( - self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream - ) -> None: + async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None: """存储消息到数据库""" try: message_data = { diff --git a/src/plugins/memory_system/Hippocampus.py b/src/plugins/memory_system/Hippocampus.py index 0032fe886..aff35f002 100644 --- a/src/plugins/memory_system/Hippocampus.py +++ b/src/plugins/memory_system/Hippocampus.py @@ -11,7 +11,7 @@ from collections import Counter from ...common.database import db from ...plugins.models.utils_model import LLM_request from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG -from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler #分布生成器 +from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler # 分布生成器 from .memory_config import MemoryConfig @@ -56,6 +56,7 @@ def get_closest_chat_from_db(length: int, timestamp: str): return [] + def calculate_information_content(text): """计算文本的信息量（熵）""" char_count = Counter(text) @@ -68,6 +69,7 @@ def calculate_information_content(text): return entropy + def cosine_similarity(v1, v2): """计算余弦相似度""" dot_product = np.dot(v1, v2) @@ -223,7 +225,8 @@ class Memory_graph: return None -#负责海马体与其他部分的交互 + +# 负责海马体与其他部分的交互 class EntorhinalCortex: def __init__(self, hippocampus): self.hippocampus = hippocampus @@ -243,7 +246,7 @@ class EntorhinalCortex: n_hours2=self.config.memory_build_distribution[3], std_hours2=self.config.memory_build_distribution[4], weight2=self.config.memory_build_distribution[5], - total_samples=self.config.build_memory_sample_num + total_samples=self.config.build_memory_sample_num, ) timestamps = sample_scheduler.get_timestamp_array() @@ -251,9 +254,7 @@ class EntorhinalCortex: chat_samples = [] for timestamp in timestamps: messages = self.random_get_msg_snippet( - timestamp, - self.config.build_memory_sample_length, - max_memorized_time_per_msg + timestamp, self.config.build_memory_sample_length, max_memorized_time_per_msg ) if messages: time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600 @@ -455,25 +456,25 @@ class EntorhinalCortex: """清空数据库并重新同步所有记忆数据""" start_time = time.time() logger.info("[数据库] 开始重新同步所有记忆数据...") - + # 清空数据库 clear_start = time.time() db.graph_data.nodes.delete_many({}) db.graph_data.edges.delete_many({}) clear_end = time.time() logger.info(f"[数据库] 清空数据库耗时: {clear_end - clear_start:.2f}秒") - + # 获取所有节点和边 memory_nodes = list(self.memory_graph.G.nodes(data=True)) memory_edges = list(self.memory_graph.G.edges(data=True)) - + # 重新写入节点 node_start = time.time() for concept, data in memory_nodes: memory_items = data.get("memory_items", []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] - + node_data = { "concept": concept, "memory_items": memory_items, @@ -484,7 +485,7 @@ class EntorhinalCortex: db.graph_data.nodes.insert_one(node_data) node_end = time.time() logger.info(f"[数据库] 写入 {len(memory_nodes)} 个节点耗时: {node_end - node_start:.2f}秒") - + # 重新写入边 edge_start = time.time() for source, target, data in memory_edges: @@ -499,12 +500,13 @@ class EntorhinalCortex: db.graph_data.edges.insert_one(edge_data) edge_end = time.time() logger.info(f"[数据库] 写入 {len(memory_edges)} 条边耗时: {edge_end - edge_start:.2f}秒") - + end_time = time.time() logger.success(f"[数据库] 重新同步完成，总耗时: {end_time - start_time:.2f}秒") logger.success(f"[数据库] 同步了 {len(memory_nodes)} 个节点和 {len(memory_edges)} 条边") -#负责整合，遗忘，合并记忆 + +# 负责整合，遗忘，合并记忆 class ParahippocampalGyrus: def __init__(self, hippocampus): self.hippocampus = hippocampus @@ -567,26 +569,26 @@ class ParahippocampalGyrus: topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate) topics_response = await self.hippocampus.llm_topic_judge.generate_response( - self.hippocampus.find_topic_llm(input_text, topic_num)) + self.hippocampus.find_topic_llm(input_text, topic_num) + ) # 使用正则表达式提取<>中的内容 - topics = re.findall(r'<([^>]+)>', topics_response[0]) - + topics = re.findall(r"<([^>]+)>", topics_response[0]) + # 如果没有找到<>包裹的内容，返回['none'] if not topics: - topics = ['none'] + topics = ["none"] else: # 处理提取出的话题 topics = [ topic.strip() - for topic in ','.join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") + for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip() ] # 过滤掉包含禁用关键词的topic filtered_topics = [ - topic for topic in topics - if not any(keyword in topic for keyword in self.config.memory_ban_words) + topic for topic in topics if not any(keyword in topic for keyword in self.config.memory_ban_words) ] logger.debug(f"过滤后话题: {filtered_topics}") @@ -601,12 +603,12 @@ class ParahippocampalGyrus: # 等待所有任务完成 compressed_memory = set() similar_topics_dict = {} - + for topic, task in tasks: response = await task if response: compressed_memory.add((topic, response[0])) - + existing_topics = list(self.memory_graph.G.nodes()) similar_topics = [] @@ -651,7 +653,7 @@ class ParahippocampalGyrus: current_time = datetime.datetime.now().timestamp() logger.debug(f"添加节点: {', '.join(topic for topic, _ in compressed_memory)}") all_added_nodes.extend(topic for topic, _ in compressed_memory) - + for topic, memory in compressed_memory: self.memory_graph.add_dot(topic, memory) all_topics.append(topic) @@ -661,13 +663,13 @@ class ParahippocampalGyrus: for similar_topic, similarity in similar_topics: if topic != similar_topic: strength = int(similarity * 10) - + logger.debug(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})") all_added_edges.append(f"{topic}-{similar_topic}") - + all_connected_nodes.append(topic) all_connected_nodes.append(similar_topic) - + self.memory_graph.G.add_edge( topic, similar_topic, @@ -685,14 +687,11 @@ class ParahippocampalGyrus: logger.success(f"更新记忆: {', '.join(all_added_nodes)}") logger.debug(f"强化连接: {', '.join(all_added_edges)}") logger.info(f"强化连接节点: {', '.join(all_connected_nodes)}") - + await self.hippocampus.entorhinal_cortex.sync_memory_to_db() - + end_time = time.time() - logger.success( - f"---------------------记忆构建耗时: {end_time - start_time:.2f} " - "秒---------------------" - ) + logger.success(f"---------------------记忆构建耗时: {end_time - start_time:.2f} 秒---------------------") async def operation_forget_topic(self, percentage=0.005): start_time = time.time() @@ -714,11 +713,11 @@ class ParahippocampalGyrus: # 使用列表存储变化信息 edge_changes = { "weakened": [], # 存储减弱的边 - "removed": [] # 存储移除的边 + "removed": [], # 存储移除的边 } node_changes = { - "reduced": [], # 存储减少记忆的节点 - "removed": [] # 存储移除的节点 + "reduced": [], # 存储减少记忆的节点 + "removed": [], # 存储移除的节点 } current_time = datetime.datetime.now().timestamp() @@ -771,35 +770,40 @@ class ParahippocampalGyrus: if any(edge_changes.values()) or any(node_changes.values()): sync_start = time.time() - + await self.hippocampus.entorhinal_cortex.resync_memory_to_db() - + sync_end = time.time() logger.info(f"[遗忘] 数据库同步耗时: {sync_end - sync_start:.2f}秒") - + # 汇总输出所有变化 logger.info("[遗忘] 遗忘操作统计:") if edge_changes["weakened"]: logger.info( - f"[遗忘] 减弱的连接 ({len(edge_changes['weakened'])}个): {', '.join(edge_changes['weakened'])}") - + f"[遗忘] 减弱的连接 ({len(edge_changes['weakened'])}个): {', '.join(edge_changes['weakened'])}" + ) + if edge_changes["removed"]: logger.info( - f"[遗忘] 移除的连接 ({len(edge_changes['removed'])}个): {', '.join(edge_changes['removed'])}") - + f"[遗忘] 移除的连接 ({len(edge_changes['removed'])}个): {', '.join(edge_changes['removed'])}" + ) + if node_changes["reduced"]: logger.info( - f"[遗忘] 