feat(affinity-flow): 重构兴趣度评分系统为智能embedding匹配
- 移除传统关键词匹配方式，改用embedding计算智能兴趣匹配度 - 添加异步方法支持机器人兴趣管理器的智能匹配计算 - 增加详细的日志记录和错误处理机制 - 添加数据库关键词提取和降级处理逻辑 - 集成智能兴趣系统初始化到人设构建流程 - 防止回复自身消息的死循环保护机制 BREAKING CHANGE: 兴趣匹配评分机制完全重构，从基于关键词的硬编码匹配改为基于embedding的智能匹配，需要重新初始化兴趣系统
This commit is contained in:
Windpicker-owo
17
src/chat/interest_system/__init__.py
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17
src/chat/interest_system/__init__.py
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"""
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机器人兴趣标签系统
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基于人设生成兴趣标签，使用embedding计算匹配度
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"""
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from .bot_interest_manager import BotInterestManager, bot_interest_manager
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from src.common.data_models.bot_interest_data_model import (
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BotInterestTag, BotPersonalityInterests, InterestMatchResult
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)
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__all__ = [
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"BotInterestManager",
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"bot_interest_manager",
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"BotInterestTag",
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"BotPersonalityInterests",
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"InterestMatchResult"
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]
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666
src/chat/interest_system/bot_interest_manager.py
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666
src/chat/interest_system/bot_interest_manager.py
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"""
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机器人兴趣标签管理系统
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基于人设生成兴趣标签，并使用embedding计算匹配度
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"""
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import orjson
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import traceback
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from typing import List, Dict, Optional, Any
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from datetime import datetime
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import numpy as np
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from src.common.logger import get_logger
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from src.common.data_models.bot_interest_data_model import (
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BotPersonalityInterests, BotInterestTag, InterestMatchResult
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)
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logger = get_logger("bot_interest_manager")
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class BotInterestManager:
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"""机器人兴趣标签管理器"""
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def __init__(self):
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self.current_interests: Optional[BotPersonalityInterests] = None
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self.embedding_cache: Dict[str, List[float]] = {} # embedding缓存
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self._initialized = False
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# Embedding客户端配置
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self.embedding_request = None
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self.embedding_config = None
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self.embedding_dimension = 1024 # 默认BGE-M3 embedding维度
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@property
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def is_initialized(self) -> bool:
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"""检查兴趣系统是否已初始化"""
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return self._initialized
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async def initialize(self, personality_description: str, personality_id: str = "default"):
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"""初始化兴趣标签系统"""
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try:
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logger.info("=" * 60)
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logger.info("🚀 开始初始化机器人兴趣标签系统")
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logger.info(f"📋 人设ID: {personality_id}")
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logger.info(f"📝 人设描述长度: {len(personality_description)} 字符")
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logger.info("=" * 60)
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# 初始化embedding模型
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logger.info("🧠 正在初始化embedding模型...")
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await self._initialize_embedding_model()
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# 检查embedding客户端是否成功初始化
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if not self.embedding_request:
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raise RuntimeError("❌ Embedding客户端初始化失败，无法继续")
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# 生成或加载兴趣标签
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logger.info("🎯 正在生成或加载兴趣标签...")
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await self._load_or_generate_interests(personality_description, personality_id)
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self._initialized = True
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# 检查是否成功获取兴趣标签
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if self.current_interests and len(self.current_interests.get_active_tags()) > 0:
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active_tags_count = len(self.current_interests.get_active_tags())
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logger.info("=" * 60)
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logger.info("✅ 机器人兴趣标签系统初始化完成!")
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logger.info(f"📊 活跃兴趣标签数量: {active_tags_count}")
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logger.info(f"💾 Embedding缓存大小: {len(self.embedding_cache)}")
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logger.info("=" * 60)
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else:
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raise RuntimeError("❌ 未能成功生成或加载兴趣标签")
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except Exception as e:
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logger.error("=" * 60)
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logger.error(f"❌ 初始化机器人兴趣标签系统失败: {e}")
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logger.error("=" * 60)
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traceback.print_exc()
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raise # 重新抛出异常，不允许降级初始化
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async def _initialize_embedding_model(self):
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"""初始化embedding模型"""
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logger.info("🔧 正在配置embedding客户端...")
