mirror of https://github.com/Mai-with-u/MaiBot.git
fix：优化记忆提取和聊天压缩
SengokuCola
parent
10cd2474af
commit
71a2a4282b
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@ -269,7 +269,16 @@ class ChatHistorySummarizer:
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)
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# 使用LLM压缩聊天内容
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theme, keywords, summary = await self._compress_with_llm(original_text)
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success, theme, keywords, summary = await self._compress_with_llm(original_text)
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if not success:
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logger.warning(
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f"{self.log_prefix} LLM压缩失败，不存储到数据库 | 消息数: {len(messages)}"
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)
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# 清空当前批次，避免重复处理
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self.current_batch = None
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return
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logger.info(
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f"{self.log_prefix} LLM压缩完成 | 主题: {theme} | 关键词数: {len(keywords)} | 概括长度: {len(summary)} 字"
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)
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@ -297,12 +306,12 @@ class ChatHistorySummarizer:
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# 出错时也清空批次，避免重复处理
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self.current_batch = None
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async def _compress_with_llm(self, original_text: str) -> tuple[str, List[str], str]:
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async def _compress_with_llm(self, original_text: str) -> tuple[bool, str, List[str], str]:
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"""
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使用LLM压缩聊天内容
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Returns:
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tuple[str, List[str], str]: (主题, 关键词列表, 概括)
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tuple[bool, str, List[str], str]: (是否成功, 主题, 关键词列表, 概括)
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"""
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prompt = f"""请对以下聊天记录进行概括，提取以下信息：
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@ -353,13 +362,13 @@ class ChatHistorySummarizer:
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if isinstance(keywords, str):
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keywords = [keywords]
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return theme, keywords, summary
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return True, theme, keywords, summary
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except Exception as e:
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logger.error(f"{self.log_prefix} LLM压缩聊天内容时出错: {e}")
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logger.error(f"{self.log_prefix} LLM响应: {response if 'response' in locals() else 'N/A'}")
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# 返回默认值
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return "未命名对话", [], "压缩失败，无法生成概括"
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# 返回失败标志和默认值
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return False, "未命名对话", [], "压缩失败，无法生成概括"
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async def _store_to_database(
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self,
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@ -221,13 +221,16 @@ def split_into_sentences_w_remove_punctuation(text: str) -> list[str]:
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while i < len(text):
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char = text[i]
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if char in separators:
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# 检查分割条件：如果空格左右都是英文字母，则不分割（仅对空格应用此规则）
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# 检查分割条件：如果空格左右都是英文字母、数字，或数字和英文之间，则不分割（仅对空格应用此规则）
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can_split = True
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if 0 < i < len(text) - 1:
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prev_char = text[i - 1]
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next_char = text[i + 1]
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# 只对空格应用"不分割两个英文之间的空格"规则
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if char == ' ' and is_english_letter(prev_char) and is_english_letter(next_char):
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# 只对空格应用"不分割数字和数字、数字和英文、英文和数字、英文和英文之间的空格"规则
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if char == ' ':
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prev_is_alnum = prev_char.isdigit() or is_english_letter(prev_char)
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next_is_alnum = next_char.isdigit() or is_english_letter(next_char)
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if prev_is_alnum and next_is_alnum:
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can_split = False
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if can_split:
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@ -2,6 +2,7 @@ import time
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import json
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import re
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import random
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import asyncio
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from typing import List, Dict, Any, Optional, Tuple
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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@ -430,6 +431,100 @@ def _store_thinking_back(
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logger.error(f"存储思考过程失败: {e}")
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def _get_max_iterations_by_question_count(question_count: int) -> int:
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"""根据问题数量获取最大迭代次数
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Args:
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question_count: 问题数量
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Returns:
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int: 最大迭代次数
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"""
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if question_count == 1:
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return 5
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elif question_count == 2:
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return 3
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else: # 3个或以上
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return 1
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async def _process_single_question(
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question: str,
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chat_id: str,
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context: str,
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max_iterations: int
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) -> Optional[str]:
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"""处理单个问题的查询（包含缓存检查逻辑）
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Args:
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question: 要查询的问题
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chat_id: 聊天ID
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context: 上下文信息
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max_iterations: 最大迭代次数
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Returns:
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Optional[str]: 如果找到答案，返回格式化的结果字符串，否则返回None
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"""
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logger.info(f"开始处理问题: {question}")
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# 先检查thinking_back数据库中是否有现成答案
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cached_result = _query_thinking_back(chat_id, question)
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should_requery = False
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if cached_result:
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cached_found_answer, cached_answer = cached_result
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# 根据found_answer的值决定是否重新查询
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if cached_found_answer: # found_answer == 1 (True)
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# found_answer == 1：20%概率重新查询
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if random.random() < 0.2:
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should_requery = True
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logger.info(f"found_answer=1，触发20%概率重新查询，问题: {question[:50]}...")
