diff --git a/README.md b/README.md index de5b2e28..e7d45287 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ ## 🔥 更新和安装 -**最新版本: v0.11.5** ([更新日志](changelogs/changelog.md)) +**最新版本: v0.11.6** ([更新日志](changelogs/changelog.md)) 可前往 [Release](https://github.com/MaiM-with-u/MaiBot/releases/) 页面下载最新版本 @@ -71,16 +71,21 @@ **技术交流群：** [麦麦脑电图](https://qm.qq.com/q/RzmCiRtHEW) | - [麦麦脑磁图](https://qm.qq.com/q/wlH5eT8OmQ) | [麦麦大脑磁共振](https://qm.qq.com/q/VQ3XZrWgMs) | - [麦麦要当VTB](https://qm.qq.com/q/wGePTl1UyY) + [麦麦要当VTB](https://qm.qq.com/q/wGePTl1UyY) | + + 为了维持技术交流和互帮互助的氛围，请不要在技术交流群讨论过多无关内容~ **聊天吹水群：** - [麦麦之闲聊群](https://qm.qq.com/q/JxvHZnxyec) + 麦麦相关闲聊群 + **插件开发/测试版讨论群：** - [插件开发群](https://qm.qq.com/q/1036092828) + 进阶内容，包括插件开发，测试版使用等等 + ## 📚 文档 **部分内容可能更新不够及时，请注意版本对应** diff --git a/changelogs/changelog.md b/changelogs/changelog.md index 5f2c733f..c12e726b 100644 --- a/changelogs/changelog.md +++ b/changelogs/changelog.md @@ -1,17 +1,52 @@ # Changelog -## [0.11.5] - 2025-11-26 -### 主要功能更改 - - +## [0.11.6] - 2025-12-2 +### 🌟 重大更新 +- 大幅提高记忆检索能力，略微提高token消耗 +- 重构历史消息概括器，更好的主题记忆 +- 日志查看器性能革命性优化 +- 支持可视化查看麦麦LPMM知识图谱 +- 支持根据不同的模型提供商/模板/URL自动获取模型，可以不用手动输入模型了 +- 新增Baka引导系统，使用React-JoyTour实现很棒的用户引导系统（让Baka也能看懂！） +- 本地聊天室功能！！你可以直接在WebUI网页和麦麦聊天！！ +- 使用cookie模式替换原有的LocalStorage Token存储，可能需要重新手动输入一遍Token +- WebUI本地聊天室支持用户模拟和平台模拟的功能！ +- WebUI新增黑话管理 & 编辑界面 ### 细节功能更改 +- 可选记忆识别中是否启用jargon - 解耦表情包识别和图片识别 - 修复部分破损json的解析问题 - 黑话更高的提取效率，增加提取准确性 - 升级jargon，更快更精准 - 新增Lpmm可视化 +### webui细节更新 +- 修复侧边栏收起、UI及表格横向滚动等问题，优化Toast动画 +- 修复适配器配置、插件克隆、表情包注册等相关BUG +- 新增适配器/模型预设模式及模板，自动填写URL和类型 +- 支持模型任务列表拖拽排序 +- 更新重启弹窗和首次引导内容 +- 多处界面命名及标题优化，如模型配置相关菜单重命名和描述更新 +- 修复聊天配置“提及回复”相关开关命名错误 +- 调试配置新增“显示记忆/Planner/LPMM Prompt”选项 +- 新增卡片尺寸、排序、字号、行间距等个性化功能 +- 聊天ID及群聊选择优化，显示可读名称 +- 聊天编辑界面精简字段，新增后端聊天列表API支持 +- 默认行间距减小，显示更紧凑 +- 修复页面滚动、表情包排序、发言频率为0等问题 +- 新增React异常Traceback界面及模型列表搜索 +- 更新WebUI Icon，修复适配器docker路径等问题 +- 插件配置可视化编辑，表单控件/元数据/布局类型扩展 +- 新增插件API与开发文档 +- 新增机器人状态卡片和快速操作按钮 +- 调整饼图显示、颜色算法，修复部分统计及解析错误 +- 新增缓存、WebSocket配置 +- 表情包支持上传和缩略图 +- 修复首页极端加载、重启后CtrlC失效、主程序配置移动端适配等问题 +- 新增表达反思设置和WebUI聊天室“思考中”占位组件 +- 细节如移除部分字段或UI控件、优化按钮/弹窗/编辑逻辑等 + ## [0.11.5] - 2025-11-21 ### 🌟 重大更新 - WebUI 现支持手动重启麦麦，曲线救国版“热重载” diff --git a/src/chat/frequency_control/frequency_control.py b/src/chat/frequency_control/frequency_control.py index 78041ae7..897a92cc 100644 --- a/src/chat/frequency_control/frequency_control.py +++ b/src/chat/frequency_control/frequency_control.py @@ -1,5 +1,6 @@ from datetime import datetime import time +import asyncio from typing import Dict from src.chat.utils.chat_message_builder import ( @@ -46,6 +47,8 @@ class FrequencyControl: self.frequency_model = LLMRequest( model_set=model_config.model_task_config.utils_small, request_type="frequency.adjust" ) + # 频率调整锁，防止并发执行 + self._adjust_lock = asyncio.Lock() def get_talk_frequency_adjust(self) -> float: """获取发言频率调整值""" @@ -56,68 +59,78 @@ class FrequencyControl: self.talk_frequency_adjust = max(0.1, min(5.0, value)) async def trigger_frequency_adjust(self) -> None: - msg_list = get_raw_msg_by_timestamp_with_chat( - chat_id=self.chat_id, - timestamp_start=self.last_frequency_adjust_time, - timestamp_end=time.time(), - ) - - if time.time() - self.last_frequency_adjust_time < 160 or len(msg_list) <= 20: - return - else: - new_msg_list = get_raw_msg_by_timestamp_with_chat( + # 使用异步锁防止并发执行 + async with self._adjust_lock: + # 在锁内检查，避免并发触发 + current_time = time.time() + previous_adjust_time = self.last_frequency_adjust_time + + msg_list = get_raw_msg_by_timestamp_with_chat( chat_id=self.chat_id, - timestamp_start=self.last_frequency_adjust_time, - timestamp_end=time.time(), - limit=20, - limit_mode="latest", + timestamp_start=previous_adjust_time, + timestamp_end=current_time, ) - message_str = build_readable_messages( - new_msg_list, - replace_bot_name=True, - timestamp_mode="relative", - read_mark=0.0, - show_actions=False, - ) - time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" - bot_name = global_config.bot.nickname - bot_nickname = ( - f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else "" - ) - name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。" + if current_time - previous_adjust_time < 160 or len(msg_list) <= 20: + return - prompt = await global_prompt_manager.format_prompt( - "frequency_adjust_prompt", - name_block=name_block, - time_block=time_block, - message_str=message_str, - ) - response, (reasoning_content, _, _) = await self.frequency_model.generate_response_async( - prompt, - ) + # 立即更新调整时间，防止并发触发 + self.last_frequency_adjust_time = current_time - # logger.info(f"频率调整 prompt: {prompt}") - # logger.info(f"频率调整 response: {response}") + try: + new_msg_list = get_raw_msg_by_timestamp_with_chat( + chat_id=self.chat_id, + timestamp_start=previous_adjust_time, + timestamp_end=current_time, + limit=20, + limit_mode="latest", + ) - if global_config.debug.show_prompt: - logger.info(f"频率调整 prompt: {prompt}") - logger.info(f"频率调整 response: {response}") - logger.info(f"频率调整 reasoning_content: {reasoning_content}") + message_str = build_readable_messages( + new_msg_list, + replace_bot_name=True, + timestamp_mode="relative", + read_mark=0.0, + show_actions=False, + ) + time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" + bot_name = global_config.bot.nickname + bot_nickname = ( + f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else "" + ) + name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。" - final_value_by_api = frequency_api.get_current_talk_value(self.chat_id) + prompt = await global_prompt_manager.format_prompt( + "frequency_adjust_prompt", + name_block=name_block, + time_block=time_block, + message_str=message_str, + ) + response, (reasoning_content, _, _) = await self.frequency_model.generate_response_async( + prompt, + ) - # LLM依然输出过多内容时取消本次调整。合法最多4个字，但有的模型可能会输出一些markdown换行符等，需要长度宽限 - if len(response) < 20: - if "过于频繁" in response: - logger.info(f"频率调整: 过于频繁，调整值到{final_value_by_api}") - self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 0.8)) - elif "过少" in response: - logger.info(f"频率调整: 过少，调整值到{final_value_by_api}") - self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 1.2)) - self.last_frequency_adjust_time = time.time() - else: - logger.info("频率调整：response不符合要求，取消本次调整") + # logger.info(f"频率调整 prompt: {prompt}") + # logger.info(f"频率调整 response: {response}") + + if global_config.debug.show_prompt: + logger.info(f"频率调整 prompt: {prompt}") + logger.info(f"频率调整 response: {response}") + logger.info(f"频率调整 reasoning_content: {reasoning_content}") + + final_value_by_api = frequency_api.get_current_talk_value(self.chat_id) + + # LLM依然输出过多内容时取消本次调整。合法最多4个字，但有的模型可能会输出一些markdown换行符等，需要长度宽限 + if len(response) < 20: + if "过于频繁" in response: + logger.info(f"频率调整: 过于频繁，调整值到{final_value_by_api}") + self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 0.8)) + elif "过少" in response: + logger.info(f"频率调整: 过少，调整值到{final_value_by_api}") + self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 1.2)) + except Exception as e: + logger.error(f"频率调整失败: {e}") + # 即使失败也保持时间戳更新，避免频繁重试 class FrequencyControlManager: diff --git a/src/express/expression_learner.py b/src/express/expression_learner.py index 76cc8408..af69cad6 100644 --- a/src/express/expression_learner.py +++ b/src/express/expression_learner.py @@ -2,6 +2,7 @@ import time import json import os import re +import asyncio from typing import List, Optional, Tuple import traceback from src.common.logger import get_logger @@ -91,6 +92,9 @@ class ExpressionLearner: # 维护每个chat的上次学习时间 self.last_learning_time: float = time.time() + # 学习锁，防止并发执行学习任务 + self._learning_lock = asyncio.Lock() + # 学习参数 _, self.enable_learning, self.learning_intensity = global_config.expression.get_expression_config_for_chat( self.chat_id @@ -139,32 +143,45 @@ class ExpressionLearner: Returns: bool: 是否成功触发学习 """ - if not self.should_trigger_learning(): - return + # 使用异步锁防止并发执行 + async with self._learning_lock: + # 在锁内检查，避免并发触发 + # 如果锁被持有，其他协程会等待，但等待期间条件可能已变化，所以需要再次检查 + if not self.should_trigger_learning(): + return - try: - logger.info(f"在聊天流 {self.chat_name} 学习表达方式") - # 学习语言风格 - learnt_style = await self.learn_and_store(num=25) + # 保存学习开始前的时间戳，用于获取消息范围 + learning_start_timestamp = time.time() + previous_learning_time = self.last_learning_time + + # 立即更新学习时间，防止并发触发 + self.last_learning_time = learning_start_timestamp - # 更新学习时间 - self.last_learning_time = time.time() + try: + logger.info(f"在聊天流 {self.chat_name} 学习表达方式") + # 学习语言风格，传递学习开始前的时间戳 + learnt_style = await self.learn_and_store(num=25, timestamp_start=previous_learning_time) - if learnt_style: - logger.info(f"聊天流 {self.chat_name} 表达学习完成") - else: - logger.warning(f"聊天流 {self.chat_name} 表达学习未获得有效结果") + if learnt_style: + logger.info(f"聊天流 {self.chat_name} 表达学习完成") + else: + logger.warning(f"聊天流 {self.chat_name} 表达学习未获得有效结果") - except Exception as e: - logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}") - traceback.print_exc() - return + except Exception as e: + logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}") + traceback.print_exc() + # 即使失败也保持时间戳更新，避免频繁重试 + return - async def learn_and_store(self, num: int = 10) -> List[Tuple[str, str, str]]: + async def learn_and_store(self, num: int = 10, timestamp_start: Optional[float] = None) -> List[Tuple[str, str, str]]: """ 学习并存储表达方式 + + Args: + num: 学习数量 + timestamp_start: 学习开始的时间戳，如果为None则使用self.last_learning_time """ - learnt_expressions = await self.learn_expression(num) + learnt_expressions = await self.learn_expression(num, timestamp_start=timestamp_start) if learnt_expressions is None: logger.info("没有学习到表达风格") @@ -374,18 +391,22 @@ class ExpressionLearner: return matched_expressions - async def learn_expression(self, num: int = 10) -> Optional[List[Tuple[str, str, str, str]]]: + async def learn_expression(self, num: int = 10, timestamp_start: Optional[float] = None) -> Optional[List[Tuple[str, str, str, str]]]: """从指定聊天流学习表达方式 Args: num: 学习数量 + timestamp_start: 学习开始的时间戳，如果为None则使用self.last_learning_time """ current_time = time.time() + + # 使用传入的时间戳，如果没有则使用self.last_learning_time + start_timestamp = timestamp_start if timestamp_start is not None else self.last_learning_time # 获取上次学习之后的消息 random_msg = get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, - timestamp_start=self.last_learning_time, + timestamp_start=start_timestamp, timestamp_end=current_time, limit=num, ) diff --git a/src/jargon/jargon_explainer.py b/src/jargon/jargon_explainer.py index 02595080..28122008 100644 --- a/src/jargon/jargon_explainer.py +++ b/src/jargon/jargon_explainer.py @@ -249,3 +249,112 @@ async def explain_jargon_in_context(chat_id: str, messages: List[Any], chat_cont """ explainer = JargonExplainer(chat_id) return await explainer.explain_jargon(messages, chat_context) + + +def match_jargon_from_text(chat_text: str, chat_id: str) -> List[str]: + """直接在聊天文本中匹配已知的jargon，返回出现过的黑话列表 + + Args: + chat_text: 要匹配的聊天文本 + chat_id: 聊天ID + + Returns: + List[str]: 匹配到的黑话列表 + """ + if not chat_text or not chat_text.strip(): + return [] + + query = Jargon.select().where((Jargon.meaning.is_null(False)) & (Jargon.meaning != "")) + if global_config.jargon.all_global: + query = query.where(Jargon.is_global) + + query = query.order_by(Jargon.count.desc()) + + matched: Dict[str, None] = {} + + for jargon in query: + content = (jargon.content or "").strip() + if not content: + continue + + if not global_config.jargon.all_global and not jargon.is_global: + chat_id_list = parse_chat_id_list(jargon.chat_id) + if not chat_id_list_contains(chat_id_list, chat_id): + continue + + pattern = re.escape(content) + if re.search(r"[\u4e00-\u9fff]", content): + search_pattern = pattern + else: + search_pattern = r"\b" + pattern + r"\b" + + if re.search(search_pattern, chat_text, re.IGNORECASE): + matched[content] = None + + logger.info(f"匹配到 {len(matched)} 个黑话") + + return list(matched.keys()) + + +async def retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> str: + """对概念列表进行jargon检索 + + Args: + concepts: 概念列表 + chat_id: 聊天ID + + Returns: + str: 检索结果字符串 + """ + if not concepts: + return "" + + results = [] + exact_matches = [] # 收集所有精确匹配的概念 + for concept in concepts: + concept = concept.strip() + if not concept: + continue + + # 先尝试精确匹配 + jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=False) + + is_fuzzy_match = False + + # 如果精确匹配未找到，尝试模糊搜索 + if not jargon_results: + jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=True) + is_fuzzy_match = True + + if jargon_results: + # 找到结果 + if is_fuzzy_match: + # 模糊匹配 + output_parts = [f"未精确匹配到'{concept}'"] + for result in jargon_results: + found_content = result.get("content", "").strip() + meaning = result.get("meaning", "").strip() + if found_content and meaning: + output_parts.append(f"找到 '{found_content}' 的含义为：{meaning}") + results.append("，".join(output_parts)) + logger.info(f"在jargon库中找到匹配（模糊搜索）: {concept}，找到{len(jargon_results)}条结果") + else: + # 精确匹配 + output_parts = [] + for result in jargon_results: + meaning = result.get("meaning", "").strip() + if meaning: + output_parts.append(f"'{concept}' 为黑话或者网络简写，含义为：{meaning}") + results.append("；".join(output_parts) if len(output_parts) > 1 else output_parts[0]) + exact_matches.append(concept) # 收集精确匹配的概念，稍后统一打印 + else: + # 未找到，不返回占位信息，只记录日志 + logger.info(f"在jargon库中未找到匹配: {concept}") + + # 合并所有精确匹配的日志 + if exact_matches: + logger.info(f"找到黑话: {', '.join(exact_matches)}，共找到{len(exact_matches)}条结果") + + if results: + return "【概念检索结果】\n" + "\n".join(results) + "\n" + return "" \ No newline at end of file diff --git a/src/jargon/jargon_miner.py b/src/jargon/jargon_miner.py index 77cb15ce..b7f6c857 100644 --- a/src/jargon/jargon_miner.py +++ b/src/jargon/jargon_miner.py @@ -182,6 +182,9 @@ class JargonMiner: self.stream_name = stream_name if stream_name else self.chat_id self.cache_limit = 100 self.cache: OrderedDict[str, None] = OrderedDict() + + # 黑话提取锁，防止并发执行 + self._extraction_lock = asyncio.Lock() def _add_to_cache(self, content: str) -> None: """将提取到的黑话加入缓存，保持LRU语义""" @@ -436,261 +439,265 @@ class JargonMiner: return bool(recent_messages and len(recent_messages) >= self.min_messages_for_learning) async def run_once(self) -> None: - try: - if not self.should_trigger(): - return - - chat_stream = get_chat_manager().get_stream(self.chat_id) - if not chat_stream: - return - - # 记录本次提取的时间窗口，避免重复提取 - extraction_start_time = self.last_learning_time - extraction_end_time = time.time() - - # 拉取学习窗口内的消息 - messages = get_raw_msg_by_timestamp_with_chat_inclusive( - chat_id=self.chat_id, - timestamp_start=extraction_start_time, - timestamp_end=extraction_end_time, - limit=20, - ) - if not messages: - return - - # 按时间排序，确保编号与上下文一致 - messages = sorted(messages, key=lambda msg: msg.time or 0) - - chat_str, message_id_list = build_readable_messages_with_id( - messages=messages, - replace_bot_name=True, - timestamp_mode="relative", - truncate=False, - show_actions=False, - show_pic=True, - pic_single=True, - ) - if not chat_str.strip(): - return - - msg_id_to_index: Dict[str, int] = {} - for idx, (msg_id, _msg) in enumerate(message_id_list or []): - if not msg_id: - continue - msg_id_to_index[msg_id] = idx - if not msg_id_to_index: - logger.warning("未能生成消息ID映射，跳过本次提取") - return - - prompt: str = await global_prompt_manager.format_prompt( - "extract_jargon_prompt", - bot_name=global_config.bot.nickname, - chat_str=chat_str, - ) - - response, _ = await self.llm.generate_response_async(prompt, temperature=0.2) - if not response: - return - - if global_config.debug.show_jargon_prompt: - logger.info(f"jargon提取提示词: {prompt}") - logger.info(f"jargon提取结果: {response}") - - # 解析为JSON - entries: List[dict] = [] + # 使用异步锁防止并发执行 + async with self._extraction_lock: try: - resp = response.strip() - parsed = None - if resp.startswith("[") and resp.endswith("]"): - parsed = json.loads(resp) - else: - repaired = repair_json(resp) - if isinstance(repaired, str): - parsed = json.loads(repaired) - else: - parsed = repaired - - if isinstance(parsed, dict): - parsed = [parsed] - - if not isinstance(parsed, list): + # 在锁内检查，避免并发触发 + if not self.should_trigger(): return - for item in parsed: - if not isinstance(item, dict): - continue + chat_stream = get_chat_manager().get_stream(self.chat_id) + if not chat_stream: + return - content = str(item.get("content", "")).strip() - msg_id_value = item.get("msg_id") - - if not content: - continue - - if contains_bot_self_name(content): - logger.info(f"解析阶段跳过包含机器人昵称/别名的词条: {content}") - continue - - msg_id_str = str(msg_id_value or "").strip() - if not msg_id_str: - logger.warning(f"解析jargon失败：msg_id缺失，content={content}") - continue - - msg_index = msg_id_to_index.get(msg_id_str) - if msg_index is None: - logger.warning(f"解析jargon失败：msg_id未找到，content={content}, msg_id={msg_id_str}") - continue - - target_msg = messages[msg_index] - if is_bot_message(target_msg): - logger.info(f"解析阶段跳过引用机器人自身消息的词条: content={content}, msg_id={msg_id_str}") - continue - - context_paragraph = build_context_paragraph(messages, msg_index) - if not context_paragraph: - logger.warning(f"解析jargon失败：上下文为空，content={content}, msg_id={msg_id_str}") - continue - - entries.append({"content": content, "raw_content": [context_paragraph]}) - cached_entries = self._collect_cached_entries(messages) - if cached_entries: - entries.extend(cached_entries) - except Exception as e: - logger.error(f"解析jargon JSON失败: {e}; 原始: {response}") - return - - if not entries: - return - - # 去重并合并raw_content（按 content 聚合） - merged_entries: OrderedDict[str, Dict[str, List[str]]] = OrderedDict() - for entry in entries: - content_key = entry["content"] - raw_list = entry.get("raw_content", []) or [] - if content_key in merged_entries: - merged_entries[content_key]["raw_content"].extend(raw_list) - else: - merged_entries[content_key] = { - "content": content_key, - "raw_content": list(raw_list), - } - - uniq_entries = [] - for merged_entry in merged_entries.values(): - raw_content_list = merged_entry["raw_content"] - if raw_content_list: - merged_entry["raw_content"] = list(dict.fromkeys(raw_content_list)) - uniq_entries.append(merged_entry) - - saved = 0 - updated = 0 - for entry in uniq_entries: - content = entry["content"] - raw_content_list = entry["raw_content"] # 已经是列表 - - try: - # 查询所有content匹配的记录 - query = Jargon.select().where(Jargon.content == content) - - # 查找匹配的记录 - matched_obj = None - for obj in query: - if global_config.jargon.all_global: - # 开启all_global：所有content匹配的记录都可以 - matched_obj = obj - break - else: - # 关闭all_global：需要检查chat_id列表是否包含目标chat_id - chat_id_list = parse_chat_id_list(obj.chat_id) - if chat_id_list_contains(chat_id_list, self.chat_id): - matched_obj = obj - break - - if matched_obj: - obj = matched_obj - try: - obj.count = (obj.count or 0) + 1 - except Exception: - obj.count = 1 - - # 合并raw_content列表：读取现有列表，追加新值，去重 - existing_raw_content = [] - if obj.raw_content: - try: - existing_raw_content = ( - json.loads(obj.raw_content) if isinstance(obj.raw_content, str) else obj.raw_content - ) - if not isinstance(existing_raw_content, list): - existing_raw_content = [existing_raw_content] if existing_raw_content else [] - except (json.JSONDecodeError, TypeError): - existing_raw_content = [obj.raw_content] if obj.raw_content else [] - - # 合并并去重 - merged_list = list(dict.fromkeys(existing_raw_content + raw_content_list)) - obj.raw_content = json.dumps(merged_list, ensure_ascii=False) - - # 更新chat_id列表：增加当前chat_id的计数 - chat_id_list = parse_chat_id_list(obj.chat_id) - updated_chat_id_list = update_chat_id_list(chat_id_list, self.chat_id, increment=1) - obj.chat_id = json.dumps(updated_chat_id_list, ensure_ascii=False) - - # 开启all_global时，确保记录标记为is_global=True - if global_config.jargon.all_global: - obj.is_global = True - # 关闭all_global时，保持原有is_global不变（不修改） - - obj.save() - - # 检查是否需要推断（达到阈值且超过上次判定值） - if _should_infer_meaning(obj): - # 异步触发推断，不阻塞主流程 - # 重新加载对象以确保数据最新 - jargon_id = obj.id - asyncio.create_task(self._infer_meaning_by_id(jargon_id)) - - updated += 1 - else: - # 没找到匹配记录，创建新记录 - if global_config.jargon.all_global: - # 开启all_global：新记录默认为is_global=True - is_global_new = True - else: - # 关闭all_global：新记录is_global=False - is_global_new = False - - # 使用新格式创建chat_id列表：[[chat_id, count]] - chat_id_list = [[self.chat_id, 1]] - chat_id_json = json.dumps(chat_id_list, ensure_ascii=False) - - Jargon.create( - content=content, - raw_content=json.dumps(raw_content_list, ensure_ascii=False), - chat_id=chat_id_json, - is_global=is_global_new, - count=1, - ) - saved += 1 - except Exception as e: - logger.error(f"保存jargon失败: chat_id={self.chat_id}, content={content}, err={e}") - continue - finally: - self._add_to_cache(content) - - # 固定输出提取的jargon结果，格式化为可读形式（只要有提取结果就输出） - if uniq_entries: - # 收集所有提取的jargon内容 - jargon_list = [entry["content"] for entry in uniq_entries] - jargon_str = ",".join(jargon_list) - - # 输出格式化的结果（使用logger.info会自动应用jargon模块的颜色） - logger.info(f"[{self.stream_name}]疑似黑话: {jargon_str}") - - # 更新为本次提取的结束时间，确保不会重复提取相同的消息窗口 + # 记录本次提取的时间窗口，避免重复提取 + extraction_start_time = self.last_learning_time + extraction_end_time = time.time() + + # 立即更新学习时间，防止并发触发 self.last_learning_time = extraction_end_time - if saved or updated: - logger.info(f"jargon写入: 新增 {saved} 条，更新 {updated} 条，chat_id={self.chat_id}") - except Exception as e: - logger.error(f"JargonMiner 运行失败: {e}") + # 拉取学习窗口内的消息 + messages = get_raw_msg_by_timestamp_with_chat_inclusive( + chat_id=self.chat_id, + timestamp_start=extraction_start_time, + timestamp_end=extraction_end_time, + limit=20, + ) + if not messages: + return + + # 按时间排序，确保编号与上下文一致 + messages = sorted(messages, key=lambda msg: msg.time or 0) + + chat_str, message_id_list = build_readable_messages_with_id( + messages=messages, + replace_bot_name=True, + timestamp_mode="relative", + truncate=False, + show_actions=False, + show_pic=True, + pic_single=True, + ) + if not chat_str.strip(): + return + + msg_id_to_index: Dict[str, int] = {} + for idx, (msg_id, _msg) in enumerate(message_id_list or []): + if not msg_id: + continue + msg_id_to_index[msg_id] = idx + if not msg_id_to_index: + logger.warning("未能生成消息ID映射，跳过本次提取") + return + + prompt: str = await global_prompt_manager.format_prompt( + "extract_jargon_prompt", + bot_name=global_config.bot.nickname, + chat_str=chat_str, + ) + + response, _ = await self.llm.generate_response_async(prompt, temperature=0.2) + if not response: + return + + if global_config.debug.show_jargon_prompt: + logger.info(f"jargon提取提示词: {prompt}") + logger.info(f"jargon提取结果: {response}") + + # 解析为JSON + entries: List[dict] = [] + try: + resp = response.strip() + parsed = None + if resp.startswith("[") and resp.endswith("]"): + parsed = json.loads(resp) + else: + repaired = repair_json(resp) + if isinstance(repaired, str): + parsed = json.loads(repaired) + else: + parsed = repaired + + if isinstance(parsed, dict): + parsed = [parsed] + + if not isinstance(parsed, list): + return + + for item in parsed: + if not isinstance(item, dict): + continue + + content = str(item.get("content", "")).strip() + msg_id_value = item.get("msg_id") + + if not content: + continue + + if contains_bot_self_name(content): + logger.info(f"解析阶段跳过包含机器人昵称/别名的词条: {content}") + continue + + msg_id_str = str(msg_id_value or "").strip() + if not msg_id_str: + logger.warning(f"解析jargon失败：msg_id缺失，content={content}") + continue + + msg_index = msg_id_to_index.get(msg_id_str) + if msg_index is None: + logger.warning(f"解析jargon失败：msg_id未找到，content={content}, msg_id={msg_id_str}") + continue + + target_msg = messages[msg_index] + if is_bot_message(target_msg): + logger.info(f"解析阶段跳过引用机器人自身消息的词条: content={content}, msg_id={msg_id_str}") + continue + + context_paragraph = build_context_paragraph(messages, msg_index) + if not context_paragraph: + logger.warning(f"解析jargon失败：上下文为空，content={content}, msg_id={msg_id_str}") + continue + + entries.append({"content": content, "raw_content": [context_paragraph]}) + cached_entries = self._collect_cached_entries(messages) + if cached_entries: + entries.extend(cached_entries) + except Exception as e: + logger.error(f"解析jargon JSON失败: {e}; 原始: {response}") + return + + if not entries: + return + + # 去重并合并raw_content（按 content 聚合） + merged_entries: OrderedDict[str, Dict[str, List[str]]] = OrderedDict() + for entry in entries: + content_key = entry["content"] + raw_list = entry.get("raw_content", []) or [] + if content_key in merged_entries: + merged_entries[content_key]["raw_content"].extend(raw_list) + else: + merged_entries[content_key] = { + "content": content_key, + "raw_content": list(raw_list), + } + + uniq_entries = [] + for merged_entry in merged_entries.values(): + raw_content_list = merged_entry["raw_content"] + if raw_content_list: + merged_entry["raw_content"] = list(dict.fromkeys(raw_content_list)) + uniq_entries.append(merged_entry) + + saved = 0 + updated = 0 + for entry in uniq_entries: + content = entry["content"] + raw_content_list = entry["raw_content"] # 已经是列表 + + try: + # 查询所有content匹配的记录 + query = Jargon.select().where(Jargon.content == content) + + # 查找匹配的记录 + matched_obj = None + for obj in query: + if global_config.jargon.all_global: + # 