diff --git a/main.py b/main.py index 9099e34..6ee69fa 100644 --- a/main.py +++ b/main.py @@ -1,4 +1,4 @@ -""" +""" QQ群日常分析插件 基于群聊记录生成精美的日常分析报告，包含话题总结、用户画像、统计数据等 @@ -7,12 +7,13 @@ QQ群日常分析插件 import asyncio from typing import Optional -from pathlib import Path from astrbot.api.event import filter from astrbot.api.star import Context, Star from astrbot.api import logger, AstrBotConfig -from astrbot.core.platform.sources.aiocqhttp.aiocqhttp_message_event import AiocqhttpMessageEvent +from astrbot.core.platform.sources.aiocqhttp.aiocqhttp_message_event import ( + AiocqhttpMessageEvent, +) from astrbot.core.message.components import File from astrbot.core.star.filter.permission import PermissionType @@ -39,7 +40,12 @@ class QQGroupDailyAnalysis(Star): self.config = config # 初始化模块化组件 - global config_manager, bot_manager, message_analyzer, report_generator, auto_scheduler + global \ + config_manager, \ + bot_manager, \ + message_analyzer, \ + report_generator, \ + auto_scheduler config_manager = ConfigManager(config) bot_manager = BotManager(config_manager) @@ -52,7 +58,7 @@ class QQGroupDailyAnalysis(Star): message_analyzer, report_generator, bot_manager, - self.html_render # 传入html_render函数 + self.html_render, # 传入html_render函数 ) # 延迟启动自动调度器，给系统时间初始化 @@ -85,45 +91,52 @@ class QQGroupDailyAnalysis(Star): """插件被卸载/停用时调用，清理资源""" try: logger.info("开始清理QQ群日常分析插件资源...") - - global auto_scheduler, bot_manager, message_analyzer, report_generator, config_manager - + + global \ + auto_scheduler, \ + bot_manager, \ + message_analyzer, \ + report_generator, \ + config_manager + # 停止自动调度器 if auto_scheduler: logger.info("正在停止自动调度器...") await auto_scheduler.stop_scheduler() logger.info("自动调度器已停止") - + # 清理bot管理器资源 # if bot_manager: # logger.info("正在清理bot管理器资源...") # # 如果有其他需要清理的资源，可以在这里添加 - + # # 清理消息分析器资源 # if message_analyzer: # logger.info("正在清理消息分析器资源...") # # 如果有其他需要清理的资源，可以在这里添加 - - # # 清理报告生成器资源 + + # # 清理报告生成器资源 # if report_generator: # logger.info("正在清理报告生成器资源...") # # 如果有其他需要清理的资源，可以在这里添加 - + # 重置全局变量 auto_scheduler = None bot_manager = None message_analyzer = None report_generator = None config_manager = None - + logger.info("QQ群日常分析插件资源清理完成") - + except Exception as e: logger.error(f"插件资源清理失败: {e}") @filter.command("群分析") @filter.permission_type(PermissionType.ADMIN) - async def analyze_group_daily(self, event: AiocqhttpMessageEvent, days: Optional[int] = None): + async def analyze_group_daily( + self, event: AiocqhttpMessageEvent, days: Optional[int] = None + ): """ 分析群聊日常活动 用法: /群分析 [天数] @@ -147,7 +160,9 @@ class QQGroupDailyAnalysis(Star): return # 设置分析天数 - analysis_days = days if days and 1 <= days <= 7 else config_manager.get_analysis_days() + analysis_days = ( + days if days and 1 <= days <= 7 else config_manager.get_analysis_days() + ) yield event.plain_result(f"🔍 开始分析群聊近{analysis_days}天的活动，请稍候...") @@ -156,21 +171,31 @@ class QQGroupDailyAnalysis(Star): try: # 获取群聊消息 - messages = await message_analyzer.message_handler.fetch_group_messages(bot_manager.get_bot_instance(), group_id, analysis_days) + messages = await message_analyzer.message_handler.fetch_group_messages( + bot_manager.get_bot_instance(), group_id, analysis_days + ) if not messages: - yield event.plain_result("❌ 未找到足够的群聊记录，请确保群内有足够的消息历史") + yield event.plain_result( + "❌ 未找到足够的群聊记录，请确保群内有足够的消息历史" + ) return # 检查消息数量是否足够分析 min_threshold = config_manager.get_min_messages_threshold() if len(messages) < min_threshold: - yield event.plain_result(f"❌ 消息数量不足（{len(messages)}条），至少需要{min_threshold}条消息才能进行有效分析") + yield event.plain_result( + f"❌ 消息数量不足（{len(messages)}条），至少需要{min_threshold}条消息才能进行有效分析" + ) return - yield event.plain_result(f"📊 已获取{len(messages)}条消息，正在进行智能分析...") + yield event.plain_result( + f"📊 已获取{len(messages)}条消息，正在进行智能分析..." + ) # 进行分析 - 传递 unified_msg_origin 以获取正确的 LLM 提供商 - analysis_result = await message_analyzer.analyze_messages(messages, group_id, event.unified_msg_origin) + analysis_result = await message_analyzer.analyze_messages( + messages, group_id, event.unified_msg_origin + ) # 检查分析结果 if not analysis_result or not analysis_result.get("statistics"): @@ -180,23 +205,32 @@ class QQGroupDailyAnalysis(Star): # 生成报告 output_format = config_manager.get_output_format() if output_format == "image": - image_url = await report_generator.generate_image_report(analysis_result, group_id, self.html_render) + image_url = await report_generator.generate_image_report( + analysis_result, group_id, self.html_render + ) if image_url: yield event.image_result(image_url) else: # 如果图片生成失败，回退到文本报告 logger.warning("图片报告生成失败，回退到文本报告") text_report = report_generator.generate_text_report(analysis_result) - yield event.plain_result(f"⚠️ 图片报告生成失败，以下是文本版本：\n\n{text_report}") + yield event.plain_result( + f"⚠️ 图片报告生成失败，以下是文本版本：\n\n{text_report}" + ) elif output_format == "pdf": if not config_manager.pyppeteer_available: - yield event.plain_result("❌ PDF 功能不可用，请使用 /安装PDF 命令安装 pyppeteer==1.0.2") + yield event.plain_result( + "❌ PDF 功能不可用，请使用 /安装PDF 命令安装 pyppeteer==1.0.2" + ) return - pdf_path = await report_generator.generate_pdf_report(analysis_result, group_id) + pdf_path = await report_generator.generate_pdf_report( + analysis_result, group_id + ) if pdf_path: # 发送 PDF 文件 from pathlib import Path + pdf_file = File(name=Path(pdf_path).name, file=pdf_path) result = event.make_result() result.chain.append(pdf_file) @@ -212,20 +246,24 @@ class QQGroupDailyAnalysis(Star): # 回退到文本报告 logger.warning("PDF 报告生成失败，回退到文本报告") text_report = report_generator.generate_text_report(analysis_result) - yield event.plain_result(f"\n📝 以下是文本版本的分析报告：\n\n{text_report}") + yield event.plain_result( + f"\n📝 以下是文本版本的分析报告：\n\n{text_report}" + ) else: text_report = report_generator.generate_text_report(analysis_result) yield event.plain_result(text_report) except Exception as e: logger.error(f"群分析失败: {e}", exc_info=True) - yield event.plain_result(f"❌ 分析失败: {str(e)}。请检查网络连接和LLM配置，或联系管理员") - - + yield event.plain_result( + f"❌ 分析失败: {str(e)}。请检查网络连接和LLM配置，或联系管理员" + ) @filter.command("设置格式") @filter.permission_type(PermissionType.ADMIN) - async def set_output_format(self, event: AiocqhttpMessageEvent, format_type: str = ""): + async def set_output_format( + self, event: AiocqhttpMessageEvent, format_type: str = "" + ): """ 设置分析报告输出格式 用法: /设置格式 [image|text|pdf] @@ -241,7 +279,9 @@ class QQGroupDailyAnalysis(Star): if not format_type: current_format = config_manager.get_output_format() - pdf_status = '✅' if config_manager.pyppeteer_available else '❌ (需安装 pyppeteer)' + pdf_status = ( + "✅" if config_manager.pyppeteer_available else "❌ (需安装 pyppeteer)" + ) yield event.plain_result(f"""📊 当前输出格式: {current_format} 可用格式: @@ -258,7 +298,9 @@ class QQGroupDailyAnalysis(Star): return if format_type == "pdf" and not config_manager.pyppeteer_available: - yield event.plain_result("❌ PDF 格式不可用，请使用 /安装PDF 命令安装 pyppeteer==1.0.2") + yield event.plain_result( + "❌ PDF 格式不可用，请使用 /安装PDF 命令安装 pyppeteer==1.0.2" + ) return config_manager.set_output_format(format_type) @@ -281,7 +323,7 @@ class QQGroupDailyAnalysis(Star): # 安装 pyppeteer result = await PDFInstaller.install_pyppeteer(config_manager) yield event.plain_result(result) - + # 提供系统依赖安装指导 system_deps_result = await PDFInstaller.install_system_deps() yield event.plain_result(system_deps_result) @@ -292,7 +334,9 @@ class QQGroupDailyAnalysis(Star): @filter.command("分析设置") @filter.permission_type(PermissionType.ADMIN) - async def analysis_settings(self, event: AiocqhttpMessageEvent, action: str = "status"): + async def analysis_settings( + self, event: AiocqhttpMessageEvent, action: str = "status" + ): """ 管理分析设置 用法: /分析设置 [enable|disable|status|reload|test] @@ -360,7 +404,9 @@ class QQGroupDailyAnalysis(Star): else: # status enabled_groups = config_manager.get_enabled_groups() status = "已启用" if group_id in enabled_groups else "未启用" - auto_status = "已启用" if config_manager.get_enable_auto_analysis() else "未启用" + auto_status = ( + "已启用" if config_manager.get_enable_auto_analysis() else "未启用" + ) auto_time = config_manager.get_auto_analysis_time() pdf_status = PDFInstaller.get_pdf_status(config_manager) @@ -379,5 +425,3 @@ class QQGroupDailyAnalysis(Star): 💡 可用命令: enable, disable, status, reload, test 💡 支持的输出格式: image, text, pdf (图片和PDF包含活跃度可视化) 💡 其他命令: /设置格式, /安装PDF""") - - diff --git a/src/__init__.py b/src/__init__.py index 33c66e7..5cf2d5f 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -2,4 +2,4 @@ QQ群日常分析插件 - 源代码包 """ -__author__ = "SXP-Simon" \ No newline at end of file +__author__ = "SXP-Simon" diff --git a/src/analysis/__init__.py b/src/analysis/__init__.py index d1d48ee..8a9ab59 100644 --- a/src/analysis/__init__.py +++ b/src/analysis/__init__.py @@ -6,7 +6,4 @@ from .llm_analyzer import LLMAnalyzer from .statistics import UserAnalyzer -__all__ = [ - 'LLMAnalyzer', - 'UserAnalyzer' -] \ No newline at end of file +__all__ = ["LLMAnalyzer", "UserAnalyzer"] diff --git a/src/analysis/analyzers/__init__.py b/src/analysis/analyzers/__init__.py index e4d49df..fe370dd 100644 --- a/src/analysis/analyzers/__init__.py +++ b/src/analysis/analyzers/__init__.py @@ -8,9 +8,4 @@ from .topic_analyzer import TopicAnalyzer from .user_title_analyzer import UserTitleAnalyzer from .golden_quote_analyzer import GoldenQuoteAnalyzer -__all__ = [ - 'BaseAnalyzer', - 'TopicAnalyzer', - 'UserTitleAnalyzer', - 'GoldenQuoteAnalyzer' -] \ No newline at end of file +__all__ = ["BaseAnalyzer", "TopicAnalyzer", "UserTitleAnalyzer", "GoldenQuoteAnalyzer"] diff --git a/src/analysis/analyzers/base_analyzer.py b/src/analysis/analyzers/base_analyzer.py index cf11bcd..261cb09 100644 --- a/src/analysis/analyzers/base_analyzer.py +++ b/src/analysis/analyzers/base_analyzer.py @@ -4,184 +4,206 @@ """ from abc import ABC, abstractmethod -from typing import List, Dict, Tuple, Any, Optional -from datetime import datetime +from typing import List, Dict, Tuple, Any from astrbot.api import logger from ...models.data_models import TokenUsage from ..utils.json_utils import parse_json_response -from ..utils.llm_utils import call_provider_with_retry, extract_token_usage, extract_response_text -import re +from ..utils.llm_utils import ( + call_provider_with_retry, + extract_token_usage, + extract_response_text, +) + class BaseAnalyzer(ABC): """ 基础分析器抽象类 定义所有分析器的通用接口和流程 """ - + def __init__(self, context, config_manager): """ 初始化基础分析器 - + Args: context: AstrBot上下文对象 config_manager: 配置管理器 """ self.context = context self.config_manager = config_manager - + @abstractmethod def get_data_type(self) -> str: """ 获取数据类型标识 - + Returns: 数据类型字符串 """ pass - + @abstractmethod def get_max_count(self) -> int: """ 获取最大提取数量 - + Returns: 最大数量 """ pass - + @abstractmethod def build_prompt(self, data: Any) -> str: """ 构建LLM提示词 - + Args: data: 输入数据 - + Returns: 提示词字符串 """ pass - + @abstractmethod def extract_with_regex(self, result_text: str, max_count: int) -> List[Dict]: """ 使用正则表达式提取数据 - + Args: result_text: LLM响应文本 max_count: 最大提取数量 - + Returns: 提取到的数据列表 """ pass - + @abstractmethod def create_data_objects(self, data_list: List[Dict]) -> List[Any]: """ 创建数据对象列表 - + Args: data_list: 原始数据列表 - + Returns: 数据对象列表 """ pass - + async def analyze(self, data: Any, umo: str = None) -> Tuple[List[Any], TokenUsage]: """ 统一的分析流程 - + Args: data: 输入数据 umo: 模型唯一标识符 - + Returns: (分析结果列表, Token使用统计) """ try: # 1. 构建提示词 - logger.debug(f"{self.get_data_type()}分析开始构建prompt，输入数据类型: {type(data)}") - logger.debug(f"{self.get_data_type()}分析输入数据长度: {len(data) if hasattr(data, '__len__') else 'N/A'}") - + logger.debug( + f"{self.get_data_type()}分析开始构建prompt，输入数据类型: {type(data)}" + ) + logger.debug( + f"{self.get_data_type()}分析输入数据长度: {len(data) if hasattr(data, '__len__') else 'N/A'}" + ) + prompt = self.build_prompt(data) logger.info(f"开始{self.get_data_type()}分析，构建提示词完成") - logger.debug(f"{self.get_data_type()}分析prompt长度: {len(prompt) if prompt else 0}") - logger.debug(f"{self.get_data_type()}分析prompt前100字符: {prompt[:100] if prompt else 'None'}...") - + logger.debug( + f"{self.get_data_type()}分析prompt长度: {len(prompt) if prompt else 0}" + ) + logger.debug( + f"{self.get_data_type()}分析prompt前100字符: {prompt[:100] if prompt else 'None'}..." + ) + # 检查 prompt 是否为空 if not prompt or not prompt.strip(): - logger.warning(f"{self.get_data_type()}分析: prompt 为空或只包含空白字符，跳过LLM调用") + logger.warning( + f"{self.get_data_type()}分析: prompt 为空或只包含空白字符，跳过LLM调用" + ) return [], TokenUsage() - + # 2. 调用LLM max_tokens = self.get_max_tokens() temperature = self.get_temperature() - + response = await call_provider_with_retry( - self.context, self.config_manager, prompt, - max_tokens, temperature, umo + self.context, self.config_manager, prompt, max_tokens, temperature, umo ) - + if response is None: - logger.error(f"{self.get_data_type()}分析调用LLM失败: provider返回None（重试失败）") + logger.error( + f"{self.get_data_type()}分析调用LLM失败: provider返回None（重试失败）" + ) return [], TokenUsage() - + # 3. 提取token使用统计 token_usage_dict = extract_token_usage(response) token_usage = TokenUsage( prompt_tokens=token_usage_dict["prompt_tokens"], completion_tokens=token_usage_dict["completion_tokens"], - total_tokens=token_usage_dict["total_tokens"] + total_tokens=token_usage_dict["total_tokens"], ) - + # 4. 提取响应文本 result_text = extract_response_text(response) logger.debug(f"{self.get_data_type()}分析原始响应: {result_text[:500]}...") - + # 5. 