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else: # found_answer == 0 (False)
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# found_answer == 0：40%概率重新查询
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if random.random() < 0.4:
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should_requery = True
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logger.info(f"found_answer=0，触发40%概率重新查询，问题: {question[:50]}...")
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# 如果不需要重新查询，使用缓存答案
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if not should_requery:
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if cached_answer:
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logger.info(f"从thinking_back缓存中获取答案，问题: {question[:50]}...")
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return f"问题：{question}\n答案：{cached_answer}"
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else:
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# 缓存中没有答案，需要查询
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should_requery = True
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# 如果没有缓存答案或需要重新查询，使用ReAct Agent查询
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if not cached_result or should_requery:
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if should_requery:
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logger.info(f"概率触发重新查询，使用ReAct Agent查询，问题: {question[:50]}...")
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else:
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logger.info(f"未找到缓存答案，使用ReAct Agent查询，问题: {question[:50]}...")
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found_answer, answer, thinking_steps = await _react_agent_solve_question(
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question=question,
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chat_id=chat_id,
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max_iterations=max_iterations,
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timeout=30.0
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)
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# 存储到数据库
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_store_thinking_back(
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chat_id=chat_id,
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question=question,
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context=context,
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found_answer=found_answer,
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answer=answer,
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thinking_steps=thinking_steps
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)
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if found_answer and answer:
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return f"问题：{question}\n答案：{answer}"
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return None
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async def build_memory_retrieval_prompt(
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message: str,
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sender: str,
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@ -498,68 +593,31 @@ async def build_memory_retrieval_prompt(
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logger.info(f"解析到 {len(questions)} 个问题: {questions}")
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# 第二步：对每个问题查询答案
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# 第二步：根据问题数量确定最大迭代次数
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max_iterations = _get_max_iterations_by_question_count(len(questions))
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logger.info(f"问题数量: {len(questions)}，设置最大迭代次数: {max_iterations}")
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# 并行处理所有问题
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question_tasks = [
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_process_single_question(
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question=question,
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chat_id=chat_id,
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context=message,
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max_iterations=max_iterations
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)
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for question in questions
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]
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# 并行执行所有查询任务
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results = await asyncio.gather(*question_tasks, return_exceptions=True)
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# 收集所有有效结果
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all_results = []
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for question in questions:
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logger.info(f"开始处理问题: {question}")
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# 先检查thinking_back数据库中是否有现成答案
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cached_result = _query_thinking_back(chat_id, question)
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should_requery = False
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if cached_result:
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cached_found_answer, cached_answer = cached_result
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# 根据found_answer的值决定是否重新查询
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if cached_found_answer: # found_answer == 1 (True)
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# found_answer == 1：20%概率重新查询
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if random.random() < 0.2:
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should_requery = True
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logger.info(f"found_answer=1，触发20%概率重新查询，问题: {question[:50]}...")
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else:
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# 使用缓存答案
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if cached_answer:
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logger.info(f"从thinking_back缓存中获取答案（found_answer=1），问题: {question[:50]}...")
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all_results.append(f"问题：{question}\n答案：{cached_answer}")
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continue # 跳过ReAct Agent查询
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else: # found_answer == 0 (False)
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# found_answer == 0：40%概率重新查询
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if random.random() < 0.4:
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should_requery = True
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logger.info(f"found_answer=0，触发40%概率重新查询，问题: {question[:50]}...")
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else:
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# 使用缓存答案（即使found_answer=0，也可能有部分答案）
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if cached_answer:
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logger.info(f"从thinking_back缓存中获取答案（found_answer=0），问题: {question[:50]}...")