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# 使用项目配置的embedding模型
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from src.config.config import model_config
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from src.llm_models.utils_model import LLMRequest
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logger.debug("✅ 成功导入embedding相关模块")
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# 检查embedding配置是否存在
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if not hasattr(model_config.model_task_config, 'embedding'):
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raise RuntimeError("❌ 未找到embedding模型配置")
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logger.info("📋 找到embedding模型配置")
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self.embedding_config = model_config.model_task_config.embedding
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self.embedding_dimension = 1024 # BGE-M3的维度
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logger.info(f"📐 使用模型维度: {self.embedding_dimension}")
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# 创建LLMRequest实例用于embedding
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self.embedding_request = LLMRequest(model_set=self.embedding_config, request_type="interest_embedding")
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logger.info("✅ Embedding请求客户端初始化成功")
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logger.info(f"🔗 客户端类型: {type(self.embedding_request).__name__}")
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# 获取第一个embedding模型的ModelInfo
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if hasattr(self.embedding_config, 'model_list') and self.embedding_config.model_list:
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first_model_name = self.embedding_config.model_list[0]
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logger.info(f"🎯 使用embedding模型: {first_model_name}")
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else:
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logger.warning("⚠️ 未找到embedding模型列表")
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logger.info("✅ Embedding模型初始化完成")
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async def _load_or_generate_interests(self, personality_description: str, personality_id: str):
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"""加载或生成兴趣标签"""
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logger.info(f"📚 正在为 '{personality_id}' 加载或生成兴趣标签...")
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# 首先尝试从数据库加载
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logger.info("💾 尝试从数据库加载现有兴趣标签...")
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loaded_interests = await self._load_interests_from_database(personality_id)
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if loaded_interests:
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self.current_interests = loaded_interests
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active_count = len(loaded_interests.get_active_tags())
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logger.info(f"✅ 成功从数据库加载 {active_count} 个兴趣标签")
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logger.info(f"📅 最后更新时间: {loaded_interests.last_updated}")
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logger.info(f"🔄 版本号: {loaded_interests.version}")
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else:
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# 生成新的兴趣标签
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logger.info("🆕 数据库中未找到兴趣标签，开始生成新的...")
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logger.info("🤖 正在调用LLM生成个性化兴趣标签...")
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generated_interests = await self._generate_interests_from_personality(personality_description, personality_id)
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if generated_interests:
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self.current_interests = generated_interests
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active_count = len(generated_interests.get_active_tags())
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logger.info(f"✅ 成功生成 {active_count} 个兴趣标签")
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# 保存到数据库
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logger.info("💾 正在保存兴趣标签到数据库...")
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await self._save_interests_to_database(generated_interests)
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else:
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raise RuntimeError("❌ 兴趣标签生成失败")
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async def _generate_interests_from_personality(self, personality_description: str, personality_id: str) -> Optional[BotPersonalityInterests]:
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"""根据人设生成兴趣标签"""
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try:
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logger.info("🎨 开始根据人设生成兴趣标签...")
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logger.info(f"📝 人设长度: {len(personality_description)} 字符")
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# 检查embedding客户端是否可用
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if not hasattr(self, 'embedding_request'):
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raise RuntimeError("❌ Embedding客户端未初始化，无法生成兴趣标签")
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# 构建提示词
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logger.info("📝 构建LLM提示词...")
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prompt = f"""
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基于以下机器人人设描述，生成一套合适的兴趣标签：
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人设描述：
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{personality_description}
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请生成一系列兴趣关键词标签，要求：
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1. 标签应该符合人设特点和性格
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2. 每个标签都有权重（0.1-1.0），表示对该兴趣的喜好程度
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3. 生成15-25个不等的标签
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4. 标签应该是具体的关键词，而不是抽象概念
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请以JSON格式返回，格式如下：
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{{
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"interests": [
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{{"name": "标签名", "weight": 0.8}},
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{{"name": "标签名", "weight": 0.6}},
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{{"name": "标签名", "weight": 0.9}}
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]
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}}
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注意：
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- 权重范围0.1-1.0，权重越高表示越感兴趣
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- 标签要具体，如"编程"、"游戏"、"旅行"等
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- 根据人设生成个性化的标签
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"""
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# 调用LLM生成兴趣标签
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logger.info("🤖 正在调用LLM生成兴趣标签...")