开启all_global：所有content匹配的记录都可以 + matched_obj = obj + break + else: + # 关闭all_global：需要检查chat_id列表是否包含目标chat_id + chat_id_list = parse_chat_id_list(obj.chat_id) + if chat_id_list_contains(chat_id_list, self.chat_id): + matched_obj = obj + break + + if matched_obj: + obj = matched_obj + try: + obj.count = (obj.count or 0) + 1 + except Exception: + obj.count = 1 + + # 合并raw_content列表：读取现有列表，追加新值，去重 + existing_raw_content = [] + if obj.raw_content: + try: + existing_raw_content = ( + json.loads(obj.raw_content) if isinstance(obj.raw_content, str) else obj.raw_content + ) + if not isinstance(existing_raw_content, list): + existing_raw_content = [existing_raw_content] if existing_raw_content else [] + except (json.JSONDecodeError, TypeError): + existing_raw_content = [obj.raw_content] if obj.raw_content else [] + + # 合并并去重 + merged_list = list(dict.fromkeys(existing_raw_content + raw_content_list)) + obj.raw_content = json.dumps(merged_list, ensure_ascii=False) + + # 更新chat_id列表：增加当前chat_id的计数 + chat_id_list = parse_chat_id_list(obj.chat_id) + updated_chat_id_list = update_chat_id_list(chat_id_list, self.chat_id, increment=1) + obj.chat_id = json.dumps(updated_chat_id_list, ensure_ascii=False) + + # 开启all_global时，确保记录标记为is_global=True + if global_config.jargon.all_global: + obj.is_global = True + # 关闭all_global时，保持原有is_global不变（不修改） + + obj.save() + + # 检查是否需要推断（达到阈值且超过上次判定值） + if _should_infer_meaning(obj): + # 异步触发推断，不阻塞主流程 + # 重新加载对象以确保数据最新 + jargon_id = obj.id + asyncio.create_task(self._infer_meaning_by_id(jargon_id)) + + updated += 1 + else: + # 没找到匹配记录，创建新记录 + if global_config.jargon.all_global: + # 开启all_global：新记录默认为is_global=True + is_global_new = True + else: + # 关闭all_global：新记录is_global=False + is_global_new = False + + # 使用新格式创建chat_id列表：[[chat_id, count]] + chat_id_list = [[self.chat_id, 1]] + chat_id_json = json.dumps(chat_id_list, ensure_ascii=False) + + Jargon.create( + content=content, + raw_content=json.dumps(raw_content_list, ensure_ascii=False), + chat_id=chat_id_json, + is_global=is_global_new, + count=1, + ) + saved += 1 + except Exception as e: + logger.error(f"保存jargon失败: chat_id={self.chat_id}, content={content}, err={e}") + continue + finally: + self._add_to_cache(content) + + # 固定输出提取的jargon结果，格式化为可读形式（只要有提取结果就输出） + if uniq_entries: + # 收集所有提取的jargon内容 + jargon_list = [entry["content"] for entry in uniq_entries] + jargon_str = ",".join(jargon_list) + + # 输出格式化的结果（使用logger.info会自动应用jargon模块的颜色） + logger.info(f"[{self.stream_name}]疑似黑话: {jargon_str}") + + if saved or updated: + logger.info(f"jargon写入: 新增 {saved} 条，更新 {updated} 条，chat_id={self.chat_id}") + except Exception as e: + logger.error(f"JargonMiner 运行失败: {e}") + # 即使失败也保持时间戳更新，避免频繁重试 class JargonMinerManager: diff --git a/src/memory_system/memory_retrieval.py b/src/memory_system/memory_retrieval.py index dc7755ea..2471c8fa 100644 --- a/src/memory_system/memory_retrieval.py +++ b/src/memory_system/memory_retrieval.py @@ -1,17 +1,16 @@ import time import json -import re import asyncio from typing import List, Dict, Any, Optional, Tuple, Set from src.common.logger import get_logger from src.config.config import global_config, model_config from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.plugin_system.apis import llm_api -from src.common.database.database_model import ThinkingBack, Jargon -from json_repair import repair_json +from src.common.database.database_model import ThinkingBack from src.memory_system.retrieval_tools import get_tool_registry, init_all_tools +from src.memory_system.memory_utils import parse_questions_json from src.llm_models.payload_content.message import MessageBuilder, RoleType, Message -from src.jargon.jargon_utils import parse_chat_id_list, chat_id_list_contains +from src.jargon.jargon_explainer import match_jargon_from_text, retrieve_concepts_with_jargon logger = get_logger("memory_retrieval") @@ -101,10 +100,6 @@ def init_memory_retrieval_prompt(): Prompt( """你的名字是{bot_name}。现在是{time_now}。 你正在参与聊天，你需要搜集信息来回答问题，帮助你参与聊天。 - -**重要限制：** -- 思考要简短，直接切入要点 - 当前需要解答的问题：{question} 已收集的信息： {collected_info} @@ -113,11 +108,13 @@ def init_memory_retrieval_prompt(): - 如果涉及过往事件，或者查询某个过去可能提到过的概念，或者某段时间发生的事件。可以使用聊天记录查询工具查询过往事件 - 如果涉及人物，可以使用人物信息查询工具查询人物信息 - 如果没有可靠信息，且查询时间充足，或者不确定查询类别，也可以使用lpmm知识库查询，作为辅助信息 -- 如果信息不足需要使用tool，说明需要查询什么，并输出为纯文本说明，然后调用相应工具查询（可并行调用多个工具） +- **如果信息不足需要使用tool，说明需要查询什么，并输出为纯文本说明，然后调用相应工具查询（可并行调用多个工具）** +- **如果当前已收集的信息足够回答问题，且能找到明确答案，调用found_answer工具标记已找到答案** **思考** -- 你可以对查询思路给出简短的思考 -- 你必须给出使用什么工具进行查询 +- 你可以对查询思路给出简短的思考：思考要简短，直接切入要点 +- 如果信息不足，你必须给出使用什么工具进行查询 +- 如果信息足够，你必须调用found_answer工具 """, name="memory_retrieval_react_prompt_head", ) @@ -149,198 +146,40 @@ def init_memory_retrieval_prompt(): ) -def _parse_react_response(response: str) -> Optional[Dict[str, Any]]: - """解析ReAct Agent的响应 - - Args: - response: LLM返回的响应 - - Returns: - Dict[str, Any]: 解析后的动作信息，如果解析失败返回None - 格式: {"thought": str, "actions": List[Dict[str, Any]]} - 每个action格式: {"action_type": str, "action_params": dict} - """ - try: - # 尝试提取JSON（可能包含在```json代码块中） - json_pattern = r"```json\s*(.*?)\s*```" - matches = re.findall(json_pattern, response, re.DOTALL) - - if matches: - json_str = matches[0] - else: - # 尝试直接解析整个响应 - json_str = response.strip() - - # 修复可能的JSON错误 - repaired_json = repair_json(json_str) - - # 解析JSON - action_info = json.loads(repaired_json) - - if not isinstance(action_info, dict): - logger.warning(f"解析的JSON不是对象格式: {action_info}") - return None - - # 确保actions字段存在且为列表 - if "actions" not in action_info: - logger.warning(f"响应中缺少actions字段: {action_info}") - return None - - if not isinstance(action_info["actions"], list): - logger.warning(f"actions字段不是数组格式: {action_info['actions']}") - return None - - # 确保actions不为空 - if len(action_info["actions"]) == 0: - logger.warning("actions数组为空") - return None - - return action_info - - except Exception as e: - logger.error(f"解析ReAct响应失败: {e}, 响应内容: {response[:200]}...") - return None -async def _retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> str: - """对概念列表进行jargon检索 - - Args: - concepts: 概念列表 - chat_id: 聊天ID - - Returns: - str: 检索结果字符串 - """ - if not concepts: - return "" - - from src.jargon.jargon_miner import search_jargon - - results = [] - exact_matches = [] # 收集所有精确匹配的概念 - for concept in concepts: - concept = concept.strip() - if not concept: - continue - - # 先尝试精确匹配 - jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=False) - - is_fuzzy_match = False - - # 如果精确匹配未找到，尝试模糊搜索 - if not jargon_results: - jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=True) - is_fuzzy_match = True - - if jargon_results: - # 找到结果 - if is_fuzzy_match: - # 模糊匹配 - output_parts = [f"未精确匹配到'{concept}'"] - for result in jargon_results: - found_content = result.get("content", "").strip() - meaning = result.get("meaning", "").strip() - if found_content and meaning: - output_parts.append(f"找到 '{found_content}' 的含义为：{meaning}") - results.append("，".join(output_parts)) - logger.info(f"在jargon库中找到匹配（模糊搜索）: {concept}，找到{len(jargon_results)}条结果") - else: - # 精确匹配 - output_parts = [] - for result in jargon_results: - meaning = result.get("meaning", "").strip() - if meaning: - output_parts.append(f"'{concept}' 为黑话或者网络简写，含义为：{meaning}") - results.append("；".join(output_parts) if len(output_parts) > 1 else output_parts[0]) - exact_matches.append(concept) # 收集精确匹配的概念，稍后统一打印 - else: - # 未找到，不返回占位信息，只记录日志 - logger.info(f"在jargon库中未找到匹配: {concept}") - - # 合并所有精确匹配的日志 - if exact_matches: - logger.info(f"找到黑话: {', '.join(exact_matches)}，共找到{len(exact_matches)}条结果") - - if results: - return "【概念检索结果】\n" + "\n".join(results) + "\n" - return "" - - -def _match_jargon_from_text(chat_text: str, chat_id: str) -> List[str]: - """直接在聊天文本中匹配已知的jargon，返回出现过的黑话列表""" - # print(chat_text) - if not chat_text or not chat_text.strip(): - return [] - - start_time = time.time() - - query = Jargon.select().where((Jargon.meaning.is_null(False)) & (Jargon.meaning != "")) - if global_config.jargon.all_global: - query = query.where(Jargon.is_global) - - query = query.order_by(Jargon.count.desc()) - - query_time = time.time() - matched: Dict[str, None] = {} - - for jargon in query: - content = (jargon.content or "").strip() - if not content: - continue - - if not global_config.jargon.all_global and not jargon.is_global: - chat_id_list = parse_chat_id_list(jargon.chat_id) - if not chat_id_list_contains(chat_id_list, chat_id): - continue - - pattern = re.escape(content) - if re.search(r"[\u4e00-\u9fff]", content): - search_pattern = pattern - else: - search_pattern = r"\b" + pattern + r"\b" - - if re.search(search_pattern, chat_text, re.IGNORECASE): - matched[content] = None - - # end_time = time.time() - logger.info( - # f"记忆检索黑话匹配: 查询耗时 {(query_time - start_time):.3f}s, " - # f"匹配耗时 {(end_time - query_time):.3f}s, 总耗时 {(end_time - start_time):.3f}s, " - f"匹配到 {len(matched)} 个黑话" - ) - - return list(matched.keys()) - - -def _log_conversation_messages(conversation_messages: List[Message], head_prompt: Optional[str] = None) -> None: +def _log_conversation_messages( + conversation_messages: List[Message], + head_prompt: Optional[str] = None, + final_status: Optional[str] = None, +) -> None: """输出对话消息列表的日志 - + Args: conversation_messages: 对话消息列表 head_prompt: 第一条系统消息（head_prompt）的内容，可选 + final_status: 最终结果状态描述（例如：找到答案/未找到答案），可选 """ if not global_config.debug.show_memory_prompt: return - - log_lines = [] - + + log_lines: List[str] = [] + # 如果有head_prompt，先添加为第一条消息 if head_prompt: - msg_info = "\n[消息 1] 角色: System 内容类型: 文本\n========================================" + msg_info = "========================================\n[消息 1] 角色: System 内容类型: 文本\n-----------------------------" msg_info += f"\n{head_prompt}" log_lines.append(msg_info) start_idx = 2 else: start_idx = 1 - + if not conversation_messages and not head_prompt: return - + for idx, msg in enumerate(conversation_messages, start_idx): role_name = msg.role.value if hasattr(msg.role, "value") else str(msg.role) - + # 处理内容 - 显示完整内容，不截断 if isinstance(msg.content, str): full_content = msg.content @@ -353,25 +192,28 @@ def _log_conversation_messages(conversation_messages: List[Message], head_prompt else: full_content = str(msg.content) content_type = "未知" - + # 构建单条消息的日志信息 - msg_info = f"\n[消息 {idx}] 角色: {role_name} 内容类型: {content_type}\n========================================" - + msg_info = f"\n========================================\n[消息 {idx}] 角色: {role_name} 内容类型: {content_type}\n-----------------------------" + if full_content: msg_info += f"\n{full_content}" - + if msg.tool_calls: msg_info += f"\n 工具调用: {len(msg.tool_calls)}个" for tool_call in msg.tool_calls: msg_info += f"\n - {tool_call}" - + if msg.tool_call_id: msg_info += f"\n 工具调用ID: {msg.tool_call_id}" - + log_lines.append(msg_info) - + total_count = len(conversation_messages) + (1 if head_prompt else 0) - logger.info(f"消息列表 (共{total_count}条):{''.join(log_lines)}") + log_text = f"消息列表 (共{total_count}条):{''.join(log_lines)}" + if final_status: + log_text += f"\n\n[最终结果] {final_status}" + logger.info(log_text) async def _react_agent_solve_question( @@ -407,7 +249,7 @@ async def _react_agent_solve_question( thinking_steps = [] is_timeout = False conversation_messages: List[Message] = [] - last_head_prompt: Optional[str] = None # 保存最后一次使用的head_prompt + first_head_prompt: Optional[str] = None # 保存第一次使用的head_prompt（用于日志显示） for iteration in range(max_iterations): # 检查超时 @@ -430,40 +272,6 @@ async def _react_agent_solve_question( remaining_iterations = max_iterations - current_iteration is_final_iteration = current_iteration >= max_iterations - # 每次迭代开始时，先评估当前信息是否足够回答问题 - evaluation_prompt = await global_prompt_manager.format_prompt( - "memory_retrieval_react_final_prompt", - bot_name=bot_name, - time_now=time_now, - question=question, - collected_info=collected_info if collected_info else "暂无信息", - current_iteration=current_iteration, - remaining_iterations=remaining_iterations, - max_iterations=max_iterations, - ) - - # if global_config.debug.show_memory_prompt: - # logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 评估Prompt: {evaluation_prompt}") - - eval_success, eval_response, eval_reasoning_content, eval_model_name, eval_tool_calls = await llm_api.generate_with_model_with_tools( - evaluation_prompt, - model_config=model_config.model_task_config.tool_use, - tool_options=[], # 评估阶段不提供工具 - request_type="memory.react.eval", - ) - - if not eval_success: - logger.error(f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段 LLM调用失败: {eval_response}") - # 评估失败，如果还有剩余迭代次数，尝试继续查询 - if not is_final_iteration: - continue - else: - break - - logger.info( - f"ReAct Agent 第 {iteration + 1} 次迭代 评估响应: {eval_response}" - ) - # 提取函数调用中参数的值，支持单引号和双引号 def extract_quoted_content(text, func_name, param_name): """从文本中提取函数调用中参数的值，支持单引号和双引号 @@ -522,88 +330,132 @@ async def _react_agent_solve_question( return None - # 从评估响应中提取found_answer或not_enough_info - found_answer_content = None - not_enough_info_reason = None + # 如果是最后一次迭代，使用final_prompt进行总结 + if is_final_iteration: + evaluation_prompt = await global_prompt_manager.format_prompt( + "memory_retrieval_react_final_prompt", + bot_name=bot_name, + time_now=time_now, + question=question, + collected_info=collected_info if collected_info else "暂无信息", + current_iteration=current_iteration, + remaining_iterations=remaining_iterations, + max_iterations=max_iterations, + ) - if eval_response: - found_answer_content = extract_quoted_content(eval_response, "found_answer", "answer") - if not found_answer_content: - not_enough_info_reason = extract_quoted_content(eval_response, "not_enough_info", "reason") + if global_config.debug.show_memory_prompt: + logger.info(f"ReAct Agent 最终评估Prompt: {evaluation_prompt}") - # 如果找到答案，直接返回 - if found_answer_content: - eval_step = { - "iteration": iteration + 1, - "thought": f"[评估] {eval_response}", - "actions": [{"action_type": "found_answer", "action_params": {"answer": found_answer_content}}], - "observations": ["评估阶段检测到found_answer"] - } - thinking_steps.append(eval_step) - logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段找到关于问题{question}的答案: {found_answer_content}") - - # React完成时输出消息列表 - _log_conversation_messages(conversation_messages, last_head_prompt) - - return True, found_answer_content, thinking_steps, False + eval_success, eval_response, eval_reasoning_content, eval_model_name, eval_tool_calls = await llm_api.generate_with_model_with_tools( + evaluation_prompt, + model_config=model_config.model_task_config.tool_use, + tool_options=[], # 最终评估阶段不提供工具 + request_type="memory.react.final", + ) - # 如果评估为not_enough_info，且是最终迭代，返回not_enough_info - if not_enough_info_reason: - if is_final_iteration: + if not eval_success: + logger.error(f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段 LLM调用失败: {eval_response}") + _log_conversation_messages( + conversation_messages, + head_prompt=first_head_prompt, + final_status="未找到答案：最终评估阶段LLM调用失败", + ) + return False, "最终评估阶段LLM调用失败", thinking_steps, False + + logger.info( + f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估响应: {eval_response}" + ) + + # 从最终评估响应中提取found_answer或not_enough_info + found_answer_content = None + not_enough_info_reason = None + + if eval_response: + found_answer_content = extract_quoted_content(eval_response, "found_answer", "answer") + if not found_answer_content: + not_enough_info_reason = extract_quoted_content(eval_response, "not_enough_info", "reason") + + # 如果找到答案，返回 + if found_answer_content: eval_step = { "iteration": iteration + 1, - "thought": f"[评估] {eval_response}", + "thought": f"[最终评估] {eval_response}", + "actions": [{"action_type": "found_answer", "action_params": {"answer": found_answer_content}}], + "observations": ["最终评估阶段检测到found_answer"] + } + thinking_steps.append(eval_step) + logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段找到关于问题{question}的答案: {found_answer_content}") + + _log_conversation_messages( + conversation_messages, + head_prompt=first_head_prompt, + final_status=f"找到答案：{found_answer_content}", + ) + + return True, found_answer_content, thinking_steps, False + + # 如果评估为not_enough_info，返回空字符串（不返回任何信息） + if not_enough_info_reason: + eval_step = { + "iteration": iteration + 1, + "thought": f"[最终评估] {eval_response}", "actions": [{"action_type": "not_enough_info", "action_params": {"reason": not_enough_info_reason}}], - "observations": ["评估阶段检测到not_enough_info"] + "observations": ["最终评估阶段检测到not_enough_info"] } thinking_steps.append(eval_step) logger.info( - f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段判断信息不足: {not_enough_info_reason}" + f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段判断信息不足: {not_enough_info_reason}" ) - # React完成时输出消息列表 - _log_conversation_messages(conversation_messages, last_head_prompt) - - return