From 242347489c07ec49ba72cdd496f4f9f63975469f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=9B=9E=E5=BD=92=E5=A4=A9=E7=A9=BA?= Date: Sun, 24 Aug 2025 20:55:52 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=80=E4=B8=AA=E5=9F=BA=E4=BA=8EAstrBot?= =?UTF-8?q?=E7=9A=84=E6=99=BA=E8=83=BD=E7=BE=A4=E8=81=8A=E5=88=86=E6=9E=90?= =?UTF-8?q?=E6=8F=92=E4=BB=B6=EF=BC=8C=E8=83=BD=E5=A4=9F=E7=94=9F=E6=88=90?= =?UTF-8?q?=E7=B2=BE=E7=BE=8E=E7=9A=84=E7=BE=A4=E8=81=8A=E6=97=A5=E5=B8=B8?= =?UTF-8?q?=E5=88=86=E6=9E=90=E6=8A=A5=E5=91=8A=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- LICENSE | 661 --------------- README.md | 128 ++- _conf_schema.json | 78 ++ config.json | 16 + demo.jpg | Bin 0 -> 179996 bytes main.py | 2007 ++++++++++++++++++++++++++++++++++++++++++++- metadata.yaml | 23 +- 7 files changed, 2223 insertions(+), 690 deletions(-) create mode 100644 _conf_schema.json create mode 100644 config.json create mode 100644 demo.jpg diff --git a/LICENSE b/LICENSE index 3423cec..e69de29 100644 --- a/LICENSE +++ b/LICENSE @@ -1,661 +0,0 @@ - GNU AFFERO GENERAL PUBLIC LICENSE - Version 3, 19 November 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU Affero General Public License is a free, copyleft license for -software and other kinds of works, specifically designed to ensure -cooperation with the community in the case of network server software. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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It is safest -to attach them to the start of each source file to most effectively -state the exclusion of warranty; and each file should have at least -the "copyright" line and a pointer to where the full notice is found. - - - Copyright (C) 2022-2099 AstrBot Plugin Authors - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU Affero General Public License as published - by the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU Affero General Public License for more details. - - You should have received a copy of the GNU Affero General Public License - along with this program. If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If your software can interact with users remotely through a computer -network, you should also make sure that it provides a way for users to -get its source. For example, if your program is a web application, its -interface could display a "Source" link that leads users to an archive -of the code. There are many ways you could offer source, and different -solutions will be better for different programs; see section 13 for the -specific requirements. - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU AGPL, see -. diff --git a/README.md b/README.md index ae9c1f0..27b1a48 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,127 @@ -# helloworld +
-AstrBot 插件模板 +# QQ群日常分析插件 -A template plugin for AstrBot plugin feature -# 支持 +[![Plugin Version](https://img.shields.io/badge/Latest_Version-1.1.0-blue.svg?style=for-the-badge&color=76bad9)](https://github.com/SXP-Simon/astrbot-qq-group-daily-analysis) +[![AstrBot](https://img.shields.io/badge/AstrBot-Plugin-ff69b4?style=for-the-badge)](https://github.com/AstrBotDevs/AstrBot) +[![License](https://img.shields.io/badge/License-AGPL3.0-green.svg?style=for-the-badge)](LICENSE) -[帮助文档](https://astrbot.app) +_✨ 一个基于AstrBot的智能群聊分析插件，能够生成精美的群聊日常分析报告。[灵感来源](https://github.com/LSTM-Kirigaya/openmcp-tutorial/tree/main/qq-group-summary)。 ✨_ + +count +
+ + +## 功能特色 + +### 🎯 智能分析 +- **话题分析**: 使用LLM智能提取群聊中的热门话题和讨论要点 +- **用户画像**: 基于聊天行为分析用户特征，分配个性化称号 +- **统计数据**: 全面的群聊活跃度和参与度统计 + +### 📊 可视化报告 +- **PDF报告**: 生成专业的PDF格式分析报告（推荐） +- **精美图片**: 生成美观的HTML+CSS可视化报告 +- **多种格式**: 支持图片和文本输出格式 +- **用户头像**: 自动获取QQ头像，让报告更加生动 +- **详细数据**: 包含消息统计、时间分布、关键词等 + +### 🛠️ 灵活配置 +- **群组管理**: 支持指定特定群组启用功能 +- **参数调节**: 可自定义分析天数、消息数量等参数 + +## 效果 +![效果图](./demo.jpg) + +## 使用方法 + +### 基础命令 + +#### 群分析 +``` +/群分析 [天数] +``` +- 分析群聊近期活动 +- 天数可选，默认为1天 +- 例如：`/群分析 3` 分析最近3天的群聊 + +#### 分析设置 +``` +/分析设置 [操作] +``` +- `enable`: 为当前群启用分析功能 +- `disable`: 为当前群禁用分析功能 +- `status`: 查看当前群的启用状态 +- 例如：`/分析设置 enable` + +### 配置选项 + +| 配置项 | 说明 | 默认值 | +|--------|------|--------| +| enabled_groups | 启用分析的QQ群列表 | [] (所有群) | +| max_messages | 最大分析消息数量 | 1000 | +| analysis_days | 默认分析天数 | 1 | +| output_format | 输出格式 (pdf/image/text) | pdf | +| require_admin | 仅管理员可用 | false | +| topic_analysis_enabled | 启用话题分析 | true | +| user_title_analysis_enabled | 启用用户称号分析 | true | + +## 分析内容 + +### 📊 基础统计 +- 消息总数 +- 参与人数 +- 总字符数 +- 表情数量 +- 最活跃时段 + +### 💬 话题分析 +- 自动提取3-5个主要话题 +- 识别话题参与者 +- 总结讨论要点和结论 + +### 🏆 用户称号 +基于用户行为特征分配称号： +- **水群小能手**: 发言频繁的活跃用户 +- **技术专家**: 经常讨论技术话题 +- **夜猫子**: 深夜活跃用户 +- **表情包批发商**: 经常发表情 +- **沉默终结者**: 经常开启话题 +- **剧作家**: 平均发言长度很长 +- **互动达人**: 经常回复他人 +- **KOL**: 群内意见领袖 + +### 🔥 数据洞察 +- 热门关键词统计 +- 用户活跃度排行 +- 时间分布分析 +- MBTI性格类型推测 + +## 安装要求 + +### 基础要求 +- 已配置LLM提供商（用于智能分析） +- QQ平台适配器 + + +## 注意事项 + +1. **性能考虑**: 大量消息分析可能消耗较多LLM tokens +2. **数据准确性**: 分析结果基于可获取的群聊记录，可能不完全准确 + +## 更新日志 + +### v1.0.0 +- 初始版本发布 +- 支持基础群聊分析功能 +- 智能话题和用户称号分析 +- 精美可视化报告生成 + +## 许可证 + +MIT License + +## 贡献 + +欢迎提交Issue和Pull Request来改进这个插件！ diff --git a/_conf_schema.json b/_conf_schema.json new file mode 100644 index 0000000..602b04a --- /dev/null +++ b/_conf_schema.json @@ -0,0 +1,78 @@ +{ + "enabled_groups": { + "type": "list", + "description": "启用分析功能的QQ群列表", + "default": [], + "hint": "填入允许使用分析功能的QQ群号，为空则所有群都可使用", + "items": { + "type": "string" + } + }, + "max_messages": { + "type": "int", + "description": "最大分析消息数量", + "default": 1000, + "hint": "单次分析的最大消息条数，建议500-2000" + }, + "analysis_days": { + "type": "int", + "description": "默认分析天数", + "default": 1, + "hint": "默认分析最近几天的消息，建议1-7天" + }, + "auto_analysis_time": { + "type": "string", + "description": "自动分析时间", + "default": "22:00", + "hint": "每日自动分析的时间，格式HH:MM" + }, + "enable_auto_analysis": { + "type": "bool", + "description": "启用自动分析", + "default": false, + "hint": "是否在指定时间自动进行群聊分析，启用需要在群内手动触发一次分析 /分析设置 test ，否则获取不到实例，不会自动分析" + }, + "output_format": { + "type": "string", + "description": "输出格式", + "default": "image", + "hint": "分析报告的输出格式：image(图片)、text(文本)" + }, + + "min_messages_threshold": { + "type": "int", + "description": "最小消息数阈值", + "default": 50, + "hint": "进行分析所需的最小消息数量" + }, + "topic_analysis_enabled": { + "type": "bool", + "description": "启用话题分析", + "default": true, + "hint": "是否使用LLM进行智能话题分析" + }, + "user_title_analysis_enabled": { + "type": "bool", + "description": "启用用户称号分析", + "default": true, + "hint": "是否使用LLM进行用户称号分析" + }, + "max_topics": { + "type": "int", + "description": "最大话题数量", + "default": 5, + "hint": "分析报告中显示的最大话题数量" + }, + "max_user_titles": { + "type": "int", + "description": "最大用户称号数量", + "default": 8, + "hint": "分析报告中显示的最大用户称号数量" + }, + "max_query_rounds": { + "type": "int", + "description": "最大消息查询轮数", + "default": 35, + "hint": "⚠️ 警告：增加此值会导致大量Token消耗！默认值35轮（有一定消耗）。设置过高可能导致LLM费用激增" + } +} diff --git a/config.json b/config.json new file mode 100644 index 0000000..2297161 --- /dev/null +++ b/config.json @@ -0,0 +1,16 @@ +{ + "enabled_groups": [ + "936617127" + ], + "max_messages": 1000, + "analysis_days": 1, + "auto_analysis_time": "19:41", + "enable_auto_analysis": true, + "output_format": "image", + "min_messages_threshold": 50, + "topic_analysis_enabled": true, + "user_title_analysis_enabled": true, + "max_topics": 5, + "max_user_titles": 8, + "max_query_rounds": 50 +} \ No newline at end of file diff --git a/demo.jpg b/demo.jpg new file mode 100644 index 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z{4+TDyv^)KH)F3rY-d$+vsQAP+-;m~aeB24yV(qrm!hX5LN>1LLDbb}HWyU=afh4} z_xgJft;4E1R(=qZNX}k;Fk)dlnxg}gmtDz)FK#1DZM+Bd4&DDQ&`qG{2b+78LlfOR zoiqh3c~PSR??DG|kJC(KR$dF$qA-6u&WLa|*t^L{p=8?=AvS4BN`F*V@6XNcto5jN ztSNlTDg-U$oMMRQ?i^VMOE0RPjT?_WL@**$<4d5AjsF;?fF4K<1j-MYK5neAw^rcw1f4;UsSuHH~sXdpitg7H2=*eKKS9-6>w46Pg9fjkrV>Lkv_6^khBtI z6W{#0InU-dTXmE?zWKeTx08lix=tjMx|?NV&H9%>TfCRplq4b;ufGQcRcjaxMcd^L zI&HC*<|z4+xJz)Inu!NBwi+e6E$^2+^SmOM786TVXSaXDkh z*?e%q`@1$z7k41WL#bPHe>tshL?9R-47E{*wb$o*PA)n|J!S&abvQipf%#qgLl#hHiU~7CovdL%i z^1>d_7v51`kAJKG7tRik@&4D&WK)B z8}3TmgzDGTSKovD3acy1cL*!Y&!X9vuWT9Z$b4)L5Ms|4{qfvW@UsW+;aScP{Q>l@ zzsDO{Ai)Z?<>h@BmlxKMAKKXGbEC>M4VWMq?*FJw6|q7WKqbKlEJ6sczKW$xGy++N7+;RE3pHK@b?|A?=HZXKrw h0r#%^*TuizpZhgVK0kU4^x%JF2V8dlS8wmT{~s^9fr bool: + """检查是否为管理员 - 已简化为允许所有用户""" + # 允许所有用户使用设置功能 + return True + + async def _fetch_group_messages_unified(self, client, group_id: str, days: int) -> List[Dict]: + """统一的群聊消息获取方法""" + try: + if not client or not group_id: + logger.error(f"群 {group_id} 无效的客户端或群组ID") + return [] + + # 计算时间范围 + end_time = datetime.now() + start_time = end_time - timedelta(days=days) + + messages = [] + message_seq = 0 + query_rounds = 0 + max_rounds = self.max_query_rounds # 从配置读取最大查询轮数 + consecutive_failures = 0 + max_failures = 3 # 最大连续失败次数 + + logger.info(f"开始获取群 {group_id} 近 {days} 天的消息记录") + logger.info(f"时间范围: {start_time.strftime('%Y-%m-%d %H:%M:%S')} 到 {end_time.strftime('%Y-%m-%d %H:%M:%S')}") + + while len(messages) < self.max_messages and query_rounds < max_rounds: + try: + payloads = { + "group_id": group_id, + "message_seq": message_seq, + "count": 