290 lines
12 KiB
Python
290 lines
12 KiB
Python
"""
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根据person_name查询用户信息 - 工具实现
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支持模糊查询，可以查询某个用户的所有信息
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"""
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import json
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from datetime import datetime
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from src.common.logger import get_logger
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from src.common.database.database_model import PersonInfo
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from .tool_registry import register_memory_retrieval_tool
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logger = get_logger("memory_retrieval_tools")
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def _format_group_nick_names(group_nick_name_field) -> str:
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"""格式化群昵称信息
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Args:
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group_nick_name_field: 群昵称字段（可能是字符串JSON或None）
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Returns:
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str: 格式化后的群昵称信息字符串
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"""
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if not group_nick_name_field:
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return ""
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try:
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# 解析JSON格式的群昵称列表
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group_nick_names_data = (
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json.loads(group_nick_name_field) if isinstance(group_nick_name_field, str) else group_nick_name_field
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)
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if not isinstance(group_nick_names_data, list) or not group_nick_names_data:
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return ""
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# 格式化群昵称列表
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group_nick_list = []
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for item in group_nick_names_data:
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if isinstance(item, dict):
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group_id = item.get("group_id", "未知群号")
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group_nick_name = item.get("group_nick_name", "未知群昵称")
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group_nick_list.append(f" - 群号 {group_id}：{group_nick_name}")
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elif isinstance(item, str):
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# 兼容旧格式（如果存在）
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group_nick_list.append(f" - {item}")
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if group_nick_list:
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return "群昵称：\n" + "\n".join(group_nick_list)
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return ""
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except (json.JSONDecodeError, TypeError, ValueError) as e:
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logger.warning(f"解析群昵称信息失败: {e}")
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# 如果解析失败，尝试显示原始内容（截断）
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if isinstance(group_nick_name_field, str):
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preview = group_nick_name_field[:200]
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if len(group_nick_name_field) > 200:
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preview += "..."
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return f"群昵称（原始数据）：{preview}"
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return ""
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async def query_person_info(person_name: str) -> str:
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"""根据person_name查询用户信息，使用模糊查询
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Args:
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person_name: 用户名称（person_name字段）
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Returns:
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str: 查询结果，包含用户的所有信息
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"""
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try:
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person_name = str(person_name).strip()
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if not person_name:
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return "用户名称为空"
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# 构建查询条件（使用模糊查询）
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query = PersonInfo.select().where(PersonInfo.person_name.contains(person_name))
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# 执行查询
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records = list(query.limit(20)) # 最多返回20条记录
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if not records:
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return f"未找到模糊匹配'{person_name}'的用户信息"
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# 区分精确匹配和模糊匹配的结果
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exact_matches = []
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fuzzy_matches = []
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for record in records:
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# 检查是否是精确匹配
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if record.person_name and record.person_name.strip() == person_name:
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exact_matches.append(record)
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else:
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fuzzy_matches.append(record)
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# 构建结果文本
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results = []
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# 先处理精确匹配的结果
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for record in exact_matches:
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result_parts = []
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result_parts.append("【精确匹配】") # 标注为精确匹配
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# 基本信息
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if record.person_name:
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result_parts.append(f"用户名称：{record.person_name}")
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if record.nickname:
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result_parts.append(f"昵称：{record.nickname}")
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if record.person_id:
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result_parts.append(f"用户ID：{record.person_id}")
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if record.platform:
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result_parts.append(f"平台：{record.platform}")
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if record.user_id:
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result_parts.append(f"平台用户ID：{record.user_id}")
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# 群昵称信息
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group_nick_name_str = _format_group_nick_names(getattr(record, "group_nick_name", None))
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if group_nick_name_str:
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result_parts.append(group_nick_name_str)
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# 名称设定原因
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if record.name_reason:
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result_parts.append(f"名称设定原因：{record.name_reason}")
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# 认识状态
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result_parts.append(f"是否已认识：{'是' if record.is_known else '否'}")
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# 时间信息
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if record.know_since:
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know_since_str = datetime.fromtimestamp(record.know_since).strftime("%Y-%m-%d %H:%M:%S")
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result_parts.append(f"首次认识时间：{know_since_str}")
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if record.last_know:
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last_know_str = datetime.fromtimestamp(record.last_know).strftime("%Y-%m-%d %H:%M:%S")
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result_parts.append(f"最后认识时间：{last_know_str}")
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if record.know_times:
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result_parts.append(f"认识次数：{int(record.know_times)}")
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# 记忆点（memory_points）
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if record.memory_points:
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try:
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memory_points_data = (
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json.loads(record.memory_points)
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if isinstance(record.memory_points, str)
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else record.memory_points
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)
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if isinstance(memory_points_data, list) and memory_points_data:
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# 解析记忆点格式：category:content:weight
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memory_list = []
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for memory_point in memory_points_data:
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if memory_point and isinstance(memory_point, str):
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parts = memory_point.split(":", 2)
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if len(parts) >= 3:
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category = parts[0].strip()
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content = parts[1].strip()
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weight = parts[2].strip()
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memory_list.append(f" - [{category}] {content} (权重: {weight})")
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else:
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memory_list.append(f" - {memory_point}")
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if memory_list:
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result_parts.append("记忆点：\n" + "\n".join(memory_list))
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except (json.JSONDecodeError, TypeError, ValueError) as e:
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logger.warning(f"解析用户 {record.person_id} 的memory_points失败: {e}")
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# 如果解析失败，直接显示原始内容（截断）
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memory_preview = str(record.memory_points)[:200]
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if len(str(record.memory_points)) > 200:
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memory_preview += "..."