减少记忆的节点 ({len(node_changes['reduced'])}个): {', '.join(node_changes['reduced'])}") - + f"[遗忘] 减少记忆的节点 ({len(node_changes['reduced'])}个): {', '.join(node_changes['reduced'])}" + ) + if node_changes["removed"]: logger.info( - f"[遗忘] 移除的节点 ({len(node_changes['removed'])}个): {', '.join(node_changes['removed'])}") + f"[遗忘] 移除的节点 ({len(node_changes['removed'])}个): {', '.join(node_changes['removed'])}" + ) else: logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件") end_time = time.time() logger.info(f"[遗忘] 总耗时: {end_time - start_time:.2f}秒") + # 海马体 class Hippocampus: def __init__(self): @@ -817,8 +821,8 @@ class Hippocampus: self.parahippocampal_gyrus = ParahippocampalGyrus(self) # 从数据库加载记忆图 self.entorhinal_cortex.sync_memory_from_db() - self.llm_topic_judge = LLM_request(self.config.llm_topic_judge,request_type="memory") - self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic,request_type="memory") + self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory") + self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory") def get_all_node_names(self) -> list: """获取记忆图中所有节点的名字列表""" @@ -901,16 +905,21 @@ class Hippocampus: memory_items = node_data.get("memory_items", []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] - + memories.append((node, memory_items, similarity)) # 按相似度降序排序 memories.sort(key=lambda x: x[2], reverse=True) return memories - async def get_memory_from_text(self, text: str, max_memory_num: int = 3, max_memory_length: int = 2, - max_depth: int = 3, - fast_retrieval: bool = False) -> list: + async def get_memory_from_text( + self, + text: str, + max_memory_num: int = 3, + max_memory_length: int = 2, + max_depth: int = 3, + fast_retrieval: bool = False, + ) -> list: """从文本中提取关键词并获取相关记忆。 Args: @@ -943,18 +952,16 @@ class Hippocampus: # 使用LLM提取关键词 topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量 # logger.info(f"提取关键词数量: {topic_num}") - topics_response = await self.llm_topic_judge.generate_response( - self.find_topic_llm(text, topic_num) - ) + topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num)) # 提取关键词 - keywords = re.findall(r'<([^>]+)>', topics_response[0]) + keywords = re.findall(r"<([^>]+)>", topics_response[0]) if not keywords: keywords = [] else: keywords = [ keyword.strip() - for keyword in ','.join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") + for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if keyword.strip() ] @@ -965,7 +972,7 @@ class Hippocampus: if not valid_keywords: logger.info("没有找到有效的关键词节点") return [] - + logger.info(f"有效的关键词: {', '.join(valid_keywords)}") # 从每个关键词获取记忆 @@ -981,35 +988,36 @@ class Hippocampus: visited_nodes = {keyword} # 待处理的节点队列，每个元素是(节点, 激活值, 当前深度) nodes_to_process = [(keyword, 1.0, 0)] - + while nodes_to_process: current_node, current_activation, current_depth = nodes_to_process.pop(0) - + # 如果激活值小于0或超过最大深度，停止扩散 if current_activation <= 0 or current_depth >= max_depth: continue - + # 获取当前节点的所有邻居 neighbors = list(self.memory_graph.G.neighbors(current_node)) - + for neighbor in neighbors: if neighbor in visited_nodes: continue - + # 获取连接强度 edge_data = self.memory_graph.G[current_node][neighbor] strength = edge_data.get("strength", 1) - + # 计算新的激活值 new_activation = current_activation - (1 / strength) - + if new_activation > 0: activation_values[neighbor] = new_activation visited_nodes.add(neighbor) nodes_to_process.append((neighbor, new_activation, current_depth + 1)) logger.debug( - f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501 - + f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})" + ) # noqa: E501 + # 更新激活映射 for node, activation_value in activation_values.items(): if activation_value > 0: @@ -1017,7 +1025,7 @@ class Hippocampus: activate_map[node] += activation_value else: activate_map[node] = activation_value - + # 输出激活映射 # logger.info("激活映射统计:") # for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True): @@ -1026,28 +1034,24 @@ class Hippocampus: # 基于激活值平方的独立概率选择 remember_map = {} # logger.info("基于激活值平方的归一化选择:") - + # 计算所有激活值的平方和 - total_squared_activation = sum(activation ** 2 for activation in activate_map.values()) + total_squared_activation = sum(activation**2 for activation in activate_map.values()) if total_squared_activation > 0: # 计算归一化的激活值 normalized_activations = { - node: (activation ** 2) / total_squared_activation - for node, activation in activate_map.items() + node: (activation**2) / total_squared_activation for node, activation in activate_map.items() } - + # 按归一化激活值排序并选择前max_memory_num个 - sorted_nodes = sorted( - normalized_activations.items(), - key=lambda x: x[1], - reverse=True - )[:max_memory_num] - + sorted_nodes = sorted(normalized_activations.items(), key=lambda x: x[1], reverse=True)[:max_memory_num] + # 将选中的节点添加到remember_map for node, normalized_activation in sorted_nodes: remember_map[node] = activate_map[node] # 使用原始激活值 logger.debug( - f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})") + f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})" + ) else: logger.info("没有有效的激活值") @@ -1060,7 +1064,7 @@ class Hippocampus: memory_items = node_data.get("memory_items", []) if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] - + if memory_items: logger.debug(f"节点包含 {len(memory_items)} 条记忆") # 计算每条记忆与输入文本的相似度 @@ -1079,7 +1083,7 @@ class Hippocampus: memory_similarities.sort(key=lambda x: x[1], reverse=True) # 获取最匹配的记忆 top_memories = memory_similarities[:max_memory_length] - + # 添加到结果中 for memory, similarity in top_memories: all_memories.append((node, [memory], similarity)) @@ -1106,11 +1110,10 @@ class Hippocampus: memory = memory_items[0] # 因为每个topic只有一条记忆 result.append((topic, memory)) logger.info(f"选中记忆: {memory} (来自节点: {topic})") - + return result - - async def get_activate_from_text(self, text: str, max_depth: int = 3, - fast_retrieval: bool = False) -> float: + + async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float: """从文本中提取关键词并获取相关记忆。 Args: @@ -1140,18 +1143,16 @@ class Hippocampus: # 使用LLM提取关键词 topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量 # logger.info(f"提取关键词数量: {topic_num}") - topics_response = await self.llm_topic_judge.generate_response( - self.find_topic_llm(text, topic_num) - ) + topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num)) # 提取关键词 - keywords = re.findall(r'<([^>]+)>', topics_response[0]) + keywords = re.findall(r"<([^>]+)>", topics_response[0]) if not keywords: keywords = [] else: keywords = [ keyword.strip() - for keyword in ','.join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") + for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if keyword.strip() ] @@ -1162,7 +1163,7 @@ class Hippocampus: if not valid_keywords: logger.info("没有找到有效的关键词节点") return 0 - + logger.info(f"有效的关键词: {', '.join(valid_keywords)}") # 从每个关键词获取记忆 @@ -1177,35 +1178,35 @@ class Hippocampus: visited_nodes = {keyword} # 待处理的节点队列，每个元素是(节点, 激活值, 当前深度) nodes_to_process = [(keyword, 1.0, 0)] - + while nodes_to_process: current_node, current_activation, current_depth = nodes_to_process.pop(0) - + # 如果激活值小于0或超过最大深度，停止扩散 if current_activation <= 0 or current_depth >= max_depth: continue - + # 获取当前节点的所有邻居 neighbors = list(self.memory_graph.G.neighbors(current_node)) - + for neighbor in neighbors: if neighbor in visited_nodes: continue - + # 获取连接强度 edge_data = self.memory_graph.G[current_node][neighbor] strength = edge_data.get("strength", 1) - + # 计算新的激活值 new_activation = current_activation - (1 / strength) - + if new_activation > 0: activation_values[neighbor] = new_activation visited_nodes.add(neighbor) nodes_to_process.append((neighbor, new_activation, current_depth + 1)) # logger.debug( - # f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501 - + # f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501 + # 更新激活映射 for node, activation_value in activation_values.items(): if activation_value > 0: @@ -1213,23 +1214,24 @@ class Hippocampus: activate_map[node] += activation_value else: activate_map[node] = activation_value - + # 输出激活映射 # logger.info("激活映射统计:") # for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True): # logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}") - + # 计算激活节点数与总节点数的比值 total_activation = sum(activate_map.values()) logger.info(f"总激活值: {total_activation:.2f}") total_nodes = len(self.memory_graph.G.nodes()) # activated_nodes = len(activate_map) activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0 - activation_ratio = activation_ratio*60 + activation_ratio = activation_ratio * 60 logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}") - + return activation_ratio + class HippocampusManager: _instance = None _hippocampus = None @@ -1252,12 +1254,12 @@ class HippocampusManager: """初始化海马体实例""" if self._initialized: return self._hippocampus - + self._global_config = global_config self._hippocampus = Hippocampus() self._hippocampus.initialize(global_config) self._initialized = True - + # 输出记忆系统参数信息 config = self._hippocampus.config @@ -1265,16 +1267,15 @@ class HippocampusManager: memory_graph = self._hippocampus.memory_graph.G node_count = len(memory_graph.nodes()) edge_count = len(memory_graph.edges()) - - logger.success(f'''-------------------------------- + + logger.success(f"""-------------------------------- 记忆系统参数配置: 构建间隔: {global_config.build_memory_interval}秒|样本数: {config.build_memory_sample_num},长度: {config.build_memory_sample_length}|压缩率: {config.memory_compress_rate} 记忆构建分布: {config.memory_build_distribution} 遗忘间隔: {global_config.forget_memory_interval}秒|遗忘比例: {global_config.memory_forget_percentage}|遗忘: {config.memory_forget_time}小时之后 记忆图统计信息: 节点数量: {node_count}, 连接数量: {edge_count} - --------------------------------''') #noqa: E501 - - + --------------------------------""") # noqa: E501 + return self._hippocampus async def build_memory(self): @@ -1289,17 +1290,22 @@ class HippocampusManager: raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法") return await self._hippocampus.parahippocampal_gyrus.operation_forget_topic(percentage) - async def get_memory_from_text(self, text: str, max_memory_num: int = 3, - max_memory_length: int = 2, max_depth: int = 3, - fast_retrieval: bool = False) -> list: + async def get_memory_from_text( + self, + text: str, + max_memory_num: int = 3, + max_memory_length: int = 2, + max_depth: int = 3, + fast_retrieval: bool = False, + ) -> list: """从文本中获取相关记忆的公共接口""" if not self._initialized: raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法") return await self._hippocampus.get_memory_from_text( - text, max_memory_num, max_memory_length, max_depth, fast_retrieval) + text, max_memory_num, max_memory_length, max_depth, fast_retrieval + ) - async def get_activate_from_text(self, text: str, max_depth: int = 3, - fast_retrieval: bool = False) -> float: + async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float: """从文本中获取激活值的公共接口""" if not self._initialized: raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法") @@ -1316,5 +1322,3 @@ class HippocampusManager: if not self._initialized: raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法") return self._hippocampus.get_all_node_names() - - diff --git a/src/plugins/memory_system/debug_memory.py b/src/plugins/memory_system/debug_memory.py index 9baf2e520..657811ac6 100644 --- a/src/plugins/memory_system/debug_memory.py +++ b/src/plugins/memory_system/debug_memory.py @@ -3,11 +3,13 @@ import asyncio import time import sys import os + # 添加项目根目录到系统路径 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))) from src.plugins.memory_system.Hippocampus import HippocampusManager from src.plugins.config.config import global_config + async def test_memory_system(): """测试记忆系统的主要功能""" try: @@ -24,7 +26,7 @@ async def test_memory_system(): # 测试记忆检索 test_text = "千石可乐在群里聊天" - test_text = '''[03-24 10:39:37] 麦麦(ta的id:2814567326): 早说散步结果下雨改成室内运动啊 + test_text = """[03-24 10:39:37] 麦麦(ta的id:2814567326): 早说散步结果下雨改成室内运动啊 [03-24 10:39:37] 麦麦(ta的id:2814567326): [回复：变量] 变量就像今天计划总变 [03-24 10:39:44] 状态异常(ta的id:535554838): 要把本地文件改成弹出来的路径吗 [03-24 10:40:35] 状态异常(ta的id:535554838): [图片：这张图片显示的是Windows系统的环境变量设置界面。界面左侧列出了多个环境变量的值，包括Intel Dev Redist、Windows、Windows PowerShell、OpenSSH、NVIDIA Corporation的目录等。右侧有新建、编辑、浏览、删除、上移、下移和编辑文本等操作按钮。图片下方有一个错误提示框，显示"Windows找不到文件'mongodb\\bin\\mongod.exe'。请确定文件名是否正确后，再试一次。"这意味着用户试图运行MongoDB的mongod.exe程序时，系统找不到该文件。这可能是因为MongoDB的安装路径未正确添加到系统环境变量中，或者文件路径有误。 @@ -39,28 +41,21 @@ async def test_memory_system(): [03-24 10:46:12] (ta的id:3229291803): [表情包：这张表情包显示了一只手正在做"点赞"的动作，通常表示赞同、喜欢或支持。这个表情包所表达的情感是积极的、赞同的或支持的。] [03-24 10:46:37] 星野風禾(ta的id:2890165435): 还能思考高达 [03-24 10:46:39] 星野風禾(ta的id:2890165435): 什么知识库 -[03-24 10:46:49] ❦幻凌慌てない(ta的id:2459587037): 为什么改了回复系数麦麦还是不怎么回复？大佬们''' # noqa: E501 - +[03-24 10:46:49] ❦幻凌慌てない(ta的id:2459587037): 为什么改了回复系数麦麦还是不怎么回复？大佬们""" # noqa: E501 # test_text = '''千石可乐：分不清AI的陪伴和人类的陪伴,是这样吗？''' print(f"开始测试记忆检索，测试文本: {test_text}\n") memories = await hippocampus_manager.get_memory_from_text( - text=test_text, - max_memory_num=3, - max_memory_length=2, - max_depth=3, - fast_retrieval=False + text=test_text, max_memory_num=3, max_memory_length=2, max_depth=3, fast_retrieval=False ) - + await asyncio.sleep(1) - + print("检索到的记忆:") for topic, memory_items in memories: print(f"主题: {topic}") print(f"- {memory_items}") - - # 测试记忆遗忘 # forget_start_time = time.time() # # print("开始测试记忆遗忘...") @@ -80,6 +75,7 @@ async def test_memory_system(): print(f"测试过程中出现错误: {e}") raise + async def main(): """主函数""" try: @@ -91,5 +87,6 @@ async def main(): print(f"程序执行出错: {e}") raise + if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file + asyncio.run(main()) diff --git a/src/plugins/memory_system/memory_config.py b/src/plugins/memory_system/memory_config.py index 6c49d15fc..73f9c1dbd 100644 --- a/src/plugins/memory_system/memory_config.py +++ b/src/plugins/memory_system/memory_config.py @@ -1,24 +1,26 @@ from dataclasses import dataclass from typing import List + @dataclass class MemoryConfig: """记忆系统配置类""" + # 记忆构建相关配置 memory_build_distribution: List[float] # 记忆构建的时间分布参数 build_memory_sample_num: int # 每次构建记忆的样本数量 build_memory_sample_length: int # 每个样本的消息长度 memory_compress_rate: float # 记忆压缩率 - + # 记忆遗忘相关配置 memory_forget_time: int # 记忆遗忘时间（小时） - + # 记忆过滤相关配置 memory_ban_words: List[str] # 记忆过滤词列表 llm_topic_judge: str # 话题判断模型 llm_summary_by_topic: str # 话题总结模型 - + @classmethod def from_global_config(cls, global_config): """从全局配置创建记忆系统配置""" @@ -30,5 +32,5 @@ class MemoryConfig: memory_forget_time=global_config.memory_forget_time, memory_ban_words=global_config.memory_ban_words, llm_topic_judge=global_config.llm_topic_judge, - llm_summary_by_topic=global_config.llm_summary_by_topic - ) \ No newline at end of file + llm_summary_by_topic=global_config.llm_summary_by_topic, + ) diff --git a/src/plugins/memory_system/sample_distribution.py b/src/plugins/memory_system/sample_distribution.py index dbe4b88a4..29218d21f 100644 --- a/src/plugins/memory_system/sample_distribution.py +++ b/src/plugins/memory_system/sample_distribution.py @@ -2,11 +2,12 @@ import numpy as np from scipy import stats from datetime import datetime, timedelta + class DistributionVisualizer: def __init__(self, mean=0, std=1, skewness=0, sample_size=10): """ 初始化分布可视化器 - + 参数: mean (float): 期望均值 std (float): 标准差 @@ -18,7 +19,7 @@ class DistributionVisualizer: self.skewness = skewness self.sample_size = sample_size self.samples = None - + def generate_samples(self): """生成具有指定参数的样本""" if self.skewness == 0: @@ -26,37 +27,28 @@ class DistributionVisualizer: self.samples = np.random.normal(loc=self.mean, scale=self.std, size=self.sample_size) else: # 使用 scipy.stats 生成具有偏度的分布 - self.samples = stats.skewnorm.rvs(a=self.skewness, - loc=self.mean, - scale=self.std, - size=self.sample_size) - + self.samples = stats.skewnorm.rvs(a=self.skewness, loc=self.mean, scale=self.std, size=self.sample_size) + def get_weighted_samples(self): """获取加权后的样本数列""" if self.samples is None: self.generate_samples() # 将样本值乘以样本大小 return self.samples * self.sample_size - + def get_statistics(self): """获取分布的统计信息""" if self.samples is None: self.generate_samples() - - return { - "均值": np.mean(self.samples), - "标准差": np.std(self.samples), - "实际偏度": stats.skew(self.samples) - } + + return {"均值": np.mean(self.samples), "标准差": np.std(self.samples), "实际偏度": stats.skew(self.samples)} + class MemoryBuildScheduler: - def __init__(self, - n_hours1, std_hours1, weight1, - n_hours2, std_hours2, weight2, - total_samples=50): + def __init__(self, n_hours1, std_hours1, weight1, n_hours2, std_hours2, weight2, total_samples=50): """ 初始化记忆构建调度器 - + 参数: n_hours1 (float): 第一个分布的均值（距离现在的小时数） std_hours1 (float): 第一个分布的标准差（小时） @@ -70,39 +62,31 @@ class MemoryBuildScheduler: total_weight = weight1 + weight2 self.weight1 = weight1 / total_weight self.weight2 = weight2 / total_weight - + self.n_hours1 = n_hours1 self.std_hours1 = std_hours1 self.n_hours2 = n_hours2 self.std_hours2 = std_hours2 self.total_samples = total_samples self.base_time = datetime.now() - + def generate_time_samples(self): """生成混合分布的时间采样点""" # 根据权重计算每个分布的样本数 samples1 = int(self.total_samples * self.weight1) samples2 = self.total_samples - samples1 - + # 生成两个正态分布的小时偏移 - hours_offset1 = np.random.normal( - loc=self.n_hours1, - scale=self.std_hours1, - size=samples1 - ) - - hours_offset2 = np.random.normal( - loc=self.n_hours2, - scale=self.std_hours2, - size=samples2 - ) - + hours_offset1 = np.random.normal(loc=self.n_hours1, scale=self.std_hours1, size=samples1) + + hours_offset2 = np.random.normal(loc=self.n_hours2, scale=self.std_hours2, size=samples2) + # 合并两个分布的偏移 hours_offset = np.concatenate([hours_offset1, hours_offset2]) - + # 将偏移转换为实际时间戳（使用绝对值确保时间点在过去） timestamps = [self.base_time - timedelta(hours=abs(offset)) for offset in hours_offset] - + # 按时间排序（从最早到最近） return sorted(timestamps) @@ -111,54 +95,56 @@ class MemoryBuildScheduler: timestamps = self.generate_time_samples() return [int(t.timestamp()) for t in timestamps] + def print_time_samples(timestamps, show_distribution=True): """打印时间样本和分布信息""" print(f"\n生成的{len(timestamps)}个时间点分布：") print("序号".ljust(5), "时间戳".ljust(25), "距现在（小时）") print("-" * 50) - + now = datetime.now() time_diffs = [] - + for i, timestamp in enumerate(timestamps, 1): hours_diff = (now - timestamp).total_seconds() / 3600 time_diffs.append(hours_diff) print(f"{str(i).ljust(5)} {timestamp.strftime('%Y-%m-%d %H:%M:%S').ljust(25)} {hours_diff:.2f}") - + # 打印统计信息 print("\n统计信息：") print(f"平均时间偏移：{np.mean(time_diffs):.2f}小时") print(f"标准差：{np.std(time_diffs):.2f}小时") print(f"最早时间：{min(timestamps).strftime('%Y-%m-%d %H:%M:%S')} ({max(time_diffs):.2f}小时前)") print(f"最近时间：{max(timestamps).strftime('%Y-%m-%d %H:%M:%S')} ({min(time_diffs):.2f}小时前)") - + if show_distribution: # 计算时间分布的直方图 hist, bins = np.histogram(time_diffs, bins=40) print("\n时间分布（每个*代表一个时间点）：") for i in range(len(hist)): if hist[i] > 0: - print(f"{bins[i]:6.1f}-{bins[i+1]:6.1f}小时: {'*' * int(hist[i])}") + print(f"{bins[i]:6.1f}-{bins[i + 1]:6.1f}小时: {'*' * int(hist[i])}") + # 使用示例 if __name__ == "__main__": # 创建一个双峰分布的记忆调度器 scheduler = MemoryBuildScheduler( - n_hours1=12, # 第一个分布均值（12小时前） - std_hours1=8, # 第一个分布标准差 - weight1=0.7, # 第一个分布权重 70% - n_hours2=36, # 第二个分布均值（36小时前） - std_hours2=24, # 第二个分布标准差 - weight2=0.3, # 第二个分布权重 30% - total_samples=50 # 总共生成50个时间点 + n_hours1=12, # 第一个分布均值（12小时前） + std_hours1=8, # 第一个分布标准差 + weight1=0.7, # 第一个分布权重 70% + n_hours2=36, # 第二个分布均值（36小时前） + std_hours2=24, # 第二个分布标准差 + weight2=0.3, # 第二个分布权重 30% + total_samples=50, # 总共生成50个时间点 ) - + # 生成时间分布 timestamps = scheduler.generate_time_samples() - + # 打印结果，包含分布可视化 print_time_samples(timestamps, show_distribution=True) - + # 打印时间戳数组 timestamp_array = scheduler.get_timestamp_array() print("\n时间戳数组（Unix时间戳）：") @@ -167,4 +153,4 @@ if __name__ == "__main__": if i > 0: print(", ", end="") print(ts, end="") - print("]") \ No newline at end of file + print("]") diff --git a/src/plugins/message/test.py b/src/plugins/message/test.py index bc4ba4d8c..1efd6c63f 100644 --- a/src/plugins/message/test.py +++ b/src/plugins/message/test.py @@ -54,9 +54,7 @@ class TestLiveAPI(unittest.IsolatedAsyncioTestCase): # 准备测试消息 user_info = UserInfo(user_id=12345678, user_nickname="测试用户", platform="qq") group_info = GroupInfo(group_id=12345678, group_name="测试群", platform="qq") - format_info = FormatInfo( - content_format=["text"], accept_format=["text", "emoji", "reply"] - ) + format_info = FormatInfo(content_format=["text"], accept_format=["text", "emoji", "reply"]) template_info = None message_info = BaseMessageInfo( platform="qq", diff --git a/src/plugins/personality/can_i_recog_u.py b/src/plugins/personality/can_i_recog_u.py index d340f8a1b..c21048e6d 100644 --- a/src/plugins/personality/can_i_recog_u.py +++ b/src/plugins/personality/can_i_recog_u.py @@ -35,6 +35,7 @@ else: print(f"未找到环境变量文件: {env_path}") print("将使用默认配置") + class ChatBasedPersonalityEvaluator: def __init__(self): self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0} @@ -50,16 +51,14 @@ class ChatBasedPersonalityEvaluator: continue scene_keys = list(scenes.keys()) selected_scenes = random.sample(scene_keys, min(3, len(scene_keys))) - + for scene_key in selected_scenes: scene = scenes[scene_key] other_traits = [t for t in PERSONALITY_SCENES if t != trait] secondary_trait = random.choice(other_traits) - self.scenarios.append({ - "场景": scene["scenario"], - "评估维度": [trait, secondary_trait], - "场景编号": scene_key - }) + self.scenarios.append( + {"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key} + ) def analyze_chat_context(self, messages: List[Dict]) -> str: """ @@ -67,20 +66,21 @@ class ChatBasedPersonalityEvaluator: """ context = "" for msg in messages: - nickname = msg.get('user_info', {}).get('user_nickname', '未知用户') - content = msg.get('processed_plain_text', msg.get('detailed_plain_text', '')) + nickname = msg.get("user_info", {}).get("user_nickname", "未知用户") + content = msg.get("processed_plain_text", msg.get("detailed_plain_text", "")) if