False, not_enough_info_reason, thinking_steps, False - else: - # 非最终迭代，信息不足，继续搜集信息 - logger.info( - f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段判断信息不足: {not_enough_info_reason}，继续查询" + _log_conversation_messages( + conversation_messages, + head_prompt=first_head_prompt, + final_status=f"未找到答案：{not_enough_info_reason}", ) + + return False, "", thinking_steps, False - # 如果是最终迭代但没有明确判断，视为not_enough_info - if is_final_iteration: + # 如果没有明确判断，视为not_enough_info，返回空字符串（不返回任何信息） eval_step = { "iteration": iteration + 1, - "thought": f"[评估] {eval_response}", + "thought": f"[最终评估] {eval_response}", "actions": [{"action_type": "not_enough_info", "action_params": {"reason": "已到达最后一次迭代，无法找到答案"}}], "observations": ["已到达最后一次迭代，无法找到答案"] } thinking_steps.append(eval_step) logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 已到达最后一次迭代，无法找到答案") - # React完成时输出消息列表 - _log_conversation_messages(conversation_messages, last_head_prompt) + _log_conversation_messages( + conversation_messages, + head_prompt=first_head_prompt, + final_status="未找到答案：已到达最后一次迭代，无法找到答案", + ) - return False, "已到达最后一次迭代，无法找到答案", thinking_steps, False + return False, "", thinking_steps, False - # 非最终迭代且信息不足，使用head_prompt决定调用哪些工具 + # 前n-1次迭代，使用head_prompt决定调用哪些工具（包含found_answer工具） tool_definitions = tool_registry.get_tool_definitions() logger.info( f"ReAct Agent 第 {iteration + 1} 次迭代，问题: {question}|可用工具数量: {len(tool_definitions)}" ) - head_prompt = await global_prompt_manager.format_prompt( - "memory_retrieval_react_prompt_head", - bot_name=bot_name, - time_now=time_now, - question=question, - collected_info=collected_info if collected_info else "", - current_iteration=current_iteration, - remaining_iterations=remaining_iterations, - max_iterations=max_iterations, - ) - last_head_prompt = head_prompt # 保存最后一次使用的head_prompt + # head_prompt应该只构建一次，使用初始的collected_info，后续迭代都复用同一个 + if first_head_prompt is None: + # 第一次构建，使用初始的collected_info（即initial_info） + initial_collected_info = initial_info if initial_info else "" + first_head_prompt = await global_prompt_manager.format_prompt( + "memory_retrieval_react_prompt_head", + bot_name=bot_name, + time_now=time_now, + question=question, + collected_info=initial_collected_info, + current_iteration=current_iteration, + remaining_iterations=remaining_iterations, + max_iterations=max_iterations, + ) + + # 后续迭代都复用第一次构建的head_prompt + head_prompt = first_head_prompt def message_factory( _client, @@ -635,7 +487,7 @@ async def _react_agent_solve_question( request_type="memory.react", ) - logger.info( + logger.debug( f"ReAct Agent 第 {iteration + 1} 次迭代 模型: {model_name} ，调用工具数量: {len(tool_calls) if tool_calls else 0} ，调用工具响应: {response}" ) @@ -643,8 +495,7 @@ async def _react_agent_solve_question( logger.error(f"ReAct Agent LLM调用失败: {response}") break - # 注意：这里不检查found_answer或not_enough_info，这些只在评估阶段（memory_retrieval_react_final_prompt）检查 - # memory_retrieval_react_prompt_head只用于决定调用哪些工具来搜集信息 + # 注意：这里会检查found_answer工具调用，如果检测到found_answer工具，会直接返回答案 assistant_message: Optional[Message] = None if tool_calls: @@ -686,16 +537,42 @@ async def _react_agent_solve_question( continue # 处理工具调用 + # 首先检查是否有found_answer工具调用，如果有则立即返回，不再处理其他工具 + found_answer_from_tool = None + for tool_call in tool_calls: + tool_name = tool_call.func_name + tool_args = tool_call.args or {} + + if tool_name == "found_answer": + found_answer_from_tool = tool_args.get("answer", "") + if found_answer_from_tool: + step["actions"].append({"action_type": "found_answer", "action_params": {"answer": found_answer_from_tool}}) + step["observations"] = ["检测到found_answer工具调用"] + thinking_steps.append(step) + logger.debug(f"ReAct Agent 第 {iteration + 1} 次迭代 通过found_answer工具找到关于问题{question}的答案: {found_answer_from_tool}") + + _log_conversation_messages( + conversation_messages, + head_prompt=first_head_prompt, + final_status=f"找到答案：{found_answer_from_tool}", + ) + + return True, found_answer_from_tool, thinking_steps, False + + # 如果没有found_answer工具调用，或者found_answer工具调用没有答案，继续处理其他工具 tool_tasks = [] - for i, tool_call in enumerate(tool_calls): tool_name = tool_call.func_name tool_args = tool_call.args or {} - logger.info( + logger.debug( f"ReAct Agent 第 {iteration + 1} 次迭代 工具调用 {i + 1}/{len(tool_calls)}: {tool_name}({tool_args})" ) + # 跳过found_answer工具调用（已经在上面处理过了） + if tool_name == "found_answer": + continue + # 普通工具调用 tool = tool_registry.get_tool(tool_name) if tool: @@ -740,15 +617,10 @@ async def _react_agent_solve_question( step["observations"].append(observation_text) collected_info += f"\n{observation_text}\n" if stripped_observation: - tool_builder = MessageBuilder() - tool_builder.set_role(RoleType.Tool) - tool_builder.add_text_content(observation_text) - tool_builder.add_tool_call(tool_call_item.call_id) - conversation_messages.append(tool_builder.build()) + # 检查工具输出中是否有新的jargon，如果有则追加到工具结果中 if enable_jargon_detection: - jargon_concepts = _match_jargon_from_text(stripped_observation, chat_id) + jargon_concepts = match_jargon_from_text(stripped_observation, chat_id) if jargon_concepts: - jargon_info = "" new_concepts = [] for concept in jargon_concepts: normalized_concept = concept.strip() @@ -756,10 +628,18 @@ async def _react_agent_solve_question( new_concepts.append(normalized_concept) seen_jargon_concepts.add(normalized_concept) if new_concepts: - jargon_info = await _retrieve_concepts_with_jargon(new_concepts, chat_id) - if jargon_info: - collected_info += f"\n{jargon_info}\n" - logger.info(f"工具输出触发黑话解析: {new_concepts}") + jargon_info = await retrieve_concepts_with_jargon(new_concepts, chat_id) + if jargon_info: + # 将jargon查询结果追加到工具结果中 + observation_text += f"\n\n{jargon_info}" + collected_info += f"\n{jargon_info}\n" + logger.info(f"工具输出触发黑话解析: {new_concepts}") + + tool_builder = MessageBuilder() + tool_builder.set_role(RoleType.Tool) + tool_builder.add_text_content(observation_text) + tool_builder.add_tool_call(tool_call_item.call_id) + conversation_messages.append(tool_builder.build()) thinking_steps.append(step) @@ -776,13 +656,18 @@ async def _react_agent_solve_question( logger.warning("ReAct Agent达到最大迭代次数，直接视为not_enough_info") # React完成时输出消息列表 - _log_conversation_messages(conversation_messages, last_head_prompt) + timeout_reason = "超时" if is_timeout else "达到最大迭代次数" + _log_conversation_messages( + conversation_messages, + head_prompt=first_head_prompt, + final_status=f"未找到答案：{timeout_reason}", + ) - return False, "未找到相关信息", thinking_steps, is_timeout + return False, "", thinking_steps, is_timeout -def _get_recent_query_history(chat_id: str, time_window_seconds: float = 300.0) -> str: - """获取最近一段时间内的查询历史 +def _get_recent_query_history(chat_id: str, time_window_seconds: float = 600.0) -> str: + """获取最近一段时间内的查询历史（用于避免重复查询） Args: chat_id: 聊天ID @@ -832,6 +717,49 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 300.0) return "" +def _get_recent_found_answers(chat_id: str, time_window_seconds: float = 600.0) -> List[str]: + """获取最近一段时间内已找到答案的查询记录（用于返回给 replyer） + + Args: + chat_id: 聊天ID + time_window_seconds: 时间窗口（秒），默认10分钟 + + Returns: + List[str]: 格式化的答案列表，每个元素格式为 "问题：xxx\n答案：xxx" + """ + try: + current_time = time.time() + start_time = current_time - time_window_seconds + + # 查询最近时间窗口内已找到答案的记录，按更新时间倒序 + records = ( + ThinkingBack.select() + .where( + (ThinkingBack.chat_id == chat_id) + & (ThinkingBack.update_time >= start_time) + & (ThinkingBack.found_answer == 1) + & (ThinkingBack.answer.is_null(False)) + & (ThinkingBack.answer != "") + ) + .order_by(ThinkingBack.update_time.desc()) + .limit(3) # 最多返回5条最近的记录 + ) + + if not records.exists(): + return [] + + found_answers = [] + for record in records: + if record.answer: + found_answers.append(f"问题：{record.question}\n答案：{record.answer}") + + return found_answers + + except Exception as e: + logger.error(f"获取最近已找到答案的记录失败: {e}") + return [] + + def _store_thinking_back( chat_id: str, question: str, context: str, found_answer: bool, answer: str, thinking_steps: List[Dict[str, Any]] ) -> None: @@ -969,8 +897,8 @@ async def build_memory_retrieval_prompt( bot_name = global_config.bot.nickname chat_id = chat_stream.stream_id - # 获取最近查询历史（最近1小时内的查询） - recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=300.0) + # 获取最近查询历史（最近10分钟内的查询，用于避免重复查询） + recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=600.0) if not recent_query_history: recent_query_history = "最近没有查询记录。" @@ -1000,7 +928,7 @@ async def