fix：修复私聊记忆
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SengokuCola
@@ -1,34 +0,0 @@
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{
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"manifest_version": 1,
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"name": "Memory Build组件",
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"version": "1.0.0",
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"description": "可以构建和管理记忆",
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"author": {
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"name": "Mai",
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"url": "https://github.com/MaiM-with-u"
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},
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"license": "GPL-v3.0-or-later",
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"host_application": {
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"min_version": "0.10.4"
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},
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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"keywords": ["memory", "build", "built-in"],
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"categories": ["Memory"],
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"default_locale": "zh-CN",
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"locales_path": "_locales",
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"plugin_info": {
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"is_built_in": true,
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"plugin_type": "action_provider",
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"components": [
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{
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"type": "build_memory",
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"name": "build_memory",
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"description": "构建记忆"
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}
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]
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}
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}
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@@ -1,245 +0,0 @@
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import asyncio
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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.llm_models.payload_content.tool_option import ToolParamType
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from src.memory_system.Memory_chest import global_memory_chest
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from src.plugin_system.base.base_tool import BaseTool
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from src.plugin_system.apis.message_api import get_messages_by_time_in_chat, build_readable_messages
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config
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from typing import Any
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logger = get_logger("memory")
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def parse_datetime_to_timestamp(value: str) -> float:
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"""
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接受多种常见格式并转换为时间戳（秒）
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支持示例：
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- 2025-09-29
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- 2025-09-29 00:00:00
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- 2025/09/29 00:00
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- 2025-09-29T00:00:00
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"""
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value = value.strip()
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fmts = [
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"%Y-%m-%d %H:%M:%S",
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"%Y-%m-%d %H:%M",
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"%Y/%m/%d %H:%M:%S",
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"%Y/%m/%d %H:%M",
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"%Y-%m-%d",
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"%Y/%m/%d",
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"%Y-%m-%dT%H:%M:%S",
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"%Y-%m-%dT%H:%M",
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]
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last_err = None
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for fmt in fmts:
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try:
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dt = datetime.strptime(value, fmt)
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return dt.timestamp()
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except Exception as e:
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last_err = e
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raise ValueError(f"无法解析时间: {value} ({last_err})")
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def parse_time_range(time_range: str) -> tuple[float, float]:
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"""
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解析时间范围字符串，返回开始和结束时间戳
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格式: "YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS"
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"""
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if " - " not in time_range:
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raise ValueError("时间范围格式错误，应使用 ' - ' 分隔开始和结束时间")
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start_str, end_str = time_range.split(" - ", 1)
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start_timestamp = parse_datetime_to_timestamp(start_str.strip())
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end_timestamp = parse_datetime_to_timestamp(end_str.strip())
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if start_timestamp > end_timestamp:
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raise ValueError("开始时间不能晚于结束时间")
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return start_timestamp, end_timestamp
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class GetMemoryTool(BaseTool):
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"""获取用户信息"""
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name = "get_memory"
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description = "在记忆中搜索，获取某个问题的答案，可以指定搜索的时间范围或时间点"
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parameters = [
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("question", ToolParamType.STRING, "需要获取答案的问题", True, None),
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("time_point", ToolParamType.STRING, "需要获取记忆的时间点，格式为YYYY-MM-DD HH:MM:SS", False, None),
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("time_range", ToolParamType.STRING, "需要获取记忆的时间范围，格式为YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS", False, None)
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行记忆搜索
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Args:
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function_args: 工具参数
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Returns:
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dict: 工具执行结果
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"""
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question: str = function_args.get("question") # type: ignore
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time_point: str = function_args.get("time_point") # type: ignore
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time_range: str = function_args.get("time_range") # type: ignore
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# 检查是否指定了时间参数
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has_time_params = bool(time_point or time_range)
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if has_time_params and not self.chat_id:
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return {"content": f"问题：{question}，无法获取聊天记录：缺少chat_id"}
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# 创建并行任务
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tasks = []
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# 原任务：从记忆仓库获取答案
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memory_task = asyncio.create_task(
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global_memory_chest.get_answer_by_question(question=question)
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)
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tasks.append(("memory", memory_task))
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# 新任务：从聊天记录获取答案（如果指定了时间参数）
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chat_task = None
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if has_time_params:
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chat_task = asyncio.create_task(
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self._get_answer_from_chat_history(question, time_point, time_range)
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)
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tasks.append(("chat", chat_task))
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# 等待所有任务完成
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results = {}
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for task_name, task in tasks:
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try:
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results[task_name] = await task
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except Exception as e:
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logger.error(f"任务 {task_name} 执行失败: {e}")
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results[task_name] = None
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# 处理结果
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memory_answer = results.get("memory")
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chat_answer = results.get("chat")
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# 构建返回内容
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content_parts = []
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if memory_answer:
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content_parts.append(f"对问题'{question}'，你回忆的信息是：{memory_answer}")
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if chat_answer:
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content_parts.append(f"对问题'{question}'，基于聊天记录的回答：{chat_answer}")
