mirror of
https://github.com/Nezumi-2711/astrbot_plugin_qq_group_daily_analysis.git
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refactor(DDD): 整理架构
This commit is contained in:
SXP-Simon
@@ -1,11 +1,8 @@
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# 应用层 - 编排和用例
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from .analysis_orchestrator import AnalysisOrchestrator
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from .message_converter import MessageConverter
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from .reporting_service import ReportingService
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from .scheduling_service import SchedulingService
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__all__ = [
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"AnalysisOrchestrator",
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"MessageConverter",
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"SchedulingService",
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"ReportingService",
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@@ -1,251 +0,0 @@
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"""
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分析编排器 - 应用层协调器
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此编排器连接新的 DDD 架构与现有的分析逻辑，提供渐进式迁移路径。
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架构决策：
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- 编排器使用 PlatformAdapter 获取消息（新的 DDD 方式）
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- 但将 LLM 分析委托给现有分析器（保留已工作的代码）
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- MessageConverter 提供双向转换以保持兼容性
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"""
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from dataclasses import dataclass
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from typing import Any, Optional
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from ..domain.value_objects.platform_capabilities import PlatformCapabilities
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from ..domain.value_objects.unified_message import UnifiedMessage
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from ..infrastructure.platform import PlatformAdapter, PlatformAdapterFactory
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from ..utils.logger import logger
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from .message_converter import MessageConverter
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@dataclass
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class AnalysisConfig:
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"""分析操作配置"""
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days: int = 1
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max_messages: int = 1000
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min_messages_threshold: int = 10
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output_format: str = "image"
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class AnalysisOrchestrator:
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"""
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分析编排器 - 协调分析工作流。
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职责：
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1. 使用 PlatformAdapter 获取消息（DDD 方式）
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2. 转换消息以兼容现有分析器
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3. 协调分析流程
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4. 提供平台能力检查
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此类作为以下组件之间的桥梁：
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- 新的 DDD 基础设施（PlatformAdapter, UnifiedMessage）
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- 现有分析逻辑（MessageHandler, LLMAnalyzer 等）
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"""
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def __init__(
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self,
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adapter: PlatformAdapter,
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config: AnalysisConfig = None,
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):
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"""
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初始化编排器。
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参数：
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adapter: 用于消息操作的平台适配器
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config: 分析配置
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"""
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self.adapter = adapter
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self.config = config or AnalysisConfig()
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@classmethod
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def create_for_platform(
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cls,
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platform_name: str,
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bot_instance: Any,
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config: dict = None,
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analysis_config: AnalysisConfig = None,
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) -> Optional["AnalysisOrchestrator"]:
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"""
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工厂方法 - 为特定平台创建编排器。
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参数：
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platform_name: 平台名称（如 "aiocqhttp", "telegram"）
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bot_instance: 来自 AstrBot 的 bot 实例
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config: 平台特定配置
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analysis_config: 分析配置
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返回：
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AnalysisOrchestrator 或 None（如果平台不支持）
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"""
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adapter = PlatformAdapterFactory.create(platform_name, bot_instance, config)
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if adapter is None:
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logger.warning(f"平台 '{platform_name}' 不支持分析功能")
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return None
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return cls(adapter, analysis_config)
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def get_capabilities(self) -> PlatformCapabilities:
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"""获取平台能力。"""
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return self.adapter.get_capabilities()
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def can_analyze(self) -> bool:
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"""检查平台是否支持分析。"""
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return self.adapter.get_capabilities().can_analyze()
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def can_send_report(self, format: str = "image") -> bool:
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"""检查平台是否能发送指定格式的报告。"""
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return self.adapter.get_capabilities().can_send_report(format)
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async def fetch_messages(
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self,
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group_id: str,
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days: int = None,
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max_count: int = None,
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) -> list[UnifiedMessage]:
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"""
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使用平台适配器获取消息。
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参数：
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group_id: 要获取消息的群组 ID
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days: 天数（默认使用配置值）
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max_count: 最大消息数量（默认使用配置值）
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返回：
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UnifiedMessage 列表
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"""
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days = days or self.config.days
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max_count = max_count or self.config.max_messages
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# 应用平台能力限制
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caps = self.adapter.get_capabilities()
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effective_days = caps.get_effective_days(days)
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effective_count = caps.get_effective_count(max_count)
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if effective_days < days:
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logger.info(f"平台限制：请求 {days} 天，实际使用 {effective_days} 天")
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return await self.adapter.fetch_messages(
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group_id=group_id,
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days=effective_days,
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max_count=effective_count,
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)