build_memory_retrieval_prompt( return "" # 解析概念列表和问题列表 - _, questions = _parse_questions_json(response) + _, questions = parse_questions_json(response) if questions: logger.info(f"解析到 {len(questions)} 个问题: {questions}") @@ -1009,7 +937,7 @@ async def build_memory_retrieval_prompt( if enable_jargon_detection: # 使用匹配逻辑自动识别聊天中的黑话概念 - concepts = _match_jargon_from_text(message, chat_id) + concepts = match_jargon_from_text(message, chat_id) if concepts: logger.info(f"黑话匹配命中 {len(concepts)} 个概念: {concepts}") else: @@ -1020,12 +948,12 @@ async def build_memory_retrieval_prompt( # 对匹配到的概念进行jargon检索，作为初始信息 initial_info = "" if enable_jargon_detection and concepts: - concept_info = await _retrieve_concepts_with_jargon(concepts, chat_id) + concept_info = await retrieve_concepts_with_jargon(concepts, chat_id) if concept_info: initial_info += concept_info - logger.info(f"概念检索完成，结果: {concept_info}") + logger.debug(f"概念检索完成，结果: {concept_info}") else: - logger.info("概念检索未找到任何结果") + logger.debug("概念检索未找到任何结果") if not questions: logger.debug("模型认为不需要检索记忆或解析失败，不返回任何查询结果") @@ -1060,67 +988,47 @@ async def build_memory_retrieval_prompt( elif result is not None: question_results.append(result) + # 获取最近10分钟内已找到答案的缓存记录 + cached_answers = _get_recent_found_answers(chat_id, time_window_seconds=600.0) + + # 合并当前查询结果和缓存答案（去重：如果当前查询的问题在缓存中已存在，优先使用当前结果） + all_results = [] + + # 先添加当前查询的结果 + current_questions = set() + for result in question_results: + # 提取问题（格式为 "问题：xxx\n答案：xxx"） + if result.startswith("问题："): + question_end = result.find("\n答案：") + if question_end != -1: + current_questions.add(result[4:question_end]) + all_results.append(result) + + # 添加缓存答案（排除当前查询中已存在的问题） + for cached_answer in cached_answers: + if cached_answer.startswith("问题："): + question_end = cached_answer.find("\n答案：") + if question_end != -1: + cached_question = cached_answer[4:question_end] + if cached_question not in current_questions: + all_results.append(cached_answer) + end_time = time.time() - if question_results: - retrieved_memory = "\n\n".join(question_results) - logger.info(f"记忆检索成功，耗时: {(end_time - start_time):.3f}秒，包含 {len(question_results)} 条记忆") + if all_results: + retrieved_memory = "\n\n".join(all_results) + current_count = len(question_results) + cached_count = len(all_results) - current_count + logger.info( + f"记忆检索成功，耗时: {(end_time - start_time):.3f}秒，" + f"当前查询 {current_count} 条记忆，缓存 {cached_count} 条记忆，共 {len(all_results)} 条记忆" + ) return f"你回忆起了以下信息：\n{retrieved_memory}\n如果与回复内容相关，可以参考这些回忆的信息。\n" else: - logger.debug("所有问题均未找到答案") + logger.debug("所有问题均未找到答案，且无缓存答案") return "" except Exception as e: logger.error(f"记忆检索时发生异常: {str(e)}") return "" - -def _parse_questions_json(response: str) -> Tuple[List[str], List[str]]: - """解析问题JSON，返回概念列表和问题列表 - - Args: - response: LLM返回的响应 - - Returns: - Tuple[List[str], List[str]]: (概念列表, 问题列表) - """ - try: - # 尝试提取JSON（可能包含在```json代码块中） - json_pattern = r"```json\s*(.*?)\s*```" - matches = re.findall(json_pattern, response, re.DOTALL) - - if matches: - json_str = matches[0] - else: - # 尝试直接解析整个响应 - json_str = response.strip() - - # 修复可能的JSON错误 - repaired_json = repair_json(json_str) - - # 解析JSON - parsed = json.loads(repaired_json) - - # 只支持新格式：包含concepts和questions的对象 - if not isinstance(parsed, dict): - logger.warning(f"解析的JSON不是对象格式: {parsed}") - return [], [] - - concepts_raw = parsed.get("concepts", []) - questions_raw = parsed.get("questions", []) - - # 确保是列表 - if not isinstance(concepts_raw, list): - concepts_raw = [] - if not isinstance(questions_raw, list): - questions_raw = [] - - # 确保所有元素都是字符串 - concepts = [c for c in concepts_raw if isinstance(c, str) and c.strip()] - questions = [q for q in questions_raw if isinstance(q, str) and q.strip()] - - return concepts, questions - - except Exception as e: - logger.error(f"解析问题JSON失败: {e}, 响应内容: {response[:200]}...") - return [], [] diff --git a/src/memory_system/memory_utils.py b/src/memory_system/memory_utils.py index bff39f95..7aa33a52 100644 --- a/src/memory_system/memory_utils.py +++ b/src/memory_system/memory_utils.py @@ -8,7 +8,8 @@ import json import re from datetime import datetime from typing import Tuple -from difflib import SequenceMatcher +from typing import List +from json_repair import repair_json from src.common.logger import get_logger @@ -16,101 +17,56 @@ from src.common.logger import get_logger logger = get_logger("memory_utils") -def parse_md_json(json_text: str) -> list[str]: - """从Markdown格式的内容中提取JSON对象和推理内容""" - json_objects = [] - reasoning_content = "" - # 使用正则表达式查找```json包裹的JSON内容 - json_pattern = r"```json\s*(.*?)\s*```" - matches = re.findall(json_pattern, json_text, re.DOTALL) - - # 提取JSON之前的内容作为推理文本 - if matches: - # 找到第一个```json的位置 - first_json_pos = json_text.find("```json") - if first_json_pos > 0: - reasoning_content = json_text[:first_json_pos].strip() - # 清理推理内容中的注释标记 - reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE) - reasoning_content = reasoning_content.strip() - - for match in matches: - try: - # 清理可能的注释和格式问题 - json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释 - json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释 - if json_str := json_str.strip(): - json_obj = json.loads(json_str) - if isinstance(json_obj, dict): - json_objects.append(json_obj) - elif isinstance(json_obj, list): - for item in json_obj: - if isinstance(item, dict): - json_objects.append(item) - except Exception as e: - logger.warning(f"解析JSON块失败: {e}, 块内容: {match[:100]}...") - continue - - return json_objects, reasoning_content - - -def calculate_similarity(text1: str, text2: str) -> float: - """ - 计算两个文本的相似度 +def parse_questions_json(response: str) -> Tuple[List[str], List[str]]: + """解析问题JSON，返回概念列表和问题列表 Args: - text1: 第一个文本 - text2: 第二个文本 + response: LLM返回的响应 Returns: - float: 相似度分数 (0-1) + Tuple[List[str], List[str]]: (概念列表, 问题列表) """ try: - # 预处理文本 - text1 = preprocess_text(text1) - text2 = preprocess_text(text2) + # 尝试提取JSON（可能包含在```json代码块中） + json_pattern = r"```json\s*(.*?)\s*```" + matches = re.findall(json_pattern, response, re.DOTALL) - # 使用SequenceMatcher计算相似度 - similarity = SequenceMatcher(None, text1, text2).ratio() + if matches: + json_str = matches[0] + else: + # 尝试直接解析整个响应 + json_str = response.strip() - # 如果其中一个文本包含另一个，提高相似度 - if text1 in text2 or text2 in text1: - similarity = max(similarity, 0.8) + # 修复可能的JSON错误 + repaired_json = repair_json(json_str) - return similarity + # 解析JSON + parsed = json.loads(repaired_json) + + # 只支持新格式：包含concepts和questions的对象 + if not isinstance(parsed, dict): + logger.warning(f"解析的JSON不是对象格式: {parsed}") + return [], [] + + concepts_raw = parsed.get("concepts", []) + questions_raw = parsed.get("questions", []) + + # 确保是列表 + if not isinstance(concepts_raw, list): + concepts_raw = [] + if not isinstance(questions_raw, list): + questions_raw = [] + + # 确保所有元素都是字符串 + concepts = [c for c in concepts_raw if isinstance(c, str) and c.strip()] + questions = [q for q in questions_raw if isinstance(q, str) and q.strip()] + + return concepts, questions except Exception as e: - logger.error(f"计算相似度时出错: {e}") - return 0.0 - - -def preprocess_text(text: str) -> str: - """ - 预处理文本，提高匹配准确性 - - Args: - text: 原始文本 - - Returns: - str: 预处理后的文本 - """ - try: - # 转换为小写 - text = text.lower() - - # 移除标点符号和特殊字符 - text = re.sub(r"[^\w\s]", "", text) - - # 移除多余空格 - text = re.sub(r"\s+", " ", text).strip() - - return text - - except Exception as e: - logger.error(f"预处理文本时出错: {e}") - return text - + logger.error(f"解析问题JSON失败: {e}, 响应内容: {response[:200]}...") + return [], [] def parse_datetime_to_timestamp(value: str) -> float: """ @@ -140,29 +96,3 @@ def parse_datetime_to_timestamp(value: str) -> float: except Exception as e: last_err = e raise ValueError(f"无法解析时间: {value} ({last_err})") - - -def parse_time_range(time_range: str) -> Tuple[float, float]: - """ - 解析时间范围字符串，返回开始和结束时间戳 - - Args: - time_range: 时间范围字符串，格式："YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS" - - Returns: - Tuple[float, float]: (开始时间戳, 结束时间戳) - """ - if " - " not in time_range: - raise ValueError(f"时间范围格式错误，应为 '开始时间 - 结束时间': {time_range}") - - parts = time_range.split(" - ", 1) - if len(parts) != 2: - raise ValueError(f"时间范围格式错误: {time_range}") - - start_str = parts[0].strip() - end_str = parts[1].strip() - - start_timestamp = parse_datetime_to_timestamp(start_str) - end_timestamp = parse_datetime_to_timestamp(end_str) - - return