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response = await self._call_llm_for_interest_generation(prompt)
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if not response:
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raise RuntimeError("❌ LLM未返回有效响应")
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logger.info("✅ LLM响应成功，开始解析兴趣标签...")
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interests_data = orjson.loads(response)
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bot_interests = BotPersonalityInterests(
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personality_id=personality_id,
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personality_description=personality_description
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)
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# 解析生成的兴趣标签
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interests_list = interests_data.get("interests", [])
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logger.info(f"📋 解析到 {len(interests_list)} 个兴趣标签")
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for i, tag_data in enumerate(interests_list):
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tag_name = tag_data.get("name", f"标签_{i}")
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weight = tag_data.get("weight", 0.5)
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tag = BotInterestTag(
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tag_name=tag_name,
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weight=weight
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)
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bot_interests.interest_tags.append(tag)
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logger.debug(f" 🏷️ {tag_name} (权重: {weight:.2f})")
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# 为所有标签生成embedding
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logger.info("🧠 开始为兴趣标签生成embedding向量...")
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await self._generate_embeddings_for_tags(bot_interests)
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logger.info("✅ 兴趣标签生成完成")
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return bot_interests
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except orjson.JSONDecodeError as e:
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logger.error(f"❌ 解析LLM响应JSON失败: {e}")
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raise
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except Exception as e:
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logger.error(f"❌ 根据人设生成兴趣标签失败: {e}")
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traceback.print_exc()
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raise
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async def _call_llm_for_interest_generation(self, prompt: str) -> Optional[str]:
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"""调用LLM生成兴趣标签"""
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try:
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logger.info("🔧 配置LLM客户端...")
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# 使用llm_api来处理请求
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from src.plugin_system.apis import llm_api
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from src.config.config import model_config
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# 构建完整的提示词，明确要求只返回纯JSON
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full_prompt = f"""你是一个专业的机器人人设分析师，擅长根据人设描述生成合适的兴趣标签。
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{prompt}
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请确保返回格式为有效的JSON，不要包含任何额外的文本、解释或代码块标记。只返回JSON对象本身。"""
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# 使用replyer模型配置
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replyer_config = model_config.model_task_config.replyer
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# 调用LLM API
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logger.info("🚀 正在通过LLM API发送请求...")
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success, response, reasoning_content, model_name = await llm_api.generate_with_model(
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prompt=full_prompt,
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model_config=replyer_config,
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request_type="interest_generation",
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temperature=0.7,
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max_tokens=2000
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)
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if success and response:
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logger.info(f"✅ LLM响应成功，模型: {model_name}, 响应长度: {len(response)} 字符")
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logger.debug(f"📄 LLM响应内容: {response[:200]}..." if len(response) > 200 else f"📄 LLM响应内容: {response}")
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if reasoning_content:
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logger.debug(f"🧠 推理内容: {reasoning_content[:100]}...")
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# 清理响应内容，移除可能的代码块标记
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cleaned_response = self._clean_llm_response(response)
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return cleaned_response
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else:
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logger.warning("⚠️ LLM返回空响应或调用失败")
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return None
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except Exception as e:
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logger.error(f"❌ 调用LLM生成兴趣标签失败: {e}")
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logger.error("🔍 错误详情:")
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traceback.print_exc()
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return None
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def _clean_llm_response(self, response: str) -> str:
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"""清理LLM响应，移除代码块标记和其他非JSON内容"""
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import re
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# 移除 ```json 和 ``` 标记
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cleaned = re.sub(r'```json\s*', '', response)
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cleaned = re.sub(r'\s*```', '', cleaned)
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# 移除可能的多余空格和换行
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cleaned = cleaned.strip()
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# 尝试提取JSON对象（如果响应中有其他文本）
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json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
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if json_match:
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cleaned = json_match.group(0)
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logger.debug(f"🧹 清理后的响应: {cleaned[:200]}..." if len(cleaned) > 200 else f"🧹 清理后的响应: {cleaned}")
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return cleaned
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async def _generate_embeddings_for_tags(self, interests: BotPersonalityInterests):
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"""为所有兴趣标签生成embedding"""
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if not hasattr(self, 'embedding_request'):
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raise RuntimeError("❌ Embedding客户端未初始化，无法生成embedding")
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total_tags = len(interests.interest_tags)
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logger.info(f"🧠 开始为 {total_tags} 个兴趣标签生成embedding向量...")