尝试JSON解析 - success, parsed_data, error_msg = parse_json_response(result_text, self.get_data_type()) - + success, parsed_data, error_msg = parse_json_response( + result_text, self.get_data_type() + ) + if success and parsed_data: # JSON解析成功，创建数据对象 data_objects = self.create_data_objects(parsed_data) - logger.info(f"{self.get_data_type()}分析成功，解析到 {len(data_objects)} 条数据") + logger.info( + f"{self.get_data_type()}分析成功，解析到 {len(data_objects)} 条数据" + ) return data_objects, token_usage - + # 6. JSON解析失败，使用正则表达式降级 - logger.warning(f"{self.get_data_type()}JSON解析失败，尝试正则表达式提取: {error_msg}") + logger.warning( + f"{self.get_data_type()}JSON解析失败，尝试正则表达式提取: {error_msg}" + ) regex_data = self.extract_with_regex(result_text, self.get_max_count()) - + if regex_data: - logger.info(f"{self.get_data_type()}正则表达式提取成功，获得 {len(regex_data)} 条数据") + logger.info( + f"{self.get_data_type()}正则表达式提取成功，获得 {len(regex_data)} 条数据" + ) data_objects = self.create_data_objects(regex_data) return data_objects, token_usage else: # 最后的降级方案 - 两种方法都失败 - logger.error(f"{self.get_data_type()}分析失败: JSON解析和正则表达式提取均未成功，返回空列表") + logger.error( + f"{self.get_data_type()}分析失败: JSON解析和正则表达式提取均未成功，返回空列表" + ) return [], token_usage - + except Exception as e: logger.error(f"{self.get_data_type()}分析失败: {e}", exc_info=True) return [], TokenUsage() - + def get_max_tokens(self) -> int: """ 获取最大token数，子类可重写 - + Returns: 最大token数 """ return 10000 - + def get_temperature(self) -> float: """ 获取温度参数，子类可重写 - + Returns: 温度参数 """ return 0.6 - - \ No newline at end of file diff --git a/src/analysis/analyzers/golden_quote_analyzer.py b/src/analysis/analyzers/golden_quote_analyzer.py index 5417860..5921547 100644 --- a/src/analysis/analyzers/golden_quote_analyzer.py +++ b/src/analysis/analyzers/golden_quote_analyzer.py @@ -12,59 +12,56 @@ from ..utils.json_utils import extract_golden_quotes_with_regex from ..utils import InfoUtils - class GoldenQuoteAnalyzer(BaseAnalyzer): """ 金句分析器 专门处理群聊金句的提取和分析 """ - + def get_data_type(self) -> str: """获取数据类型标识""" return "金句" - + def get_max_count(self) -> int: """获取最大金句数量""" return self.config_manager.get_max_golden_quotes() - + def get_max_tokens(self) -> int: """获取最大token数""" return self.config_manager.get_golden_quote_max_tokens() - + def get_temperature(self) -> float: """获取温度参数""" return 0.7 - + def build_prompt(self, messages: List[Dict]) -> str: """ 构建金句分析提示词 - + Args: messages: 群聊的文本消息列表 - + Returns: 提示词字符串 """ if not messages: return "" - + # 构建消息文本 - messages_text = "\n".join([ - f"[{msg['time']}] {msg['sender']}: {msg['content']}" - for msg in messages - ]) - + messages_text = "\n".join( + [f"[{msg['time']}] {msg['sender']}: {msg['content']}" for msg in messages] + ) + max_golden_quotes = self.get_max_count() - + # 从配置读取 prompt 模板（默认使用 "default" 风格） prompt_template = self.config_manager.get_golden_quote_analysis_prompt() - + if prompt_template: # 使用配置中的 prompt 并替换变量 try: prompt = prompt_template.format( - max_golden_quotes=max_golden_quotes, - messages_text=messages_text + max_golden_quotes=max_golden_quotes, messages_text=messages_text ) logger.info("使用配置中的金句分析提示词") return prompt @@ -72,117 +69,117 @@ class GoldenQuoteAnalyzer(BaseAnalyzer): logger.warning(f"金句分析提示词变量格式错误: {e}") except Exception as e: logger.warning(f"应用金句分析提示词失败: {e}") - + logger.warning("未找到有效的金句分析提示词配置，请检查配置文件") return "" - + def extract_with_regex(self, result_text: str, max_count: int) -> List[Dict]: """ 使用正则表达式提取金句信息 - + Args: result_text: LLM响应文本 max_count: 最大提取数量 - + Returns: 金句数据列表 """ return extract_golden_quotes_with_regex(result_text, max_count) - + def create_data_objects(self, quotes_data: List[Dict]) -> List[GoldenQuote]: """ 创建金句对象列表 - + Args: quotes_data: 原始金句数据列表 - + Returns: GoldenQuote对象列表 """ try: quotes = [] max_quotes = self.get_max_count() - + for quote_data in quotes_data[:max_quotes]: # 确保数据格式正确 content = quote_data.get("content", "").strip() sender = quote_data.get("sender", "").strip() reason = quote_data.get("reason", "").strip() - + # 验证必要字段 if not content or not sender or not reason: logger.warning(f"金句数据格式不完整，跳过: {quote_data}") continue - - quotes.append(GoldenQuote( - content=content, - sender=sender, - reason=reason - )) - + + quotes.append( + GoldenQuote(content=content, sender=sender, reason=reason) + ) + return quotes - + except Exception as e: logger.error(f"创建金句对象失败: {e}") return [] - + def extract_interesting_messages(self, messages: List[Dict]) -> List[Dict]: """ 提取圣经的文本消息 - + Args: messages: 群聊消息列表 - + Returns: 圣经的文本消息列表 """ try: interesting_messages = [] - + for msg in messages: sender = msg.get("sender", {}) nickname = InfoUtils.get_user_nickname(self.config_manager, sender) 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、可能圣经的消息 - if 5 <= len(text) <= 100 and not text.startswith(("http", "www", "/")): - interesting_messages.append({ - "sender": nickname, - "time": msg_time, - "content": text - }) - + if 5 <= len(text) <= 100 and not text.startswith( + ("http", "www", "/") + ): + interesting_messages.append( + {"sender": nickname, "time": msg_time, "content": text} + ) + return interesting_messages - + except Exception as e: logger.error(f"提取圣经消息失败: {e}") return [] - - async def analyze_golden_quotes(self, messages: List[Dict], umo: str = None) -> Tuple[List[GoldenQuote], TokenUsage]: + + async def analyze_golden_quotes( + self, messages: List[Dict], umo: str = None + ) -> Tuple[List[GoldenQuote], TokenUsage]: """ 分析群聊金句 - + Args: messages: 群聊消息列表 umo: 模型唯一标识符 - + Returns: (金句列表, Token使用统计) """ try: # 提取圣经的文本消息 interesting_messages = self.extract_interesting_messages(messages) - + if not interesting_messages: logger.info("没有符合条件的圣经消息，返回空结果") return [], TokenUsage() - + logger.info(f"开始从 {len(interesting_messages)} 条圣经消息中提取金句") return await self.analyze(interesting_messages, umo) - + except Exception as e: logger.error(f"金句分析失败: {e}") - return [], TokenUsage() \ No newline at end of file + return [], TokenUsage() diff --git a/src/analysis/analyzers/topic_analyzer.py b/src/analysis/analyzers/topic_analyzer.py index 8399224..27ac694 100644 --- a/src/analysis/analyzers/topic_analyzer.py +++ b/src/analysis/analyzers/topic_analyzer.py @@ -18,30 +18,30 @@ class TopicAnalyzer(BaseAnalyzer): 话题分析器 专门处理群聊话题的提取和分析 """ - + def get_data_type(self) -> str: """获取数据类型标识""" return "话题" - + def get_max_count(self) -> int: """获取最大话题数量""" return self.config_manager.get_max_topics() - + def get_max_tokens(self) -> int: """获取最大token数""" return self.config_manager.get_topic_max_tokens() - + def get_temperature(self) -> float: """获取温度参数""" return 0.6 - + def build_prompt(self, messages: List[Dict]) -> str: """ 构建话题分析提示词 - + Args: messages: 群聊消息列表 - + Returns: 提示词字符串 """ @@ -49,49 +49,61 @@ class TopicAnalyzer(BaseAnalyzer): if not isinstance(messages, list): logger.error(f"build_prompt 期望列表，但收到: {type(messages)}") return "" - + # 检查消息列表是否为空 if not messages: logger.warning("build_prompt 收到空消息列表") return "" - + # 提取文本消息 text_messages = [] for i, msg in enumerate(messages): - logger.debug(f"build_prompt 处理第 {i+1} 条消息，类型: {type(msg)}") - + logger.debug(f"build_prompt 处理第 {i + 1} 条消息，类型: {type(msg)}") + # 确保msg是字典类型，避免'str' object has no attribute 'get'错误 if not isinstance(msg, dict): - logger.warning(f"build_prompt 跳过非字典类型的消息: {type(msg)} - {msg}") + logger.warning( + f"build_prompt 跳过非字典类型的消息: {type(msg)} - {msg}" + ) continue - + try: sender = msg.get("sender", {}) # 确保sender是字典类型，避免'str' object has no attribute 'get'错误 if not isinstance(sender, dict): - logger.warning(f"build_prompt 跳过sender非字典类型的消息: {type(sender)} - {sender}") + logger.warning( + f"build_prompt 跳过sender非字典类型的消息: {type(sender)} - {sender}" + ) continue - + nickname = InfoUtils.get_user_nickname(self.config_manager, sender) msg_time = datetime.fromtimestamp(msg.get("time", 0)).strftime("%H:%M") - + message_list = msg.get("message", []) - logger.debug(f"build_prompt 消息 {i+1} 的 message 字段类型: {type(message_list)}, 长度: {len(message_list) if hasattr(message_list, '__len__') else 'N/A'}") - + logger.debug( + f"build_prompt 消息 {i + 1} 的 message 字段类型: {type(message_list)}, 长度: {len(message_list) if hasattr(message_list, '__len__') else 'N/A'}" + ) + # 提取文本内容，可能分布在多个 content 中 text_parts = [] for j, content in enumerate(message_list): - logger.debug(f"build_prompt 处理消息 {i+1} 的内容 {j+1}, 类型: {type(content)}") + logger.debug( + f"build_prompt 处理消息 {i + 1} 的内容 {j + 1}, 类型: {type(content)}" + ) if not isinstance(content, dict): - logger.warning(f"build_prompt 跳过非字典类型的内容: {type(content)} - {content}") + logger.warning( + f"build_prompt 跳过非字典类型的内容: {type(content)} - {content}" + ) continue - + content_type = content.get("type", "") logger.debug(f"build_prompt 内容类型: {content_type}") - + if content_type == "text": text = content.get("data", {}).get("text", "").strip() - logger.debug(f"build_prompt 提取到的文本: '{text}' (长度: {len(text)})") + logger.debug( + f"build_prompt 提取到的文本: '{text}' (长度: {len(text)})" + ) if text: text_parts.append(text) elif content_type == "at": @@ -108,53 +120,62 @@ class TopicAnalyzer(BaseAnalyzer): reply_text = f"[回复:{reply_id}]" text_parts.append(reply_text) logger.debug(f"build_prompt 提取到回复消息: {reply_text}") - + # 合并所有文本部分 combined_text = "".join(text_parts).strip() - logger.debug(f"build_prompt 合并后的文本: '{combined_text}' (长度: {len(combined_text)})") - - if combined_text and len(combined_text) > 2 and not combined_text.startswith("/"): + logger.debug( + f"build_prompt 合并后的文本: '{combined_text}' (长度: {len(combined_text)})" + ) + + if ( + combined_text + and len(combined_text) > 2 + and not combined_text.startswith("/") + ): # 清理消息内容 - cleaned_text = combined_text.replace('“', '"').replace('”', '"') - cleaned_text = cleaned_text.replace('‘', "'").replace('’', "'") - cleaned_text = cleaned_text.replace('\n', ' ').replace('\r', ' ') - cleaned_text = cleaned_text.replace('\t', ' ') - cleaned_text = re.sub(r'[\x00-\x1f\x7f-\x9f]', '', cleaned_text) - + cleaned_text = combined_text.replace("“", '"').replace("”", '"') + cleaned_text = cleaned_text.replace("‘", "'").replace("’", "'") + cleaned_text = cleaned_text.replace("\n", " ").replace("\r", " ") + cleaned_text = cleaned_text.replace("\t", " ") + cleaned_text = re.sub(r"[\x00-\x1f\x7f-\x9f]", "", cleaned_text) + logger.debug(f"build_prompt 清理后的文本: '{cleaned_text}'") - - text_messages.append({ - "sender": nickname, - "time": msg_time, - "content": cleaned_text - }) + + text_messages.append( + {"sender": nickname, "time": msg_time, "content": cleaned_text} + ) else: - logger.debug(f"build_prompt 跳过文本: '{combined_text}' (长度不足或以/开头)") + logger.debug( + f"build_prompt 跳过文本: '{combined_text}' (长度不足或以/开头)" + ) except Exception as e: - logger.error(f"build_prompt 处理第 {i+1} 条消息时出错: {e}", exc_info=True) + logger.error( + f"build_prompt 处理第 {i + 1} 条消息时出错: {e}", exc_info=True + ) continue - + if not text_messages: logger.warning("build_prompt 没有提取到有效的文本消息，返回空prompt") return "" - + # 构建消息文本 - messages_text = "\n".join([ - f"[{msg['time']}] {msg['sender']}: {msg['content']}" - for msg in text_messages - ]) - + messages_text = "\n".join( + [ + f"[{msg['time']}] {msg['sender']}: {msg['content']}" + for msg in text_messages + ] + ) + max_topics = self.get_max_count() - + # 从配置读取 prompt 模板（默认使用 "default" 风格） prompt_template = self.config_manager.get_topic_analysis_prompt() - + if prompt_template: # 使用配置中的 prompt 并替换变量 try: prompt = prompt_template.format( - max_topics=max_topics, - messages_text=messages_text + max_topics=max_topics, messages_text=messages_text ) logger.info("使用配置中的话题分析提示词") return prompt @@ -162,181 +183,205 @@ class TopicAnalyzer(BaseAnalyzer): logger.warning(f"话题分析提示词变量格式错误: {e}") except Exception as e: logger.warning(f"应用话题分析提示词失败: {e}") - + logger.warning("未找到有效的话题分析提示词配置，请检查配置文件") return "" - + def extract_with_regex(self, result_text: str, max_topics: int) -> List[Dict]: """ 使用正则表达式提取话题信息 - + Args: result_text: LLM响应文本 max_topics: 最大话题数量 - + Returns: 话题数据列表 """ return extract_topics_with_regex(result_text, max_topics) - + def create_data_objects(self, topics_data: List[Dict]) -> List[SummaryTopic]: """ 创建话题对象列表 - + Args: topics_data: 原始话题数据列表 - + Returns: SummaryTopic对象列表 """ - logger.debug(f"create_data_objects 开始处理，输入数据数量: {len(topics_data) if topics_data else 0}") + logger.debug( + f"create_data_objects 开始处理，输入数据数量: {len(topics_data) if topics_data else 0}" + ) logger.debug(f"输入数据类型: {type(topics_data)}") - + try: topics = [] max_topics = self.get_max_count() - + logger.debug(f"处理前 {max_topics} 条话题数据") - + for i, topic_data in enumerate(topics_data[:max_topics]): - logger.debug(f"处理第 {i+1} 条话题数据，类型: {type(topic_data)}") - + logger.debug(f"处理第 {i + 1} 条话题数据，类型: {type(topic_data)}") + # 确保topic_data是字典类型，避免'str' object has no attribute 'get'错误 if not isinstance(topic_data, dict): - logger.warning(f"跳过非字典类型的话题数据: {type(topic_data)} - {topic_data}") + logger.warning( + f"跳过非字典类型的话题数据: {type(topic_data)} - {topic_data}" + ) continue - + try: # 确保数据格式正确 topic_name = topic_data.get("topic", "").strip() contributors = topic_data.get("contributors", []) detail = topic_data.get("detail", "").strip() - - logger.debug(f"话题数据 - 名称: {topic_name}, 参与者: {contributors}, 详情: {detail[:50]}...") - + + logger.debug( + f"话题数据 - 名称: {topic_name}, 参与者: {contributors}, 详情: {detail[:50]}..." + ) + # 验证必要字段 if not topic_name or not detail: logger.warning(f"话题数据格式不完整，跳过: {topic_data}") continue - + # 确保参与者列表有效 if not contributors or not isinstance(contributors, list): contributors = ["群友"] else: # 清理参与者名称 - contributors = [str(c).strip() for c in contributors if c and str(c).strip()] or ["群友"] - - topics.append(SummaryTopic( - topic=topic_name, - contributors=contributors[:5], # 最多5个参与者 - detail=detail - )) + contributors = [ + str(c).strip() for c in contributors if c and str(c).strip() + ] or ["群友"] + + topics.append( + SummaryTopic( + topic=topic_name, + contributors=contributors[:5], # 最多5个参与者 + detail=detail, + ) + ) except Exception as e: - logger.error(f"处理第 {i+1} 条话题数据时出错: {e}", exc_info=True) + logger.error(f"处理第 {i + 1} 条话题数据时出错: {e}", exc_info=True) continue - + logger.debug(f"create_data_objects 完成，创建了 {len(topics)} 个话题对象") return topics - + except Exception as e: logger.error(f"创建话题对象失败: {e}", exc_info=True) return [] - + def extract_text_messages(self, messages: List[Dict]) -> List[Dict]: """ 从群聊消息中提取文本消息 - + Args: messages: 群聊消息列表 - + Returns: 提取的文本消息列表 """ - logger.debug(f"extract_text_messages 开始处理，输入消息数量: {len(messages) if messages else 0}") + logger.debug( + f"extract_text_messages 开始处理，输入消息数量: {len(messages) if messages else 0}" + ) logger.debug(f"extract_text_messages 输入消息类型: {type(messages)}") - + if not messages: logger.warning("extract_text_messages 收到空消息列表") return [] - + text_messages = [] - + for i, msg in enumerate(messages): - logger.debug(f"处理第 {i+1} 条消息，类型: {type(msg)}") + logger.debug(f"处理第 {i + 1} 条消息，类型: {type(msg)}") # 确保msg是字典类型，避免'str' object has no attribute 'get'错误 if not isinstance(msg, dict): logger.warning(f"跳过非字典类型的消息: {type(msg)} - {msg}") continue - + try: sender = msg.get("sender", {}) # 确保sender是字典类型，避免'str' object has no attribute 'get'错误 if not isinstance(sender, dict): - logger.warning(f"extract_text_messages 跳过sender非字典类型的消息: {type(sender)} - {sender}") + logger.warning( + f"extract_text_messages 跳过sender非字典类型的消息: {type(sender)} - {sender}" + ) continue - + nickname = InfoUtils.get_user_nickname(self.config_manager, sender) 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 and not text.startswith("/"): # 清理消息内容 text = text.replace('""', '"').replace('""', '"') - text = text.replace(''', "'").replace(''', "'") - text = text.replace('\n', ' ').replace('\r', ' ') - text = text.replace('\t', ' ') - text = re.sub(r'[\x00-\x1f\x7f-\x9f]', '', text) - text_messages.append({ - "sender": nickname, - "time": msg_time, - "content": text.strip() - }) + text = text.replace(""", "'").replace(""", "'") + text = text.replace("\n", " ").replace("\r", " ") + text = text.replace("\t", " ") + text = re.sub(r"[\x00-\x1f\x7f-\x9f]", "", text) + text_messages.append( + { + "sender": nickname, + "time": msg_time, + "content": text.strip(), + } + ) except Exception as e: - logger.error(f"处理第 {i+1} 条消息时出错: {e}", exc_info=True) + logger.error(f"处理第 {i + 1} 条消息时出错: {e}", exc_info=True) continue - - logger.debug(f"extract_text_messages 完成，提取到 {len(text_messages)} 条文本消息") + + logger.debug( + f"extract_text_messages 完成，提取到 {len(text_messages)} 条文本消息" + ) if text_messages: logger.debug(f"extract_text_messages 第一条文本消息: {text_messages[0]}") return text_messages - - async def analyze_topics(self, messages: List[Dict], umo: str = None) -> Tuple[List[SummaryTopic], TokenUsage]: + + async def analyze_topics( + self, messages: List[Dict], umo: str = None + ) -> Tuple[List[SummaryTopic], TokenUsage]: """ 分析群聊话题 - + Args: messages: 群聊消息列表 umo: 模型唯一标识符 - + Returns: (话题列表, Token使用统计) """ try: - logger.debug(f"analyze_topics 开始处理，消息数量: {len(messages) if messages else 0}") + logger.debug( + f"analyze_topics 开始处理，消息数量: {len(messages) if messages else 0}" + ) logger.debug(f"消息类型: {type(messages)}") if messages: - logger.debug(f"第一条消息类型: {type(messages[0]) if messages else '无'}") + logger.debug( + f"第一条消息类型: {type(messages[0]) if messages else '无'}" + ) logger.debug(f"第一条消息内容: {messages[0] if messages else '无'}") - + # 检查是否有有效的文本消息 text_messages = self.extract_text_messages(messages) logger.debug(f"提取到 {len(text_messages)} 条文本消息") - + if not text_messages: logger.info("没有有效的文本消息，返回空结果") return [], TokenUsage() - + logger.info(f"开始分析 {len(text_messages)} 条文本消息中的话题") logger.debug(f"文本消息类型: {type(text_messages)}") if text_messages: logger.debug(f"第一条文本消息类型: {type(text_messages[0])}") logger.debug(f"第一条文本消息内容: {text_messages[0]}") - + # 直接传入原始消息，让 build_prompt 方法处理 return await self.analyze(messages, umo) - + except Exception as e: logger.error(f"话题分析失败: {e}", exc_info=True) - return [], TokenUsage() \ No newline at end of file + return [], TokenUsage() diff --git a/src/analysis/analyzers/user_title_analyzer.py b/src/analysis/analyzers/user_title_analyzer.py index 127c24d..ed8e11c 100644 --- a/src/analysis/analyzers/user_title_analyzer.py +++ b/src/analysis/analyzers/user_title_analyzer.py @@ -15,93 +15,93 @@ class UserTitleAnalyzer(BaseAnalyzer): 用户称号分析器 专门处理用户称号分配和MBTI类型分析 """ - + def get_data_type(self) -> str: """获取数据类型标识""" return "用户称号" - + def get_max_count(self) -> int: """获取最大用户称号数量""" return self.config_manager.get_max_user_titles() - + def get_max_tokens(self) -> int: """获取最大token数""" return self.config_manager.get_user_title_max_tokens() - + def get_temperature(self) -> float: """获取温度参数""" return 0.5 - + def build_prompt(self, user_data: Dict) -> str: """ 构建用户称号分析提示词 - + Args: user_data: 用户数据字典，包含用户统计信息 - + Returns: 提示词字符串 """ user_summaries = user_data.get("user_summaries", []) - + if not user_summaries: return "" - + # 构建用户数据文本 - 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 - ]) - + 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 + ] + ) + # 从配置读取 prompt 模板（默认使用 "default" 风格） prompt_template = self.config_manager.get_user_title_analysis_prompt() - + if prompt_template: # 使用配置中的 prompt 并替换变量 try: - prompt = prompt_template.format( - users_text=users_text - ) + prompt = prompt_template.format(users_text=users_text) logger.info("使用配置中的用户称号分析提示词") return prompt except KeyError as e: logger.warning(f"用户称号分析提示词变量格式错误: {e}") except Exception as e: logger.warning(f"应用用户称号分析提示词失败: {e}") - + logger.warning("未找到有效的用户称号分析提示词配置，请检查配置文件") return "" - + def extract_with_regex(self, result_text: str, max_count: int) -> List[Dict]: """ 使用正则表达式提取用户称号信息 - + Args: result_text: LLM响应文本 max_count: 最大提取数量 - + Returns: 用户称号数据列表 """ return extract_user_titles_with_regex(result_text, max_count) - + def create_data_objects(self, titles_data: List[Dict]) -> List[UserTitle]: """ 创建用户称号对象列表 - + Args: titles_data: 原始用户称号数据列表 - + Returns: UserTitle对象列表 """ try: titles = [] max_titles = self.get_max_count() - + for title_data in titles_data[:max_titles]: # 确保数据格式正确 name = title_data.get("name", "").strip() @@ -109,122 +109,147 @@ class UserTitleAnalyzer(BaseAnalyzer): title = title_data.get("title", "").strip() mbti = title_data.get("mbti", "").strip() reason = title_data.get("reason", "").strip() - + # 验证必要字段 if not name or not title or not mbti or not reason: logger.warning(f"用户称号数据格式不完整，跳过: {title_data}") continue - + # 验证QQ号格式 try: qq = int(qq) except (ValueError, TypeError): logger.warning(f"QQ号格式无效，跳过: {qq}") continue - - titles.append(UserTitle( - name=name, - qq=qq, - title=title, - mbti=mbti, - reason=reason - )) - + + titles.append( + UserTitle(name=name, qq=qq, title=title, mbti=mbti, reason=reason) + ) + return titles - + except Exception as e: logger.error(f"创建用户称号对象失败: {e}") return [] - - def prepare_user_data(self, messages: List[Dict], user_analysis: Dict, top_users: List[Dict] = None) -> Dict: + + def prepare_user_data( + self, messages: List[Dict], user_analysis: Dict, top_users: List[Dict] = None + ) -> Dict: """ 准备用户数据 - + Args: messages: 群聊消息列表 user_analysis: 用户分析统计 top_users: 活跃用户列表(从get_top_users获取) - + Returns: 准备好的用户数据字典 """ try: # 获取机器人QQ号用于过滤 bot_qq_id = self.config_manager.get_bot_qq_id() - + user_summaries = [] - + # 如果提供了top_users列表,只分析这些活跃用户 if top_users: - logger.info(f"使用get_top_users筛选出的 {len(top_users)} 个活跃用户进行称号分析") - target_user_ids = {str(user['user_id']) for user in top_users} + logger.info( + f"使用get_top_users筛选出的 {len(top_users)} 个活跃用户进行称号分析" + ) + target_user_ids = {str(user["user_id"]) for user in top_users} else: # 兼容旧逻辑:如果没有提供top_users,则使用所有消息数>=5的用户 logger.info("未提供活跃用户列表,使用消息数>=5的用户") - target_user_ids = {user_id for user_id, stats in user_analysis.items() - if stats["message_count"] >= 5} - + target_user_ids = { + user_id + for user_id, stats in user_analysis.items() + if stats["message_count"] >= 5 + } + for user_id, stats in user_analysis.items(): # 过滤机器人自己的消息 if bot_qq_id and str(user_id) == str(bot_qq_id): logger.debug(f"过滤掉机器人QQ号: {user_id}") continue - + # 只处理活跃用户 if user_id not in target_user_ids: continue - + # 分析用户特征 night_messages = sum(stats["hours"][h] for h in range(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) if stats["message_count"] > 0 else 0, - "night_ratio": round(night_messages / stats["message_count"], 2) if stats["message_count"] > 0 else 0, - "reply_ratio": round(stats["reply_count"] / stats["message_count"], 2) if stats["message_count"] > 0 else 0 - }) - + 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 + ) + if stats["message_count"] > 0 + else 0, + "night_ratio": round(night_messages / stats["message_count"], 2) + if stats["message_count"] > 0 + else 0, + "reply_ratio": round( + stats["reply_count"] / stats["message_count"], 2 + ) + if stats["message_count"] > 0 + else 0, + } + ) + if not user_summaries: return {"user_summaries": []} - + # 按消息数量排序 user_summaries.sort(key=lambda x: x["message_count"], reverse=True) - + return {"user_summaries": user_summaries} - + except Exception as e: logger.error(f"准备用户数据失败: {e}") return {"user_summaries": []} - - async def analyze_user_titles(self, messages: List[Dict], user_analysis: Dict, umo: str = None, top_users: List[Dict] = None) -> Tuple[List[UserTitle], TokenUsage]: + + async def analyze_user_titles( + self, + messages: List[Dict], + user_analysis: Dict, + umo: str = None, + top_users: List[Dict] = None, + ) -> Tuple[List[UserTitle], TokenUsage]: """ 分析用户称号 - + Args: messages: 群聊消息列表 user_analysis: 用户分析统计 umo: 模型唯一标识符 top_users: 活跃用户列表(从get_top_users获取,可选) - + Returns: (用户称号列表, Token使用统计) """ try: # 准备用户数据,传入活跃用户列表 user_data = self.prepare_user_data(messages, user_analysis, top_users) - + if not user_data["user_summaries"]: logger.info("没有符合条件的用户，返回空结果") return [], TokenUsage() - + logger.info(f"开始分析 {len(user_data['user_summaries'])} 个活跃用户的称号") return await self.analyze(user_data, umo) - + except Exception as e: logger.error(f"用户称号分析失败: {e}") - return [], TokenUsage() \ No newline at end of file + return [], TokenUsage() diff --git a/src/analysis/llm_analyzer.py b/src/analysis/llm_analyzer.py index ebd11f9..c5a3583 100644 --- a/src/analysis/llm_analyzer.py +++ b/src/analysis/llm_analyzer.py @@ -12,7 +12,6 @@ from .analyzers.user_title_analyzer import UserTitleAnalyzer from .analyzers.golden_quote_analyzer import GoldenQuoteAnalyzer from .utils.llm_utils import call_provider_with_retry from .utils.json_utils import fix_json -from .utils.json_utils import extract_topics_with_regex, extract_user_titles_with_regex, extract_golden_quotes_with_regex class LLMAnalyzer: @@ -21,32 +20,34 @@ class LLMAnalyzer: 作为统一入口，协调各个专门的分析器进行不同类型的分析 保持向后兼容性，提供原有的接口 """ - + def __init__(self, context, config_manager): """ 初始化LLM分析器 - + Args: context: AstrBot上下文对象 config_manager: 配置管理器 """ self.context = context self.config_manager = config_manager - + # 初始化各个专门的分析器 self.topic_analyzer = TopicAnalyzer(context, config_manager) self.user_title_analyzer = UserTitleAnalyzer(context, config_manager) self.golden_quote_analyzer = GoldenQuoteAnalyzer(context, config_manager) - - async def analyze_topics(self, messages: List[Dict], umo: str = None) -> Tuple[List[SummaryTopic], TokenUsage]: + + async def analyze_topics( + self, messages: List[Dict], umo: str = None + ) -> Tuple[List[SummaryTopic], TokenUsage]: """ 使用LLM分析话题 保持原有接口，委托给专门的TopicAnalyzer处理 - + Args: messages: 群聊消息列表 umo: 模型唯一标识符 - + Returns: (话题列表, Token使用统计) """ @@ -56,37 +57,47 @@ class LLMAnalyzer: except Exception as e: logger.error(f"话题分析失败: {e}") return [], TokenUsage() - - async def analyze_user_titles(self, messages: List[Dict], user_analysis: Dict, umo: str = None, top_users: List[Dict] = None) -> Tuple[List[UserTitle], TokenUsage]: + + async def analyze_user_titles( + self, + messages: List[Dict], + user_analysis: Dict, + umo: str = None, + top_users: List[Dict] = None, + ) -> Tuple[List[UserTitle], TokenUsage]: """ 使用LLM分析用户称号 保持原有接口，委托给专门的UserTitleAnalyzer处理 - + Args: messages: 群聊消息列表 user_analysis: 用户分析统计 umo: 模型唯一标识符 top_users: 活跃用户列表(可选) - + Returns: (用户称号列表, Token使用统计) """ try: logger.info("开始用户称号分析") - return await self.user_title_analyzer.analyze_user_titles(messages, user_analysis, umo, top_users) + return await self.user_title_analyzer.analyze_user_titles( + messages, user_analysis, umo, top_users + ) except Exception as e: logger.error(f"用户称号分析失败: {e}") return [], TokenUsage() - - async def analyze_golden_quotes(self, messages: List[Dict], umo: str = None) -> Tuple[List[GoldenQuote], TokenUsage]: + + async def analyze_golden_quotes( + self, messages: List[Dict], umo: str = None + ) -> Tuple[List[GoldenQuote], TokenUsage]: """ 使用LLM分析群聊金句 保持原有接口，委托给专门的GoldenQuoteAnalyzer处理 - + Args: messages: 群聊消息列表 umo: 模型唯一标识符 - + Returns: (金句列表, Token使用统计) """ @@ -96,98 +107,120 @@ class LLMAnalyzer: except Exception as e: logger.error(f"金句分析失败: {e}") return [], TokenUsage() - - async def analyze_all_concurrent(self, messages: List[Dict], user_analysis: Dict, umo: str = None, top_users: List[Dict] = None) -> Tuple[List[SummaryTopic], List[UserTitle], List[GoldenQuote], TokenUsage]: + + async def analyze_all_concurrent( + self, + messages: List[Dict], + user_analysis: Dict, + umo: str = None, + top_users: List[Dict] = None, + ) -> Tuple[List[SummaryTopic], List[UserTitle], List[GoldenQuote], TokenUsage]: """ 并发执行所有分析任务（话题、用户称号、金句） - + Args: messages: 群聊消息列表 user_analysis: 用户分析统计 umo: 模型唯一标识符 top_users: 活跃用户列表(可选) - + Returns: (话题列表, 用户称号列表, 金句列表, 总Token使用统计) """ try: logger.info("开始并发执行所有分析任务") - + # 并发执行三个分析任务 results = await asyncio.gather( self.topic_analyzer.analyze_topics(messages, umo), - self.user_title_analyzer.analyze_user_titles(messages, user_analysis, umo, top_users), + self.user_title_analyzer.analyze_user_titles( + messages, user_analysis, umo, top_users + ), self.golden_quote_analyzer.analyze_golden_quotes(messages, umo), - return_exceptions=True + return_exceptions=True, ) - + # 处理结果 topics, topic_usage = [], TokenUsage() user_titles, title_usage = [], TokenUsage() golden_quotes, quote_usage = [], TokenUsage() - + # 话题分析结果 if isinstance(results[0], Exception): logger.error(f"话题分析失败: {results[0]}") else: topics, topic_usage = results[0] - + # 用户称号分析结果 if isinstance(results[1], Exception): logger.error(f"用户称号分析失败: {results[1]}") else: user_titles, title_usage = results[1] - + # 金句分析结果 if isinstance(results[2], Exception): logger.error(f"金句分析失败: {results[2]}") else: golden_quotes, quote_usage = results[2] - + # 合并Token使用统计 total_usage = TokenUsage( - prompt_tokens=topic_usage.prompt_tokens + title_usage.prompt_tokens + quote_usage.prompt_tokens, - completion_tokens=topic_usage.completion_tokens + title_usage.completion_tokens + quote_usage.completion_tokens, - total_tokens=topic_usage.total_tokens + title_usage.total_tokens + quote_usage.total_tokens + prompt_tokens=topic_usage.prompt_tokens + + title_usage.prompt_tokens + + quote_usage.prompt_tokens, + completion_tokens=topic_usage.completion_tokens + + title_usage.completion_tokens + + quote_usage.completion_tokens, + total_tokens=topic_usage.total_tokens + + title_usage.total_tokens + + quote_usage.total_tokens, + ) + + logger.info( + f"并发分析完成 - 话题: {len(topics)}, 称号: {len(user_titles)}, 金句: {len(golden_quotes)}" ) - - logger.info(f"并发分析完成 - 话题: {len(topics)}, 称号: {len(user_titles)}, 金句: {len(golden_quotes)}") return topics, user_titles, golden_quotes, total_usage - + except Exception as e: logger.error(f"并发分析失败: {e}") return [], [], [], TokenUsage() - + # 向后兼容的方法，保持原有调用方式 - async def _call_provider_with_retry(self, provider, prompt: str, max_tokens: int, - temperature: float, umo: str = None): + async def _call_provider_with_retry( + self, + provider, + prompt: str, + max_tokens: int, + temperature: float, + umo: str = None, + ): """ 向后兼容的LLM调用方法 现在委托给llm_utils模块处理 - + Args: provider: LLM服务商实例或None prompt: 输入的提示语 max_tokens: 最大生成token数 temperature: 采样温度 umo: 指定使用的模型唯一标识符 - + Returns: LLM生成的结果 """ - return await call_provider_with_retry(self.context, self.config_manager, - prompt, max_tokens, temperature, umo) - + return await call_provider_with_retry( + self.context, self.config_manager, prompt, max_tokens, temperature, umo + ) + def _fix_json(self, text: str) -> str: """ 向后兼容的JSON修复方法 现在委托给json_utils模块处理 - + Args: text: 需要修复的JSON文本 - + Returns: 修复后的JSON文本 """ return fix_json(text) - \ No newline at end of file diff --git a/src/analysis/statistics.py b/src/analysis/statistics.py index 473ac5d..937478c 100644 --- a/src/analysis/statistics.py +++ b/src/analysis/statistics.py @@ -19,24 +19,26 @@ class UserAnalyzer: """分析用户活跃度""" # 获取机器人QQ号用于过滤 bot_qq_id = self.config_manager.get_bot_qq_id() - - user_stats = defaultdict(lambda: { - "message_count": 0, - "char_count": 0, - "emoji_count": 0, - "nickname": "", - "hours": defaultdict(int), - "reply_count": 0 - }) + + 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", "")) - + # 跳过机器人自己的消息，避免进入统计 if bot_qq_id and user_id == str(bot_qq_id): continue - + nickname = InfoUtils.get_user_nickname(self.config_manager, sender) user_stats[user_id]["message_count"] += 1 @@ -75,31 +77,37 @@ class UserAnalyzer: return dict(user_stats) - def get_top_users(self, user_analysis: Dict[str, Dict], limit: int = 10) -> List[Dict]: + def get_top_users( + self, user_analysis: Dict[str, Dict], limit: int = 10 + ) -> List[Dict]: """获取最活跃的用户""" # 获取机器人QQ号用于过滤 bot_qq_id = self.config_manager.get_bot_qq_id() - + users = [] for user_id, stats in user_analysis.items(): # 过滤机器人自己 if bot_qq_id and str(user_id) == str(bot_qq_id): continue - - users.append({ - "user_id": user_id, - "nickname": stats["nickname"], - "message_count": stats["message_count"], - "char_count": stats["char_count"], - "emoji_count": stats["emoji_count"], - "reply_count": stats["reply_count"] - }) + + users.append( + { + "user_id": user_id, + "nickname": stats["nickname"], + "message_count": stats["message_count"], + "char_count": stats["char_count"], + "emoji_count": stats["emoji_count"], + "reply_count": stats["reply_count"], + } + ) # 按消息数量排序 users.sort(key=lambda x: x["message_count"], reverse=True) return users[:limit] - def get_user_activity_pattern(self, user_analysis: Dict[str, Dict], user_id: str) -> Dict: + def get_user_activity_pattern( + self, user_analysis: Dict[str, Dict], user_id: str + ) -> Dict: """获取用户活动模式""" if user_id not in user_analysis: return {} @@ -112,11 +120,12 @@ class UserAnalyzer: # 计算夜间活跃度 night_messages = sum(hours[h] for h in range(0, 6)) - night_ratio = night_messages / stats["message_count"] if stats["message_count"] > 0 else 0 + night_ratio = ( + night_messages / stats["message_count"] if stats["message_count"] > 0 else 0 + ) return { "most_active_hour": most_active_hour, "night_ratio": night_ratio, - "hourly_distribution": dict(hours) + "hourly_distribution": dict(hours), } - diff --git a/src/analysis/utils/__init__.py b/src/analysis/utils/__init__.py index b935e13..8388263 100644 --- a/src/analysis/utils/__init__.py +++ b/src/analysis/utils/__init__.py @@ -8,30 +8,28 @@ from .json_utils import ( parse_json_response, extract_topics_with_regex, extract_user_titles_with_regex, - extract_golden_quotes_with_regex + extract_golden_quotes_with_regex, ) from .llm_utils import ( call_provider_with_retry, extract_token_usage, - extract_response_text + extract_response_text, ) from .info_utils import InfoUtils __all__ = [ # JSON处理工具 - 'fix_json', - 'parse_json_response', - 'extract_topics_with_regex', - 'extract_user_titles_with_regex', - 'extract_golden_quotes_with_regex', - + "fix_json", + "parse_json_response", + "extract_topics_with_regex", + "extract_user_titles_with_regex", + "extract_golden_quotes_with_regex", # LLM工具 - 'call_provider_with_retry', - 'extract_token_usage', - 'extract_response_text', - + "call_provider_with_retry", + "extract_token_usage", + "extract_response_text", # 信息工具 - 'InfoUtils' -] \ No newline at end of file + "InfoUtils", +] diff --git a/src/analysis/utils/json_utils.py b/src/analysis/utils/json_utils.py index 3af948b..d17fcd5 100644 --- a/src/analysis/utils/json_utils.py +++ b/src/analysis/utils/json_utils.py @@ -1,4 +1,3 @@ - """ JSON处理工具模块 提供JSON解析、修复和正则提取功能 @@ -6,41 +5,41 @@ JSON处理工具模块 import json import re -from typing import List, Dict, Tuple, Any, Optional +from typing import List, Dict, Tuple, Optional from astrbot.api import logger def fix_json(text: str) -> str: """ 修复JSON格式问题，包括中文符号替换 - + Args: text: 需要修复的JSON文本 - + Returns: 修复后的JSON文本 """ try: # 1. 