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all_results.append(f"问题：{question}\n答案：{cached_answer}")
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continue # 跳过ReAct Agent查询
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# 如果没有缓存答案或需要重新查询，使用ReAct Agent查询
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if not cached_result or should_requery:
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if should_requery:
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logger.info(f"概率触发重新查询，使用ReAct Agent查询，问题: {question[:50]}...")
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else:
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logger.info(f"未找到缓存答案，使用ReAct Agent查询，问题: {question[:50]}...")
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found_answer, answer, thinking_steps = await _react_agent_solve_question(
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question=question,
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chat_id=chat_id,
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max_iterations=5,
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timeout=30.0
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)
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# 存储到数据库
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_store_thinking_back(
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chat_id=chat_id,
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question=question,
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context=message, # 只存储前500字符作为上下文
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found_answer=found_answer,
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answer=answer,
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thinking_steps=thinking_steps
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)
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if found_answer and answer:
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all_results.append(f"问题：{question}\n答案：{answer}")
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for i, result in enumerate(results):
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if isinstance(result, Exception):
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logger.error(f"处理问题 '{questions[i]}' 时发生异常: {result}")
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elif result is not None:
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all_results.append(result)
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end_time = time.time()
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@ -9,6 +9,7 @@ from src.common.logger import get_logger
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from src.config.config import model_config
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from src.common.database.database_model import ChatHistory
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from src.llm_models.utils_model import LLMRequest
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from src.chat.utils.utils import parse_keywords_string
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from .tool_registry import register_memory_retrieval_tool
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from .tool_utils import parse_datetime_to_timestamp, parse_time_range
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@ -18,51 +19,46 @@ logger = get_logger("memory_retrieval_tools")
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async def query_chat_history(
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chat_id: str,
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keyword: Optional[str] = None,
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time_point: Optional[str] = None,
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time_range: Optional[str] = None
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) -> str:
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"""根据时间或关键词在chat_history表中查询聊天记录概述
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Args:
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chat_id: 聊天ID
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keyword: 关键词（可选）
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time_point: 时间点，格式：YYYY-MM-DD HH:MM:SS（可选）
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time_range: 时间范围，格式："YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS"（可选）
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keyword: 关键词（可选，支持多个关键词，可用空格、逗号等分隔）
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time_range: 时间范围或时间点，格式：
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- 时间范围："YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS"
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- 时间点："YYYY-MM-DD HH:MM:SS"（查询包含该时间点的记录）
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Returns:
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str: 查询结果
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"""
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try:
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# 检查参数
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if not keyword and not time_point and not time_range:
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return "未指定查询参数（需要提供keyword、time_point或time_range之一）"
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if not keyword and not time_range:
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return "未指定查询参数（需要提供keyword或time_range之一）"
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# 构建查询条件