start_timestamp, end_timestamp diff --git a/src/memory_system/retrieval_tools/__init__.py b/src/memory_system/retrieval_tools/__init__.py index 17c3effb..7832985f 100644 --- a/src/memory_system/retrieval_tools/__init__.py +++ b/src/memory_system/retrieval_tools/__init__.py @@ -14,6 +14,7 @@ from .tool_registry import ( from .query_chat_history import register_tool as register_query_chat_history from .query_lpmm_knowledge import register_tool as register_lpmm_knowledge from .query_person_info import register_tool as register_query_person_info +from .found_answer import register_tool as register_found_answer from src.config.config import global_config @@ -21,6 +22,7 @@ def init_all_tools(): """初始化并注册所有记忆检索工具""" register_query_chat_history() register_query_person_info() + register_found_answer() # 注册found_answer工具 if global_config.lpmm_knowledge.lpmm_mode == "agent": register_lpmm_knowledge() diff --git a/src/memory_system/retrieval_tools/found_answer.py b/src/memory_system/retrieval_tools/found_answer.py new file mode 100644 index 00000000..0efd02be --- /dev/null +++ b/src/memory_system/retrieval_tools/found_answer.py @@ -0,0 +1,40 @@ +""" +found_answer工具 - 用于在记忆检索过程中标记找到答案 +""" + +from src.common.logger import get_logger +from .tool_registry import register_memory_retrieval_tool + +logger = get_logger("memory_retrieval_tools") + + +async def found_answer(answer: str) -> str: + """标记已找到问题的答案 + + Args: + answer: 找到的答案内容 + + Returns: + str: 确认信息 + """ + # 这个工具主要用于标记，实际答案会通过返回值传递 + logger.info(f"找到答案: {answer}") + return f"已确认找到答案: {answer}" + + +def register_tool(): + """注册found_answer工具""" + register_memory_retrieval_tool( + name="found_answer", + description="当你在已收集的信息中找到了问题的明确答案时，调用此工具标记已找到答案。只有在检索到明确、具体的答案时才使用此工具，不要编造信息。", + parameters=[ + { + "name": "answer", + "type": "string", + "description": "找到的答案内容，必须基于已收集的信息，不要编造", + "required": True, + }, + ], + execute_func=found_answer, + ) + diff --git a/src/memory_system/retrieval_tools/query_chat_history.py b/src/memory_system/retrieval_tools/query_chat_history.py index 478ccb97..097632c4 100644 --- a/src/memory_system/retrieval_tools/query_chat_history.py +++ b/src/memory_system/retrieval_tools/query_chat_history.py @@ -275,10 +275,6 @@ async def get_chat_history_detail(chat_id: str, memory_ids: str) -> str: except (json.JSONDecodeError, TypeError, ValueError): pass - # 添加原文内容 - if record.original_text: - result_parts.append(f"原文内容：\n{record.original_text}") - results.append("\n".join(result_parts)) if not results: @@ -318,7 +314,7 @@ def register_tool(): # 注册工具2：获取记忆详情 register_memory_retrieval_tool( name="get_chat_history_detail", - description="根据记忆ID，展示某条或某几条记忆的具体内容。包括主题、时间、参与人、关键词、概括、关键信息点和原文内容等详细信息。需要先使用search_chat_history工具获取记忆ID。", + description="根据记忆ID，展示某条或某几条记忆的具体内容。包括主题、时间、参与人、关键词、概括和关键信息点等详细信息。需要先使用search_chat_history工具获取记忆ID。", parameters=[ { "name": "memory_ids", diff --git a/src/webui/expression_routes.py b/src/webui/expression_routes.py index f92219ab..bef93b30 100644 --- a/src/webui/expression_routes.py +++ b/src/webui/expression_routes.py @@ -5,7 +5,6 @@ from pydantic import BaseModel from typing import Optional, List, Dict from src.common.logger import get_logger from src.common.database.database_model import Expression, ChatStreams -from .token_manager import get_token_manager from .auth import verify_auth_token_from_cookie_or_header import time diff --git a/src/webui/jargon_routes.py b/src/webui/jargon_routes.py index 51012267..318912e8 100644 --- a/src/webui/jargon_routes.py +++ b/src/webui/jargon_routes.py @@ -1,7 +1,7 @@ """黑话（俚语）管理路由""" import json -from typing import Optional, List +from typing import Optional, List, Annotated from fastapi import APIRouter, HTTPException, Query from pydantic import BaseModel, Field from peewee import fn @@ -331,19 +331,19 @@ async def get_jargon_stats(): total = Jargon.select().count() # 已确认是黑话的数量 - confirmed_jargon = Jargon.select().where(Jargon.is_jargon == True).count() + confirmed_jargon = Jargon.select().where(Jargon.is_jargon).count() # 已确认不是黑话的数量 - confirmed_not_jargon = Jargon.select().where(Jargon.is_jargon == False).count() + confirmed_not_jargon = Jargon.select().where(~Jargon.is_jargon).count() # 未判定的数量 pending = Jargon.select().where(Jargon.is_jargon.is_null()).count() # 全局黑话数量 - global_count = Jargon.select().where(Jargon.is_global == True).count() + global_count = Jargon.select().where(Jargon.is_global).count() # 已完成推断的数量 - complete_count = Jargon.select().where(Jargon.is_complete == True).count() + complete_count = Jargon.select().where(Jargon.is_complete).count() # 关联的聊天数量 chat_count = ( @@ -519,8 +519,8 @@ async def batch_delete_jargons(request: BatchDeleteRequest): @router.post("/batch/set-jargon", response_model=JargonUpdateResponse) async def batch_set_jargon_status( - ids: List[int] = Query(..., description="黑话ID列表"), - is_jargon: bool = Query(..., description="是否是黑话"), + ids: Annotated[List[int], Query(description="黑话ID列表")], + is_jargon: Annotated[bool, Query(description="是否是黑话")], ): """批量设置黑话状态""" try: diff --git a/src/webui/person_routes.py b/src/webui/person_routes.py index 0b70a3a2..5c039371 100644 --- a/src/webui/person_routes.py +++ b/src/webui/person_routes.py @@ -5,7 +5,6 @@ from pydantic import BaseModel from typing import Optional, List, Dict from src.common.logger import get_logger from src.common.database.database_model import PersonInfo -from .token_manager import get_token_manager from .auth import verify_auth_token_from_cookie_or_header import json import time diff --git a/src/webui/routers/system.py b/src/webui/routers/system.py index 7499c06d..d6932896 100644 --- a/src/webui/routers/system.py +++ b/src/webui/routers/system.py @@ -5,7 +5,6 @@ """ import os -import sys import time from datetime import datetime from fastapi import APIRouter, HTTPException diff --git a/webui/dist/assets/index-DuFwC87p.js b/webui/dist/assets/index-DJb_iiTR.js similarity index 92% rename from webui/dist/assets/index-DuFwC87p.js rename to webui/dist/assets/index-DJb_iiTR.js index ee71a6a6..886bc66f 100644 --- a/webui/dist/assets/index-DuFwC87p.js +++ b/webui/dist/assets/index-DJb_iiTR.js @@ -12,7 +12,7 @@ ${c.map(([x,f])=>{const j=f.theme?.[d]||f.color;return j?` --color-${x}: ${j};` `)} } `).join(` 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a0{grad3;p;perm;constructor(i=0){this.grad3=[[1,1,0],[-1,1,0],[1,-1,0],[-1,-1,0],[1,0,1],[-1,0,1],[1,0,-1],[-1,0,-1],[0,1,1],[0,-1,1],[0,1,-1],[0,-1,-1]],this.p=[];for(let c=0;c<256;c++)this.p[c]=Math.floor(Math.random()*256);this.perm=[];for(let c=0;c<512;c++)this.perm[c]=this.p[c&255]}dot(i,c,d){return i[0]*c+i[1]*d}mix(i,c,d){return(1-d)*i+d*c}fade(i){return i*i*i*(i*(i*6-15)+10)}perlin2(i,c){const d=Math.floor(i)&255,h=Math.floor(c)&255;i-=Math.floor(i),c-=Math.floor(c);const x=this.fade(i),f=this.fade(c),j=this.perm[d]+h,p=this.perm[j],w=this.perm[j+1],v=this.perm[d+1]+h,y=this.perm[v],S=this.perm[v+1];return this.mix(this.mix(this.dot(this.grad3[p%12],i,c),this.dot(this.grad3[y%12],i-1,c),x),this.mix(this.dot(this.grad3[w%12],i,c-1),this.dot(this.grad3[S%12],i-1,c-1),x),f)}}function ap(){const n=u.useRef(null),i=u.useRef(null),c=u.useRef(void 0),d=u.useRef({mouse:{x:-10,y:0,lx:0,ly:0,sx:0,sy:0,v:0,vs:0,a:0,set:!1},lines:[],paths:[],noise:new 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j(),p(),window.addEventListener("resize",M),window.addEventListener("mousemove",C),c.current=requestAnimationFrame(S),()=>{window.removeEventListener("resize",M),window.removeEventListener("mousemove",C),c.current&&cancelAnimationFrame(c.current)}},[]),e.jsxs("div",{ref:i,className:"waves-background",style:{position:"absolute",top:0,left:0,width:"100%",height:"100%",overflow:"hidden",pointerEvents:"none"},children:[e.jsx("div",{className:"waves-cursor",style:{position:"absolute",top:0,left:0,width:"0.5rem",height:"0.5rem",background:"hsl(var(--primary) / 0.3)",borderRadius:"50%",transform:"translate3d(calc(var(--x, -0.5rem) - 50%), calc(var(--y, 50%) - 50%), 0)",willChange:"transform",pointerEvents:"none"}}),e.jsx("svg",{ref:n,style:{display:"block",width:"100%",height:"100%"},children:e.jsx("style",{children:` path { fill: none; stroke: hsl(var(--primary) / 0.20); diff --git a/webui/dist/index.html b/webui/dist/index.html index 6aa56064..66ce72c0 100644 --- a/webui/dist/index.html +++ b/webui/dist/index.html @@ -7,7 +7,7 @@ MaiBot Dashboard - +