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result_parts.append(f"记忆点（原始数据）：{memory_preview}")
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results.append("\n".join(result_parts))
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# 再处理模糊匹配的结果
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for record in fuzzy_matches:
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result_parts = []
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result_parts.append("【模糊匹配】") # 标注为模糊匹配
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# 基本信息
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if record.person_name:
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result_parts.append(f"用户名称：{record.person_name}")
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if record.nickname:
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result_parts.append(f"昵称：{record.nickname}")
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if record.person_id:
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result_parts.append(f"用户ID：{record.person_id}")
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if record.platform:
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result_parts.append(f"平台：{record.platform}")
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if record.user_id:
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result_parts.append(f"平台用户ID：{record.user_id}")
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# 群昵称信息
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group_nick_name_str = _format_group_nick_names(getattr(record, "group_nick_name", None))
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if group_nick_name_str:
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result_parts.append(group_nick_name_str)
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# 名称设定原因
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if record.name_reason:
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result_parts.append(f"名称设定原因：{record.name_reason}")
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# 认识状态
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result_parts.append(f"是否已认识：{'是' if record.is_known else '否'}")
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# 时间信息
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if record.know_since:
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know_since_str = datetime.fromtimestamp(record.know_since).strftime("%Y-%m-%d %H:%M:%S")
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result_parts.append(f"首次认识时间：{know_since_str}")
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if record.last_know:
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last_know_str = datetime.fromtimestamp(record.last_know).strftime("%Y-%m-%d %H:%M:%S")
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result_parts.append(f"最后认识时间：{last_know_str}")
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if record.know_times:
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result_parts.append(f"认识次数：{int(record.know_times)}")
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# 记忆点（memory_points）
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if record.memory_points:
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try:
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memory_points_data = (
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json.loads(record.memory_points)
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if isinstance(record.memory_points, str)
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else record.memory_points
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)
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if isinstance(memory_points_data, list) and memory_points_data:
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# 解析记忆点格式：category:content:weight
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memory_list = []
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for memory_point in memory_points_data:
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if memory_point and isinstance(memory_point, str):
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parts = memory_point.split(":", 2)
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if len(parts) >= 3:
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category = parts[0].strip()
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content = parts[1].strip()
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weight = parts[2].strip()
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memory_list.append(f" - [{category}] {content} (权重: {weight})")
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else:
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memory_list.append(f" - {memory_point}")
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if memory_list:
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result_parts.append("记忆点：\n" + "\n".join(memory_list))
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except (json.JSONDecodeError, TypeError, ValueError) as e:
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logger.warning(f"解析用户 {record.person_id} 的memory_points失败: {e}")
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# 如果解析失败，直接显示原始内容（截断）
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memory_preview = str(record.memory_points)[:200]
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if len(str(record.memory_points)) > 200:
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memory_preview += "..."
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result_parts.append(f"记忆点（原始数据）：{memory_preview}")
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results.append("\n".join(result_parts))
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# 组合所有结果
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if not results:
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return f"未找到匹配'{person_name}'的用户信息"
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response_text = "\n\n---\n\n".join(results)
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# 添加统计信息
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total_count = len(records)
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exact_count = len(exact_matches)
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fuzzy_count = len(fuzzy_matches)
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# 显示精确匹配和模糊匹配的统计
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if exact_count > 0 or fuzzy_count > 0:
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stats_parts = []
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if exact_count > 0:
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stats_parts.append(f"精确匹配：{exact_count} 条")
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if fuzzy_count > 0:
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stats_parts.append(f"模糊匹配：{fuzzy_count} 条")
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stats_text = "，".join(stats_parts)
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response_text = f"找到 {total_count} 条匹配的用户信息（{stats_text}）：\n\n{response_text}"
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elif total_count > 1:
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response_text = f"找到 {total_count} 条匹配的用户信息：\n\n{response_text}"
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else:
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response_text = f"找到用户信息：\n\n{response_text}"
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# 如果结果数量达到限制，添加提示
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if total_count >= 20:
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response_text += "\n\n(已显示前20条结果，可能还有更多匹配记录)"
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return response_text
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except Exception as e:
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logger.error(f"查询用户信息失败: {e}")
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return f"查询失败: {str(e)}"
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def register_tool():
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"""注册工具"""
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register_memory_retrieval_tool(
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name="query_person_info",
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description="根据查询某个用户的所有信息。名称、昵称、平台、用户ID、qq号、群昵称等",
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parameters=[
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{"name": "person_name", "type": "string", "description": "用户名称，用于查询用户信息", "required": True}
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],
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execute_func=query_person_info,
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)
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