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query = ChatHistory.select().where(ChatHistory.chat_id == chat_id)
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# 时间过滤条件
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time_conditions = []
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if time_point:
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# 时间点：查询包含该时间点的记录（start_time <= time_point <= end_time）
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target_timestamp = parse_datetime_to_timestamp(time_point)
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time_conditions.append(
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(ChatHistory.start_time <= target_timestamp) &
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(ChatHistory.end_time >= target_timestamp)
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)
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elif time_range:
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if time_range:
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# 判断是时间点还是时间范围
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if " - " in time_range:
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# 时间范围：查询与时间范围有交集的记录
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start_timestamp, end_timestamp = parse_time_range(time_range)
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# 交集条件：start_time < end_timestamp AND end_time > start_timestamp
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time_conditions.append(
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time_filter = (
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(ChatHistory.start_time < end_timestamp) &
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(ChatHistory.end_time > start_timestamp)
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)
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if time_conditions:
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# 合并所有时间条件（OR关系）
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time_filter = time_conditions[0]
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for condition in time_conditions[1:]:
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time_filter = time_filter | condition
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else:
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# 时间点：查询包含该时间点的记录（start_time <= time_point <= end_time）
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target_timestamp = parse_datetime_to_timestamp(time_range)
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time_filter = (
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(ChatHistory.start_time <= target_timestamp) &
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(ChatHistory.end_time >= target_timestamp)
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)
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query = query.where(time_filter)
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# 执行查询
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@ -73,7 +69,17 @@ async def query_chat_history(
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# 如果有关键词，进一步过滤
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if keyword:
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keyword_lower = keyword.lower()
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# 解析多个关键词（支持空格、逗号等分隔符）
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keywords_list = parse_keywords_string(keyword)
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if not keywords_list:
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keywords_list = [keyword.strip()] if keyword.strip() else []
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# 转换为小写以便匹配
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keywords_lower = [kw.lower() for kw in keywords_list if kw.strip()]
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if not keywords_lower:
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return "关键词为空"
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filtered_records = []
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for record in records:
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@ -82,25 +88,32 @@ async def query_chat_history(
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summary = (record.summary or "").lower()
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original_text = (record.original_text or "").lower()
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# 解析keywords JSON
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keywords_list = []
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# 解析record中的keywords JSON
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record_keywords_list = []
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if record.keywords:
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try:
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keywords_data = json.loads(record.keywords) if isinstance(record.keywords, str) else record.keywords
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if isinstance(keywords_data, list):
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keywords_list = [str(k).lower() for k in keywords_data]
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record_keywords_list = [str(k).lower() for k in keywords_data]
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except (json.JSONDecodeError, TypeError, ValueError):
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pass