200, + "reverseOrder": True, + } + + result = await client.api.call_action("get_group_msg_history", **payloads) + + if not result or "messages" not in result: + logger.warning(f"群 {group_id} API返回无效结果: {result}") + consecutive_failures += 1 + if consecutive_failures >= max_failures: + break + continue + + round_messages = result.get("messages", []) + + if not round_messages: + logger.info(f"群 {group_id} 没有更多消息，结束获取") + break + + # 重置失败计数 + consecutive_failures = 0 + + # 过滤时间范围内的消息 + valid_messages_in_round = 0 + oldest_msg_time = None + + for msg in round_messages: + try: + msg_time = datetime.fromtimestamp(msg.get("time", 0)) + oldest_msg_time = msg_time # 记录最老的消息时间 + + # 过滤掉机器人自己的消息 + sender_id = str(msg.get("sender", {}).get("user_id", "")) + if self.bot_qq_id and sender_id == self.bot_qq_id: + continue + + if msg_time >= start_time and msg_time <= end_time: + messages.append(msg) + valid_messages_in_round += 1 + except Exception as msg_error: + logger.warning(f"群 {group_id} 处理单条消息失败: {msg_error}") + continue + + # 如果最老的消息时间已经超出范围，停止获取 + if oldest_msg_time and oldest_msg_time < start_time: + logger.info(f"群 {group_id} 已获取到时间范围外的消息，停止获取。共获取 {len(messages)} 条消息") + break + + if valid_messages_in_round == 0: + logger.warning(f"群 {group_id} 本轮未获取到有效消息") + break + + message_seq = round_messages[0]["message_id"] + query_rounds += 1 + + # 添加延迟避免请求过快 + if query_rounds % 5 == 0: + await asyncio.sleep(0.5) + + except Exception as e: + logger.error(f"群 {group_id} 获取消息失败 (第{query_rounds+1}轮): {e}") + consecutive_failures += 1 + if consecutive_failures >= max_failures: + logger.error(f"群 {group_id} 连续失败 {max_failures} 次，停止获取") + break + await asyncio.sleep(1) # 失败后等待1秒再重试 + + logger.info(f"群 {group_id} 消息获取完成，共获取 {len(messages)} 条消息，查询轮数: {query_rounds}") + return messages + + except Exception as e: + logger.error(f"群 {group_id} 获取群聊消息记录失败: {e}", exc_info=True) + return [] + + async def _fetch_group_messages(self, event: AiocqhttpMessageEvent, days: int) -> List[Dict]: + """获取群聊消息记录（手动分析）""" + return await self._fetch_group_messages_unified(event.bot, event.get_group_id(), days) + + async def _analyze_messages(self, messages: List[Dict], group_id: str) -> Dict: + """分析消息内容""" + # 基础统计 + stats = self._calculate_statistics(messages) + + # 用户活跃度分析 + user_analysis = self._analyze_users(messages) + + # 话题分析（根据配置决定是否启用） + topics = [] + if self.topic_analysis_enabled: + topics = await self._analyze_topics(messages) + + # 用户称号分析（根据配置决定是否启用） + user_titles = [] + if self.user_title_analysis_enabled: + user_titles = await self._analyze_user_titles(messages, user_analysis) + + # 群聊金句分析 + golden_quotes = await self._analyze_golden_quotes(messages) + stats.golden_quotes = golden_quotes + + return { + "group_id": group_id, + "analysis_time": datetime.now().isoformat(), + "statistics": stats, + "user_analysis": user_analysis, + "topics": topics, + "user_titles": user_titles, + "message_count": len(messages) + } + + def _calculate_statistics(self, messages: List[Dict]) -> GroupStatistics: + """计算基础统计数据""" + total_chars = 0 + participants = set() + hour_counts = defaultdict(int) + emoji_count = 0 + + for msg in messages: + sender_id = str(msg.get("sender", {}).get("user_id", "")) + participants.add(sender_id) + + # 统计时间分布 + msg_time = datetime.fromtimestamp(msg.get("time", 0)) + hour_counts[msg_time.hour] += 1 + + # 处理消息内容 + for content in msg.get("message", []): + if content.get("type") == "text": + text = content.get("data", {}).get("text", "") + total_chars += len(text) + elif content.get("type") == "face": + emoji_count += 1 + + # 找出最活跃时段 + most_active_hour = max(hour_counts.items(), key=lambda x: x[1])[0] if hour_counts else 0 + most_active_period = f"{most_active_hour:02d}:00-{(most_active_hour+1)%24:02d}:00" + + return GroupStatistics( + message_count=len(messages), + total_characters=total_chars, + participant_count=len(participants), + most_active_period=most_active_period, + golden_quotes=[], # 将在后续LLM分析中填充 + emoji_count=emoji_count + ) + + def _analyze_users(self, messages: List[Dict]) -> Dict[str, Dict]: + """分析用户活跃度""" + user_stats = defaultdict(lambda: { + "message_count": 0, + "char_count": 0, + "emoji_count": 0, + "nickname": "", + "hours": defaultdict(int), + "reply_count": 0 + }) + + for msg in messages: + sender = msg.get("sender", {}) + user_id = str(sender.get("user_id", "")) + nickname = sender.get("nickname", "") or sender.get("card", "") + + user_stats[user_id]["message_count"] += 1 + user_stats[user_id]["nickname"] = nickname + + # 统计时间分布 + msg_time = datetime.fromtimestamp(msg.get("time", 0)) + user_stats[user_id]["hours"][msg_time.hour] += 1 + + # 处理消息内容 + for content in msg.get("message", []): + if content.get("type") == "text": + text = content.get("data", {}).get("text", "") + user_stats[user_id]["char_count"] += len(text) + elif content.get("type") == "face": + user_stats[user_id]["emoji_count"] += 1 + elif content.get("type") == "reply": + user_stats[user_id]["reply_count"] += 1 + + return dict(user_stats) + + async def _get_user_avatar(self, user_id: str) -> Optional[str]: + """获取用户头像的base64编码""" + try: + avatar_url = f"https://q4.qlogo.cn/headimg_dl?dst_uin={user_id}&spec=640" + async with aiohttp.ClientSession() as client: + response = await client.get(avatar_url) + response.raise_for_status() + avatar_data = await response.read() + # 转换为base64编码 + avatar_base64 = base64.b64encode(avatar_data).decode('utf-8') + return f"data:image/jpeg;base64,{avatar_base64}" + except Exception as e: + logger.error(f"获取用户头像失败 {user_id}: {e}") + return None + + async def _analyze_topics(self, messages: List[Dict]) -> List[SummaryTopic]: + """使用LLM分析话题""" + try: + # 提取文本消息 + text_messages = [] + for msg in messages: + sender = msg.get("sender", {}) + nickname = sender.get("nickname", "") or sender.get("card", "") + msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M") + + for content in msg.get("message", []): + if content.get("type") == "text": + text = content.get("data", {}).get("text", "").strip() + if text and len(text) > 2: # 过滤太短的消息 + text_messages.append({ + "sender": nickname, + "time": msg_time, + "content": text + }) + + if not text_messages: + return [] + + # 限制消息数量以避免token过多 + if len(text_messages) > 100: + # 均匀采样 + step = len(text_messages) // 100 + text_messages = text_messages[::step] + + # 构建LLM提示词 + messages_text = "\n".join([ + f"[{msg['time']}] {msg['sender']}: {msg['content']}" + for msg in text_messages + ]) + + prompt = f""" +请分析以下群聊记录，提取出3-5个主要话题。对于每个话题，请提供： +1. 话题名称（简洁明了） +2. 主要参与者（最多3人） +3. 话题详细描述（包含关键信息和结论） + +群聊记录： +{messages_text} + +请以JSON格式返回，格式如下： +[ + {{ + "topic": "话题名称", + "contributors": ["参与者1", "参与者2"], + "detail": "详细描述话题内容、讨论要点和结论" + }} +] +""" + + # 调用LLM + provider = self.context.get_using_provider() + if not provider: + logger.warning("未配置LLM提供商，跳过话题分析") + return [] + + response = await provider.text_chat( + prompt=prompt, + max_tokens=2000, + temperature=0.3 + ) + + # 解析响应 + if hasattr(response, 'completion_text'): + result_text = response.completion_text + else: + result_text = str(response) + + # 尝试解析JSON + try: + import re + json_match = re.search(r'\[.*\]', result_text, re.DOTALL) + if json_match: + topics_data = json.loads(json_match.group()) + return [SummaryTopic(**topic) for topic in topics_data[:5]] + except: + pass + + return [] + + except Exception as e: + logger.error(f"话题分析失败: {e}") + return [] + + async def _analyze_user_titles(self, messages: List[Dict], user_analysis: Dict) -> List[UserTitle]: + """使用LLM分析用户称号""" + try: + # 准备用户数据 + user_summaries = [] + for user_id, stats in user_analysis.items(): + if stats["message_count"] < 5: # 过滤活跃度太低的用户 + continue + + # 分析用户特征 + night_messages = sum(stats["hours"][h] for h in range(0, 6)) + day_messages = stats["message_count"] - night_messages + avg_chars = stats["char_count"] / stats["message_count"] if stats["message_count"] > 0 else 0 + + user_summaries.append({ + "name": stats["nickname"], + "qq": int(user_id), + "message_count": stats["message_count"], + "avg_chars": round(avg_chars, 1), + "emoji_ratio": round(stats["emoji_count"] / stats["message_count"], 2), + "night_ratio": round(night_messages / stats["message_count"], 2), + "reply_ratio": round(stats["reply_count"] / stats["message_count"], 2) + }) + + if not user_summaries: + return [] + + # 按消息数量排序，取前8名 + user_summaries.sort(key=lambda x: x["message_count"], reverse=True) + user_summaries = user_summaries[:8] + + # 构建LLM提示词 + users_text = "\n".join([ + f"- {user['name']} (QQ:{user['qq']}): " + f"发言{user['message_count']}条, 平均{user['avg_chars']}字, " + f"表情比例{user['emoji_ratio']}, 夜间发言比例{user['night_ratio']}, " + f"回复比例{user['reply_ratio']}" + for user in user_summaries + ]) + + prompt = f""" +请为以下群友分配合适的称号和MBTI类型。每个人只能有一个称号，每个称号只能给一个人。 + +可选称号： +- 水群小能手: 发言频繁但内容轻松的人 +- 技术专家: 经常讨论技术话题的人 +- 夜猫子: 经常在深夜发言的人 +- 表情包批发商: 经常发表情的人 +- 沉默终结者: 经常开启话题的人 +- 剧作家: 平均发言长度很长的人 +- KOL: 在群里很有影响力的人 +- 互动达人: 经常回复别人的人 + +用户数据： +{users_text} + +请以JSON格式返回，格式如下： +[ + {{ + "name": "用户名", + "qq": 123456789, + "title": "称号", + "mbti": "MBTI类型", + "reason": "获得此称号的原因" + }} +] +""" + + # 调用LLM + provider = self.context.get_using_provider() + if not provider: + logger.warning("未配置LLM提供商，跳过用户称号分析") + return [] + + response = await provider.text_chat( + prompt=prompt, + max_tokens=1500, + temperature=0.5 + ) + + # 解析响应 + if hasattr(response, 'completion_text'): + result_text = response.completion_text + else: + result_text = str(response) + + # 尝试解析JSON + try: + import re + json_match = re.search(r'\[.*\]', result_text, re.DOTALL) + if json_match: + titles_data = json.loads(json_match.group()) + return [UserTitle(**title) for title in titles_data] + except: + pass + + return [] + + except Exception as e: + logger.error(f"用户称号分析失败: {e}") + return [] + + async def _analyze_golden_quotes(self, messages: List[Dict]) -> List[GoldenQuote]: + """使用LLM分析群聊金句""" + try: + # 提取有趣的文本消息 + interesting_messages = [] + for msg in messages: + sender = msg.get("sender", {}) + nickname = sender.get("nickname", "") or sender.get("card", "") + msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M") + + for content in msg.get("message", []): + if content.get("type") == "text": + text = content.get("data", {}).get("text", "").strip() + # 过滤长度适中、可能有趣的消息 + if 5 <= len(text) <= 100 and not text.startswith(("http", "www")): + interesting_messages.append({ + "sender": nickname, + "time": msg_time, + "content": text + }) + + if not interesting_messages: + return [] + + # 限制消息数量以避免token过多 + if len(interesting_messages) > 50: + # 均匀采样 + step = len(interesting_messages) // 50 + interesting_messages = interesting_messages[::step] + + # 构建LLM提示词 + messages_text = "\n".join([ + f"[{msg['time']}] {msg['sender']}: {msg['content']}" + for msg in interesting_messages + ]) + + prompt = f""" +请从以下群聊记录中挑选出3-5句最有趣、最有意思的"金句"。这些金句可以是： +- 幽默搞笑的发言 +- 富有哲理的话语 +- 意外的神回复 +- 有创意的表达 +- 让人印象深刻的句子 + +对于每个金句，请提供： +1. 原文内容 +2. 发言人昵称 +3. 选择理由（为什么觉得这句话有趣） + +群聊记录： +{messages_text} + +请以JSON格式返回，格式如下： +[ + {{ + "content": "金句原文", + "sender": "发言人昵称", + "reason": "选择这句话的理由" + }} +] +""" + + # 调用LLM + provider = self.context.get_using_provider() + if not provider: + logger.warning("未配置LLM提供商，跳过金句分析") + return [] + + response = await provider.text_chat( + prompt=prompt, + max_tokens=1500, + temperature=0.7 + ) + + # 解析响应 + if hasattr(response, 'completion_text'): + result_text = response.completion_text + else: + result_text = str(response) + + # 尝试解析JSON + try: + import re + json_match = re.search(r'\[.*\]', result_text, re.DOTALL) + if json_match: + quotes_data = json.loads(json_match.group()) + return [GoldenQuote(**quote) for quote in quotes_data[:5]] + except: + pass + + return [] + + except Exception as e: + logger.error(f"金句分析失败: {e}") + return [] + + async def _generate_image_report(self, analysis_result: Dict, group_id: str) -> Optional[str]: + """生成图片格式的分析报告""" + try: + # 准备渲染数据 + render_payload = await self._prepare_render_data(analysis_result) + + # 使用AstrBot内置的HTML渲染服务 + image_url = await self.html_render(self._get_html_template(), render_payload) + return image_url + + except Exception as e: + logger.error(f"生成图片报告失败: {e}") + return None + + + + async def _prepare_render_data(self, analysis_result: Dict) -> Dict: + """准备渲染数据""" + stats = analysis_result["statistics"] + topics = analysis_result["topics"] + user_titles = analysis_result["user_titles"] + + # 构建话题HTML + topics_html = "" + for i, topic in enumerate(topics[:self.max_topics], 1): + contributors_str = "、".join(topic.contributors) + topics_html += f""" +
+
+ {i} + {topic.topic} +
+
参与者: {contributors_str}
+
{topic.detail}
+
+ """ + + # 构建用户称号HTML（包含头像） + titles_html = "" + for title in user_titles[:self.max_user_titles]: + # 获取用户头像 + avatar_data = await self._get_user_avatar(str(title.qq)) + avatar_html = f'头像' if avatar_data else '
👤
' + + titles_html += f""" +
+ +
{title.reason}
+
+ """ + + # 构建金句HTML + quotes_html = "" + for quote in stats.golden_quotes[:5]: + quotes_html += f""" +
+
"{quote.content}"
+
—— {quote.sender}
+
{quote.reason}
+
+ """ + + # 返回扁平化的渲染数据 + return { + "current_date": datetime.now().strftime('%Y年%m月%d日'), + "current_datetime": datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + "message_count": stats.message_count, + "participant_count": stats.participant_count, + "total_characters": stats.total_characters, + "emoji_count": stats.emoji_count, + "most_active_period": stats.most_active_period, + "topics_html": topics_html, + "titles_html": titles_html, + "quotes_html": quotes_html + } + + def _get_html_template(self) -> str: + """获取HTML模板""" + return """ + + + + + + 群聊日常分析报告 + + + + +
+
+