移除markdown代码块标记 - text = re.sub(r'```json\s*', '', text) - text = re.sub(r'```\s*$', '', text) - + text = re.sub(r"```json\s*", "", text) + text = re.sub(r"```\s*$", "", text) + # 2. 基础清理 - text = text.replace('\n', ' ').replace('\r', ' ') - text = re.sub(r'\s+', ' ', text) - + text = text.replace("\n", " ").replace("\r", " ") + text = re.sub(r"\s+", " ", text) + # 3. 替换中文符号为英文符号（修复） # 中文引号 -> 英文引号 - text = text.replace('“', '"').replace('”', '"') - text = text.replace('‘', "'").replace('’', "'") + text = text.replace("“", '"').replace("”", '"') + text = text.replace("‘", "'").replace("’", "'") # 中文逗号 -> 英文逗号 - text = text.replace('，', ',') + text = text.replace("，", ",") # 中文冒号 -> 英文冒号 - text = text.replace('：', ':') + text = text.replace("：", ":") # 中文括号 -> 英文括号 - text = text.replace('（', '(').replace('）', ')') - text = text.replace('【', '[').replace('】', ']') - + text = text.replace("（", "(").replace("）", ")") + text = text.replace("【", "[").replace("】", "]") + # 4. 处理字符串内容中的特殊字符 # 转义字符串内的双引号 def escape_quotes_in_strings(match): @@ -48,71 +47,73 @@ def fix_json(text: str) -> str: # 转义内部的双引号 content = content.replace('"', '\\"') return f'"{content}"' - + # 先处理字段值中的引号 text = re.sub(r'"([^"]*(?:"[^"]*)*)"', escape_quotes_in_strings, text) - + # 5. 修复截断的JSON - if not text.endswith(']'): - last_complete = text.rfind('}') + if not text.endswith("]"): + last_complete = text.rfind("}") if last_complete > 0: - text = text[:last_complete + 1] + ']' - + text = text[: last_complete + 1] + "]" + # 6. 修复常见的JSON格式问题 # 1. 修复缺失的逗号 - text = re.sub(r'}\s*{', '}, {', text) - + text = re.sub(r"}\s*{", "}, {", text) + # 2. 确保字段名有引号（仅在对象开始或逗号后，避免破坏字符串值） def quote_field_names(match): prefix = match.group(1) key = match.group(2) return f'{prefix}"{key}":' - + # 只在 { 或 , 后面匹配字段名，避免在字符串值中误匹配 - text = re.sub(r'([{,]\s*)([a-zA-Z_][a-zA-Z0-9_]*)\s*:', quote_field_names, text) - + text = re.sub(r"([{,]\s*)([a-zA-Z_][a-zA-Z0-9_]*)\s*:", quote_field_names, text) + # 3. 移除多余的逗号 - text = re.sub(r',\s*}', '}', text) - text = re.sub(r',\s*]', ']', text) - + text = re.sub(r",\s*}", "}", text) + text = re.sub(r",\s*]", "]", text) + return text.strip() - + except Exception as e: logger.error(f"JSON修复失败: {e}") return text -def parse_json_response(result_text: str, data_type: str) -> Tuple[bool, Optional[List[Dict]], Optional[str]]: +def parse_json_response( + result_text: str, data_type: str +) -> Tuple[bool, Optional[List[Dict]], Optional[str]]: """ 统一的JSON解析方法 - + Args: result_text: LLM返回的原始文本 data_type: 数据类型 ('topics' | 'user_titles' | 'golden_quotes') - + Returns: (成功标志, 解析后的数据列表, 错误消息) """ try: # 1. 提取JSON部分 - json_match = re.search(r'\[.*?\]', result_text, re.DOTALL) + json_match = re.search(r"\[.*?\]", result_text, re.DOTALL) if not json_match: error_msg = f"{data_type}响应中未找到JSON格式" logger.warning(error_msg) return False, None, error_msg - + json_text = json_match.group() logger.debug(f"{data_type}分析JSON原文: {json_text[:500]}...") - + # 2. 修复JSON json_text = fix_json(json_text) logger.debug(f"{data_type}修复后的JSON: {json_text[:300]}...") - + # 3. 解析JSON data = json.loads(json_text) logger.info(f"{data_type}分析成功，解析到 {len(data)} 条数据") return True, data, None - + except json.JSONDecodeError as e: error_msg = f"{data_type}JSON解析失败: {e}" logger.warning(error_msg) @@ -127,11 +128,11 @@ def parse_json_response(result_text: str, data_type: str) -> Tuple[bool, Optiona def extract_topics_with_regex(result_text: str, max_topics: int) -> List[Dict]: """ 使用正则表达式提取话题信息 - + Args: result_text: 需要提取的文本 max_topics: 最大话题数量 - + Returns: 话题数据列表 """ @@ -140,33 +141,38 @@ def extract_topics_with_regex(result_text: str, max_topics: int) -> List[Dict]: # 匹配每个完整的话题对象 topic_pattern = r'\{\s*"topic":\s*"([^"]+)"\s*,\s*"contributors":\s*\[([^\]]+)\]\s*,\s*"detail":\s*"([^"]*(?:\\.[^"]*)*)"\s*\}' matches = re.findall(topic_pattern, result_text, re.DOTALL) - + if not matches: # 尝试更宽松的匹配 topic_pattern = r'"topic":\s*"([^"]+)"[^}]*"contributors":\s*\[([^\]]+)\][^}]*"detail":\s*"([^"]*(?:\\.[^"]*)*)"' matches = re.findall(topic_pattern, result_text, re.DOTALL) - + topics = [] for match in matches[:max_topics]: topic_name = match[0].strip() contributors_str = match[1].strip() detail = match[2].strip() - + # 清理detail中的转义字符 - detail = detail.replace('\\"', '"').replace('\\n', ' ').replace('\\t', ' ') - + detail = detail.replace('\\"', '"').replace("\\n", " ").replace("\\t", " ") + # 解析参与者列表 - contributors = [contrib.strip() for contrib in re.findall(r'"([^"]+)"', contributors_str)] or ["群友"] - - topics.append({ - "topic": topic_name, - "contributors": contributors[:5], # 最多5个参与者 - "detail": detail - }) - + contributors = [ + contrib.strip() + for contrib in re.findall(r'"([^"]+)"', contributors_str) + ] or ["群友"] + + topics.append( + { + "topic": topic_name, + "contributors": contributors[:5], # 最多5个参与者 + "detail": detail, + } + ) + logger.info(f"话题正则表达式提取成功，提取到 {len(topics)} 条有效话题内容") return topics - + except Exception as e: logger.error(f"话题正则表达式提取失败: {e}") return [] @@ -175,47 +181,43 @@ def extract_topics_with_regex(result_text: str, max_topics: int) -> List[Dict]: def extract_user_titles_with_regex(result_text: str, max_count: int) -> List[Dict]: """ 使用正则表达式提取用户称号信息 - + Args: result_text: 需要提取的文本 max_count: 最大提取数量 - + Returns: 用户称号数据列表 """ try: titles = [] - + # 正则模式：匹配完整的用户称号对象 pattern = r'\{\s*"name":\s*"([^"]+)"\s*,\s*"qq":\s*(\d+)\s*,\s*"title":\s*"([^"]+)"\s*,\s*"mbti":\s*"([^"]+)"\s*,\s*"reason":\s*"([^"]*(?:\\.[^"]*)*)"\s*\}' matches = re.findall(pattern, result_text, re.DOTALL) - + if not matches: # 尝试更宽松的匹配（字段顺序可变） pattern = r'"name":\s*"([^"]+)"[^}]*"qq":\s*(\d+)[^}]*"title":\s*"([^"]+)"[^}]*"mbti":\s*"([^"]+)"[^}]*"reason":\s*"([^"]*(?:\\.[^"]*)*)"' matches = re.findall(pattern, result_text, re.DOTALL) - + for match in matches[:max_count]: name = match[0].strip() qq = int(match[1]) title = match[2].strip() mbti = match[3].strip() reason = match[4].strip() - + # 清理转义字符 - reason = reason.replace('\\"', '"').replace('\\n', ' ').replace('\\t', ' ') - - titles.append({ - "name": name, - "qq": qq, - "title": title, - "mbti": mbti, - "reason": reason - }) - + reason = reason.replace('\\"', '"').replace("\\n", " ").replace("\\t", " ") + + titles.append( + {"name": name, "qq": qq, "title": title, "mbti": mbti, "reason": reason} + ) + logger.info(f"用户称号正则表达式提取成功，提取到 {len(titles)} 条有效用户称号") return titles - + except Exception as e: logger.error(f"用户称号正则表达式提取失败: {e}") return [] @@ -224,44 +226,42 @@ def extract_user_titles_with_regex(result_text: str, max_count: int) -> List[Dic def extract_golden_quotes_with_regex(result_text: str, max_count: int) -> List[Dict]: """ 使用正则表达式提取金句信息 - + Args: result_text: 需要提取的文本 max_count: 最大提取数量 - + Returns: 金句数据列表 """ try: quotes = [] - + # 正则模式：匹配完整的金句对象 pattern = r'\{\s*"content":\s*"([^"]*(?:\\.[^"]*)*)"\s*,\s*"sender":\s*"([^"]+)"\s*,\s*"reason":\s*"([^"]*(?:\\.[^"]*)*)"\s*\}' matches = re.findall(pattern, result_text, re.DOTALL) - + if not matches: # 尝试更宽松的匹配（字段顺序可变） pattern = r'"content":\s*"([^"]*(?:\\.[^"]*)*)"[^}]*"sender":\s*"([^"]+)"[^}]*"reason":\s*"([^"]*(?:\\.[^"]*)*)"' matches = re.findall(pattern, result_text, re.DOTALL) - + for match in matches[:max_count]: content = match[0].strip() sender = match[1].strip() reason = match[2].strip() - + # 清理转义字符 - content = content.replace('\\"', '"').replace('\\n', ' ').replace('\\t', ' ') - reason = reason.replace('\\"', '"').replace('\\n', ' ').replace('\\t', ' ') - - quotes.append({ - "content": content, - "sender": sender, - "reason": reason - }) - + content = ( + content.replace('\\"', '"').replace("\\n", " ").replace("\\t", " ") + ) + reason = reason.replace('\\"', '"').replace("\\n", " ").replace("\\t", " ") + + quotes.append({"content": content, "sender": sender, "reason": reason}) + logger.info(f"金句正则表达式提取成功，提取到 {len(quotes)} 条有效金句") return quotes - + except Exception as e: logger.error(f"金句正则表达式提取失败: {e}") return [] diff --git a/src/analysis/utils/llm_utils.py b/src/analysis/utils/llm_utils.py index 002e9fc..aba04e2 100644 --- a/src/analysis/utils/llm_utils.py +++ b/src/analysis/utils/llm_utils.py @@ -9,11 +9,17 @@ from astrbot.api import logger import aiohttp -async def call_provider_with_retry(context, config_manager, prompt: str, max_tokens: int, - temperature: float, umo: str = None) -> Optional[Any]: +async def call_provider_with_retry( + context, + config_manager, + prompt: str, + max_tokens: int, + temperature: float, + umo: str = None, +) -> Optional[Any]: """ 调用LLM提供者，带超时、重试与退避。支持自定义服务商。 - + Args: context: AstrBot上下文对象 config_manager: 配置管理器 @@ -21,59 +27,80 @@ async def call_provider_with_retry(context, config_manager, prompt: str, max_tok max_tokens: 最大生成token数 temperature: 采样温度 umo: 指定使用的模型唯一标识符 - + Returns: LLM生成的结果，失败时返回None """ timeout = config_manager.get_llm_timeout() retries = config_manager.get_llm_retries() backoff = config_manager.get_llm_backoff() - + # 获取自定义服务商参数 custom_api_key = config_manager.get_custom_api_key() custom_api_base = config_manager.get_custom_api_base_url() custom_model = config_manager.get_custom_model_name() - + last_exc = None for attempt in range(1, retries + 1): try: if custom_api_key and custom_api_base and custom_model: - logger.info(f"使用自定义LLM提供商: {custom_api_base} model={custom_model}, max_tokens={max_tokens}, temperature={temperature}") - logger.debug(f"自定义LLM提供商 prompt 长度: {len(prompt) if prompt else 0}") - logger.debug(f"自定义LLM提供商 prompt 前100字符: {prompt[:100] if prompt else 'None'}...") - + logger.info( + f"使用自定义LLM提供商: {custom_api_base} model={custom_model}, max_tokens={max_tokens}, temperature={temperature}" + ) + logger.debug( + f"自定义LLM提供商 prompt 长度: {len(prompt) if prompt else 0}" + ) + logger.debug( + f"自定义LLM提供商 prompt 前100字符: {prompt[:100] if prompt else 'None'}..." + ) + # 检查 prompt 是否为空 if not prompt or not prompt.strip(): - logger.error("自定义LLM提供商: prompt 为空或只包含空白字符，无法发送请求") + logger.error( + "自定义LLM提供商: prompt 为空或只包含空白字符，无法发送请求" + ) return None - + async with aiohttp.ClientSession() as session: headers = { "Authorization": f"Bearer {custom_api_key}", - "Content-Type": "application/json" + "Content-Type": "application/json", } payload = { "model": custom_model, "messages": [{"role": "user", "content": prompt}], "max_tokens": max_tokens, - "temperature": temperature + "temperature": temperature, } aio_timeout = aiohttp.ClientTimeout(total=timeout) - async with session.post(custom_api_base, json=payload, headers=headers, timeout=aio_timeout) as resp: + async with session.post( + custom_api_base, + json=payload, + headers=headers, + timeout=aio_timeout, + ) as resp: if resp.status != 200: error_text = await resp.text() - logger.error(f"自定义LLM服务商请求失败: HTTP {resp.status}, 内容: {error_text}") + logger.error( + f"自定义LLM服务商请求失败: HTTP {resp.status}, 内容: {error_text}" + ) try: response_json = await resp.json() except Exception as json_err: error_text = await resp.text() - logger.error(f"自定义LLM服务商响应JSON解析失败: {json_err}, 内容: {error_text}") + logger.error( + f"自定义LLM服务商响应JSON解析失败: {json_err}, 内容: {error_text}" + ) return None # 兼容 OpenAI 格式，安全访问嵌套字段 content = None try: choices = response_json.get("choices") - if choices and isinstance(choices, list) and len(choices) > 0: + if ( + choices + and isinstance(choices, list) + and len(choices) > 0 + ): message = choices[0].get("message") if message and isinstance(message, dict): content = message.get("content") @@ -81,17 +108,21 @@ async def call_provider_with_retry(context, config_manager, prompt: str, max_tok logger.error(f"自定义LLM响应格式异常: {response_json}") return None except Exception as key_err: - logger.error(f"自定义LLM响应结构解析失败: {key_err}, 响应内容: {response_json}") + logger.error( + f"自定义LLM响应结构解析失败: {key_err}, 响应内容: {response_json}" + ) return None + # 构造一个兼容原有逻辑的对象 class CustomResponse: completion_text = content raw_completion = response_json + return CustomResponse() else: # 确保使用当前指定的模型 provider = context.get_using_provider(umo=umo) - provider_id = 'unknown' + provider_id = "unknown" if provider: try: meta = provider.meta() @@ -99,23 +130,33 @@ async def call_provider_with_retry(context, config_manager, prompt: str, max_tok except Exception as e: logger.debug(f"获取提供商ID失败: {e}") logger.info(f"获取到的 provider ID: {provider_id}") - if not provider or provider_id == 'unknown': + if not provider or provider_id == "unknown": logger.warning(f"获取的提供商不正确 (Provider ID: {provider_id})") - - logger.info(f"使用LLM provider: {provider}, max_tokens={max_tokens}, temperature={temperature}") + + logger.info( + f"使用LLM provider: {provider}, max_tokens={max_tokens}, temperature={temperature}" + ) if not provider: logger.error("provider 为空，无法调用 text_chat，直接返回 None") return None - - logger.debug(f"LLM provider prompt 长度: {len(prompt) if prompt else 0}") - logger.debug(f"LLM provider prompt 前100字符: {prompt[:100] if prompt else 'None'}...") - + + logger.debug( + f"LLM provider prompt 长度: {len(prompt) if prompt else 0}" + ) + logger.debug( + f"LLM provider prompt 前100字符: {prompt[:100] if prompt else 'None'}..." + ) + # 检查 prompt 是否为空 if not prompt or not prompt.strip(): - logger.error("LLM provider: prompt 为空或只包含空白字符，无法调用 text_chat") + logger.error( + "LLM provider: prompt 为空或只包含空白字符，无法调用 text_chat" + ) return None - - coro = provider.text_chat(prompt=prompt, max_tokens=max_tokens, temperature=temperature) + + coro = provider.text_chat( + prompt=prompt, max_tokens=max_tokens, temperature=temperature + ) return await asyncio.wait_for(coro, timeout=timeout) except asyncio.TimeoutError as e: last_exc = e @@ -126,7 +167,7 @@ async def call_provider_with_retry(context, config_manager, prompt: str, max_tok # 若非最后一次，等待退避后重试 if attempt < retries: await asyncio.sleep(backoff * attempt) - + # 最终仍失败，记录错误并返回 None 由调用方处理降级，避免抛出异常 logger.error(f"LLM请求全部重试失败: {last_exc}") return None @@ -135,55 +176,49 @@ async def call_provider_with_retry(context, config_manager, prompt: str, max_tok def extract_token_usage(response) -> Optional[dict]: """ 从LLM响应中提取token使用统计 - + Args: response: LLM响应对象 - + Returns: Token使用统计字典，包含prompt_tokens, completion_tokens, total_tokens """ try: - token_usage = { - "prompt_tokens": 0, - "completion_tokens": 0, - "total_tokens": 0 - } - + token_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} + # 安全地提取 usage，避免 response.raw_completion.usage 为 None 导致的 AttributeError usage = None - if getattr(response, 'raw_completion', None) is not None: - usage = getattr(response.raw_completion, 'usage', None) + if getattr(response, "raw_completion", None) is not None: + usage = getattr(response.raw_completion, "usage", None) if usage: - token_usage["prompt_tokens"] = getattr(usage, 'prompt_tokens', 0) or 0 - token_usage["completion_tokens"] = getattr(usage, 'completion_tokens', 0) or 0 - token_usage["total_tokens"] = getattr(usage, 'total_tokens', 0) or 0 - + token_usage["prompt_tokens"] = getattr(usage, "prompt_tokens", 0) or 0 + token_usage["completion_tokens"] = ( + getattr(usage, "completion_tokens", 0) or 0 + ) + token_usage["total_tokens"] = getattr(usage, "total_tokens", 0) or 0 + return token_usage - + except Exception as e: logger.error(f"提取token使用统计失败: {e}") - return { - "prompt_tokens": 0, - "completion_tokens": 0, - "total_tokens": 0 - } + return {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} def extract_response_text(response) -> str: """ 从LLM响应中提取文本内容 - + Args: response: LLM响应对象 - + Returns: 响应文本内容 """ try: - if hasattr(response, 'completion_text'): + if hasattr(response, "completion_text"): return response.completion_text else: return str(response) except Exception as e: logger.error(f"提取响应文本失败: {e}") - return "" \ No newline at end of file + return "" diff --git a/src/core/__init__.py b/src/core/__init__.py index 9e7dda8..0999975 100644 --- a/src/core/__init__.py +++ b/src/core/__init__.py @@ -5,7 +5,4 @@ from .config import ConfigManager from .message_handler import MessageHandler -__all__ = [ - 'ConfigManager', - 'MessageHandler' -] \ No newline at end of file +__all__ = ["ConfigManager", "MessageHandler"] diff --git a/src/core/bot_manager.py b/src/core/bot_manager.py index 505fcec..5340121 100644 --- a/src/core/bot_manager.py +++ b/src/core/bot_manager.py @@ -5,20 +5,21 @@ Bot实例管理模块 from typing import Dict, Any + class BotManager: """Bot实例管理器 - 统一管理所有bot相关操作""" - + def __init__(self, config_manager): self.config_manager = config_manager self._bot_instance = None self._bot_qq_id = None self._context = None self._is_initialized = False - + def set_context(self, context): """设置AstrBot上下文""" self._context = context - + def set_bot_instance(self, bot_instance): """设置bot实例""" if bot_instance: @@ -29,48 +30,46 @@ class BotManager: if bot_qq_id: self._bot_qq_id = str(bot_qq_id) - def set_bot_qq_id(self, bot_qq_id: str): """设置bot QQ号""" if bot_qq_id: self._bot_qq_id = str(bot_qq_id) - def get_bot_instance(self): """获取当前bot实例""" return self._bot_instance - + def has_bot_instance(self) -> bool: """检查是否有可用的bot实例""" return self._bot_instance is not None - + def has_bot_qq_id(self) -> bool: """检查是否有配置的bot QQ号""" return self._bot_qq_id is not None - + def is_ready_for_auto_analysis(self) -> bool: """检查是否准备好进行自动分析""" return self.has_bot_instance() and self.has_bot_qq_id() - + async def auto_discover_bot_instance(self): """自动发现可用的bot实例""" - if not self._context or not hasattr(self._context, 'platform_manager'): + if not self._context or not hasattr(self._context, "platform_manager"): return None - platforms = getattr(self._context.platform_manager, 'platform_insts', []) + platforms = getattr(self._context.platform_manager, "platform_insts", []) for platform in platforms: # 获取bot实例 bot_client = None - if hasattr(platform, 'get_client'): + if hasattr(platform, "get_client"): bot_client = platform.get_client() - elif hasattr(platform, 'bot'): + elif hasattr(platform, "bot"): bot_client = platform.bot if bot_client: self.set_bot_instance(bot_client) return bot_client return None - + async def initialize_from_config(self): """从配置初始化bot管理器""" # 设置配置的bot QQ号 @@ -84,19 +83,19 @@ class BotManager: # 返回是否成功初始化（至少有bot实例） return self.has_bot_instance() - + def get_status_info(self) -> Dict[str, Any]: """获取bot管理器状态信息""" return { "has_bot_instance": self.has_bot_instance(), "has_bot_qq_id": self.has_bot_qq_id(), "bot_qq_id": self._bot_qq_id, - "ready_for_auto_analysis": self.is_ready_for_auto_analysis() + "ready_for_auto_analysis": self.is_ready_for_auto_analysis(), } - + def update_from_event(self, event): """从事件更新bot实例（用于手动命令）""" - if hasattr(event, 'bot') and event.bot: + if hasattr(event, "bot") and event.bot: self.set_bot_instance(event.bot) # 每次都尝试从bot实例提取QQ号 bot_qq_id = self._extract_bot_qq_id(event.bot) @@ -113,18 +112,18 @@ class BotManager: def _extract_bot_qq_id(self, bot_instance): """从bot实例中提取QQ号""" # 尝试多种方式获取bot QQ号 - if hasattr(bot_instance, 'self_id') and bot_instance.self_id: + if hasattr(bot_instance, "self_id") and bot_instance.self_id: return str(bot_instance.self_id) - elif hasattr(bot_instance, 'qq') and bot_instance.qq: + elif hasattr(bot_instance, "qq") and bot_instance.qq: return str(bot_instance.qq) - elif hasattr(bot_instance, 'user_id') and bot_instance.user_id: + elif hasattr(bot_instance, "user_id") and bot_instance.user_id: return str(bot_instance.user_id) return None - + def validate_for_message_fetching(self, group_id: str) -> bool: """验证是否可以进行消息获取""" return self.has_bot_instance() and bool(group_id) - + def should_filter_bot_message(self, sender_id: str) -> bool: """判断是否应该过滤bot自己的消息""" if not self._bot_qq_id: diff --git a/src/core/config.py b/src/core/config.py index 82fcb9e..6f89fb5 100644 --- a/src/core/config.py +++ b/src/core/config.py @@ -4,8 +4,6 @@ """ import sys -import importlib -from pathlib import Path from typing import Optional, List from astrbot.api import logger, AstrBotConfig @@ -110,25 +108,30 @@ class ConfigManager: def get_custom_model_name(self) -> str: """获取自定义 LLM 服务的模型名称""" return self.config.get("custom_model_name", "") + def get_pdf_output_dir(self) -> str: """获取PDF输出目录""" - return self.config.get("pdf_output_dir", "data/plugins/astrbot-qq-group-daily-analysis/reports") - + return self.config.get( + "pdf_output_dir", "data/plugins/astrbot-qq-group-daily-analysis/reports" + ) + def get_bot_qq_id(self) -> str: """获取bot QQ号""" return str(self.config.get("bot_qq_id", "")) def get_pdf_filename_format(self) -> str: """获取PDF文件名格式""" - return self.config.get("pdf_filename_format", "群聊分析报告_{group_id}_{date}.pdf") + return self.config.get( + "pdf_filename_format", "群聊分析报告_{group_id}_{date}.pdf" + ) def get_topic_analysis_prompt(self, style: str = "topic_prompt") -> str: """ 获取话题分析提示词模板 - + Args: style: 提示词风格，默认为 "topic_prompt" - + Returns: 提示词模板字符串 """ @@ -144,10 +147,10 @@ class ConfigManager: def get_user_title_analysis_prompt(self, style: str = "user_title_prompt") -> str: """ 获取用户称号分析提示词模板 - + Args: style: 提示词风格，默认为 "user_title_prompt" - + Returns: 提示词模板字符串 """ @@ -160,13 +163,15 @@ class ConfigManager: # 兼容旧配置 return self.config.get("user_title_analysis_prompt", "") - def get_golden_quote_analysis_prompt(self, style: str = "golden_quote_prompt") -> str: + def get_golden_quote_analysis_prompt( + self, style: str = "golden_quote_prompt" + ) -> str: """ 获取金句分析提示词模板 - + Args: style: 提示词风格，默认为 "golden_quote_prompt" - + Returns: 提示词模板字符串 """ @@ -309,6 +314,7 @@ class ConfigManager: try: import pyppeteer from pyppeteer import launch + self._pyppeteer_available = True # 检查版本 @@ -322,7 +328,9 @@ class ConfigManager: except ImportError: self._pyppeteer_available = False self._pyppeteer_version = None - logger.warning("pyppeteer 未安装，PDF 功能将不可用。请使用 /安装PDF 命令安装 pyppeteer==1.0.2") + logger.warning( + "pyppeteer 未安装，PDF 功能将不可用。请使用 /安装PDF 命令安装 pyppeteer==1.0.2" + ) def reload_pyppeteer(self) -> bool: """重新加载 pyppeteer 模块""" @@ -330,7 +338,9 @@ class ConfigManager: logger.info("开始重新加载 pyppeteer 模块...") # 移除所有 pyppeteer 相关模块 - modules_to_remove = [mod for mod in sys.modules.keys() if mod.startswith('pyppeteer')] + modules_to_remove = [ + mod for mod in sys.modules.keys() if mod.startswith("pyppeteer") + ] logger.info(f"移除模块: {modules_to_remove}") for mod in modules_to_remove: del sys.modules[mod] @@ -344,22 +354,28 @@ class ConfigManager: self._pyppeteer_available = True try: self._pyppeteer_version = pyppeteer.__version__ - logger.info(f"重新加载成功，pyppeteer 版本: {self._pyppeteer_version}") + logger.info( + f"重新加载成功，pyppeteer 版本: {self._pyppeteer_version}" + ) except AttributeError: self._pyppeteer_version = "unknown" logger.info("重新加载成功，pyppeteer 版本未知") return True - except ImportError as e: - logger.info(f"pyppeteer 重新导入需要重启 AstrBot 才能生效") - logger.info("💡 提示：pyppeteer 安装成功，但需要重启 AstrBot 后才能使用 PDF 功能") + except ImportError: + logger.info("pyppeteer 重新导入需要重启 AstrBot 才能生效") + logger.info( + "💡 提示：pyppeteer 安装成功，但需要重启 AstrBot 后才能使用 PDF 功能" + ) self._pyppeteer_available = False self._pyppeteer_version = None return False - except Exception as e: - logger.info(f"pyppeteer 重新导入需要重启 AstrBot 才能生效") - logger.info("💡 提示：pyppeteer 安装成功，但需要重启 AstrBot 后才能使用 PDF 功能") + except Exception: + logger.info("pyppeteer 重新导入需要重启 AstrBot 才能生效") + logger.info( + "💡 提示：pyppeteer 安装成功，但需要重启 AstrBot 后才能使用 PDF 功能" + ) self._pyppeteer_available = False self._pyppeteer_version = None return False @@ -384,4 +400,4 @@ class ConfigManager: # 配置会自动从self.config中重新读取 logger.info("配置重载完成") except Exception as e: - logger.error(f"重新加载配置失败: {e}") \ No newline at end of file + logger.error(f"重新加载配置失败: {e}") diff --git a/src/core/message_handler.py b/src/core/message_handler.py index 2aa07b9..5f6e3c1 100644 --- a/src/core/message_handler.py +++ b/src/core/message_handler.py @@ -3,12 +3,11 @@ 负责群聊消息的获取、过滤和预处理 """ -import asyncio from datetime import datetime, timedelta -from typing import List, Dict, Optional +from typing import List, Dict from collections import defaultdict from astrbot.api import logger -from ...src.models.data_models import GroupStatistics, TokenUsage, EmojiStatistics, ActivityVisualization +from ...src.models.data_models import GroupStatistics, TokenUsage, EmojiStatistics from ...src.visualization.activity_charts import ActivityVisualizer @@ -35,15 +34,17 @@ class MessageHandler: def _extract_bot_qq_id_from_instance(self, bot_instance): """从bot实例中提取QQ号""" - if hasattr(bot_instance, 'self_id') and bot_instance.self_id: + if hasattr(bot_instance, "self_id") and bot_instance.self_id: return str(bot_instance.self_id) - elif hasattr(bot_instance, 'qq') and bot_instance.qq: + elif hasattr(bot_instance, "qq") and bot_instance.qq: return str(bot_instance.qq) - elif hasattr(bot_instance, 'user_id') and bot_instance.user_id: + elif hasattr(bot_instance, "user_id") and bot_instance.user_id: return str(bot_instance.user_id) return None - async def fetch_group_messages(self, bot_instance, group_id: str, days: int) -> List[Dict]: + async def fetch_group_messages( + self, bot_instance, group_id: str, days: int + ) -> List[Dict]: """获取群聊消息记录""" try: # 验证参数 @@ -68,7 +69,9 @@ class MessageHandler: query_rounds = 0 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')}") + 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')}" + ) # 单次请求，按配置的 max_messages 作为 count try: @@ -78,21 +81,29 @@ class MessageHandler: } result = None - if hasattr(bot_instance, 'call_action'): + if hasattr(bot_instance, "call_action"): try: # aiocqhttp (CQHttp) 方式 - result = await bot_instance.call_action("get_group_msg_history", **payloads) + result = await bot_instance.call_action( + "get_group_msg_history", **payloads + ) query_rounds = 1 except Exception as api_err: logger.error(f"群 {group_id} API 调用失败: {api_err}") - logger.error(f"群 {group_id} 当前 OneBot 实现可能不支持 get_group_msg_history API") + logger.error( + f"群 {group_id} 当前 OneBot 实现可能不支持 get_group_msg_history API" + ) return [] - elif hasattr(bot_instance, 'api'): + elif hasattr(bot_instance, "api"): # QQ 官方 bot (botClient) 不支持历史消息 - logger.error(f"群 {group_id} 检测到 QQ 官方 Bot，官方 API 不支持获取历史消息") + logger.error( + f"群 {group_id} 检测到 QQ 官方 Bot，官方 API 不支持获取历史消息" + ) return [] else: - logger.error(f"群 {group_id} 未知的 bot_instance 类型，无法调用 API，类型: {type(bot_instance)}") + logger.error( + f"群 {group_id} 未知的 bot_instance 类型，无法调用 API，类型: {type(bot_instance)}" + ) return [] if not result or "messages" not in result: @@ -102,7 +113,7 @@ class MessageHandler: round_messages = result.get("messages", []) if not round_messages: logger.info(f"群 {group_id} 未获取到消息") - + # 过滤时间范围内的消息并过滤机器人自身消息 for msg in round_messages: try: @@ -110,7 +121,10 @@ class MessageHandler: if not (start_time <= msg_time <= end_time): continue sender_id = str(msg.get("sender", {}).get("user_id", "")) - if self.bot_manager and self.bot_manager.should_filter_bot_message(sender_id): + if ( + self.bot_manager + and self.bot_manager.should_filter_bot_message(sender_id) + ): continue messages.append(msg) except Exception as msg_error: @@ -122,25 +136,32 @@ class MessageHandler: # ========== 最终清理步骤:严格过滤和限制 ========== original_count = len(messages) - + # 1. 严格过滤时间范围外的消息 messages = [ - msg for msg in messages + msg + for msg in messages if start_time <= datetime.fromtimestamp(msg.get("time", 0)) <= end_time ] time_filtered_count = len(messages) - + # 2. 