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cached_count = 0
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generated_count = 0
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failed_count = 0
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for i, tag in enumerate(interests.interest_tags, 1):
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if tag.tag_name in self.embedding_cache:
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# 使用缓存的embedding
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tag.embedding = self.embedding_cache[tag.tag_name]
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cached_count += 1
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logger.debug(f" [{i}/{total_tags}] 🏷️ '{tag.tag_name}' - 使用缓存")
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else:
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# 生成新的embedding
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embedding_text = tag.tag_name
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logger.debug(f" [{i}/{total_tags}] 🔄 正在为 '{tag.tag_name}' 生成embedding...")
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embedding = await self._get_embedding(embedding_text)
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if embedding:
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tag.embedding = embedding
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self.embedding_cache[tag.tag_name] = embedding
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generated_count += 1
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logger.debug(f" ✅ '{tag.tag_name}' embedding生成成功")
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else:
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failed_count += 1
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logger.warning(f" ❌ '{tag.tag_name}' embedding生成失败")
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if failed_count > 0:
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raise RuntimeError(f"❌ 有 {failed_count} 个兴趣标签embedding生成失败")
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interests.last_updated = datetime.now()
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logger.info("=" * 50)
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logger.info("✅ Embedding生成完成!")
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logger.info(f"📊 总标签数: {total_tags}")
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logger.info(f"💾 缓存命中: {cached_count}")
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logger.info(f"🆕 新生成: {generated_count}")
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logger.info(f"❌ 失败: {failed_count}")
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logger.info(f"🗃️ 总缓存大小: {len(self.embedding_cache)}")
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logger.info("=" * 50)
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async def _get_embedding(self, text: str) -> List[float]:
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"""获取文本的embedding向量"""
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if not hasattr(self, 'embedding_request'):
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raise RuntimeError("❌ Embedding请求客户端未初始化")
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# 检查缓存
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if text in self.embedding_cache:
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logger.debug(f"💾 使用缓存的embedding: '{text[:30]}...'")
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return self.embedding_cache[text]
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# 使用LLMRequest获取embedding
|
||||
logger.debug(f"🔄 正在获取embedding: '{text[:30]}...'")
|
||||
embedding, model_name = await self.embedding_request.get_embedding(text)
|
||||
|
||||
if embedding and len(embedding) > 0:
|
||||
self.embedding_cache[text] = embedding
|
||||
logger.debug(f"✅ Embedding获取成功，维度: {len(embedding)}, 模型: {model_name}")
|
||||
return embedding
|
||||
else:
|
||||
raise RuntimeError(f"❌ 返回的embedding为空: {embedding}")
|
||||
|
||||
async def _generate_message_embedding(self, message_text: str, keywords: List[str]) -> List[float]:
|
||||
"""为消息生成embedding向量"""
|
||||
# 组合消息文本和关键词作为embedding输入
|
||||
if keywords:
|
||||
combined_text = f"{message_text} {' '.join(keywords)}"
|
||||
else:
|
||||
combined_text = message_text
|
||||
|
||||
logger.debug(f"🔄 正在为消息生成embedding，输入长度: {len(combined_text)}")
|
||||
|
||||
# 生成embedding
|
||||
embedding = await self._get_embedding(combined_text)
|
||||
logger.debug(f"✅ 消息embedding生成成功，维度: {len(embedding)}")
|
||||
return embedding
|
||||
|
||||
async def _calculate_similarity_scores(self, result: InterestMatchResult, message_embedding: List[float], keywords: List[str]):
|
||||
"""计算消息与兴趣标签的相似度分数"""
|
||||
try:
|
||||
if not self.current_interests:
|
||||
return
|
||||
|
||||
active_tags = self.current_interests.get_active_tags()
|
||||
if not active_tags:
|
||||
return
|
||||
|
||||
logger.debug(f"🔍 开始计算与 {len(active_tags)} 个兴趣标签的相似度")
|
||||
|
||||
for tag in active_tags:
|
||||
if tag.embedding:
|
||||
# 计算余弦相似度
|
||||
similarity = self._calculate_cosine_similarity(message_embedding, tag.embedding)
|
||||
weighted_score = similarity * tag.weight
|
||||
|
||||
# 设置相似度阈值为0.3
|
||||
if similarity > 0.3:
|
||||
result.add_match(tag.tag_name, weighted_score, keywords)
|
||||
logger.debug(f" 🏷️ '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 加权分数={weighted_score:.3f}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 计算相似度分数失败: {e}")
|
||||
|
||||
async def calculate_interest_match(self, message_text: str, keywords: List[str] = None) -> InterestMatchResult:
|
||||
"""计算消息与机器人兴趣的匹配度"""
|
||||
if not self.current_interests or not self._initialized:
|
||||
raise RuntimeError("❌ 兴趣标签系统未初始化")
|
||||
|
||||
logger.info("🎯 开始计算兴趣匹配度...")
