mirror of https://github.com/Mai-with-u/MaiBot.git
better：优化记忆检索占用
SengokuCola
parent
85864c7013
commit
26784b00a5
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@ -390,6 +390,7 @@ class ChatHistorySummarizer:
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"theme": theme,
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"keywords": json.dumps(keywords, ensure_ascii=False),
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"summary": summary,
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"count": 0,
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}
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# 使用db_save存储（使用start_time和chat_id作为唯一标识）
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@ -377,6 +377,7 @@ class ChatHistory(BaseModel):
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theme = TextField() # 主题：这段对话的主要内容，一个简短的标题
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keywords = TextField() # 关键词：这段对话的关键词，JSON格式存储
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summary = TextField() # 概括：对这段话的平文本概括
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count = IntegerField(default=0) # 被检索次数
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class Meta:
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table_name = "chat_history"
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@ -280,12 +280,12 @@ async def _react_agent_solve_question(
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return False, "未找到相关信息", thinking_steps
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def _get_recent_query_history(chat_id: str, time_window_seconds: float = 3600.0) -> str:
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def _get_recent_query_history(chat_id: str, time_window_seconds: float = 300.0) -> str:
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"""获取最近一段时间内的查询历史
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Args:
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chat_id: 聊天ID
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time_window_seconds: 时间窗口（秒），默认1小时
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time_window_seconds: 时间窗口（秒），默认10分钟
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Returns:
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str: 格式化的查询历史字符串
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@ -302,7 +302,7 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 3600.0)
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(ThinkingBack.update_time >= start_time)
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)
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.order_by(ThinkingBack.update_time.desc())
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.limit(8) # 最多返回10条最近的记录
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.limit(5) # 最多返回5条最近的记录
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)
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if not records.exists():
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@ -314,7 +314,8 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 3600.0)
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for record in records:
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status = "✓ 已找到答案" if record.found_answer else "✗ 未找到答案"
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answer_preview = ""
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if record.answer:
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# 只有找到答案时才显示答案内容
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if record.found_answer and record.answer:
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# 截取答案前100字符
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answer_preview = record.answer[:100]
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if len(record.answer) > 100:
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@ -554,7 +555,7 @@ async def build_memory_retrieval_prompt(
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chat_id = chat_stream.stream_id
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# 获取最近查询历史（最近1小时内的查询）
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recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=600.0)
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recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=300.0)
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if not recent_query_history:
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recent_query_history = "最近没有查询记录。"
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@ -6,9 +6,7 @@
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import json
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from typing import Optional
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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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@ -116,10 +114,19 @@ async def query_chat_history(
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return f"未找到包含关键词'{keywords_str}'的聊天记录概述"
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records = filtered_records
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# 对即将返回的记录增加使用计数
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records_to_use = records[:3]
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for record in records_to_use:
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try:
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ChatHistory.update(count=ChatHistory.count + 1).where(ChatHistory.id == record.id).execute()
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record.count = (record.count or 0) + 1
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except Exception as update_error:
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logger.error(f"更新聊天记录概述计数失败: {update_error}")
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# 构建结果文本
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results = []
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for record in records[:10]: # 最多返回10条记录
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for record in records_to_use: # 最多返回3条记录
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result_parts = []
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# 添加主题
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@ -146,66 +153,11 @@ async def query_chat_history(
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if not results:
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return "未找到相关聊天记录概述"
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# 如果只有一条记录，直接返回
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if len(results) == 1:
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return results[0]
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# 多条记录，使用LLM总结
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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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query_desc = []
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if keyword:
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# 解析关键词列表用于显示
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keywords_list = parse_keywords_string(keyword)
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if keywords_list:
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keywords_str = "、".join(keywords_list)
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query_desc.append(f"关键词：{keywords_str}")