content: context += f"{nickname}: {content}\n" return context def evaluate_chat_response( - self, user_nickname: str, chat_context: str, dimensions: List[str] = None) -> Dict[str, float]: + self, user_nickname: str, chat_context: str, dimensions: List[str] = None + ) -> Dict[str, float]: """ 评估聊天内容在各个人格维度上的得分 """ # 使用所有维度进行评估 dimensions = list(self.personality_traits.keys()) - + dimension_descriptions = [] for dim in dimensions: desc = FACTOR_DESCRIPTIONS.get(dim, "") @@ -136,18 +136,19 @@ class ChatBasedPersonalityEvaluator: def evaluate_user_personality(self, qq_id: str, num_samples: int = 10, context_length: int = 5) -> Dict: """ 基于用户的聊天记录评估人格特征 - + Args: qq_id (str): 用户QQ号 num_samples (int): 要分析的聊天片段数量 context_length (int): 每个聊天片段的上下文长度 - + Returns: Dict: 评估结果 """ # 获取用户的随机消息及其上下文 chat_contexts, user_nickname = self.message_analyzer.get_user_random_contexts( - qq_id, num_messages=num_samples, context_length=context_length) + qq_id, num_messages=num_samples, context_length=context_length + ) if not chat_contexts: return {"error": f"没有找到QQ号 {qq_id} 的消息记录"} @@ -155,7 +156,7 @@ class ChatBasedPersonalityEvaluator: final_scores = defaultdict(float) dimension_counts = defaultdict(int) chat_samples = [] - + # 清空历史记录 self.trait_scores_history.clear() @@ -163,13 +164,11 @@ class ChatBasedPersonalityEvaluator: for chat_context in chat_contexts: # 评估这段聊天内容的所有维度 scores = self.evaluate_chat_response(user_nickname, chat_context) - + # 记录样本 - chat_samples.append({ - "聊天内容": chat_context, - "评估维度": list(self.personality_traits.keys()), - "评分": scores - }) + chat_samples.append( + {"聊天内容": chat_context, "评估维度": list(self.personality_traits.keys()), "评分": scores} + ) # 更新总分和历史记录 for dimension, score in scores.items(): @@ -196,7 +195,7 @@ class ChatBasedPersonalityEvaluator: "人格特征评分": average_scores, "维度评估次数": dict(dimension_counts), "详细样本": chat_samples, - "特质得分历史": {k: v for k, v in self.trait_scores_history.items()} + "特质得分历史": {k: v for k, v in self.trait_scores_history.items()}, } # 保存结果 @@ -215,40 +214,41 @@ class ChatBasedPersonalityEvaluator: chinese_fonts = [] for f in fm.fontManager.ttflist: try: - if '简' in f.name or 'SC' in f.name or '黑' in f.name or '宋' in f.name or '微软' in f.name: + if "简" in f.name or "SC" in f.name or "黑" in f.name or "宋" in f.name or "微软" in f.name: chinese_fonts.append(f.name) except Exception: continue - + if chinese_fonts: - plt.rcParams['font.sans-serif'] = chinese_fonts + ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS'] + plt.rcParams["font.sans-serif"] = chinese_fonts + ["SimHei", "Microsoft YaHei", "Arial Unicode MS"] else: # 如果没有找到中文字体，使用默认字体，并将中文昵称转换为拼音或英文 try: from pypinyin import lazy_pinyin - user_nickname = ''.join(lazy_pinyin(user_nickname)) + + user_nickname = "".join(lazy_pinyin(user_nickname)) except ImportError: user_nickname = "User" # 如果无法转换为拼音，使用默认英文 - - plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题 - + + plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题 + plt.figure(figsize=(12, 6)) - plt.style.use('bmh') # 使用内置的bmh样式，它有类似seaborn的美观效果 - + plt.style.use("bmh") # 使用内置的bmh样式，它有类似seaborn的美观效果 + colors = { "开放性": "#FF9999", "严谨性": "#66B2FF", "外向性": "#99FF99", "宜人性": "#FFCC99", - "神经质": "#FF99CC" + "神经质": "#FF99CC", } - + # 计算每个维度在每个时间点的累计平均分 cumulative_averages = {} for trait, scores in self.trait_scores_history.items(): if not scores: continue - + averages = [] total = 0 valid_count = 0 @@ -264,25 +264,25 @@ class ChatBasedPersonalityEvaluator: averages.append(averages[-1]) else: continue # 跳过无效分数 - + if averages: # 只有在有有效分数的情况下才添加到累计平均中 cumulative_averages[trait] = averages - + # 绘制每个维度的累计平均分变化趋势 for trait, averages in cumulative_averages.items(): x = range(1, len(averages) + 1) - plt.plot(x, averages, 'o-', label=trait, color=colors.get(trait), linewidth=2, markersize=8) - + plt.plot(x, averages, "o-", label=trait, color=colors.get(trait), linewidth=2, markersize=8) + # 添加趋势线 z = np.polyfit(x, averages, 1) p = np.poly1d(z) - plt.plot(x, p(x), '--', color=colors.get(trait), alpha=0.5) + plt.plot(x, p(x), "--", color=colors.get(trait), alpha=0.5) plt.title(f"{user_nickname} 的人格特质累计平均分变化趋势", fontsize=14, pad=20) plt.xlabel("评估次数", fontsize=12) plt.ylabel("累计平均分", fontsize=12) - plt.grid(True, linestyle='--', alpha=0.7) - plt.legend(loc='center left', bbox_to_anchor=(1, 0.5)) + plt.grid(True, linestyle="--", alpha=0.7) + plt.legend(loc="center left", bbox_to_anchor=(1, 0.5)) plt.ylim(0, 7) plt.tight_layout() @@ -290,38 +290,39 @@ class ChatBasedPersonalityEvaluator: os.makedirs("results/plots", exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") plot_file = f"results/plots/personality_trend_{qq_id}_{timestamp}.png" - plt.savefig(plot_file, dpi=300, bbox_inches='tight') + plt.savefig(plot_file, dpi=300, bbox_inches="tight") plt.close() + def analyze_user_personality(qq_id: str, num_samples: int = 10, context_length: int = 5) -> str: """ 分析用户人格特征的便捷函数 - + Args: qq_id (str): 用户QQ号 num_samples (int): 要分析的聊天片段数量 context_length (int): 每个聊天片段的上下文长度 - + Returns: str: 格式化的分析结果 """ evaluator = ChatBasedPersonalityEvaluator() result = evaluator.evaluate_user_personality(qq_id, num_samples, context_length) - + if "error" in result: return result["error"] - + # 格式化输出 output = f"QQ号 {qq_id} ({result['用户昵称']}) 的人格特征分析结果：\n" output += "=" * 50 + "\n\n" - + output += "人格特征评分：\n" for trait, score in result["人格特征评分"].items(): if score == 0: output += f"{trait}: 数据不足，无法判断 (评估次数: {result['维度评估次数'].get(trait, 0)})\n" else: output += f"{trait}: {score}/6 (评估次数: {result['维度评估次数'].get(trait, 0)})\n" - + # 添加变化趋势描述 if trait in result["特质得分历史"] and len(result["特质得分历史"][trait]) > 1: scores = [s for s in result["特质得分历史"][trait] if s != 0] # 过滤掉无效分数 @@ -334,13 +335,14 @@ def analyze_user_personality(qq_id: str, num_samples: int = 10, context_length: else: trend_desc = "呈下降趋势" output += f" 变化趋势: {trend_desc} (斜率: {trend:.2f})\n" - + output += f"\n分析样本数量：{result['样本数量']}\n" output += f"结果已保存至：results/personality_result_{qq_id}.json\n" output += "变化趋势图已保存至：results/plots/目录\n" - + return output + if __name__ == "__main__": # 测试代码 # test_qq = "" # 替换为要测试的QQ号 diff --git a/src/plugins/personality/renqingziji_with_mymy.py b/src/plugins/personality/renqingziji_with_mymy.py index 92c1341a8..04cbec099 100644 --- a/src/plugins/personality/renqingziji_with_mymy.py +++ b/src/plugins/personality/renqingziji_with_mymy.py @@ -82,7 +82,6 @@ class PersonalityEvaluator_direct: dimensions_text = "\n".join(dimension_descriptions) - prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。 