|
||||
logger.debug(f"💬 消息长度: {len(message_text)} 字符")
|
||||
if keywords:
|
||||
logger.debug(f"🏷️ 关键词数量: {len(keywords)}")
|
||||
|
||||
message_id = f"msg_{datetime.now().timestamp()}"
|
||||
result = InterestMatchResult(message_id=message_id)
|
||||
|
||||
# 获取活跃的兴趣标签
|
||||
active_tags = self.current_interests.get_active_tags()
|
||||
if not active_tags:
|
||||
raise RuntimeError("❌ 没有活跃的兴趣标签")
|
||||
|
||||
logger.info(f"📊 有 {len(active_tags)} 个活跃兴趣标签参与匹配")
|
||||
|
||||
# 生成消息的embedding
|
||||
logger.debug("🔄 正在生成消息embedding...")
|
||||
message_embedding = await self._get_embedding(message_text)
|
||||
logger.debug(f"✅ 消息embedding生成成功，维度: {len(message_embedding)}")
|
||||
|
||||
# 计算与每个兴趣标签的相似度
|
||||
match_count = 0
|
||||
high_similarity_count = 0
|
||||
similarity_threshold = 0.3
|
||||
|
||||
logger.debug(f"🔍 使用相似度阈值: {similarity_threshold}")
|
||||
|
||||
for tag in active_tags:
|
||||
if tag.embedding:
|
||||
similarity = self._calculate_cosine_similarity(message_embedding, tag.embedding)
|
||||
weighted_score = similarity * tag.weight
|
||||
|
||||
if similarity > similarity_threshold:
|
||||
match_count += 1
|
||||
result.add_match(tag.tag_name, weighted_score, [tag.tag_name])
|
||||
|
||||
if similarity > 0.7:
|
||||
high_similarity_count += 1
|
||||
|
||||
logger.debug(f" 🏷️ '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 加权分数={weighted_score:.3f}")
|
||||
|
||||
logger.info(f"📈 匹配统计: {match_count}/{len(active_tags)} 个标签超过阈值")
|
||||
logger.info(f"🔥 高相似度匹配(>0.7): {high_similarity_count} 个")
|
||||
|
||||
# 计算总体分数
|
||||
result.calculate_overall_score()
|
||||
|
||||
# 确定最佳匹配标签
|
||||
if result.matched_tags:
|
||||
top_tag_name = max(result.match_scores.items(), key=lambda x: x[1])[0]
|
||||
result.top_tag = top_tag_name
|
||||
logger.info(f"🏆 最佳匹配标签: '{top_tag_name}' (分数: {result.match_scores[top_tag_name]:.3f})")
|
||||
|
||||
logger.info(f"📊 最终结果: 总分={result.overall_score:.3f}, 置信度={result.confidence:.3f}, 匹配标签数={len(result.matched_tags)}")
|
||||
return result
|
||||
|
||||
|
||||
def _calculate_cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
|
||||
"""计算余弦相似度"""
|
||||
try:
|
||||
vec1 = np.array(vec1)
|
||||
vec2 = np.array(vec2)
|
||||
|
||||
dot_product = np.dot(vec1, vec2)
|
||||
norm1 = np.linalg.norm(vec1)
|
||||
norm2 = np.linalg.norm(vec2)
|
||||
|
||||
if norm1 == 0 or norm2 == 0:
|
||||
return 0.0
|
||||
|
||||
return dot_product / (norm1 * norm2)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算余弦相似度失败: {e}")
|
||||
return 0.0
|
||||
|
||||
async def _load_interests_from_database(self, personality_id: str) -> Optional[BotPersonalityInterests]:
|
||||