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else:
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query_desc.append(f"关键词：{keyword}")
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if time_range:
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if " - " in time_range:
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query_desc.append(f"时间范围：{time_range}")
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else:
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query_desc.append(f"时间点：{time_range}")
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query_info = "，".join(query_desc) if query_desc else "聊天记录概述"
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combined_results = "\n\n---\n\n".join(results)
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analysis_prompt = f"""请根据以下聊天记录概述，总结与查询条件相关的信息。请输出一段平文本，不要有特殊格式。
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查询条件：{query_info}
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聊天记录概述：
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{combined_results}
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请仔细分析聊天记录概述，提取与查询条件相关的信息并给出总结。如果概述中没有相关信息，输出"无有效信息"即可，不要输出其他内容。
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总结："""
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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=512
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)
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logger.info(f"查询聊天历史概述提示词: {analysis_prompt}")
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logger.info(f"查询聊天历史概述响应: {response}")
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logger.info(f"查询聊天历史概述推理: {reasoning}")
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logger.info(f"查询聊天历史概述模型: {model_name}")
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if "无有效信息" in response:
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return "无有效信息"
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return response
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except Exception as llm_error:
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logger.error(f"LLM分析聊天记录概述失败: {llm_error}")
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# 如果LLM分析失败，返回前3条记录的摘要
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return "\n\n---\n\n".join(results[:3])
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response_text = "\n\n---\n\n".join(results)
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if len(records) > len(records_to_use):
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omitted_count = len(records) - len(records_to_use)
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response_text += f"\n\n(还有{omitted_count}条历史记录已省略)"
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return response_text
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except Exception as e:
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logger.error(f"查询聊天历史概述失败: {e}")
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@ -1,391 +0,0 @@
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"""
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StyleLearner 数据库测试脚本
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使用数据库中的expression数据测试style_learner功能
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"""
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import os
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import sys
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from typing import List, Dict, Tuple
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import precision_recall_fscore_support
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# 添加项目根目录到Python路径
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from src.common.database.database_model import Expression, db
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from src.express.style_learner import StyleLearnerManager
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from src.common.logger import get_logger
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logger = get_logger("style_learner_test")
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class StyleLearnerDatabaseTest:
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"""使用数据库数据测试StyleLearner"""
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def __init__(self, random_state: int = 42):
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self.random_state = random_state
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self.manager = StyleLearnerManager(model_save_path="data/test_style_models")
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# 测试结果
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self.test_results = {
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"total_samples": 0,
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"train_samples": 0,
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"test_samples": 0,
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"unique_styles": 0,
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"unique_chat_ids": 0,
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"accuracy": 0.0,
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"precision": 0.0,
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"recall": 0.0,
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"f1_score": 0.0,
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"predictions": [],
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"ground_truth": [],
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"model_save_success": False,
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"model_save_path": self.manager.model_save_path
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}
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def load_data_from_database(self) -> List[Dict]:
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"""
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从数据库加载expression数据
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Returns:
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List[Dict]: 包含up_content, style, chat_id的数据列表
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"""
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try:
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# 连接数据库
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db.connect(reuse_if_open=True)
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# 查询所有expression数据
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expressions = Expression.select().where(
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(Expression.up_content.is_null(False)) &
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(Expression.style.is_null(False)) &
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(Expression.chat_id.is_null(False)) &
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(Expression.type == "style")
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)
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data = []
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for expr in expressions:
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if expr.up_content and expr.style and expr.chat_id:
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data.append({
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"up_content": expr.up_content,
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"style": expr.style,