严格限制消息数量 if len(messages) > max_messages: # 保留最新的消息(假设messages已按时间排序,从新到旧) messages = messages[:max_messages] - logger.info(f"群 {group_id} 消息数量超过限制，已截断: {time_filtered_count} -> {max_messages} 条") - + logger.info( + f"群 {group_id} 消息数量超过限制，已截断: {time_filtered_count} -> {max_messages} 条" + ) + # 记录清理结果 if original_count != len(messages): - logger.info(f"群 {group_id} 最终清理: 原始 {original_count} 条 -> 时间过滤 {time_filtered_count} 条 -> 最终 {len(messages)} 条") - - logger.info(f"群 {group_id} 消息获取完成，共获取到 {len(messages)} 条有效消息（时间范围: 近{days}天），查询轮数: {query_rounds}") + logger.info( + f"群 {group_id} 最终清理: 原始 {original_count} 条 -> 时间过滤 {time_filtered_count} 条 -> 最终 {len(messages)} 条" + ) + + logger.info( + f"群 {group_id} 消息获取完成，共获取到 {len(messages)} 条有效消息（时间范围: 近{days}天），查询轮数: {query_rounds}" + ) return messages except Exception as e: @@ -171,22 +192,30 @@ class MessageHandler: # QQ基础表情 emoji_statistics.face_count += 1 face_id = content.get("data", {}).get("id", "unknown") - emoji_statistics.face_details[f"face_{face_id}"] = emoji_statistics.face_details.get(f"face_{face_id}", 0) + 1 + emoji_statistics.face_details[f"face_{face_id}"] = ( + emoji_statistics.face_details.get(f"face_{face_id}", 0) + 1 + ) elif content.get("type") == "mface": # 动画表情/魔法表情 emoji_statistics.mface_count += 1 emoji_id = content.get("data", {}).get("emoji_id", "unknown") - emoji_statistics.face_details[f"mface_{emoji_id}"] = emoji_statistics.face_details.get(f"mface_{emoji_id}", 0) + 1 + emoji_statistics.face_details[f"mface_{emoji_id}"] = ( + emoji_statistics.face_details.get(f"mface_{emoji_id}", 0) + 1 + ) elif content.get("type") == "bface": # 超级表情 emoji_statistics.bface_count += 1 emoji_id = content.get("data", {}).get("p", "unknown") - emoji_statistics.face_details[f"bface_{emoji_id}"] = emoji_statistics.face_details.get(f"bface_{emoji_id}", 0) + 1 + emoji_statistics.face_details[f"bface_{emoji_id}"] = ( + emoji_statistics.face_details.get(f"bface_{emoji_id}", 0) + 1 + ) elif content.get("type") == "sface": # 小表情 emoji_statistics.sface_count += 1 emoji_id = content.get("data", {}).get("id", "unknown") - emoji_statistics.face_details[f"sface_{emoji_id}"] = emoji_statistics.face_details.get(f"sface_{emoji_id}", 0) + 1 + emoji_statistics.face_details[f"sface_{emoji_id}"] = ( + emoji_statistics.face_details.get(f"sface_{emoji_id}", 0) + 1 + ) elif content.get("type") == "image": # 检查是否是动画表情（通过summary字段判断） data = content.get("data", {}) @@ -195,20 +224,34 @@ class MessageHandler: # 动画表情（以image形式发送） emoji_statistics.mface_count += 1 file_name = data.get("file", "unknown") - emoji_statistics.face_details[f"animated_{file_name}"] = emoji_statistics.face_details.get(f"animated_{file_name}", 0) + 1 + emoji_statistics.face_details[f"animated_{file_name}"] = ( + emoji_statistics.face_details.get( + f"animated_{file_name}", 0 + ) + + 1 + ) else: # 普通图片，不计入表情统计 pass - elif content.get("type") in ["record", "video"] and "emoji" in str(content.get("data", {})).lower(): + elif ( + content.get("type") in ["record", "video"] + and "emoji" in str(content.get("data", {})).lower() + ): # 其他可能的表情类型 emoji_statistics.other_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" + 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" + ) # 生成活跃度可视化数据 - activity_visualization = self.activity_visualizer.generate_activity_visualization(messages) + activity_visualization = ( + self.activity_visualizer.generate_activity_visualization(messages) + ) return GroupStatistics( message_count=len(messages), @@ -219,5 +262,5 @@ class MessageHandler: emoji_count=emoji_statistics.total_emoji_count, # 保持向后兼容 emoji_statistics=emoji_statistics, activity_visualization=activity_visualization, - token_usage=TokenUsage() + token_usage=TokenUsage(), ) diff --git a/src/models/__init__.py b/src/models/__init__.py index 5a42962..1630360 100644 --- a/src/models/__init__.py +++ b/src/models/__init__.py @@ -7,13 +7,7 @@ from .data_models import ( UserTitle, GoldenQuote, TokenUsage, - GroupStatistics + GroupStatistics, ) -__all__ = [ - 'SummaryTopic', - 'UserTitle', - 'GoldenQuote', - 'TokenUsage', - 'GroupStatistics' -] \ No newline at end of file +__all__ = ["SummaryTopic", "UserTitle", "GoldenQuote", "TokenUsage", "GroupStatistics"] diff --git a/src/models/data_models.py b/src/models/data_models.py index c6210c3..688c25d 100644 --- a/src/models/data_models.py +++ b/src/models/data_models.py @@ -10,6 +10,7 @@ from typing import List @dataclass class SummaryTopic: """话题总结数据结构""" + topic: str contributors: List[str] detail: str @@ -18,6 +19,7 @@ class SummaryTopic: @dataclass class UserTitle: """用户称号数据结构""" + name: str qq: int title: str @@ -28,6 +30,7 @@ class UserTitle: @dataclass class GoldenQuote: """群聊金句数据结构""" + content: str sender: str reason: str @@ -36,6 +39,7 @@ class GoldenQuote: @dataclass class TokenUsage: """Token使用统计""" + prompt_tokens: int = 0 completion_tokens: int = 0 total_tokens: int = 0 @@ -44,6 +48,7 @@ class TokenUsage: @dataclass class EmojiStatistics: """表情统计数据结构""" + face_count: int = 0 # QQ基础表情数量 mface_count: int = 0 # 动画表情数量 bface_count: int = 0 # 超级表情数量 @@ -54,14 +59,21 @@ class EmojiStatistics: @property def total_emoji_count(self) -> int: """总表情数量""" - return self.face_count + self.mface_count + self.bface_count + self.sface_count + self.other_emoji_count + return ( + self.face_count + + self.mface_count + + self.bface_count + + self.sface_count + + self.other_emoji_count + ) @dataclass class ActivityVisualization: """活跃度可视化数据结构""" + hourly_activity: dict = field(default_factory=dict) # {hour: count} - daily_activity: dict = field(default_factory=dict) # {date: count} + daily_activity: dict = field(default_factory=dict) # {date: count} user_activity_ranking: list = field(default_factory=list) # 用户活跃度排行 peak_hours: list = field(default_factory=list) # 高峰时段 activity_heatmap_data: dict = field(default_factory=dict) # 热力图数据 @@ -70,6 +82,7 @@ class ActivityVisualization: @dataclass class GroupStatistics: """群聊统计数据结构""" + message_count: int total_characters: int participant_count: int @@ -77,5 +90,7 @@ class GroupStatistics: golden_quotes: List[GoldenQuote] emoji_count: int # 保持向后兼容 emoji_statistics: EmojiStatistics = field(default_factory=EmojiStatistics) - activity_visualization: ActivityVisualization = field(default_factory=ActivityVisualization) - token_usage: TokenUsage = field(default_factory=TokenUsage) \ No newline at end of file + activity_visualization: ActivityVisualization = field( + default_factory=ActivityVisualization + ) + token_usage: TokenUsage = field(default_factory=TokenUsage) diff --git a/src/reports/__init__.py b/src/reports/__init__.py index 267df1e..6ff4113 100644 --- a/src/reports/__init__.py +++ b/src/reports/__init__.py @@ -6,7 +6,4 @@ from .generators import ReportGenerator from .templates import HTMLTemplates -__all__ = [ - 'ReportGenerator', - 'HTMLTemplates' -] \ No newline at end of file +__all__ = ["ReportGenerator", "HTMLTemplates"] diff --git a/src/reports/generators.py b/src/reports/generators.py index fb3c04e..8ea212d 100644 --- a/src/reports/generators.py +++ b/src/reports/generators.py @@ -21,7 +21,9 @@ class ReportGenerator: self.config_manager = config_manager self.activity_visualizer = ActivityVisualizer() - async def generate_image_report(self, analysis_result: Dict, group_id: str, html_render_func) -> Optional[str]: + async def generate_image_report( + self, analysis_result: Dict, group_id: str, html_render_func + ) -> Optional[str]: """生成图片格式的分析报告""" try: # 准备渲染数据 @@ -31,13 +33,13 @@ class ReportGenerator: image_options = { "full_page": True, "type": "jpeg", # 使用默认的jpeg格式提高兼容性 - "quality": 95, # 设置合理的质量 + "quality": 95, # 设置合理的质量 } image_url = await html_render_func( HTMLTemplates.get_image_template(), render_payload, True, # return_url=True，返回URL而不是下载文件 - image_options + image_options, ) logger.info(f"图片生成成功: {image_url}") @@ -51,13 +53,13 @@ class ReportGenerator: simple_options = { "full_page": True, "type": "jpeg", - "quality": 70 # 降低质量以提高兼容性 + "quality": 70, # 降低质量以提高兼容性 } image_url = await html_render_func( HTMLTemplates.get_image_template(), render_payload, True, - simple_options + simple_options, ) logger.info(f"使用低质量选项生成成功: {image_url}") return image_url @@ -65,9 +67,9 @@ class ReportGenerator: logger.error(f"后备低质量方案也失败: {fallback_e}") return None - - - async def generate_pdf_report(self, analysis_result: Dict, group_id: str) -> Optional[str]: + async def generate_pdf_report( + self, analysis_result: Dict, group_id: str + ) -> Optional[str]: """生成PDF格式的分析报告""" try: # 确保输出目录存在 @@ -75,10 +77,9 @@ class ReportGenerator: output_dir.mkdir(parents=True, exist_ok=True) # 生成文件名 - current_date = datetime.now().strftime('%Y%m%d') + current_date = datetime.now().strftime("%Y%m%d") filename = self.config_manager.get_pdf_filename_format().format( - group_id=group_id, - date=current_date + group_id=group_id, date=current_date ) pdf_path = output_dir / filename @@ -87,7 +88,9 @@ class ReportGenerator: logger.info(f"PDF 渲染数据准备完成，包含 {len(render_data)} 个字段") # 生成 HTML 内容（PDF模板使用{}占位符） - html_content = self._render_html_template(HTMLTemplates.get_pdf_template(), render_data, use_jinja_style=False) + html_content = self._render_html_template( + HTMLTemplates.get_pdf_template(), render_data, use_jinja_style=False + ) logger.info(f"HTML 内容生成完成，长度: {len(html_content)} 字符") # 转换为 PDF @@ -110,7 +113,7 @@ class ReportGenerator: report = f""" 🎯 群聊日常分析报告 -📅 {datetime.now().strftime('%Y年%m月%d日')} +📅 {datetime.now().strftime("%Y年%m月%d日")} 📊 基础统计 • 消息总数: {stats.message_count} @@ -138,7 +141,7 @@ class ReportGenerator: report += "💬 群圣经\n" max_golden_quotes = self.config_manager.get_max_golden_quotes() for i, quote in enumerate(stats.golden_quotes[:max_golden_quotes], 1): - report += f"{i}. \"{quote.content}\" —— {quote.sender}\n" + report += f'{i}. "{quote.content}" —— {quote.sender}\n' report += f" {quote.reason}\n\n" return report @@ -172,7 +175,11 @@ class ReportGenerator: for title in user_titles[: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 '
👤
' + avatar_html = ( + f'头像' + if avatar_data + else '
👤
' + ) titles_html += f"""
@@ -209,8 +216,8 @@ class ReportGenerator: # 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'), + "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, @@ -220,15 +227,20 @@ class ReportGenerator: "titles_html": titles_html, "quotes_html": quotes_html, "hourly_chart_html": hourly_chart_html, - "total_tokens": stats.token_usage.total_tokens if stats.token_usage.total_tokens else 0, - "prompt_tokens": stats.token_usage.prompt_tokens if stats.token_usage.prompt_tokens else 0, - "completion_tokens": stats.token_usage.completion_tokens if stats.token_usage.completion_tokens else 0 + "total_tokens": stats.token_usage.total_tokens + if stats.token_usage.total_tokens + else 0, + "prompt_tokens": stats.token_usage.prompt_tokens + if stats.token_usage.prompt_tokens + else 0, + "completion_tokens": stats.token_usage.completion_tokens + if stats.token_usage.completion_tokens + else 0, } - - - - def _render_html_template(self, template: str, data: Dict, use_jinja_style: bool = False) -> str: + def _render_html_template( + self, template: str, data: Dict, use_jinja_style: bool = False + ) -> str: """HTML模板渲染，支持两种占位符格式 Args: @@ -239,7 +251,9 @@ class ReportGenerator: result = template # 调试：记录渲染数据 - logger.info(f"渲染数据键: {list(data.keys())}, 使用Jinja风格: {use_jinja_style}") + logger.info( + f"渲染数据键: {list(data.keys())}, 使用Jinja风格: {use_jinja_style}" + ) for key, value in data.items(): if use_jinja_style: @@ -256,10 +270,11 @@ class ReportGenerator: # 检查是否还有未替换的占位符 import re + if use_jinja_style: - remaining_placeholders = re.findall(r'\{\{[^}]+\}\}', result) + remaining_placeholders = re.findall(r"\{\{[^}]+\}\}", result) else: - remaining_placeholders = re.findall(r'\{[^}]+\}', result) + remaining_placeholders = re.findall(r"\{[^}]+\}", result) if remaining_placeholders: logger.warning(f"未替换的占位符: {remaining_placeholders[:10]}") @@ -275,7 +290,7 @@ class ReportGenerator: response.raise_for_status() avatar_data = await response.read() # 转换为base64编码 - avatar_base64 = base64.b64encode(avatar_data).decode('utf-8') + 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}") @@ -300,91 +315,98 @@ class ReportGenerator: # 配置浏览器启动参数，解决Docker环境中的沙盒问题 launch_options = { - 'headless': True, - 'args': [ - '--no-sandbox', # Docker环境必需 - 禁用沙盒 - '--disable-setuid-sandbox', # Docker环境必需 - 禁用setuid沙盒 - '--disable-dev-shm-usage', # 避免共享内存问题 - '--disable-gpu', # 禁用GPU加速 - '--no-first-run', - '--disable-extensions', - '--disable-default-apps', - '--disable-background-timer-throttling', - '--disable-backgrounding-occluded-windows', - '--disable-renderer-backgrounding', - '--disable-features=TranslateUI', - '--disable-ipc-flooding-protection', - '--disable-background-networking', - '--enable-features=NetworkService,NetworkServiceInProcess', - '--force-color-profile=srgb', - '--metrics-recording-only', - '--disable-breakpad', - '--disable-component-extensions-with-background-pages', - '--disable-features=Translate,BackForwardCache,AcceptCHFrame,AvoidUnnecessaryBeforeUnloadCheckSync', - '--enable-automation', - '--password-store=basic', - '--use-mock-keychain', - '--export-tagged-pdf', - '--disable-web-security', - '--disable-features=VizDisplayCompositor', - '--disable-blink-features=AutomationControlled', # 隐藏自动化特征 - ] + "headless": True, + "args": [ + "--no-sandbox", # Docker环境必需 - 禁用沙盒 + "--disable-setuid-sandbox", # Docker环境必需 - 禁用setuid沙盒 + "--disable-dev-shm-usage", # 避免共享内存问题 + "--disable-gpu", # 禁用GPU加速 + "--no-first-run", + "--disable-extensions", + "--disable-default-apps", + "--disable-background-timer-throttling", + "--disable-backgrounding-occluded-windows", + "--disable-renderer-backgrounding", + "--disable-features=TranslateUI", + "--disable-ipc-flooding-protection", + "--disable-background-networking", + "--enable-features=NetworkService,NetworkServiceInProcess", + "--force-color-profile=srgb", + "--metrics-recording-only", + "--disable-breakpad", + "--disable-component-extensions-with-background-pages", + "--disable-features=Translate,BackForwardCache,AcceptCHFrame,AvoidUnnecessaryBeforeUnloadCheckSync", + "--enable-automation", + "--password-store=basic", + "--use-mock-keychain", + "--export-tagged-pdf", + "--disable-web-security", + "--disable-features=VizDisplayCompositor", + "--disable-blink-features=AutomationControlled", # 隐藏自动化特征 + ], } # 检测系统 Chrome/Chromium 路径 chrome_paths = [] - - if sys.platform.startswith('win'): + + if sys.platform.startswith("win"): # Windows 系统 Chrome 安装路径 - username = os.environ.get('USERNAME', '') + username = os.environ.get("USERNAME", "") chrome_paths = [ r"C:\Program Files\Google\Chrome\Application\chrome.exe", r"C:\Program Files (x86)\Google\Chrome\Application\chrome.exe", rf"C:\Users\{username}\AppData\Local\Google\Chrome\Application\chrome.exe", r"C:\Program Files\Chromium\Application\chrome.exe", ] - elif sys.platform.startswith('linux'): + elif sys.platform.startswith("linux"): # Linux 系统 Chrome/Chromium 路径 chrome_paths = [ - '/usr/bin/google-chrome', - '/usr/bin/google-chrome-stable', - '/usr/bin/chromium', - '/usr/bin/chromium-browser', - '/snap/bin/chromium', - '/usr/bin/chromium-freeworld', + "/usr/bin/google-chrome", + "/usr/bin/google-chrome-stable", + "/usr/bin/chromium", + "/usr/bin/chromium-browser", + "/snap/bin/chromium", + "/usr/bin/chromium-freeworld", ] - elif sys.platform.startswith('darwin'): + elif sys.platform.startswith("darwin"): # macOS 系统 Chrome 路径 chrome_paths = [ - '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome', - '/Applications/Chromium.app/Contents/MacOS/Chromium', + "/Applications/Google Chrome.app/Contents/MacOS/Google Chrome", + "/Applications/Chromium.app/Contents/MacOS/Chromium", ] # 查找可用的浏览器 found_browser = False for chrome_path in chrome_paths: if Path(chrome_path).exists(): - launch_options['executablePath'] = chrome_path + launch_options["executablePath"] = chrome_path logger.info(f"使用系统浏览器: {chrome_path}") found_browser = True break - + if not found_browser: logger.info("未找到系统浏览器，将使用 pyppeteer 默认下载的 Chromium") # 先尝试确保 Chromium 已下载 try: - from pyppeteer import connection, browser, launcher + from pyppeteer import browser, launcher + launcher_instance = launcher.Launcher( headless=True, - args=['--no-sandbox', '--disable-setuid-sandbox', '--disable-dev-shm-usage'] + args=[ + "--no-sandbox", + "--disable-setuid-sandbox", + "--disable-dev-shm-usage", + ], ) await launcher_instance._get_chromium_revision() await launcher_instance._download_chromium() chromium_path = pyppeteer.executablePath() - launch_options['executablePath'] = chromium_path + launch_options["executablePath"] = chromium_path logger.info(f"使用 pyppeteer 下载的 Chromium: {chromium_path}") except Exception as pre_download_err: - logger.warning(f"预下载 Chromium 失败，继续尝试直接启动: {pre_download_err}") + logger.warning( + f"预下载 Chromium 失败，继续尝试直接启动: {pre_download_err}" + ) # 尝试启动浏览器 try: @@ -398,49 +420,53 @@ class ReportGenerator: try: # 创建新页面，设置更合理的超时时间 page = await browser.newPage() - + # 设置页面视口，减少内存占用 - await page.setViewport({ - 'width': 1024, - 'height': 768, - 'deviceScaleFactor': 1, - 'isMobile': False, - 'hasTouch': False, - 'isLandscape': False - }) + await page.setViewport( + { + "width": 1024, + "height": 768, + "deviceScaleFactor": 1, + "isMobile": False, + "hasTouch": False, + "isLandscape": False, + } + ) # 设置页面内容，使用更安全的加载方式 logger.info("开始设置页面内容...") - await page.setContent(html_content, {'waitUntil': 'domcontentloaded', 'timeout': 30000}) - + await page.setContent( + html_content, {"waitUntil": "domcontentloaded", "timeout": 30000} + ) + # 等待页面基本加载完成，但不要太长时间 try: - await page.waitForSelector('body', {'timeout': 5000}) + await page.waitForSelector("body", {"timeout": 5000}) logger.info("页面基本加载完成") except Exception: logger.warning("等待页面加载超时，继续执行") - + # 减少等待时间，避免内存累积 await asyncio.sleep(1) # 导出 PDF，使用更保守的设置 logger.info("开始生成PDF...") pdf_options = { - 'path': output_path, - 'format': 'A4', - 'printBackground': True, - 'margin': { - 'top': '10mm', - 'right': '10mm', - 'bottom': '10mm', - 'left': '10mm' + "path": output_path, + "format": "A4", + "printBackground": True, + "margin": { + "top": "10mm", + "right": "10mm", + "bottom": "10mm", + "left": "10mm", }, - 'scale': 0.8, - 'displayHeaderFooter': False, - 'preferCSSPageSize': True, - 'timeout': 60000 # 增加PDF生成超时时间到60秒 + "scale": 0.8, + "displayHeaderFooter": False, + "preferCSSPageSize": True, + "timeout": 60000, # 增加PDF生成超时时间到60秒 } - + await page.pdf(pdf_options) logger.info(f"PDF 生成成功: {output_path}") return True @@ -448,7 +474,7 @@ class ReportGenerator: except Exception as e: logger.error(f"PDF生成过程中出错: {e}") return False - + finally: # 确保浏览器被正确关闭 if browser: @@ -461,10 +487,10 @@ class ReportGenerator: await page.close() except: pass - + # 等待一小段时间让资源释放 await asyncio.sleep(0.5) - + # 关闭浏览器 await browser.close() logger.info("浏览器已关闭") @@ -480,14 +506,18 @@ class ReportGenerator: error_msg = str(e) if "Chromium downloadable not found" in error_msg: logger.error("Chromium 下载失败，建议安装系统 Chrome/Chromium") - logger.info("💡 Linux 系统建议: sudo apt-get install chromium-browser 或 sudo yum install chromium") + logger.info( + "💡 Linux 系统建议: sudo apt-get install chromium-browser 或 sudo yum install chromium" + ) elif "No usable sandbox" in error_msg: logger.error("沙盒权限问题，已尝试禁用沙盒") elif "Connection refused" in error_msg or "connect" in error_msg.lower(): logger.error("浏览器连接失败，请检查系统资源或尝试重启") elif "executablePath" in error_msg and "not found" in error_msg: logger.error("未找到系统浏览器，请安装 Chrome 或 Chromium") - logger.info("💡 安装建议: sudo apt-get install chromium-browser (Ubuntu/Debian) 或 sudo yum install chromium (CentOS/RHEL)") + logger.info( + "💡 安装建议: sudo apt-get install chromium-browser (Ubuntu/Debian) 或 sudo yum install chromium (CentOS/RHEL)" + ) elif "Browser closed unexpectedly" in error_msg: logger.error("浏览器意外关闭，可能是由于内存不足或系统资源限制") logger.info("💡 建议: 检查系统内存，或重启 AstrBot 后重试") @@ -497,5 +527,7 @@ class ReportGenerator: logger.info(" 3. 考虑使用其他 PDF 生成方案") else: logger.error(f"HTML 转 PDF 失败: {e}") - logger.info("💡 可以尝试使用 /安装PDF 命令重新安装依赖，或检查系统日志获取更多信息") - return False \ No newline at end of file + logger.info( + "💡 可以尝试使用 /安装PDF 命令重新安装依赖，或检查系统日志获取更多信息" + ) + return False diff --git a/src/reports/templates.py b/src/reports/templates.py index 380ae08..4007ab3 100644 --- a/src/reports/templates.py +++ b/src/reports/templates.py @@ -6,7 +6,7 @@ HTML模板模块 class HTMLTemplates: """HTML模板管理类""" - + @staticmethod def get_image_template() -> str: """获取图片报告的HTML模板（使用{{ }}占位符）""" @@ -594,7 +594,7 @@ class HTMLTemplates:
""" - + @staticmethod def get_pdf_template() -> str: """获取PDF报告的HTML模板（使用{}占位符）""" diff --git a/src/scheduler/__init__.py b/src/scheduler/__init__.py index 67a693f..790b53f 100644 --- a/src/scheduler/__init__.py +++ b/src/scheduler/__init__.py @@ -5,6 +5,4 @@ from .auto_scheduler import AutoScheduler -__all__ = [ - 'AutoScheduler' -] \ No newline at end of file +__all__ = ["AutoScheduler"] diff --git a/src/scheduler/auto_scheduler.py b/src/scheduler/auto_scheduler.py index 54753fd..121e1a9 100644 --- a/src/scheduler/auto_scheduler.py +++ b/src/scheduler/auto_scheduler.py @@ -5,14 +5,21 @@ import asyncio from datetime import datetime, timedelta -from typing import Optional from astrbot.api import logger class AutoScheduler: """自动调度器""" - def __init__(self, config_manager, message_handler, analyzer, report_generator, bot_manager, html_render_func=None): + def __init__( + self, + config_manager, + message_handler, + analyzer, + report_generator, + bot_manager, + html_render_func=None, + ): self.config_manager = config_manager self.message_handler = message_handler self.analyzer = analyzer @@ -33,16 +40,20 @@ class AutoScheduler: def _get_platform_id(self): """获取平台ID""" try: - if hasattr(self.bot_manager, '_context') and self.bot_manager._context: + if hasattr(self.bot_manager, "_context") and self.bot_manager._context: context = self.bot_manager._context - if hasattr(context, 'platform_manager') and hasattr(context.platform_manager, 'platform_insts'): + if hasattr(context, "platform_manager") and hasattr( + context.platform_manager, "platform_insts" + ): platforms = context.platform_manager.platform_insts for platform in platforms: - if hasattr(platform, 'metadata') and hasattr(platform.metadata, 'id'): + if hasattr(platform, "metadata") and hasattr( + platform.metadata, "id" + ): platform_id = platform.metadata.id return platform_id return "aiocqhttp" # 默认值 - except Exception as e: + except Exception: return "aiocqhttp" # 默认值 async def start_scheduler(self): @@ -54,7 +65,9 @@ class AutoScheduler: # 延迟启动，给系统时间初始化 await asyncio.sleep(10) - logger.info(f"启动定时任务调度器，自动分析时间: {self.config_manager.get_auto_analysis_time()}") + logger.info( + f"启动定时任务调度器，自动分析时间: {self.config_manager.get_auto_analysis_time()}" + ) self.scheduler_task = asyncio.create_task(self._scheduler_loop()) @@ -75,9 +88,9 @@ class AutoScheduler: while True: try: now = datetime.now() - target_time = datetime.strptime(self.config_manager.get_auto_analysis_time(), "%H:%M").replace( - year=now.year, month=now.month, day=now.day - ) + target_time = datetime.strptime( + self.config_manager.get_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: @@ -85,7 +98,9 @@ class AutoScheduler: # 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") + 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) @@ -94,7 +109,9 @@ class AutoScheduler: if self.config_manager.get_enable_auto_analysis(): # 检查今天是否已经执行过，防止重复执行 if self.last_execution_date == target_time.date(): - logger.info(f"今天 {target_time.date()} 已经执行过自动分析，跳过执行") + logger.info( + f"今天 {target_time.date()} 已经执行过自动分析，跳过执行" + ) # 等待到明天再检查 await asyncio.sleep(3600) # 等待1小时后再检查 continue @@ -102,7 +119,9 @@ class AutoScheduler: logger.info("开始执行定时分析") await self._run_auto_analysis() self.last_execution_date = target_time.date() # 记录执行日期 - logger.info(f"定时分析执行完成，记录执行日期: {self.last_execution_date}") + logger.info( + f"定时分析执行完成，记录执行日期: {self.last_execution_date}" + ) else: logger.info("自动分析已禁用，跳过执行") break @@ -122,24 +141,26 @@ class AutoScheduler: logger.info("没有启用的群聊需要分析") return - logger.info(f"将为 {len(enabled_groups)} 个群聊并发执行分析: {enabled_groups}") - + logger.info( + f"将为 {len(enabled_groups)} 个群聊并发执行分析: {enabled_groups}" + ) + # 创建并发任务 - 为每个群聊创建独立的分析任务 analysis_tasks = [] for group_id in enabled_groups: task = asyncio.create_task( self._perform_auto_analysis_for_group_with_timeout(group_id), - name=f"analysis_group_{group_id}" + name=f"analysis_group_{group_id}", ) analysis_tasks.append(task) - + # 并发执行所有分析任务，使用 return_exceptions=True 确保单个任务失败不影响其他任务 results = await asyncio.gather(*analysis_tasks, return_exceptions=True) - + # 统计执行结果 success_count = 0 error_count = 0 - + for i, result in enumerate(results): group_id = enabled_groups[i] if isinstance(result, Exception): @@ -147,8 +168,10 @@ class AutoScheduler: error_count += 1 else: success_count += 1 - - logger.info(f"并发分析完成 - 成功: {success_count}, 失败: {error_count}, 总计: {len(enabled_groups)}") + + logger.info( + f"并发分析完成 - 成功: {success_count}, 失败: {error_count}, 总计: {len(enabled_groups)}" + ) except Exception as e: logger.error(f"自动分析执行失败: {e}", exc_info=True) @@ -157,30 +180,34 @@ class AutoScheduler: """为指定群执行自动分析（带超时控制）""" try: # 为每个群聊设置独立的超时时间（20分钟）- 使用 asyncio.wait_for 兼容所有 Python 版本 - await asyncio.wait_for(self._perform_auto_analysis_for_group(group_id), timeout=1200) + await asyncio.wait_for( + self._perform_auto_analysis_for_group(group_id), timeout=1200 + ) except asyncio.TimeoutError: logger.error(f"群 {group_id} 分析超时（20分钟），跳过该群分析") except Exception as e: logger.error(f"群 {group_id} 分析任务执行失败: {e}") - + async def _perform_auto_analysis_for_group(self, group_id: str): """为指定群执行自动分析（核心逻辑）""" # 为每个群聊使用独立的锁，避免全局锁导致串行化 group_lock_key = f"analysis_{group_id}" - if not hasattr(self, '_group_locks'): + if not hasattr(self, "_group_locks"): self._group_locks = {} - + if group_lock_key not in self._group_locks: self._group_locks[group_lock_key] = asyncio.Lock() - + async with self._group_locks[group_lock_key]: try: start_time = asyncio.get_event_loop().time() - + # 检查bot管理器状态 if not self.bot_manager.is_ready_for_auto_analysis(): status = self.bot_manager.get_status_info() - logger.warning(f"群 {group_id} 自动分析跳过：bot管理器未就绪 - {status}") + logger.warning( + f"群 {group_id} 自动分析跳过：bot管理器未就绪 - {status}" + ) return logger.info(f"开始为群 {group_id} 执行自动分析（并发任务）") @@ -189,8 +216,10 @@ class AutoScheduler: analysis_days = self.config_manager.get_analysis_days() bot_instance = self.bot_manager.get_bot_instance() - messages = await self.message_handler.fetch_group_messages(bot_instance, group_id, analysis_days) - + messages = await self.message_handler.fetch_group_messages( + bot_instance, group_id, analysis_days + ) + if not messages: logger.warning(f"群 {group_id} 未获取到足够的消息记录") return @@ -198,7 +227,9 @@ class AutoScheduler: # 检查消息数量 min_threshold = self.config_manager.get_min_messages_threshold() if len(messages) < min_threshold: - logger.warning(f"群 {group_id} 消息数量不足（{len(messages)}条），跳过分析") + 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") @@ -206,14 +237,16 @@ class AutoScheduler: # 进行分析 - 构造正确的 unified_msg_origin platform_id = self._get_platform_id() umo = f"{platform_id}:GroupMessage:{group_id}" if platform_id else None - analysis_result = await self.analyzer.analyze_messages(messages, group_id, umo) + analysis_result = await self.analyzer.analyze_messages( + messages, group_id, umo + ) if not analysis_result: logger.error(f"群 {group_id} 分析失败") return # 生成并发送报告 await self._send_analysis_report(group_id, analysis_result) - + # 记录执行时间 end_time = asyncio.get_event_loop().time() execution_time = end_time - start_time @@ -221,10 +254,10 @@ class AutoScheduler: except Exception as e: logger.error(f"群 {group_id} 自动分析执行失败: {e}", exc_info=True) - + finally: # 清理群聊锁资源（可选，防止内存泄漏） - if hasattr(self, '_group_locks') and len(self._group_locks) > 50: + if hasattr(self, "_group_locks") and len(self._group_locks) > 50: old_locks = list(self._group_locks.keys())[:10] for lock_key in old_locks: if not self._group_locks[lock_key].locked(): @@ -240,47 +273,87 @@ class AutoScheduler: # 使用图片格式 logger.info(f"群 {group_id} 自动分析使用图片报告格式") try: - image_url = await self.report_generator.generate_image_report(analysis_result, group_id, self.html_render_func) + image_url = await self.report_generator.generate_image_report( + analysis_result, group_id, self.html_render_func + ) if image_url: await self._send_image_message(group_id, image_url) logger.info(f"群 {group_id} 图片报告发送成功") else: # 图片生成失败，回退到文本 - logger.warning(f"群 {group_id} 图片报告生成失败（返回None），回退到文本报告") - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + logger.warning( + f"群 {group_id} 图片报告生成失败（返回None），回退到文本报告" + ) + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) except Exception as img_e: - logger.error(f"群 {group_id} 图片报告生成异常: {img_e}，回退到文本报告") - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + logger.error( + f"群 {group_id} 图片报告生成异常: {img_e}，回退到文本报告" + ) + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) else: # 没有html_render函数，回退到文本报告 logger.warning(f"群 {group_id} 缺少html_render函数，回退到文本报告") - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) elif output_format == "pdf": if not self.config_manager.pyppeteer_available: logger.warning(f"群 {group_id} PDF功能不可用，回退到文本报告") - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) else: try: - pdf_path = await self.report_generator.generate_pdf_report(analysis_result, group_id) + pdf_path = await self.report_generator.generate_pdf_report( + analysis_result, group_id + ) if pdf_path: await self._send_pdf_file(group_id, pdf_path) logger.info(f"群 {group_id} 自动分析完成，已发送PDF报告") else: - logger.error(f"群 {group_id} PDF报告生成失败（返回None），回退到文本报告") - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + logger.error( + f"群 {group_id} PDF报告生成失败（返回None），回退到文本报告" + ) + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) except Exception as pdf_e: - logger.error(f"群 {group_id} PDF报告生成异常: {pdf_e}，回退到文本报告") - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + logger.error( + f"群 {group_id} PDF报告生成异常: {pdf_e}，回退到文本报告" + ) + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) else: - text_report = self.report_generator.generate_text_report(analysis_result) - await self._send_text_message(group_id, f"📊 每日群聊分析报告：\n\n{text_report}") + text_report = self.report_generator.generate_text_report( + analysis_result + ) + await self._send_text_message( + group_id, f"📊 每日群聊分析报告：\n\n{text_report}" + ) logger.info(f"群 {group_id} 自动分析完成，已发送报告") @@ -299,13 +372,10 @@ class AutoScheduler: await 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} - }] + message=[ + {"type": "text", "data": {"text": "📊 每日群聊分析报告已生成："}}, + {"type": "image", "data": {"url": image_url}}, + ], ) logger.info(f"群 {group_id} 图片消息发送成功") @@ -322,9 +392,7 @@ class AutoScheduler: # 发送文本消息到群 await bot_instance.api.call_action( - "send_group_msg", - group_id=group_id, - message=text_content + "send_group_msg", group_id=group_id, message=text_content ) logger.info(f"群 {group_id} 文本消息发送成功") @@ -343,13 +411,10 @@ class AutoScheduler: await 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": "file", - "data": {"file": pdf_path} - }] + message=[ + {"type": "text", "data": {"text": "📊 每日群聊分析报告已生成："}}, + {"type": "file", "data": {"file": pdf_path}}, + ], ) logger.info(f"群 {group_id} PDF文件发送成功") @@ -360,7 +425,7 @@ class AutoScheduler: await bot_instance.api.call_action( "send_group_msg", group_id=group_id, - message=f"📊 每日群聊分析报告已生成，但发送PDF文件失败。PDF文件路径：{pdf_path}" + message=f"📊 每日群聊分析报告已生成，但发送PDF文件失败。