📊 群聊日常分析报告

+
{{ current_date }}
+
+ +
+
+

📈 基础统计

+
+
+
{{ message_count }}
+
消息总数
+
+
+
{{ participant_count }}
+
参与人数
+
+
+
{{ total_characters }}
+
总字符数
+
+
+
{{ emoji_count }}
+
表情数量
+
+
+ +
+
{{ most_active_period }}
+
最活跃时段
+
+
+ +
+

💬 热门话题

+ {{ topics_html | safe }} +
+ +
+

🏆 群友称号

+ {{ titles_html | safe }} +
+ +
+

💬 群聊金句

+ {{ quotes_html | safe }} +
+
+ +
+ 由 AstrBot QQ群日常分析插件 生成 | {{ current_datetime }} +
+
+ + + """ + + async def _create_html_report(self, analysis_result: Dict) -> str: + """创建HTML报告内容""" + stats = analysis_result["statistics"] + topics = analysis_result["topics"] + user_titles = analysis_result["user_titles"] + + # 构建话题HTML + topics_html = "" + for i, topic in enumerate(topics[:self.max_topics], 1): + contributors_str = "、".join(topic.contributors) + topics_html += f""" +
+
+ {i} + {topic.topic} +
+
参与者: {contributors_str}
+
{topic.detail}
+
+ """ + + # 构建用户称号HTML + titles_html = "" + for title in user_titles[:self.max_user_titles]: + titles_html += f""" +
+ +
{title.reason}
+
+ """ + + # 构建金句HTML + quotes_html = "" + for i, quote in enumerate(stats.golden_quotes[:5], 1): + quotes_html += f""" +
+
"{quote.content}"
+
—— {quote.sender}
+
{quote.reason}
+
+ """ + + # HTML模板 + html_template = f""" + + + + + + 群聊日常分析报告 + + + +
+
+

🎯 群聊日常分析报告

+
{datetime.now().strftime('%Y年%m月%d日')}
+
+ +
+
+

📊 基础统计

+
+
+
{stats.message_count}
+
消息总数
+
+
+
{stats.participant_count}
+
参与人数
+
+
+
{stats.total_characters}
+
总字符数
+
+
+
{stats.emoji_count}
+
表情数量
+
+
+
+
+
{stats.most_active_period}
+
最活跃时段
+
+
+
+ +
+

💬 热门话题

+ {topics_html} +
+ +
+

🏆 群友称号

+ {titles_html} +
+ +
+

💬 群聊金句

+ {quotes_html} +
+
+
+ + + """ + + return html_template + + async def _generate_text_report(self, analysis_result: Dict) -> str: + """生成文本格式的分析报告""" + stats = analysis_result["statistics"] + topics = analysis_result["topics"] + user_titles = analysis_result["user_titles"] + + report = f""" +🎯 群聊日常分析报告 +📅 {datetime.now().strftime('%Y年%m月%d日')} + +📊 基础统计 +• 消息总数: {stats.message_count} +• 参与人数: {stats.participant_count} +• 总字符数: {stats.total_characters} +• 表情数量: {stats.emoji_count} +• 最活跃时段: {stats.most_active_period} + +💬 热门话题 +""" + + for i, topic in enumerate(topics[:self.max_topics], 1): + contributors_str = "、".join(topic.contributors) + report += f"{i}. {topic.topic}\n" + report += f" 参与者: {contributors_str}\n" + report += f" {topic.detail}\n\n" + + report += "🏆 群友称号\n" + for title in user_titles[:self.max_user_titles]: + report += f"• {title.name} - {title.title} ({title.mbti})\n" + report += f" {title.reason}\n\n" + + report += "💬 群聊金句\n" + for i, quote in enumerate(stats.golden_quotes[:5], 1): + report += f"{i}. \"{quote.content}\" —— {quote.sender}\n" + report += f" {quote.reason}\n\n" + + return report + + + + async def _save_config(self): + """保存配置到文件""" + try: + config_file = Path("data/plugins/astrbot-qq-group-daily-analysis/config.json") + config_file.parent.mkdir(parents=True, exist_ok=True) + + config_data = { + "enabled_groups": self.enabled_groups, + "max_messages": self.max_messages, + "analysis_days": self.analysis_days, + "auto_analysis_time": self.auto_analysis_time, + "enable_auto_analysis": self.enable_auto_analysis, + "output_format": self.output_format, + + "min_messages_threshold": self.min_messages_threshold, + "topic_analysis_enabled": self.topic_analysis_enabled, + "user_title_analysis_enabled": self.user_title_analysis_enabled, + "max_topics": self.max_topics, + "max_user_titles": self.max_user_titles, + "max_query_rounds": self.max_query_rounds + } + + with open(config_file, 'w', encoding='utf-8') as f: + json.dump(config_data, f, ensure_ascii=False, indent=2) + + except Exception as e: + logger.error(f"保存配置失败: {e}") + + def _load_config_from_file(self): + """从配置文件加载配置""" + try: + config_file = Path("data/plugins/astrbot-qq-group-daily-analysis/config.json") + if config_file.exists(): + with open(config_file, 'r', encoding='utf-8') as f: + file_config = json.load(f) + + # 设置默认值 + self.enabled_groups = file_config.get("enabled_groups", []) + self.max_messages = file_config.get("max_messages", 1000) + self.analysis_days = file_config.get("analysis_days", 1) + self.auto_analysis_time = file_config.get("auto_analysis_time", "09:00") + self.enable_auto_analysis = file_config.get("enable_auto_analysis", False) + self.output_format = file_config.get("output_format", "image") + + self.min_messages_threshold = file_config.get("min_messages_threshold", 50) + self.topic_analysis_enabled = file_config.get("topic_analysis_enabled", True) + self.user_title_analysis_enabled = file_config.get("user_title_analysis_enabled", True) + self.max_topics = file_config.get("max_topics", 5) + self.max_user_titles = file_config.get("max_user_titles", 8) + self.max_query_rounds = file_config.get("max_query_rounds", 35) + + logger.info(f"从配置文件加载配置成功，自动分析: {self.enable_auto_analysis}, 时间: {self.auto_analysis_time}") + else: + # 设置默认值 + self.enabled_groups = [] + self.max_messages = 1000 + self.analysis_days = 1 + self.auto_analysis_time = "09:00" + self.enable_auto_analysis = False + self.output_format = "image" + + self.min_messages_threshold = 50 + self.topic_analysis_enabled = True + self.user_title_analysis_enabled = True + self.max_topics = 5 + self.max_user_titles = 8 + self.max_query_rounds = 35 + + except Exception as e: + logger.error(f"加载配置文件失败: {e}") + # 设置默认值 + self.enabled_groups = [] + self.max_messages = 1000 + self.analysis_days = 1 + self.auto_analysis_time = "09:00" + self.enable_auto_analysis = False + self.output_format = "image" + + self.min_messages_threshold = 50 + self.topic_analysis_enabled = True + self.user_title_analysis_enabled = True + self.max_topics = 5 + self.max_user_titles = 8 + self.max_query_rounds = 35 + + async def _reload_config_and_restart_scheduler(self): + """重新加载配置并重启调度器""" + try: + # 重新加载配置 + self._load_config_from_file() + logger.info(f"重新加载配置: 自动分析={self.enable_auto_analysis}") + + # 停止现有的调度器 + if hasattr(self, 'scheduler_task') and self.scheduler_task and not self.scheduler_task.done(): + self.scheduler_task.cancel() + logger.info("已停止现有的定时任务") + + # 如果启用了自动分析，启动新的调度器 + if self.enable_auto_analysis: + self.scheduler_task = asyncio.create_task(self._start_scheduler()) + logger.info("已启动新的定时任务") + + except Exception as e: + logger.error(f"重新加载配置失败: {e}") + + async def _start_scheduler(self): + """启动定时任务调度器""" + logger.info(f"启动定时任务调度器，自动分析时间: {self.auto_analysis_time}") + + while True: + try: + now = datetime.now() + target_time = datetime.strptime(self.auto_analysis_time, "%H:%M").replace( + year=now.year, month=now.month, day=now.day + ) + + # 如果今天的目标时间已过，设置为明天 + if now >= target_time: + target_time += timedelta(days=1) + + # 计算等待时间 + wait_seconds = (target_time - now).total_seconds() + logger.info(f"定时分析将在 {target_time.strftime('%Y-%m-%d %H:%M:%S')} 执行，等待 {wait_seconds:.0f} 秒") + + # 等待到目标时间 + await asyncio.sleep(wait_seconds) + + # 执行自动分析 + if self.enable_auto_analysis: + logger.info("开始执行定时分析") + await self._run_auto_analysis() + else: + logger.info("自动分析已禁用，跳过执行") + break + + except Exception as e: + logger.error(f"定时任务调度器错误: {e}") + # 等待5分钟后重试 + await asyncio.sleep(300) + + async def _run_auto_analysis(self): + """执行自动分析""" + try: + logger.info("开始执行自动群聊分析") + + # 为每个启用的群执行分析 + for group_id in self.enabled_groups: + try: + logger.info(f"为群 {group_id} 执行自动分析") + + # 这里需要模拟一个事件对象来调用分析功能 + # 由于自动分析没有真实的用户事件，我们直接调用内部方法 + await self._perform_auto_analysis_for_group(group_id) + + except Exception as e: + logger.error(f"群 {group_id} 自动分析失败: {e}") + + except Exception as e: + logger.error(f"自动分析执行失败: {e}") + + async def _perform_auto_analysis_for_group(self, group_id: str): + """为指定群执行自动分析""" + try: + # 尝试获取bot实例 + if not self.bot_instance: + self.bot_instance = await self._get_bot_instance() + + if not self.bot_instance: + logger.warning(f"群 {group_id} 自动分析跳过：未获取到bot实例，请先手动触发一次分析") + return + + # 确保有机器人QQ号 + if not self.bot_qq_id: + await self._get_bot_qq_id() + + logger.info(f"开始为群 {group_id} 执行自动分析") + + # 获取群聊消息 + messages = await self._fetch_group_messages_for_auto(group_id) + if not messages: + logger.warning(f"群 {group_id} 未获取到足够的消息记录") + return + + # 检查消息数量 + if len(messages) < self.min_messages_threshold: + logger.warning(f"群 {group_id} 消息数量不足（{len(messages)}条），跳过分析") + return + + logger.info(f"群 {group_id} 获取到 {len(messages)} 条消息，开始分析") + + # 进行分析 + analysis_result = await self._analyze_messages(messages, group_id) + if not analysis_result: + logger.error(f"群 {group_id} 分析失败") + return + + # 生成报告 + if self.output_format == "image": + image_url = await self._generate_image_report(analysis_result, group_id) + if image_url: + # 发送分析报告到群 + await self._send_auto_analysis_result(group_id, image_url) + logger.info(f"群 {group_id} 自动分析完成，已发送图片报告") + else: + logger.error(f"群 {group_id} 图片报告生成失败") + else: + text_report = await self._generate_text_report(analysis_result) + await self._send_auto_analysis_text(group_id, text_report) + logger.info(f"群 {group_id} 自动分析完成，已发送文本报告") + + except Exception as e: + logger.error(f"群 {group_id} 自动分析执行失败: {e}", exc_info=True) + + async def _fetch_group_messages_for_auto(self, group_id: str) -> List[Dict]: + """为自动分析获取群聊消息（使用统一方法）""" + if not self.bot_instance: + logger.error(f"群 {group_id} 获取消息失败：缺少bot实例") + return [] + + return await self._fetch_group_messages_unified(self.bot_instance, group_id, self.analysis_days) + + async def _send_auto_analysis_result(self, group_id: str, image_url: str): + """发送自动分析的图片结果到群""" + try: + if not self.bot_instance: + return + + # 发送图片消息到群 + await self.bot_instance.api.call_action( + "send_group_msg", + group_id=group_id, + message=[{ + "type": "text", + "data": {"text": "📊 每日群聊分析报告已生成："} + }, { + "type": "image", + "data": {"url": image_url} + }] + ) + + except Exception as e: + logger.error(f"发送自动分析结果到群 {group_id} 失败: {e}") + + async def _send_auto_analysis_text(self, group_id: str, text_report: str): + """发送自动分析的文本结果到群""" + try: + if not self.bot_instance: + return + + # 发送文本消息到群 + await self.bot_instance.api.call_action( + "send_group_msg", + group_id=group_id, + message=f"📊 每日群聊分析报告：\n\n{text_report}" + ) + + except Exception as e: + logger.error(f"发送自动分析文本到群 {group_id} 失败: {e}") + + async def _get_bot_instance(self): + """从Context获取bot实例""" + try: + # 如果已经有保存的实例，直接返回 + if self.bot_instance: + return self.bot_instance + + logger.info("尝试获取bot实例...") + + # 简化的获取逻辑，尝试常见的几种方式 + if hasattr(self.context, 'get_platforms') and callable(self.context.get_platforms): + platforms = self.context.get_platforms() + for platform in platforms: + if hasattr(platform, 'bot') and platform.bot: + logger.info(f"从平台获取到bot实例") + return platform.bot + + # 尝试从context的platforms属性获取 + if hasattr(self.context, 'platforms') and self.context.platforms: + for platform in self.context.platforms: + if hasattr(platform, 'bot') and platform.bot: + logger.info(f"从平台列表获取到bot实例") + return platform.bot + + logger.info("暂时无法获取bot实例，等待用户手动触发分析") + return None + + except Exception as e: + logger.error(f"获取bot实例失败: {e}") + return None + + async def _delayed_start_scheduler(self): + """延迟启动调度器，给系统时间初始化""" + try: + # 等待10秒让系统完全初始化 + await asyncio.sleep(10) + + # 尝试获取bot实例 + self.bot_instance = await self._get_bot_instance() + + if self.bot_instance: + logger.info("成功获取bot实例，启动定时任务") + # 获取机器人QQ号 + await self._get_bot_qq_id() + else: + logger.info("暂时未获取到bot实例，定时任务仍会启动。首次手动触发分析后将自动获取bot实例") + + # 启动调度器 + self.scheduler_task = asyncio.create_task(self._start_scheduler()) + + except Exception as e: + logger.error(f"延迟启动调度器失败: {e}") diff --git a/metadata.yaml b/metadata.yaml index c8d3f94..bbd461d 100644 --- a/metadata.yaml +++ b/metadata.yaml @@ -1,5 +1,18 @@ -name: helloworld # 这是你的插件的唯一识别名。 -desc: 这是 AstrBot 的默认插件，支持关键词回复。 # 插件简短描述 -version: v1.3 # 插件版本号。格式：v1.1.1 或者 v1.1 -author: Soulter # 作者 -repo: https://github.com/Soulter/helloworld # 插件的仓库地址 +name: astrbot_qq_group_daily_analysis # 这是你的插件的唯一识别名。 +desc: QQ群日常分析插件 - 生成精美的群聊分析报告，支持话题分析、用户称号、群聊金句等功能 # 插件简短描述 +help: | # 插件的帮助信息 + 智能分析群聊活动，生成精美的日常分析报告 + + 主要功能： + - 话题分析和总结 + - 用户称号分配 + - 群聊金句识别 + - 可视化报告 + - 定时自动分析 + + 命令： + /群分析 [天数] - 分析群聊活动 + /分析设置 [操作] - 管理设置（enable/disable/status/test） +version: v1.0.0 # 插件版本号。格式：v1.1.1 或者 v1.1 +author: SXP-Simon # 作者 +repo: https://github.com/SXP-Simon/astrbot-qq-group-daily-analysis # 插件的仓库地址 \ No newline at end of file