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"chat_id": expr.chat_id,
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"last_active_time": expr.last_active_time,
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"context": expr.context,
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"situation": expr.situation
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})
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logger.info(f"从数据库加载了 {len(data)} 条expression数据")
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return data
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except Exception as e:
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logger.error(f"从数据库加载数据失败: {e}")
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return []
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def preprocess_data(self, data: List[Dict]) -> List[Dict]:
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"""
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数据预处理
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Args:
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data: 原始数据
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Returns:
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List[Dict]: 预处理后的数据
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"""
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# 过滤掉空值或过短的数据
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filtered_data = []
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for item in data:
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up_content = item["up_content"].strip()
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style = item["style"].strip()
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if len(up_content) >= 2 and len(style) >= 2:
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filtered_data.append({
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"up_content": up_content,
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"style": style,
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"chat_id": item["chat_id"],
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"last_active_time": item["last_active_time"],
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"context": item["context"],
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"situation": item["situation"]
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})
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logger.info(f"预处理后剩余 {len(filtered_data)} 条数据")
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return filtered_data
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def split_data(self, data: List[Dict]) -> Tuple[List[Dict], List[Dict]]:
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"""
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分割训练集和测试集
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训练集使用所有数据，测试集从训练集中随机选择5%
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Args:
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data: 预处理后的数据
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Returns:
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Tuple[List[Dict], List[Dict]]: (训练集, 测试集)
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"""
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# 训练集使用所有数据
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train_data = data.copy()
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# 测试集从训练集中随机选择5%
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test_size = 0.05 # 5%
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test_data = train_test_split(
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train_data, test_size=test_size, random_state=self.random_state
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)[1] # 只取测试集部分
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logger.info(f"数据分割完成: 训练集 {len(train_data)} 条, 测试集 {len(test_data)} 条")
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logger.info(f"训练集使用所有数据，测试集从训练集中随机选择 {test_size*100:.1f}%")
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return train_data, test_data
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def train_model(self, train_data: List[Dict]) -> None:
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"""
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训练模型
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Args:
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train_data: 训练数据
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"""
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logger.info("开始训练模型...")
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# 统计信息
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chat_ids = set()
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styles = set()
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for item in train_data:
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chat_id = item["chat_id"]
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up_content = item["up_content"]
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style = item["style"]
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chat_ids.add(chat_id)
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styles.add(style)
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# 学习映射关系
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success = self.manager.learn_mapping(chat_id, up_content, style)
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if not success:
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logger.warning(f"学习失败: {chat_id} - {up_content} -> {style}")
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self.test_results["train_samples"] = len(train_data)
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self.test_results["unique_styles"] = len(styles)
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self.test_results["unique_chat_ids"] = len(chat_ids)
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logger.info(f"训练完成: {len(train_data)} 个样本, {len(styles)} 种风格, {len(chat_ids)} 个聊天室")
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# 保存训练好的模型
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logger.info("开始保存训练好的模型...")
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save_success = self.manager.save_all_models()
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self.test_results["model_save_success"] = save_success
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if save_success:
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logger.info(f"所有模型已成功保存到: {self.manager.model_save_path}")
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print(f"✅ 模型已保存到: {self.manager.model_save_path}")
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else:
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logger.warning("部分模型保存失败")
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print(f"⚠️ 模型保存失败，请检查路径: {self.manager.model_save_path}")
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def test_model(self, test_data: List[Dict]) -> None:
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"""
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测试模型
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Args:
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test_data: 测试数据
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"""
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logger.info("开始测试模型...")