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# 检查是否包含关键词
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if (keyword_lower in theme or
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keyword_lower in summary or
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keyword_lower in original_text or
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any(keyword_lower in k for k in keywords_list)):
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# 检查是否包含任意一个关键词（OR关系）
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matched = False
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for kw in keywords_lower:
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if (kw in theme or
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kw in summary or
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kw in original_text or
|
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any(kw in k for k in record_keywords_list)):
|
||||
matched = True
|
||||
break
|
||||
|
||||
if matched:
|
||||
filtered_records.append(record)
|
||||
|
||||
if not filtered_records:
|
||||
return f"未找到包含关键词'{keyword}'的聊天记录概述"
|
||||
keywords_str = "、".join(keywords_list)
|
||||
return f"未找到包含关键词'{keywords_str}'的聊天记录概述"
|
||||
|
||||
records = filtered_records
|
||||
|
||||
|
|
@ -146,11 +159,18 @@ async def query_chat_history(
|
|||
|
||||
query_desc = []
|
||||
if keyword:
|
||||
# 解析关键词列表用于显示
|
||||
keywords_list = parse_keywords_string(keyword)
|
||||
if keywords_list:
|
||||
keywords_str = "、".join(keywords_list)
|
||||
query_desc.append(f"关键词：{keywords_str}")
|
||||
else:
|
||||
query_desc.append(f"关键词：{keyword}")
|
||||
if time_point:
|
||||
query_desc.append(f"时间点：{time_point}")
|
||||
if time_range:
|
||||
if " - " in time_range:
|
||||
query_desc.append(f"时间范围：{time_range}")
|
||||
else:
|
||||
query_desc.append(f"时间点：{time_range}")
|
||||
|
||||
query_info = "，".join(query_desc) if query_desc else "聊天记录概述"
|
||||
|
||||
|
|
@ -201,19 +221,13 @@ def register_tool():
|
|||
{
|
||||
"name": "keyword",
|
||||
"type": "string",
|
||||
"description": "关键词（可选，用于在主题、关键词、概括、原文中搜索）",
|
||||
"required": False
|
||||
},
|
||||
{
|
||||
"name": "time_point",
|
||||
"type": "string",
|
||||
"description": "时间点，格式：YYYY-MM-DD HH:MM:SS（可选，与time_range二选一）。用于查询包含该时间点的聊天记录概述",
|
||||
"description": "关键词（可选，支持多个关键词，可用空格、逗号、斜杠等分隔，如：'麦麦 百度网盘' 或 '麦麦,百度网盘'。用于在主题、关键词、概括、原文中搜索，只要包含任意一个关键词即匹配）",
|
||||
"required": False
|
||||
},
|
||||
{
|
||||
"name": "time_range",
|
||||
"type": "string",
|
||||
"description": "时间范围，格式：'YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS'（可选，与time_point二选一）。用于查询与时间范围有交集的聊天记录概述",
|
||||
"description": "时间范围或时间点（可选）。格式：'YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS'（时间范围，查询与时间范围有交集的记录）或 'YYYY-MM-DD HH:MM:SS'（时间点，查询包含该时间点的记录）",
|
||||
"required": False
|
||||
}
|
||||
],
|
||||
|
|
|
|||
|
|
@ -12,8 +12,7 @@ logger = get_logger("memory_retrieval_tools")
|
|||
async def query_jargon(
|
||||
keyword: str,
|
||||
chat_id: str,
|
||||
fuzzy: bool = False,
|
||||
search_all: bool = False
|
||||
fuzzy: bool = False
|
||||
) -> str:
|
||||
"""根据关键词在jargon库中查询
|
||||
|
||||
|
|
@ -21,7 +20,6 @@ async def query_jargon(
|
|||
keyword: 关键词（黑话/俚语/缩写）
|
||||
chat_id: 聊天ID
|
||||
fuzzy: 是否使用模糊搜索，默认False（精确匹配）
|
||||
search_all: 是否搜索全库（不限chat_id），默认False（仅搜索当前会话或global）
|
||||
|
||||
Returns:
|
||||
str: 查询结果
|
||||
|
|
@ -31,11 +29,10 @@ async def query_jargon(
|
|||
if not content:
|
||||
return "关键词为空"
|
||||
|
||||
# 根据参数执行搜索
|
||||
search_chat_id = None if search_all else chat_id
|
||||
# 执行搜索（仅搜索当前会话或全局）
|
||||
results = search_jargon(
|
||||
keyword=content,
|
||||
chat_id=search_chat_id,
|
||||
chat_id=chat_id,
|
||||
limit=1,
|
||||
case_sensitive=False,
|
||||
fuzzy=fuzzy
|
||||
|
|
@ -46,15 +43,13 @@ async def query_jargon(
|
|||
translation = result.get("translation", "").strip()
|
||||
meaning = result.get("meaning", "").strip()
|
||||
search_type = "模糊搜索" if fuzzy else "精确匹配"
|
||||
search_scope = "全库" if search_all else "当前会话或全局"
|
||||
output = f"“{content}可能为黑话或者网络简写，翻译为：{translation}，含义为：{meaning}”"
|
||||
logger.info(f"在jargon库中找到匹配（{search_scope}，{search_type}）: {content}")
|
||||
output = f'"{content}可能为黑话或者网络简写，翻译为：{translation}，含义为：{meaning}"'
|
||||
logger.info(f"在jargon库中找到匹配（当前会话或全局，{search_type}）: {content}")
|
||||
return output
|
||||
|
||||
# 未命中
|
||||
search_type = "模糊搜索" if fuzzy else "精确匹配"
|
||||
search_scope = "全库" if search_all else "当前会话或全局"
|
||||
logger.info(f"在jargon库中未找到匹配（{search_scope}，{search_type}）: {content}")
|
||||
logger.info(f"在jargon库中未找到匹配（当前会话或全局，{search_type}）: {content}")
|
||||
return f"未在jargon库中找到'{content}'的解释"
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -66,7 +61,7 @@ def register_tool():
|
|||
"""注册工具"""
|
||||
register_memory_retrieval_tool(
|
||||
name="query_jargon",
|
||||
description="根据关键词在jargon库中查询黑话/俚语/缩写的含义。支持大小写不敏感搜索和模糊搜索。默认优先搜索当前会话或全局jargon，可以设置为搜索全库。",
|
||||
description="根据关键词在jargon库中查询黑话/俚语/缩写的含义。支持大小写不敏感搜索和模糊搜索。仅搜索当前会话或全局jargon。",
|
||||
parameters=[
|
||||
{
|
||||
"name": "keyword",
|
||||
|
|
@ -79,12 +74,6 @@ def register_tool():
|
|||
"type": "boolean",
|
||||
"description": "是否使用模糊搜索（部分匹配），默认False（精确匹配）。当精确匹配找不到时，可以尝试使用模糊搜索。",
|
||||
"required": False
|
||||
},
|
||||
{
|
||||
"name": "search_all",
|
||||
"type": "boolean",
|
||||
"description": "是否搜索全库（不限chat_id），默认False（仅搜索当前会话或global的jargon）。当在当前会话中找不到时，可以尝试搜索全库。",
|
||||
"required": False
|
||||
}
|
||||
],
|
||||
execute_func=query_jargon
|
||||
|
|
|
|||
Loading…
Reference in New Issue