场景描述： diff --git a/src/plugins/personality/who_r_u.py b/src/plugins/personality/who_r_u.py index 34c134472..4877fb8c9 100644 --- a/src/plugins/personality/who_r_u.py +++ b/src/plugins/personality/who_r_u.py @@ -14,18 +14,19 @@ sys.path.append(root_path) from src.common.database import db # noqa: E402 + class MessageAnalyzer: def __init__(self): self.messages_collection = db["messages"] - + def get_message_context(self, message_id: int, context_length: int = 5) -> Optional[List[Dict]]: """ 获取指定消息ID的上下文消息列表 - + Args: message_id (int): 消息ID context_length (int): 上下文长度（单侧，总长度为 2*context_length + 1） - + Returns: Optional[List[Dict]]: 消息列表，如果未找到则返回None """ @@ -33,110 +34,110 @@ class MessageAnalyzer: target_message = self.messages_collection.find_one({"message_id": message_id}) if not target_message: return None - + # 获取该消息的stream_id - stream_id = target_message.get('chat_info', {}).get('stream_id') + stream_id = target_message.get("chat_info", {}).get("stream_id") if not stream_id: return None - + # 获取同一stream_id的所有消息 - stream_messages = list(self.messages_collection.find({ - "chat_info.stream_id": stream_id - }).sort("time", 1)) - + stream_messages = list(self.messages_collection.find({"chat_info.stream_id": stream_id}).sort("time", 1)) + # 找到目标消息在列表中的位置 target_index = None for i, msg in enumerate(stream_messages): - if msg['message_id'] == message_id: + if msg["message_id"] == message_id: target_index = i break - + if target_index is None: return None - + # 获取目标消息前后的消息 start_index = max(0, target_index - context_length) end_index = min(len(stream_messages), target_index + context_length + 1) - + return stream_messages[start_index:end_index] - + def format_messages(self, messages: List[Dict], target_message_id: Optional[int] = None) -> str: """ 格式化消息列表为可读字符串 - + Args: messages (List[Dict]): 消息列表 target_message_id (Optional[int]): 目标消息ID，用于标记 - + Returns: str: 格式化的消息字符串 """ if not messages: return "没有消息记录" - + reply = "" for msg in messages: # 消息时间 - msg_time = datetime.datetime.fromtimestamp(int(msg['time'])).strftime("%Y-%m-%d %H:%M:%S") - + msg_time = datetime.datetime.fromtimestamp(int(msg["time"])).strftime("%Y-%m-%d %H:%M:%S") + # 获取消息内容 - message_text = msg.get('processed_plain_text', msg.get('detailed_plain_text', '无消息内容')) - nickname = msg.get('user_info', {}).get('user_nickname', '未知用户') - + message_text = msg.get("processed_plain_text", msg.get("detailed_plain_text", "无消息内容")) + nickname = msg.get("user_info", {}).get("user_nickname", "未知用户") + # 标记当前消息 - is_target = "→ " if target_message_id and msg['message_id'] == target_message_id else " " - + is_target = "→ " if target_message_id and msg["message_id"] == target_message_id else " " + reply += f"{is_target}[{msg_time}] {nickname}: {message_text}\n" - - if target_message_id and msg['message_id'] == target_message_id: + + if target_message_id and msg["message_id"] == target_message_id: reply += " " + "-" * 50 + "\n" - + return reply - + def get_user_random_contexts( - self, qq_id: str, num_messages: int = 10, context_length: int = 5) -> tuple[List[str], str]: # noqa: E501 + self, qq_id: str, num_messages: int = 10, context_length: int = 5 + ) -> tuple[List[str], str]: # noqa: E501 """ 获取用户的随机消息及其上下文 - + Args: qq_id (str): QQ号 num_messages (int): 要获取的随机消息数量 context_length (int): 每条消息的上下文长度（单侧） - + Returns: tuple[List[str], str]: (每个消息上下文的格式化字符串列表, 用户昵称) """ if not qq_id: return [], "" - + # 获取用户所有消息 all_messages = list(self.messages_collection.find({"user_info.user_id": int(qq_id)})) if not all_messages: return [], "" - + # 获取用户昵称 - user_nickname = all_messages[0].get('chat_info', {}).get('user_info', {}).get('user_nickname', '未知用户') - + user_nickname = all_messages[0].get("chat_info", {}).get("user_info", {}).get("user_nickname", "未知用户") + # 随机选择指定数量的消息 selected_messages = random.sample(all_messages, min(num_messages, len(all_messages))) # 按时间排序 - selected_messages.sort(key=lambda x: int(x['time'])) - + selected_messages.sort(key=lambda x: int(x["time"])) + # 存储所有上下文消息 context_list = [] - + # 获取每条消息的上下文 for msg in selected_messages: - message_id = msg['message_id'] - + message_id = msg["message_id"] + # 获取消息上下文 context_messages = self.get_message_context(message_id, context_length) if context_messages: formatted_context = self.format_messages(context_messages, message_id) context_list.append(formatted_context) - + return context_list, user_nickname + if __name__ == "__main__": # 测试代码 analyzer = MessageAnalyzer() @@ -145,7 +146,7 @@ if __name__ == "__main__": print("-" * 50) # 获取5条消息，每条消息前后各3条上下文 contexts, nickname = analyzer.get_user_random_contexts(test_qq, num_messages=5, context_length=3) - + print(f"用户昵称: {nickname}\n") # 打印每个上下文 for i, context in enumerate(contexts, 1): diff --git a/src/plugins/utils/statistic.py b/src/plugins/utils/statistic.py index b9efafd03..5548d1812 100644 --- a/src/plugins/utils/statistic.py +++ b/src/plugins/utils/statistic.py @@ -46,17 +46,15 @@ class LLMStatistics: """记录在线时间""" current_time = datetime.now() # 检查5分钟内是否已有记录 - recent_record = db.online_time.find_one({ - "timestamp": { - "$gte": current_time - timedelta(minutes=5) - } - }) - + recent_record = db.online_time.find_one({"timestamp": {"$gte": current_time - timedelta(minutes=5)}}) + if not recent_record: - db.online_time.insert_one({ - "timestamp": current_time, - "duration": 5 # 5分钟 - }) + db.online_time.insert_one( + { + "timestamp": current_time, + "duration": 5, # 5分钟 + } + ) def _collect_statistics_for_period(self, start_time: datetime) -> Dict[str, Any]: """收集指定时间段的LLM请求统计数据 diff --git a/src/plugins/willing/mode_custom.py b/src/plugins/willing/mode_custom.py index a131b576d..0f32c0c75 100644 --- a/src/plugins/willing/mode_custom.py +++ b/src/plugins/willing/mode_custom.py @@ -41,10 +41,9 @@ class WillingManager: interested_rate = interested_rate * config.response_interested_rate_amplifier - if interested_rate > 0.4: current_willing += interested_rate - 0.3 - + if is_mentioned_bot and current_willing < 1.0: current_willing += 1 elif is_mentioned_bot: diff --git a/src/think_flow_demo/heartflow.py b/src/think_flow_demo/heartflow.py index f8eda6237..3551340f2 100644 --- a/src/think_flow_demo/heartflow.py +++ b/src/think_flow_demo/heartflow.py @@ -5,38 +5,41 @@ from src.plugins.models.utils_model import LLM_request from src.plugins.config.config import global_config from src.plugins.schedule.schedule_generator import bot_schedule import asyncio -from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG # noqa: E402 +from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG # noqa: E402 import time heartflow_config = LogConfig( # 使用海马体专用样式 console_format=HEARTFLOW_STYLE_CONFIG["console_format"], file_format=HEARTFLOW_STYLE_CONFIG["file_format"], -) +) logger = get_module_logger("heartflow", config=heartflow_config) + class CuttentState: def __init__(self): self.willing = 0 self.current_state_info = "" - + self.mood_manager = MoodManager() self.mood = self.mood_manager.get_prompt() - + def update_current_state_info(self): self.current_state_info = self.mood_manager.get_current_mood() + class Heartflow: def __init__(self): self.current_mind = "你什么也没想" self.past_mind = [] - self.current_state : CuttentState = CuttentState() + self.current_state: CuttentState = CuttentState() self.llm_model = LLM_request( - model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow") - + model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow" + ) + self._subheartflows = {} self.active_subheartflows_nums = 0 - + self.personality_info = " ".join(global_config.PROMPT_PERSONALITY) async def _cleanup_inactive_subheartflows(self): @@ -44,46 +47,46 @@ class Heartflow: while True: current_time = time.time() inactive_subheartflows = [] - + # 检查所有子心流 for subheartflow_id, subheartflow in self._subheartflows.items(): if current_time - subheartflow.last_active_time > 600: # 10分钟 = 600秒 inactive_subheartflows.append(subheartflow_id) logger.info(f"发现不活跃的子心流: {subheartflow_id}") - + # 清理不活跃的子心流 for subheartflow_id in inactive_subheartflows: del self._subheartflows[subheartflow_id] logger.info(f"已清理不活跃的子心流: {subheartflow_id}") - + await asyncio.sleep(30) # 每分钟检查一次 async def heartflow_start_working(self): # 启动清理任务 asyncio.create_task(self._cleanup_inactive_subheartflows()) - + while True: # 检查是否存在子心流 if not self._subheartflows: logger.info("当前没有子心流，等待新的子心流创建...") await asyncio.sleep(60) # 每分钟检查一次是否有新的子心流 continue - + await self.do_a_thinking() await asyncio.sleep(300) # 5分钟思考一次 - + async def do_a_thinking(self): logger.debug("麦麦大脑袋转起来了") self.current_state.update_current_state_info() - + personality_info = self.personality_info current_thinking_info = self.current_mind mood_info = self.current_state.mood - related_memory_info = 'memory' + related_memory_info = "memory" sub_flows_info = await self.get_all_subheartflows_minds() - - schedule_info = bot_schedule.get_current_num_task(num = 4,time_info = True) - + + schedule_info = bot_schedule.get_current_num_task(num=4, time_info=True) + prompt = "" prompt += f"你刚刚在做的事情是：{schedule_info}\n" prompt += f"{personality_info}\n" @@ -93,49 +96,46 @@ class Heartflow: prompt += f"你现在{mood_info}。" prompt += "现在你接下去继续思考，产生新的想法，但是要基于原有的主要想法，不要分点输出，" prompt += "输出连贯的内心独白，不要太长，但是记得结合上述的消息，关注新内容:" - + reponse, reasoning_content = await self.llm_model.generate_response_async(prompt) - + self.update_current_mind(reponse) - + self.current_mind = reponse logger.info(f"麦麦的总体脑内状态：{self.current_mind}") # logger.info("麦麦想了想，当前活动:") await bot_schedule.move_doing(self.current_mind) - - + for _, subheartflow in self._subheartflows.items(): subheartflow.main_heartflow_info = reponse - def update_current_mind(self,reponse): + def update_current_mind(self, reponse): self.past_mind.append(self.current_mind) self.current_mind = reponse - - - + async def get_all_subheartflows_minds(self): sub_minds = "" for _, subheartflow in self._subheartflows.items(): sub_minds += subheartflow.current_mind - + return await self.minds_summary(sub_minds) - - async def minds_summary(self,minds_str): + + async def minds_summary(self, minds_str): personality_info = self.personality_info mood_info = self.current_state.mood - + prompt = "" prompt += f"{personality_info}\n" prompt += f"现在{global_config.BOT_NICKNAME}的想法是：{self.current_mind}\n" prompt += f"现在{global_config.BOT_NICKNAME}在qq群里进行聊天，聊天的话题如下：{minds_str}\n" prompt += f"你现在{mood_info}\n" - prompt += '''现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白 - 不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:''' + prompt += """现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白 + 不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:""" reponse, reasoning_content = await self.llm_model.generate_response_async(prompt) return reponse - + def create_subheartflow(self, subheartflow_id): """ 创建一个新的SubHeartflow实例 @@ -145,10 +145,10 @@ class Heartflow: if subheartflow_id not in self._subheartflows: logger.debug(f"创建 subheartflow: {subheartflow_id}") subheartflow = SubHeartflow(subheartflow_id) - #创建一个观察对象，目前只可以用chat_id创建观察对象 + # 创建一个观察对象，目前只可以用chat_id创建观察对象 logger.debug(f"创建 observation: {subheartflow_id}") observation = ChattingObservation(subheartflow_id) - + logger.debug(f"添加 observation ") subheartflow.add_observation(observation) logger.debug(f"添加 observation 成功") @@ -159,11 +159,11 @@ class Heartflow: self._subheartflows[subheartflow_id] = subheartflow logger.info(f"添加 subheartflow 成功") return self._subheartflows[subheartflow_id] - + def get_subheartflow(self, observe_chat_id): """获取指定ID的SubHeartflow实例""" return self._subheartflows.get(observe_chat_id) # 创建一个全局的管理器实例 -heartflow = Heartflow() +heartflow = Heartflow() diff --git a/src/think_flow_demo/observation.py b/src/think_flow_demo/observation.py index 2dc31c694..c71b58d05 100644 --- a/src/think_flow_demo/observation.py +++ b/src/think_flow_demo/observation.py @@ -1,119 +1,123 @@ -#定义了来自外部世界的信息 -#外部世界可以是某个聊天 不同平台的聊天 也可以是任意媒体 +# 定义了来自外部世界的信息 +# 外部世界可以是某个聊天 不同平台的聊天 也可以是任意媒体 import asyncio from datetime import datetime from src.plugins.models.utils_model import LLM_request from src.plugins.config.config import global_config from src.common.database import db + # 所有观察的基类 class Observation: - def __init__(self,observe_type,observe_id): + def __init__(self, observe_type, observe_id): self.observe_info = "" self.observe_type = observe_type self.observe_id = observe_id self.last_observe_time = datetime.now().timestamp() # 初始化为当前时间 + # 聊天观察 class ChattingObservation(Observation): - def __init__(self,chat_id): - super().__init__("chat",chat_id) + def __init__(self, chat_id): + super().__init__("chat", chat_id) self.chat_id = chat_id - + self.talking_message = [] self.talking_message_str = "" - + self.observe_times = 0 - + self.summary_count = 0 # 30秒内的更新次数 - self.max_update_in_30s = 2 #30秒内最多更新2次 - self.last_summary_time = 0 #上次更新summary的时间 - + self.max_update_in_30s = 2 # 30秒内最多更新2次 + self.last_summary_time = 0 # 上次更新summary的时间 + self.sub_observe = None - + self.llm_summary = LLM_request( - model=global_config.llm_outer_world, temperature=0.7, max_tokens=300, request_type="outer_world") - + model=global_config.llm_outer_world, temperature=0.7, max_tokens=300, request_type="outer_world" + ) + # 进行一次观察 返回观察结果observe_info async def observe(self): # 查找新消息，限制最多30条 - new_messages = list(db.messages.find({ - "chat_id": self.chat_id, - "time": {"$gt": self.last_observe_time} - }).sort("time", 1).limit(20)) # 按时间正序排列，最多20条 - + new_messages = list( + db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}}) + .sort("time", 1) + .limit(20) + ) # 按时间正序排列，最多20条 + if not new_messages: - return self.observe_info #没有新消息，返回上次观察结果 - + return self.observe_info # 没有新消息，返回上次观察结果 + # 将新消息转换为字符串格式 new_messages_str = "" for msg in new_messages: if "sender_name" in msg and "content" in msg: new_messages_str += f"{msg['sender_name']}: {msg['content']}\n" - + # 将新消息添加到talking_message，同时保持列表长度不超过20条 self.talking_message.extend(new_messages) if len(self.talking_message) > 20: self.talking_message = self.talking_message[-20:] # 只保留最新的20条 self.translate_message_list_to_str() - + # 更新观察次数 self.observe_times += 1 self.last_observe_time = new_messages[-1]["time"] - + # 检查是否需要更新summary current_time = int(datetime.now().timestamp()) if current_time - self.last_summary_time >= 30: # 如果超过30秒，重置计数 self.summary_count = 0 self.last_summary_time = current_time - + if self.summary_count < self.max_update_in_30s: # 如果30秒内更新次数小于2次 await self.update_talking_summary(new_messages_str) self.summary_count += 1 - + return self.observe_info - + async def carefully_observe(self): # 查找新消息，限制最多40条 - new_messages = list(db.messages.find({ - "chat_id": self.chat_id, - "time": {"$gt": self.last_observe_time} - }).sort("time", 1).limit(30)) # 按时间正序排列，最多30条 - + new_messages = list( + db.messages.find({"chat_id": self.chat_id, "time": {"$gt": self.last_observe_time}}) + .sort("time", 1) + .limit(30) + ) # 按时间正序排列，最多30条 + if not new_messages: - return self.observe_info #没有新消息，返回上次观察结果 - + return self.observe_info # 没有新消息，返回上次观察结果 + # 将新消息转换为字符串格式 new_messages_str = "" for msg in new_messages: if "sender_name" in msg and "content" in msg: new_messages_str += f"{msg['sender_name']}: {msg['content']}\n" - + # 将新消息添加到talking_message，同时保持列表长度不超过30条 self.talking_message.extend(new_messages) if len(self.talking_message) > 30: self.talking_message = self.talking_message[-30:] # 只保留最新的30条 self.translate_message_list_to_str() - + # 