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refactor(DDD): 整理架构

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SXP-Simon
2026-02-09 12:47:43 +08:00
parent 0b096351d5
commit 2005f0633f
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src/utils/helpers.py
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"""
通用工具函数模块
包含消息分析和其他通用功能
"""
import asyncio
from typing import Any
from ..analysis.llm_analyzer import LLMAnalyzer
from ..analysis.statistics import UserAnalyzer
from ..core.message_handler import MessageHandler
from ..models.data_models import TokenUsage
from .logger import logger
class MessageAnalyzer:
"""
业务逻辑：消息分析整合器
该类作为一个门面（Facade），将消息存储、统计计算、LLM 智能分析以及用户画像分析
等多个底层组件整合在一起，提供统一的消息分析流程接口。
Attributes:
context (Any): AstrBot 上下文环境
config_manager (Any): 配置管理者实例
bot_manager (Any, optional): 机器人多实例管理者
message_handler (MessageHandler): 负责消息过滤和基础统计
llm_analyzer (LLMAnalyzer): 负责调用大模型进行语义分析
user_analyzer (UserAnalyzer): 负责用户活跃度及角色分析
"""
def __init__(
self, context: Any, config_manager: Any, bot_manager: Any | None = None
):
"""
初始化消息分析器。
Args:
context (Any): AstrBot 核心上下文
config_manager (Any): 插件配置管理器
bot_manager (Any, optional): 多平台机器人管理器实例
"""
self.context = context
self.config_manager = config_manager
self.bot_manager = bot_manager
self.message_handler = MessageHandler(config_manager, bot_manager)
self.llm_analyzer = LLMAnalyzer(context, config_manager)
self.user_analyzer = UserAnalyzer(config_manager)
def _extract_bot_self_id_from_instance(self, bot_instance: Any) -> str | None:
"""
内部方法：从不同平台的机器人实例中探测其自身 ID。
Args:
bot_instance (Any): 宿主机器人实例 (如 OneBot, Discord 实例)
Returns:
str | None: 探测到的用户 ID 或 None
"""
if hasattr(bot_instance, "self_id") and bot_instance.self_id:
return str(bot_instance.self_id)
elif hasattr(bot_instance, "user_id") and bot_instance.user_id:
return str(bot_instance.user_id)
return None
async def set_bot_instance(
self, bot_instance: Any, platform_id: str | None = None
) -> None:
"""
向分析组件注入当前活跃的机器人实例。
Args:
bot_instance (Any): 活跃的机器人 SDK 实例
platform_id (str, optional): 平台标识符，用于多实例路由
"""
if self.bot_manager:
self.bot_manager.set_bot_instance(bot_instance, platform_id)
else:
# 降级逻辑：仅设置单个默认 ID
bot_self_id = self._extract_bot_self_id_from_instance(bot_instance)
if bot_self_id:
await self.message_handler.set_bot_self_ids([bot_self_id])
async def analyze_messages(
self, messages: list[dict], group_id: str, unified_msg_origin: str | None = None
) -> dict | None:
"""
执行完整的群消息流水化分析。
包含：消息预处理 -> 词频统计 -> 活跃用户识别 -> LLM 摘要/金句提取。
Args:
messages (list[dict]): 待处理的原始或统一格式消息字典列表
group_id (str): 群组 ID，用于上下文标识
unified_msg_origin (str, optional): 统一消息来源标识
Returns:
dict | None: 包含 statistics, topics, user_titles, user_analysis 的字典，失败返回 None
"""
try:
# 1. 基础消息统计 (耗时操作，放入线程池避免阻塞事件循环)
statistics = await asyncio.to_thread(
self.message_handler.calculate_statistics, messages
)
# 2. 用户维度分析 (等级、发言习惯等)
user_analysis = await asyncio.to_thread(
self.user_analyzer.analyze_users, messages
)
# 3. 筛选分析范围：提取 Top N 活跃用户用于深度称号分析
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"已为称号分析筛选出 {len(top_users)} 名活跃用户 (最大限制: {max_user_titles})"
)
# 4. LLM 语义分析阶段
topics = []
user_titles = []
golden_quotes = []
total_token_usage = TokenUsage()
# 检查开关设置
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()
)
# 策略：如果多项功能均开启，则通过 LLMAnalyzer 并发调用，显著降低分析总时长
if topic_enabled and user_title_enabled and golden_quote_enabled:
(
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:
# 串行降级路径：根据开关按需串行调用 (适用于 Token 敏感或单项测试)
if topic_enabled:
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.total_tokens += topic_tokens.total_tokens
if user_title_enabled:
(
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.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
)
total_token_usage.prompt_tokens += quote_tokens.prompt_tokens
total_token_usage.completion_tokens += (
quote_tokens.completion_tokens
)
total_token_usage.total_tokens += quote_tokens.total_tokens
# 5. 回填分析结果并组装返回字典
statistics.golden_quotes = golden_quotes
statistics.token_usage = total_token_usage
return {
"statistics": statistics,
"topics": topics,
"user_titles": user_titles,
"user_analysis": user_analysis,
}
except Exception as e:
logger.error(f"消息分析流水线执行失败: {e}")
return None