PDF文件路径：{pdf_path}", ) except Exception as e2: - logger.error(f"发送PDF失败提示到群 {group_id} 也失败: {e2}") \ No newline at end of file + logger.error(f"发送PDF失败提示到群 {group_id} 也失败: {e2}") diff --git a/src/utils/__init__.py b/src/utils/__init__.py index c70fe49..75bef1a 100644 --- a/src/utils/__init__.py +++ b/src/utils/__init__.py @@ -6,7 +6,4 @@ from .pdf_utils import PDFInstaller from .helpers import MessageAnalyzer -__all__ = [ - 'PDFInstaller', - 'MessageAnalyzer' -] \ No newline at end of file +__all__ = ["PDFInstaller", "MessageAnalyzer"] diff --git a/src/utils/helpers.py b/src/utils/helpers.py index b538f77..a645902 100644 --- a/src/utils/helpers.py +++ b/src/utils/helpers.py @@ -4,12 +4,13 @@ """ from typing import List, Dict -from ...src.models.data_models import GroupStatistics, SummaryTopic, UserTitle, GoldenQuote, TokenUsage +from ...src.models.data_models import TokenUsage from ...src.core.message_handler import MessageHandler from ...src.analysis.llm_analyzer import LLMAnalyzer from ...src.analysis.statistics import UserAnalyzer from astrbot.api import logger + class MessageAnalyzer: """消息分析器 - 整合所有分析功能""" @@ -28,7 +29,9 @@ class MessageAnalyzer: else: await self.message_handler.set_bot_qq_id(bot_instance) - async def analyze_messages(self, messages: List[Dict], group_id: str, unified_msg_origin: str = None) -> Dict: + async def analyze_messages( + self, messages: List[Dict], group_id: str, unified_msg_origin: str = None + ) -> Dict: """完整的消息分析流程""" try: # 基础统计 @@ -36,11 +39,15 @@ class MessageAnalyzer: # 用户分析 user_analysis = self.user_analyzer.analyze_users(messages) - + # 获取活跃用户列表 - 使用get_top_users方法,limit从配置中读取 max_user_titles = self.config_manager.get_max_user_titles() - top_users = self.user_analyzer.get_top_users(user_analysis, limit=max_user_titles) - logger.info(f"获取到 {len(top_users)} 个活跃用户用于称号分析(配置上限: {max_user_titles})") + top_users = self.user_analyzer.get_top_users( + user_analysis, limit=max_user_titles + ) + logger.info( + f"获取到 {len(top_users)} 个活跃用户用于称号分析(配置上限: {max_user_titles})" + ) # LLM分析 - 使用并发方式 topics = [] @@ -51,35 +58,61 @@ class MessageAnalyzer: # 检查各个分析功能是否启用 topic_enabled = self.config_manager.get_topic_analysis_enabled() user_title_enabled = self.config_manager.get_user_title_analysis_enabled() - golden_quote_enabled = self.config_manager.get_golden_quote_analysis_enabled() - + golden_quote_enabled = ( + self.config_manager.get_golden_quote_analysis_enabled() + ) + # 如果三个分析都启用，使用并发执行 if topic_enabled and user_title_enabled and golden_quote_enabled: # 并发执行所有三个分析任务，传入活跃用户列表 - topics, user_titles, golden_quotes, total_token_usage = await self.llm_analyzer.analyze_all_concurrent( + ( + topics, + user_titles, + golden_quotes, + total_token_usage, + ) = await self.llm_analyzer.analyze_all_concurrent( messages, user_analysis, umo=unified_msg_origin, top_users=top_users ) else: # 如果只启用部分分析，则按需执行 if topic_enabled: - topics, topic_tokens = await self.llm_analyzer.analyze_topics(messages, umo=unified_msg_origin) + topics, topic_tokens = await self.llm_analyzer.analyze_topics( + messages, umo=unified_msg_origin + ) total_token_usage.prompt_tokens += topic_tokens.prompt_tokens - total_token_usage.completion_tokens += topic_tokens.completion_tokens + total_token_usage.completion_tokens += ( + topic_tokens.completion_tokens + ) total_token_usage.total_tokens += topic_tokens.total_tokens if user_title_enabled: # 传入活跃用户列表 - user_titles, title_tokens = await self.llm_analyzer.analyze_user_titles( - messages, user_analysis, umo=unified_msg_origin, top_users=top_users + ( + user_titles, + title_tokens, + ) = await self.llm_analyzer.analyze_user_titles( + messages, + user_analysis, + umo=unified_msg_origin, + top_users=top_users, ) total_token_usage.prompt_tokens += title_tokens.prompt_tokens - total_token_usage.completion_tokens += title_tokens.completion_tokens + total_token_usage.completion_tokens += ( + title_tokens.completion_tokens + ) total_token_usage.total_tokens += title_tokens.total_tokens if golden_quote_enabled: - golden_quotes, quote_tokens = await self.llm_analyzer.analyze_golden_quotes(messages, umo=unified_msg_origin) + ( + golden_quotes, + quote_tokens, + ) = await self.llm_analyzer.analyze_golden_quotes( + messages, umo=unified_msg_origin + ) total_token_usage.prompt_tokens += quote_tokens.prompt_tokens - total_token_usage.completion_tokens += quote_tokens.completion_tokens + total_token_usage.completion_tokens += ( + quote_tokens.completion_tokens + ) total_token_usage.total_tokens += quote_tokens.total_tokens # 更新统计数据 @@ -90,7 +123,7 @@ class MessageAnalyzer: "statistics": statistics, "topics": topics, "user_titles": user_titles, - "user_analysis": user_analysis + "user_analysis": user_analysis, } except Exception as e: diff --git a/src/utils/pdf_utils.py b/src/utils/pdf_utils.py index 1a92eda..9b550f9 100644 --- a/src/utils/pdf_utils.py +++ b/src/utils/pdf_utils.py @@ -5,21 +5,22 @@ PDF工具模块 import sys import asyncio -import threading from concurrent.futures import ThreadPoolExecutor from astrbot.api import logger class PDFInstaller: """PDF功能安装器""" - + # 类级别的线程池，用于异步下载任务 - _executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="chromium_download") + _executor = ThreadPoolExecutor( + max_workers=1, thread_name_prefix="chromium_download" + ) _download_status = { "in_progress": False, "completed": False, "failed": False, - "error_message": None + "error_message": None, } @staticmethod @@ -31,10 +32,14 @@ class PDFInstaller: # 使用asyncio安装pyppeteer和兼容的websockets版本 logger.info("安装 pyppeteer==1.0.2 和兼容的依赖...") process = await asyncio.create_subprocess_exec( - sys.executable, "-m", "pip", "install", - "pyppeteer==1.0.2", "websockets==10.4", + sys.executable, + "-m", + "pip", + "install", + "pyppeteer==1.0.2", + "websockets==10.4", stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE + stderr=asyncio.subprocess.PIPE, ) stdout, stderr = await process.communicate() @@ -63,20 +68,20 @@ class PDFInstaller: """通过 pyppeteer 自动安装 Chromium（异步非阻塞方式）""" try: logger.info("正在通过 pyppeteer 自动安装 Chromium...") - + # 检查是否已经在下载中 if PDFInstaller._download_status["in_progress"]: return "⏳ Chromium 正在后台下载中，请稍候..." - + # 启动异步下载任务 PDFInstaller._download_status["in_progress"] = True PDFInstaller._download_status["completed"] = False PDFInstaller._download_status["failed"] = False PDFInstaller._download_status["error_message"] = None - + # 在后台线程中启动下载 asyncio.create_task(PDFInstaller._background_chromium_download()) - + return """⏳ Chromium 下载已在后台启动 这可能需要几分钟时间，请稍候... @@ -96,31 +101,35 @@ class PDFInstaller: """后台下载 Chromium，带超时控制""" try: logger.info("后台 Chromium 下载任务开始") - + # 设置10分钟超时 timeout_seconds = 600 - + try: # 使用 asyncio.wait_for 实现超时控制 success = await asyncio.wait_for( PDFInstaller._download_chromium_via_pyppeteer(), - timeout=timeout_seconds + timeout=timeout_seconds, ) - + if success: PDFInstaller._download_status["completed"] = True PDFInstaller._download_status["failed"] = False logger.info("✅ Chromium 后台下载完成！") else: PDFInstaller._download_status["failed"] = True - PDFInstaller._download_status["error_message"] = "下载失败，请检查网络连接" + PDFInstaller._download_status["error_message"] = ( + "下载失败，请检查网络连接" + ) logger.error("❌ Chromium 下载失败") - + except asyncio.TimeoutError: PDFInstaller._download_status["failed"] = True - PDFInstaller._download_status["error_message"] = f"下载超时（{timeout_seconds}秒）" + PDFInstaller._download_status["error_message"] = ( + f"下载超时（{timeout_seconds}秒）" + ) logger.error(f"❌ Chromium 下载超时（{timeout_seconds}秒）") - + except Exception as e: PDFInstaller._download_status["failed"] = True PDFInstaller._download_status["error_message"] = str(e) @@ -133,96 +142,101 @@ class PDFInstaller: """通过 pyppeteer 自动下载 Chromium（带重试机制）""" max_retries = 2 retry_count = 0 - + while retry_count <= max_retries: try: if retry_count > 0: - logger.info(f"正在重试下载 Chromium（第 {retry_count}/{max_retries} 次）...") + logger.info( + f"正在重试下载 Chromium（第 {retry_count}/{max_retries} 次）..." + ) else: logger.info("通过 pyppeteer 自动下载 Chromium...") - + # 导入 pyppeteer 并尝试下载 try: import pyppeteer from pyppeteer import launch from pyppeteer.errors import BrowserError - + # 方法1: 尝试直接下载 Chromium 而不启动浏览器 logger.info("尝试直接下载 Chromium...") try: from pyppeteer.launcher import Launcher - + # 创建 Launcher 实例但不启动浏览器 launcher = Launcher( headless=True, - args=['--no-sandbox', '--disable-setuid-sandbox'] + args=["--no-sandbox", "--disable-setuid-sandbox"], ) - + # 只下载 Chromium chromium_revision = launcher._get_chromium_revision() await launcher._download_chromium() - + logger.info("✅ Chromium 下载完成") return True - + except Exception as download_error: logger.warning(f"直接下载 Chromium 失败: {download_error}") logger.info("尝试通过启动浏览器来下载...") - + # 方法2: 通过启动浏览器触发自动下载 import platform + system = platform.system().lower() - + if system == "linux": browser_args = [ - '--no-sandbox', - '--disable-setuid-sandbox', - '--disable-dev-shm-usage', - '--disable-accelerated-2d-canvas', - '--no-first-run', - '--no-zygote', - '--disable-gpu', - '--disable-background-timer-throttling', - '--disable-backgrounding-occluded-windows', - '--disable-renderer-backgrounding', - '--disable-features=TranslateUI', - '--disable-ipc-flooding-protection' + "--no-sandbox", + "--disable-setuid-sandbox", + "--disable-dev-shm-usage", + "--disable-accelerated-2d-canvas", + "--no-first-run", + "--no-zygote", + "--disable-gpu", + "--disable-background-timer-throttling", + "--disable-backgrounding-occluded-windows", + "--disable-renderer-backgrounding", + "--disable-features=TranslateUI", + "--disable-ipc-flooding-protection", ] else: browser_args = [ - '--no-sandbox', - '--disable-setuid-sandbox', - '--disable-gpu' + "--no-sandbox", + "--disable-setuid-sandbox", + "--disable-gpu", ] - + logger.info("启动 pyppeteer 浏览器以触发 Chromium 自动下载...") browser = await launch( headless=True, args=browser_args, ignoreHTTPSErrors=True, - dumpio=False # 关闭浏览器日志输出以减少干扰 + dumpio=False, # 关闭浏览器日志输出以减少干扰 ) - + # 获取 Chromium 路径 chromium_path = pyppeteer.executablePath() logger.info(f"✅ Chromium 自动下载完成，路径: {chromium_path}") - + await browser.close() return True - + except BrowserError as e: logger.error(f"浏览器错误: {e}") - + # 方法3: 使用子进程命令行触发下载 try: logger.info("尝试使用命令行触发 Chromium 自动下载...") - + import platform + system = platform.system().lower() - + if system == "linux": cmd = [ - sys.executable, "-c", + sys.executable, + "-c", """ import pyppeteer import asyncio @@ -246,33 +260,34 @@ async def download_chrome(): raise asyncio.run(download_chrome()) - """ + """, ] else: cmd = [ - sys.executable, "-c", - "import pyppeteer; import asyncio; asyncio.run(pyppeteer.launch())" + sys.executable, + "-c", + "import pyppeteer; import asyncio; asyncio.run(pyppeteer.launch())", ] - + process = await asyncio.create_subprocess_exec( *cmd, stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE + stderr=asyncio.subprocess.PIPE, ) - + stdout, stderr = await process.communicate() - + if process.returncode == 0: logger.info("✅ 成功通过命令行触发 Chromium 自动下载") return True else: logger.error(f"命令行触发自动下载失败: {stderr.decode()}") raise Exception(f"命令行下载失败: {stderr.decode()}") - + except Exception as e2: logger.error(f"命令行触发自动下载失败: {e2}") raise - + except Exception as e: retry_count += 1 if retry_count <= max_retries: @@ -280,9 +295,12 @@ asyncio.run(download_chrome()) logger.warning(f"下载失败，{wait_time}秒后重试... 错误: {e}") await asyncio.sleep(wait_time) else: - logger.error(f"通过 pyppeteer 自动下载 Chromium 失败（已重试{max_retries}次）: {e}", exc_info=True) + logger.error( + f"通过 pyppeteer 自动下载 Chromium 失败（已重试{max_retries}次）: {e}", + exc_info=True, + ) return False - + return False @staticmethod @@ -292,4 +310,4 @@ asyncio.run(download_chrome()) version = config_manager.pyppeteer_version or "未知版本" return f"✅ PDF 功能可用 (pyppeteer {version})" else: - return "❌ PDF 功能不可用 - 需要安装 pyppeteer" \ No newline at end of file + return "❌ PDF 功能不可用 - 需要安装 pyppeteer" diff --git a/src/visualization/__init__.py b/src/visualization/__init__.py index de8e4e7..b980bb0 100644 --- a/src/visualization/__init__.py +++ b/src/visualization/__init__.py @@ -4,4 +4,4 @@ from .activity_charts import ActivityVisualizer -__all__ = ['ActivityVisualizer'] +__all__ = ["ActivityVisualizer"] diff --git a/src/visualization/activity_charts.py b/src/visualization/activity_charts.py index 4f2a004..dcd3977 100644 --- a/src/visualization/activity_charts.py +++ b/src/visualization/activity_charts.py @@ -3,19 +3,21 @@ 参考 astrbot_plugin_github_analyzer 的实现方式 """ -from datetime import datetime, timedelta +from datetime import datetime from collections import defaultdict -from typing import Dict, List, Any +from typing import Dict, List from ..models.data_models import ActivityVisualization class ActivityVisualizer: """活跃度可视化器""" - + def __init__(self): pass - - def generate_activity_visualization(self, messages: List[Dict]) -> ActivityVisualization: + + def generate_activity_visualization( + self, messages: List[Dict] + ) -> ActivityVisualization: """生成活跃度可视化数据 - 专注于小时级别分析""" hourly_activity = defaultdict(int) user_activity = defaultdict(int) @@ -54,15 +56,19 @@ class ActivityVisualizer: # 生成用户活跃度排行 user_ranking = [] for user_id, data in user_activity.items(): - user_ranking.append({ - "user_id": user_id, - "nickname": data["nickname"], - "message_count": data["count"] - }) + user_ranking.append( + { + "user_id": user_id, + "nickname": data["nickname"], + "message_count": data["count"], + } + ) user_ranking.sort(key=lambda x: x["message_count"], reverse=True) # 找出高峰时段（活跃度最高的3个小时） - peak_hours = sorted(hourly_activity.items(), key=lambda x: x[1], reverse=True)[:3] + peak_hours = sorted(hourly_activity.items(), key=lambda x: x[1], reverse=True)[ + :3 + ] peak_hours = [{"hour": hour, "count": count} for hour, count in peak_hours] return ActivityVisualization( @@ -70,10 +76,14 @@ class ActivityVisualizer: daily_activity={}, # 不使用日期分析 user_activity_ranking=user_ranking[:10], # 前10名 peak_hours=peak_hours, - activity_heatmap_data=self._generate_hourly_heatmap_data(hourly_activity, emoji_activity) + activity_heatmap_data=self._generate_hourly_heatmap_data( + hourly_activity, emoji_activity + ), ) - - def _generate_hourly_heatmap_data(self, hourly_activity: dict, emoji_activity: dict) -> dict: + + def _generate_hourly_heatmap_data( + self, hourly_activity: dict, emoji_activity: dict + ) -> dict: """生成小时级热力图数据""" # 计算活跃度等级 max_hourly = max(hourly_activity.values()) if hourly_activity else 1 @@ -90,7 +100,7 @@ class ActivityVisualizer: hour: (emoji_activity.get(hour, 0) / max_emoji) * 100 for hour in range(24) }, - "activity_levels": self._calculate_activity_levels(hourly_activity) + "activity_levels": self._calculate_activity_levels(hourly_activity), } def _calculate_activity_levels(self, hourly_activity: dict) -> dict: @@ -114,17 +124,17 @@ class ActivityVisualizer: levels[hour] = level return levels - + def generate_hourly_chart_html(self, hourly_activity: dict) -> str: """生成每小时活动分布的HTML图表""" html_parts = [] max_activity = max(hourly_activity.values()) if hourly_activity else 1 threshold_percentage = 20 # 数值显示阈值 - + for hour in range(24): count = hourly_activity.get(hour, 0) percentage = (count / max_activity) * 100 if max_activity > 0 else 0 - + if count > 0 and percentage >= threshold_percentage: # 活动数较多，数值显示在条形图内部 html_segment = f""" @@ -159,7 +169,5 @@ class ActivityVisualizer: """ html_parts.append(html_segment) - + return "".join(html_parts) - -