更新观察次数 self.observe_times += 1 self.last_observe_time = new_messages[-1]["time"] await self.update_talking_summary(new_messages_str) return self.observe_info - - - async def update_talking_summary(self,new_messages_str): - #基于已经有的talking_summary，和新的talking_message，生成一个summary + + async def update_talking_summary(self, new_messages_str): + # 基于已经有的talking_summary，和新的talking_message，生成一个summary # print(f"更新聊天总结：{self.talking_summary}") prompt = "" prompt = f"你正在参与一个qq群聊的讨论，这个群之前在聊的内容是：{self.observe_info}\n" prompt += f"现在群里的群友们产生了新的讨论，有了新的发言，具体内容如下：{new_messages_str}\n" - prompt += '''以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容， - 以及聊天中的一些重要信息，记得不要分点，不要太长，精简的概括成一段文本\n''' + prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容， + 以及聊天中的一些重要信息，记得不要分点，不要太长，精简的概括成一段文本\n""" prompt += "总结概括：" self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt) - + def translate_message_list_to_str(self): self.talking_message_str = "" for message in self.talking_message: diff --git a/src/think_flow_demo/sub_heartflow.py b/src/think_flow_demo/sub_heartflow.py index 0766077aa..879d3a3a6 100644 --- a/src/think_flow_demo/sub_heartflow.py +++ b/src/think_flow_demo/sub_heartflow.py @@ -7,13 +7,13 @@ import re import time from src.plugins.schedule.schedule_generator import bot_schedule from src.plugins.memory_system.Hippocampus import HippocampusManager -from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402 +from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402 subheartflow_config = LogConfig( # 使用海马体专用样式 console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"], file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"], -) +) logger = get_module_logger("subheartflow", config=subheartflow_config) @@ -21,38 +21,39 @@ class CuttentState: def __init__(self): self.willing = 0 self.current_state_info = "" - + self.mood_manager = MoodManager() self.mood = self.mood_manager.get_prompt() - + def update_current_state_info(self): self.current_state_info = self.mood_manager.get_current_mood() class SubHeartflow: - def __init__(self,subheartflow_id): + def __init__(self, subheartflow_id): self.subheartflow_id = subheartflow_id - + self.current_mind = "" self.past_mind = [] - self.current_state : CuttentState = CuttentState() + self.current_state: CuttentState = CuttentState() self.llm_model = LLM_request( - model=global_config.llm_sub_heartflow, temperature=0.7, max_tokens=600, request_type="sub_heart_flow") - + model=global_config.llm_sub_heartflow, temperature=0.7, max_tokens=600, request_type="sub_heart_flow" + ) + self.main_heartflow_info = "" - + self.last_reply_time = time.time() self.last_active_time = time.time() # 添加最后激活时间 - + if not self.current_mind: self.current_mind = "你什么也没想" - + self.personality_info = " ".join(global_config.PROMPT_PERSONALITY) - + self.is_active = False - - self.observations : list[Observation] = [] - + + self.observations: list[Observation] = [] + def add_observation(self, observation: Observation): """添加一个新的observation对象到列表中，如果已存在相同id的observation则不添加""" # 查找是否存在相同id的observation @@ -62,16 +63,16 @@ class SubHeartflow: return # 如果没有找到相同id的observation，则添加新的 self.observations.append(observation) - + def remove_observation(self, observation: Observation): """从列表中移除一个observation对象""" if observation in self.observations: self.observations.remove(observation) - + def get_all_observations(self) -> list[Observation]: """获取所有observation对象""" return self.observations - + def clear_observations(self): """清空所有observation对象""" self.observations.clear() @@ -85,50 +86,45 @@ class SubHeartflow: else: self.is_active = True self.last_active_time = current_time # 更新最后激活时间 - + observation = self.observations[0] await observation.observe() - + self.current_state.update_current_state_info() - + await self.do_a_thinking() await self.judge_willing() await asyncio.sleep(60) - + # 检查是否超过10分钟没有激活 if current_time - self.last_active_time > 600: # 5分钟无回复/不在场，销毁 logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...") break # 退出循环，销毁自己 - + async def do_a_thinking(self): - current_thinking_info = self.current_mind mood_info = self.current_state.mood - + observation = self.observations[0] chat_observe_info = observation.observe_info print(f"chat_observe_info：{chat_observe_info}") - + # 调取记忆 related_memory = await HippocampusManager.get_instance().get_memory_from_text( - text=chat_observe_info, - max_memory_num=2, - max_memory_length=2, - max_depth=3, - fast_retrieval=False + text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False ) - + if related_memory: related_memory_info = "" for memory in related_memory: related_memory_info += memory[1] else: - related_memory_info = '' - + related_memory_info = "" + # print(f"相关记忆：{related_memory_info}") - - schedule_info = bot_schedule.get_current_num_task(num = 1,time_info = False) - + + schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False) + prompt = "" prompt += f"你刚刚在做的事情是：{schedule_info}\n" # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n" @@ -142,25 +138,25 @@ class SubHeartflow: prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，" prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:" reponse, reasoning_content = await self.llm_model.generate_response_async(prompt) - + self.update_current_mind(reponse) - + self.current_mind = reponse logger.debug(f"prompt:\n{prompt}\n") logger.info(f"麦麦的脑内状态：{self.current_mind}") - - async def do_after_reply(self,reply_content,chat_talking_prompt): + + async def do_after_reply(self, reply_content, chat_talking_prompt): # print("麦麦脑袋转起来了") current_thinking_info = self.current_mind mood_info = self.current_state.mood - + observation = self.observations[0] chat_observe_info = observation.observe_info - + message_new_info = chat_talking_prompt reply_info = reply_content - schedule_info = bot_schedule.get_current_num_task(num = 1,time_info = False) - + schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False) + prompt = "" prompt += f"你现在正在做的事情是：{schedule_info}\n" prompt += f"你{self.personality_info}\n" @@ -171,16 +167,16 @@ class SubHeartflow: prompt += f"你现在{mood_info}" prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白" prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:" - + reponse, reasoning_content = await self.llm_model.generate_response_async(prompt) - + self.update_current_mind(reponse) - + self.current_mind = reponse logger.info(f"麦麦回复后的脑内状态：{self.current_mind}") - + self.last_reply_time = time.time() - + async def judge_willing(self): # print("麦麦闹情绪了1") current_thinking_info = self.current_mind @@ -193,21 +189,20 @@ class SubHeartflow: prompt += f"你现在{mood_info}。" prompt += "现在请你思考，你想不想发言或者回复，请你输出一个数字，1-10，1表示非常不想，10表示非常想。" prompt += "请你用<>包裹你的回复意愿，输出<1>表示不想回复，输出<10>表示非常想回复。请你考虑，你完全可以不回复" - + response, reasoning_content = await self.llm_model.generate_response_async(prompt) # 解析willing值 - willing_match = re.search(r'<(\d+)>', response) + willing_match = re.search(r"<(\d+)>", response) if willing_match: self.current_state.willing = int(willing_match.group(1)) else: self.current_state.willing = 0 - + return self.current_state.willing - def update_current_mind(self,reponse): + def update_current_mind(self, reponse): self.past_mind.append(self.current_mind) self.current_mind = reponse # subheartflow = SubHeartflow() -