"""从数据库加载兴趣标签"""
|
||||
try:
|
||||
logger.info(f"💾 正在从数据库加载兴趣标签，personality_id: {personality_id}")
|
||||
|
||||
# 导入SQLAlchemy相关模块
|
||||
from src.common.database.sqlalchemy_models import BotPersonalityInterests as DBBotPersonalityInterests
|
||||
from src.common.database.sqlalchemy_database_api import get_db_session
|
||||
import orjson
|
||||
|
||||
with get_db_session() as session:
|
||||
# 查询最新的兴趣标签配置
|
||||
db_interests = session.query(DBBotPersonalityInterests).filter(
|
||||
DBBotPersonalityInterests.personality_id == personality_id
|
||||
).order_by(
|
||||
DBBotPersonalityInterests.version.desc(),
|
||||
DBBotPersonalityInterests.last_updated.desc()
|
||||
).first()
|
||||
|
||||
if db_interests:
|
||||
logger.info(f"✅ 找到数据库中的兴趣标签配置，版本: {db_interests.version}")
|
||||
logger.debug(f"📅 最后更新时间: {db_interests.last_updated}")
|
||||
logger.debug(f"🧠 使用的embedding模型: {db_interests.embedding_model}")
|
||||
|
||||
# 解析JSON格式的兴趣标签
|
||||
try:
|
||||
tags_data = orjson.loads(db_interests.interest_tags)
|
||||
logger.debug(f"🏷️ 解析到 {len(tags_data)} 个兴趣标签")
|
||||
|
||||
# 创建BotPersonalityInterests对象
|
||||
interests = BotPersonalityInterests(
|
||||
personality_id=db_interests.personality_id,
|
||||
personality_description=db_interests.personality_description,
|
||||
embedding_model=db_interests.embedding_model,
|
||||
version=db_interests.version,
|
||||
last_updated=db_interests.last_updated
|
||||
)
|
||||
|
||||
# 解析兴趣标签
|
||||
for tag_data in tags_data:
|
||||
tag = BotInterestTag(
|
||||
tag_name=tag_data.get("tag_name", ""),
|
||||
weight=tag_data.get("weight", 0.5),
|
||||
created_at=datetime.fromisoformat(tag_data.get("created_at", datetime.now().isoformat())),
|
||||
updated_at=datetime.fromisoformat(tag_data.get("updated_at", datetime.now().isoformat())),
|
||||
is_active=tag_data.get("is_active", True),
|
||||
embedding=tag_data.get("embedding")
|
||||
)
|
||||
interests.interest_tags.append(tag)
|
||||
|
||||
logger.info(f"✅ 成功从数据库加载 {len(interests.interest_tags)} 个兴趣标签")
|
||||
return interests
|
||||
|
||||
except (orjson.JSONDecodeError, Exception) as e:
|
||||
logger.error(f"❌ 解析兴趣标签JSON失败: {e}")
|
||||
logger.debug(f"🔍 原始JSON数据: {db_interests.interest_tags[:200]}...")
|
||||
return None
|
||||
else:
|
||||
logger.info(f"ℹ️ 数据库中未找到personality_id为 '{personality_id}' 的兴趣标签配置")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 从数据库加载兴趣标签失败: {e}")
|
||||
logger.error("🔍 错误详情:")
|
||||
traceback.print_exc()
|
||||
return None
|
||||
|
||||
async def _save_interests_to_database(self, interests: BotPersonalityInterests):
|
||||
"""保存兴趣标签到数据库"""
|
||||
try:
|
||||
logger.info("💾 正在保存兴趣标签到数据库...")