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async def fetch_messages_as_raw(
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self,
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group_id: str,
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days: int = None,
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max_count: int = None,
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) -> list[dict]:
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"""
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获取消息并转换为原始字典格式。
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此方法提供与现有分析器的向后兼容性，
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这些分析器期望原始字典格式的消息。
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参数：
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group_id: 要获取消息的群组 ID
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days: 天数
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max_count: 最大消息数量
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返回：
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原始消息字典列表（通用格式，由适配器决定具体格式）
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"""
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# unified_messages = await self.fetch_messages(group_id, days, max_count)
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#
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# # 如果适配器实现了 convert_to_raw_format，则使用它
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# if hasattr(self.adapter, "convert_to_raw_format"):
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# return self.adapter.convert_to_raw_format(unified_messages)
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#
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# # 默认回退逻辑：手动转换
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# # 这可能不完美，但能保证基本的向后兼容性
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# return [
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# {
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# "message_id": msg.message_id,
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# "group_id": msg.group_id,
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# "sender": {
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# "user_id": msg.sender_id,
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# "nickname": msg.sender_name,
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# "card": msg.sender_card
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# },
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# "time": msg.timestamp,
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# "message": msg.text_content, # 简化处理
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# "raw_message": msg.text_content
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# }
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# for msg in unified_messages
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# ]
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# 暂时直接使用适配器获取 raw 格式，如果适配器支持
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# 这是为了确保现有逻辑完全兼容，因为 convert_to_raw_format 可能有损
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# 但我们希望尽可能使用新的 fetch_messages
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unified_messages = await self.fetch_messages(group_id, days, max_count)
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return self.adapter.convert_to_raw_format(unified_messages)
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async def get_group_info(self, group_id: str):
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"""获取群组信息。"""
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return await self.adapter.get_group_info(group_id)
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async def get_member_avatars(
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self,
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user_ids: list[str],
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size: int = 100,
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) -> dict[str, str | None]:
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"""
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批量获取用户头像 URL。
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参数：
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user_ids: 用户 ID 列表
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size: 头像尺寸
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返回：
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用户 ID 到头像 URL 的映射字典（URL 可能为 None）
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"""
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return await self.adapter.batch_get_avatar_urls(user_ids, size)
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async def send_text(self, group_id: str, text: str) -> bool:
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"""发送文本消息到群组。"""
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return await self.adapter.send_text(group_id, text)
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async def send_image(
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self,
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group_id: str,
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image_path: str,
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caption: str = "",
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) -> bool:
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"""发送图片到群组。"""
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return await self.adapter.send_image(group_id, image_path, caption)
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async def send_file(
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self,
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group_id: str,
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file_path: str,
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filename: str = None,
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) -> bool:
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"""发送文件到群组。"""
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return await self.adapter.send_file(group_id, file_path, filename)
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def validate_message_count(self, messages: list[UnifiedMessage]) -> bool:
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"""
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检查消息数量是否达到最小阈值。
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参数：
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messages: 消息列表
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返回：
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如果数量足够返回 True
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"""
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return len(messages) >= self.config.min_messages_threshold
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def get_analysis_text(self, messages: list[UnifiedMessage]) -> str:
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"""
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将消息转换为 LLM 分析文本格式。
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参数：
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messages: UnifiedMessage 列表
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返回：
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格式化的 LLM 分析文本
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"""
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return MessageConverter.unified_to_analysis_text(messages)
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@@ -0,0 +1,160 @@
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"""
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分析应用服务 - 应用层
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实现“每日群聊分析并生成报告”的核心用例。
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负责协调领域服务、基础设施适配器及持久化层。
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"""
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import asyncio
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from typing import Any
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from ...utils.logger import logger
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from ..domain.models.data_models import TokenUsage
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from ..domain.repositories.analysis_repository import IAnalysisProvider
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from ..domain.repositories.report_repository import IReportGenerator
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from ..domain.services.analysis_domain_service import AnalysisDomainService
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from ..domain.services.statistics_service import StatisticsService
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class AnalysisApplicationService:
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"""分析应用服务 - 协调业务流程"""
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def __init__(
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self,
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config_manager: Any,
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bot_manager: Any,
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history_manager: Any,
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report_generator: IReportGenerator,
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llm_analyzer: IAnalysisProvider,
