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better：优化记忆检索占用

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This commit is contained in:
SengokuCola
2025-11-11 00:40:43 +08:00
parent 85864c7013
commit 26784b00a5
5 changed files with 23 additions and 459 deletions
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1
src/chat/utils/chat_history_summarizer.py
View File

@@ -390,6 +390,7 @@ class ChatHistorySummarizer:
"theme": theme,
"keywords": json.dumps(keywords, ensure_ascii=False),
"summary": summary,
"count": 0,
}
# 使用db_save存储（使用start_time和chat_id作为唯一标识）

1
src/common/database/database_model.py
View File

@@ -377,6 +377,7 @@ class ChatHistory(BaseModel):
theme = TextField() # 主题：这段对话的主要内容，一个简短的标题
keywords = TextField() # 关键词：这段对话的关键词，JSON格式存储
summary = TextField() # 概括：对这段话的平文本概括
count = IntegerField(default=0) # 被检索次数
class Meta:
table_name = "chat_history"

11
src/memory_system/memory_retrieval.py
View File

@@ -280,12 +280,12 @@ async def _react_agent_solve_question(
return False, "未找到相关信息", thinking_steps
def _get_recent_query_history(chat_id: str, time_window_seconds: float = 3600.0) -> str:
def _get_recent_query_history(chat_id: str, time_window_seconds: float = 300.0) -> str:
"""获取最近一段时间内的查询历史
Args:
chat_id: 聊天ID
time_window_seconds: 时间窗口（秒），默认1小时
time_window_seconds: 时间窗口（秒），默认10分钟
Returns:
str: 格式化的查询历史字符串
@@ -302,7 +302,7 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 3600.0)
(ThinkingBack.update_time >= start_time)
)
.order_by(ThinkingBack.update_time.desc())
.limit(8) # 最多返回10条最近的记录
.limit(5) # 最多返回5条最近的记录
)
if not records.exists():
@@ -314,7 +314,8 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 3600.0)
for record in records:
status = "✓ 已找到答案" if record.found_answer else "✗ 未找到答案"
answer_preview = ""
if record.answer:
# 只有找到答案时才显示答案内容
if record.found_answer and record.answer:
# 截取答案前100字符
answer_preview = record.answer[:100]
if len(record.answer) > 100:
@@ -554,7 +555,7 @@ async def build_memory_retrieval_prompt(
chat_id = chat_stream.stream_id
# 获取最近查询历史（最近1小时内的查询）
recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=600.0)
recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=300.0)
if not recent_query_history:
recent_query_history = "最近没有查询记录。"

78
src/memory_system/retrieval_tools/query_chat_history.py
View File

@@ -6,9 +6,7 @@
import json
from typing import Optional
from src.common.logger import get_logger
from src.config.config import model_config
from src.common.database.database_model import ChatHistory
from src.llm_models.utils_model import LLMRequest
from src.chat.utils.utils import parse_keywords_string
from .tool_registry import register_memory_retrieval_tool
from .tool_utils import parse_datetime_to_timestamp, parse_time_range
@@ -116,10 +114,19 @@ async def query_chat_history(
return f"未找到包含关键词'{keywords_str}'的聊天记录概述"
records = filtered_records
# 对即将返回的记录增加使用计数
records_to_use = records[:3]
for record in records_to_use:
try:
ChatHistory.update(count=ChatHistory.count + 1).where(ChatHistory.id == record.id).execute()
record.count = (record.count or 0) + 1
except Exception as update_error:
logger.error(f"更新聊天记录概述计数失败: {update_error}")
# 构建结果文本
results = []
for record in records[:10]: # 最多返回10条记录
for record in records_to_use: # 最多返回3条记录
result_parts = []
# 添加主题
@@ -146,66 +153,11 @@ async def query_chat_history(
if not results:
return "未找到相关聊天记录概述"
# 如果只有一条记录，直接返回
if len(results) == 1:
return results[0]
# 多条记录，使用LLM总结
try:
llm_request = LLMRequest(
model_set=model_config.model_task_config.utils_small,
request_type="chat_history_analysis"
)
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_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"
combined_results = "\n\n---\n\n".join(results)
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。
查询条件：{query_info}
聊天记录概述：
{combined_results}
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。
总结："""
response, (reasoning, model_name, tool_calls) = await llm_request.generate_response_async(
prompt=analysis_prompt,
temperature=0.3,
max_tokens=512
)
logger.info(f"查询聊天历史概述提示词: {analysis_prompt}")
logger.info(f"查询聊天历史概述响应: {response}")
logger.info(f"查询聊天历史概述推理: {reasoning}")
logger.info(f"查询聊天历史概述模型: {model_name}")
if "无有效信息" in response:
return "无有效信息"
return response
except Exception as llm_error:
logger.error(f"LLM分析聊天记录概述失败: {llm_error}")
# 如果LLM分析失败，返回前3条记录的摘要
return "\n\n---\n\n".join(results[:3])
response_text = "\n\n---\n\n".join(results)
if len(records) > len(records_to_use):
omitted_count = len(records) - len(records_to_use)
response_text += f"\n\n(还有{omitted_count}条历史记录已省略)"
return response_text
except Exception as e:
logger.error(f"查询聊天历史概述失败: {e}")