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predictions = []
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ground_truth = []
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correct_predictions = 0
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for item in test_data:
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chat_id = item["chat_id"]
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up_content = item["up_content"]
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true_style = item["style"]
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# 预测风格
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predicted_style, scores = self.manager.predict_style(chat_id, up_content, top_k=1)
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predictions.append(predicted_style)
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ground_truth.append(true_style)
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# 检查预测是否正确
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if predicted_style == true_style:
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correct_predictions += 1
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# 记录详细预测结果
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self.test_results["predictions"].append({
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"chat_id": chat_id,
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"up_content": up_content,
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"true_style": true_style,
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"predicted_style": predicted_style,
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"scores": scores
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})
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# 计算准确率
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accuracy = correct_predictions / len(test_data) if test_data else 0
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# 计算其他指标（需要处理None值）
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valid_predictions = [p for p in predictions if p is not None]
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valid_ground_truth = [gt for p, gt in zip(predictions, ground_truth, strict=False) if p is not None]
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if valid_predictions:
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precision, recall, f1, _ = precision_recall_fscore_support(
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valid_ground_truth, valid_predictions, average='weighted', zero_division=0
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)
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else:
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precision = recall = f1 = 0.0
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self.test_results["test_samples"] = len(test_data)
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self.test_results["accuracy"] = accuracy
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self.test_results["precision"] = precision
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self.test_results["recall"] = recall
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self.test_results["f1_score"] = f1
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logger.info(f"测试完成: 准确率 {accuracy:.4f}, 精确率 {precision:.4f}, 召回率 {recall:.4f}, F1分数 {f1:.4f}")
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def analyze_results(self) -> None:
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"""分析测试结果"""
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logger.info("=== 测试结果分析 ===")
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print("\n📊 数据统计:")
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print(f" 总样本数: {self.test_results['total_samples']}")
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print(f" 训练样本数: {self.test_results['train_samples']}")
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print(f" 测试样本数: {self.test_results['test_samples']}")
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print(f" 唯一风格数: {self.test_results['unique_styles']}")
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print(f" 唯一聊天室数: {self.test_results['unique_chat_ids']}")
|
||||
|
||||
print("\n🎯 模型性能:")
|
||||
print(f" 准确率: {self.test_results['accuracy']:.4f}")
|
||||
print(f" 精确率: {self.test_results['precision']:.4f}")
|
||||
print(f" 召回率: {self.test_results['recall']:.4f}")
|
||||
print(f" F1分数: {self.test_results['f1_score']:.4f}")
|
||||
|
||||
print("\n💾 模型保存:")
|
||||
save_status = "成功" if self.test_results['model_save_success'] else "失败"