|
||||
logger.info(f"📋 personality_id: {interests.personality_id}")
|
||||
logger.info(f"🏷️ 兴趣标签数量: {len(interests.interest_tags)}")
|
||||
logger.info(f"🔄 版本: {interests.version}")
|
||||
|
||||
# 导入SQLAlchemy相关模块
|
||||
from src.common.database.sqlalchemy_models import BotPersonalityInterests as DBBotPersonalityInterests
|
||||
from src.common.database.sqlalchemy_database_api import get_db_session
|
||||
import orjson
|
||||
|
||||
# 将兴趣标签转换为JSON格式
|
||||
tags_data = []
|
||||
for tag in interests.interest_tags:
|
||||
tag_dict = {
|
||||
"tag_name": tag.tag_name,
|
||||
"weight": tag.weight,
|
||||
"created_at": tag.created_at.isoformat(),
|
||||
"updated_at": tag.updated_at.isoformat(),
|
||||
"is_active": tag.is_active,
|
||||
"embedding": tag.embedding
|
||||
}
|
||||
tags_data.append(tag_dict)
|
||||
|
||||
# 序列化为JSON
|
||||
json_data = orjson.dumps(tags_data)
|
||||
|
||||
with get_db_session() as session:
|
||||
# 检查是否已存在相同personality_id的记录
|
||||
existing_record = session.query(DBBotPersonalityInterests).filter(
|
||||
DBBotPersonalityInterests.personality_id == interests.personality_id
|
||||
).first()
|
||||
|
||||
if existing_record:
|
||||
# 更新现有记录
|
||||
logger.info("🔄 更新现有的兴趣标签配置")
|
||||
existing_record.interest_tags = json_data
|
||||
existing_record.personality_description = interests.personality_description
|
||||
existing_record.embedding_model = interests.embedding_model
|
||||
existing_record.version = interests.version
|
||||
existing_record.last_updated = interests.last_updated
|
||||
|
||||
logger.info(f"✅ 成功更新兴趣标签配置，版本: {interests.version}")
|
||||
|
||||
else:
|
||||
# 创建新记录
|
||||
logger.info("🆕 创建新的兴趣标签配置")
|
||||
new_record = DBBotPersonalityInterests(
|
||||
personality_id=interests.personality_id,
|
||||
personality_description=interests.personality_description,
|
||||
interest_tags=json_data,
|
||||
embedding_model=interests.embedding_model,
|
||||
version=interests.version,
|
||||
last_updated=interests.last_updated
|
||||
)
|
||||
session.add(new_record)
|
||||
logger.info(f"✅ 成功创建兴趣标签配置，版本: {interests.version}")
|
||||
|
||||
logger.info("✅ 兴趣标签已成功保存到数据库")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 保存兴趣标签到数据库失败: {e}")
|
||||
logger.error("🔍 错误详情:")
|
||||
traceback.print_exc()
|
||||
|
||||
def get_current_interests(self) -> Optional[BotPersonalityInterests]:
|
||||
"""获取当前的兴趣标签配置"""
|
||||
return self.current_interests
|
||||
|
||||
def get_interest_stats(self) -> Dict[str, Any]:
|
||||
"""获取兴趣系统统计信息"""
|
||||
if not self.current_interests:
|
||||
return {"initialized": False}
|
||||
|
||||
active_tags = self.current_interests.get_active_tags()
|
||||
|
||||
return {
|
||||
"initialized": self._initialized,
|
||||
"total_tags": len(active_tags),
|
||||
"embedding_model": self.current_interests.embedding_model,
|
||||
"last_updated": self.current_interests.last_updated.isoformat(),
|
||||
"cache_size": len(self.embedding_cache)
|
||||
}
|
||||
|
||||
async def update_interest_tags(self, new_personality_description: str = None):
|
||||
"""更新兴趣标签"""
|
||||
try:
|
||||
if not self.current_interests:
|
||||
logger.warning("没有当前的兴趣标签配置，无法更新")
|
||||
return
|
||||
|
||||
if new_personality_description:
|
||||
self.current_interests.personality_description = new_personality_description
|
||||
|
||||
# 重新生成兴趣标签
|
||||
new_interests = await self._generate_interests_from_personality(
|
||||
self.current_interests.personality_description,
|
||||
self.current_interests.personality_id
|
||||
)
|
||||
|
||||
if new_interests:
|
||||
new_interests.version = self.current_interests.version + 1
|
||||
self.current_interests = new_interests
|
||||
await self._save_interests_to_database(new_interests)
|
||||
logger.info(f"兴趣标签已更新，版本: {new_interests.version}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"更新兴趣标签失败: {e}")
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
# 创建全局实例（重新创建以包含新的属性）
|
||||
bot_interest_manager = BotInterestManager()
|
||||
Reference in New Issue
Block a user