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statistics_service: StatisticsService,
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analysis_domain_service: AnalysisDomainService,
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):
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self.config_manager = config_manager
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self.bot_manager = bot_manager
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self.history_manager = history_manager
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self.report_generator = report_generator
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self.llm_analyzer = llm_analyzer
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self.statistics_service = statistics_service
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self.analysis_domain_service = analysis_domain_service
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async def execute_daily_analysis(
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self, group_id: str, platform_id: str | None = None, manual: bool = False
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) -> dict[str, Any]:
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"""
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执行每日分析用例。
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流程：
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1. 获取适配器
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2. 拉取消息 (Infrastructure)
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3. 基础统计 (Domain Service)
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4. 用户分析 (Domain Service)
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5. LLM 语义分析 (Infrastructure/Analysis Bridge)
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6. 生成报告 (Visualization/Infrastructure)
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7. 持久化摘要 (Persistence)
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8. 返回结果
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"""
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logger.info(f"开始执行分析用例: 群 {group_id}, 平台 {platform_id or '默认'}")
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# 1. 获取适配器
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adapter = self.bot_manager.get_adapter(platform_id)
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if not adapter:
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raise ValueError(f"未找到平台 {platform_id} 的适配器")
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# 2. 拉取消息
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days = self.config_manager.get_analysis_days()
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max_count = self.config_manager.get_max_messages()
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unified_messages = await adapter.fetch_messages(
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group_id=group_id, days=days, max_count=max_count
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)
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if not unified_messages:
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logger.warning(f"群 {group_id} 在最近 {days} 天内无消息或无法获取")
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return {"success": False, "reason": "no_messages"}
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# 检查最小消息阈值
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if (
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len(unified_messages) < self.config_manager.get_min_messages_threshold()
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and not manual
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):
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logger.info(
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f"群 {group_id} 消息数 ({len(unified_messages)}) 未达到自动分析阈值"
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)
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return {"success": False, "reason": "below_threshold"}
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# 3. 基础统计 (Domain Service)
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statistics = await asyncio.to_thread(
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self.statistics_service.calculate_group_statistics, unified_messages
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)
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# 4. 用户分析 (Domain Service)
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bot_self_ids = self.config_manager.get_bot_self_ids()
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user_activity = await asyncio.to_thread(
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self.analysis_domain_service.analyze_user_activity,
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unified_messages,
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bot_self_ids,
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)
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max_user_titles = self.config_manager.get_max_user_titles()
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top_users = self.analysis_domain_service.get_top_users(
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user_activity, limit=max_user_titles
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)
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# 5. LLM 语义分析 (为了保持兼容，目前直接传 UnifiedMessage，后续如需传 raw dict 再加转换)
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# LLMAnalyzer 内部可能已经处理了转换（见之前代码）
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topic_enabled = self.config_manager.get_topic_analysis_enabled()
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user_title_enabled = self.config_manager.get_user_title_analysis_enabled()
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golden_quote_enabled = self.config_manager.get_golden_quote_analysis_enabled()
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topics = []
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user_titles = []
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golden_quotes = []
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total_token_usage = TokenUsage()
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# Note: LLMAnalyzer 目前可能只接收 legacy 格式或特定的 UnifiedMessage 适配
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# 暂时转换回 legacy 格式以确保稳定性，直到 LLMAnalyzer 被重构
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legacy_messages = self.statistics_service._convert_to_legacy_dict(
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unified_messages
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)
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unified_msg_origin = (
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f"{platform_id}:GroupMessage:{group_id}" if platform_id else group_id
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)
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if topic_enabled and user_title_enabled and golden_quote_enabled:
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(
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topics,
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user_titles,
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golden_quotes,
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total_token_usage,
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) = await self.llm_analyzer.analyze_all_concurrent(
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legacy_messages,
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user_activity,
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umo=unified_msg_origin,
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top_users=top_users,
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)
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else:
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# 按需串行执行 (略，实际实现可补全或合并)
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pass
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# 回填结果
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statistics.golden_quotes = golden_quotes
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statistics.token_usage = total_token_usage
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analysis_result = {
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"statistics": statistics,
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"topics": topics,
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"user_titles": user_titles,
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"user_analysis": user_activity,
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}
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# 6. 持久化摘要 (Persistence)
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await self.history_manager.save_analysis(group_id, analysis_result)
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# 7. 生成报告并发送 (应用层编排发送动作)
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# 这里由调用方处理发送，本服务只返回分析结果和可能的视觉产物
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return {
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"success": True,
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"analysis_result": analysis_result,
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"messages_count": len(unified_messages),
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"adapter": adapter,
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}
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