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elif has_time_params:
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if time_point:
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content_parts.append(f"在 {time_point} 的时间点，你没有参与聊天")
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elif time_range:
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content_parts.append(f"在 {time_range} 的时间范围内，你没有参与聊天")
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if content_parts:
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retrieval_content = f"问题：{question}" + "\n".join(content_parts)
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return {"content": retrieval_content}
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else:
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return {"content": ""}
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async def _get_answer_from_chat_history(self, question: str, time_point: str = None, time_range: str = None) -> str:
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"""从聊天记录中获取问题的答案"""
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try:
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# 确定时间范围
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print(f"time_point: {time_point}, time_range: {time_range}")
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# 检查time_range的两个时间值是否相同，如果相同则按照time_point处理
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if time_range and not time_point:
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try:
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start_timestamp, end_timestamp = parse_time_range(time_range)
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if start_timestamp == end_timestamp:
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# 两个时间值相同，按照time_point处理
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time_point = time_range.split(" - ")[0].strip()
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time_range = None
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print(f"time_range两个值相同，按照time_point处理: {time_point}")
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except Exception as e:
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logger.warning(f"解析time_range失败: {e}")
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if time_point:
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# 时间点：搜索前后25条记录
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target_timestamp = parse_datetime_to_timestamp(time_point)
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# 获取前后各25条记录，总共50条
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messages_before = get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=0,
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end_time=target_timestamp,
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limit=25,
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limit_mode="latest"
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)
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messages_after = get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=target_timestamp,
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end_time=float('inf'),
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limit=25,
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limit_mode="earliest"
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)
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messages = messages_before + messages_after
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elif time_range:
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# 时间范围：搜索范围内最多50条记录
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start_timestamp, end_timestamp = parse_time_range(time_range)
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messages = get_messages_by_time_in_chat(
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chat_id=self.chat_id,
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start_time=start_timestamp,
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end_time=end_timestamp,
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limit=50,
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limit_mode="latest"
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)
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else:
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return "未指定时间参数"
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if not messages:
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return "没有找到相关聊天记录"
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# 将消息转换为可读格式
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chat_content = build_readable_messages(messages, timestamp_mode="relative")
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if not chat_content.strip():
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return "聊天记录为空"
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# 使用LLM分析聊天内容并回答问题
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try:
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llm_request = LLMRequest(
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model_set=model_config.model_task_config.utils_small,
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request_type="chat_history_analysis"
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)
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analysis_prompt = f"""请根据以下聊天记录内容，回答用户的问题。请输出一段平文本，不要有特殊格式。
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聊天记录：
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{chat_content}
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用户问题：{question}
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请仔细分析聊天记录，提取与问题相关的信息，并给出准确的答案。如果聊天记录中没有相关信息，无法回答问题，输出"无有效信息"即可，不要输出其他内容。
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答案："""
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print(f"analysis_prompt: {analysis_prompt}")
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response, (reasoning, model_name, tool_calls) = await llm_request.generate_response_async(
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prompt=analysis_prompt,
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temperature=0.3,
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max_tokens=256
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)
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print(f"response: {response}")
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if "无有效信息" in response:
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return ""
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return response
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except Exception as llm_error:
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logger.error(f"LLM分析聊天记录失败: {llm_error}")
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# 如果LLM分析失败，返回聊天内容的摘要
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if len(chat_content) > 300:
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chat_content = chat_content[:300] + "..."
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return chat_content
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except Exception as e:
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logger.error(f"从聊天记录获取答案失败: {e}")
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return ""
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@@ -1,53 +0,0 @@
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from typing import List, Tuple, Type
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# 导入新插件系统
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from src.plugin_system import BasePlugin, ComponentInfo, register_plugin
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from src.plugin_system.base.config_types import ConfigField
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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from src.plugins.built_in.memory.build_memory import GetMemoryTool
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logger = get_logger("memory_build")
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@register_plugin
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class MemoryBuildPlugin(BasePlugin):
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"""记忆构建插件
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系统内置插件，提供基础的聊天交互功能：
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- GetMemory: 获取记忆
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注意：插件基本信息优先从_manifest.json文件中读取
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"""
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# 插件基本信息
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plugin_name: str = "memory_build" # 内部标识符
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enable_plugin: bool = True
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dependencies: list[str] = [] # 插件依赖列表
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python_dependencies: list[str] = [] # Python包依赖列表
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config_file_name: str = "config.toml"
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# 配置节描述
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config_section_descriptions = {
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"plugin": "插件启用配置",
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"components": "核心组件启用配置",
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}
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# 配置Schema定义
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config_schema: dict = {
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"plugin": {
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"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
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"config_version": ConfigField(type=str, default="1.1.1", description="配置文件版本"),
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},
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}
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def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
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"""返回插件包含的组件列表"""
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# --- 根据配置注册组件 ---
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components = []
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components.append((GetMemoryTool.get_tool_info(), GetMemoryTool))
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return components
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