|
||||
print(f" 保存状态: {save_status}")
|
||||
print(f" 保存路径: {self.test_results['model_save_path']}")
|
||||
|
||||
# 分析各聊天室的性能
|
||||
chat_performance = {}
|
||||
for pred in self.test_results["predictions"]:
|
||||
chat_id = pred["chat_id"]
|
||||
if chat_id not in chat_performance:
|
||||
chat_performance[chat_id] = {"correct": 0, "total": 0}
|
||||
|
||||
chat_performance[chat_id]["total"] += 1
|
||||
if pred["predicted_style"] == pred["true_style"]:
|
||||
chat_performance[chat_id]["correct"] += 1
|
||||
|
||||
print("\n📈 各聊天室性能:")
|
||||
for chat_id, perf in chat_performance.items():
|
||||
accuracy = perf["correct"] / perf["total"] if perf["total"] > 0 else 0
|
||||
print(f" {chat_id}: {accuracy:.4f} ({perf['correct']}/{perf['total']})")
|
||||
|
||||
# 分析风格分布
|
||||
style_counts = {}
|
||||
for pred in self.test_results["predictions"]:
|
||||
style = pred["true_style"]
|
||||
style_counts[style] = style_counts.get(style, 0) + 1
|
||||
|
||||
print("\n🎨 风格分布 (前10个):")
|
||||
sorted_styles = sorted(style_counts.items(), key=lambda x: x[1], reverse=True)
|
||||
for style, count in sorted_styles[:10]:
|
||||
print(f" {style}: {count} 次")
|
||||
|
||||
def show_sample_predictions(self, num_samples: int = 10) -> None:
|
||||
"""显示样本预测结果"""
|
||||
print(f"\n🔍 样本预测结果 (前{num_samples}个):")
|
||||
|
||||
for i, pred in enumerate(self.test_results["predictions"][:num_samples]):
|
||||
status = "✓" if pred["predicted_style"] == pred["true_style"] else "✗"
|
||||
print(f"\n {i+1}. {status}")
|
||||
print(f" 聊天室: {pred['chat_id']}")
|
||||
print(f" 输入内容: {pred['up_content']}")
|
||||
print(f" 真实风格: {pred['true_style']}")
|
||||
print(f" 预测风格: {pred['predicted_style']}")
|
||||
if pred["scores"]:
|
||||
top_scores = dict(list(pred["scores"].items())[:3])
|
||||
print(f" 分数: {top_scores}")
|
||||
|
||||
def save_results(self, output_file: str = "style_learner_test_results.txt") -> None:
|
||||
"""保存测试结果到文件"""
|
||||
try:
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
f.write("StyleLearner 数据库测试结果\n")
|
||||
f.write("=" * 50 + "\n\n")
|
||||
|
||||
f.write("数据统计:\n")
|
||||
f.write(f" 总样本数: {self.test_results['total_samples']}\n")
|
||||
f.write(f" 训练样本数: {self.test_results['train_samples']}\n")
|
||||
f.write(f" 测试样本数: {self.test_results['test_samples']}\n")
|
||||
f.write(f" 唯一风格数: {self.test_results['unique_styles']}\n")
|
||||
f.write(f" 唯一聊天室数: {self.test_results['unique_chat_ids']}\n\n")
|
||||
|
||||
f.write("模型性能:\n")
|
||||
f.write(f" 准确率: {self.test_results['accuracy']:.4f}\n")
|
||||
f.write(f" 精确率: {self.test_results['precision']:.4f}\n")
|
||||
f.write(f" 召回率: {self.test_results['recall']:.4f}\n")
|
||||
f.write(f" F1分数: {self.test_results['f1_score']:.4f}\n\n")
|
||||
|
||||
f.write("模型保存:\n")
|
||||
save_status = "成功" if self.test_results['model_save_success'] else "失败"
|
||||
f.write(f" 保存状态: {save_status}\n")
|
||||
f.write(f" 保存路径: {self.test_results['model_save_path']}\n\n")
|
||||
|
||||
f.write("详细预测结果:\n")
|
||||
for i, pred in enumerate(self.test_results["predictions"]):
|
||||
status = "✓" if pred["predicted_style"] == pred["true_style"] else "✗"
|
||||
f.write(f"{i+1}. {status} [{pred['chat_id']}] {pred['up_content']} -> {pred['predicted_style']} (真实: {pred['true_style']})\n")
|
||||
|
||||
logger.info(f"测试结果已保存到 {output_file}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"保存测试结果失败: {e}")
|
||||
|
||||
def run_test(self) -> None:
|
||||
"""运行完整测试"""
|
||||
logger.info("开始StyleLearner数据库测试...")
|
||||
|
||||
# 1. 加载数据
|
||||
raw_data = self.load_data_from_database()
|
||||
if not raw_data:
|
||||
logger.error("没有加载到数据，测试终止")
|
||||
return
|
||||
|
||||
# 2. 数据预处理
|
||||
processed_data = self.preprocess_data(raw_data)
|
||||
if not processed_data:
|
||||
logger.error("预处理后没有数据，测试终止")
|
||||
return
|
||||
|
||||
self.test_results["total_samples"] = len(processed_data)
|
||||
|
||||
# 3. 分割数据
|
||||
train_data, test_data = self.split_data(processed_data)
|
||||
|
||||
# 4. 训练模型
|
||||
self.train_model(train_data)
|
||||
|
||||
# 5. 测试模型
|
||||
self.test_model(test_data)
|
||||
|
||||
# 6. 分析结果
|
||||
self.analyze_results()
|
||||
|
||||
# 7. 显示样本预测
|
||||
self.show_sample_predictions(10)
|
||||
|
||||
# 8. 保存结果
|
||||
self.save_results()
|
||||
|
||||
logger.info("StyleLearner数据库测试完成!")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
print("StyleLearner 数据库测试脚本")
|
||||
print("=" * 50)
|
||||
|
||||
# 创建测试实例
|
||||
test = StyleLearnerDatabaseTest(random_state=42)
|
||||
|
||||
# 运行测试
|
||||
test.run_test()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue