mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of https://github.com/Ronifue/MaiBot into dev
Ronifue
commit
940e4a5f6b
11
README.md
11
README.md
|
|
@ -46,7 +46,7 @@
|
|||
## 🔥 更新和安装
|
||||
|
||||
|
||||
**最新版本: v0.11.5** ([更新日志](changelogs/changelog.md))
|
||||
**最新版本: v0.11.6** ([更新日志](changelogs/changelog.md))
|
||||
|
||||
|
||||
可前往 [Release](https://github.com/MaiM-with-u/MaiBot/releases/) 页面下载最新版本
|
||||
|
|
@ -71,16 +71,21 @@
|
|||
|
||||
**技术交流群：**
|
||||
[麦麦脑电图](https://qm.qq.com/q/RzmCiRtHEW) |
|
||||
[麦麦脑磁图](https://qm.qq.com/q/wlH5eT8OmQ) |
|
||||
[麦麦大脑磁共振](https://qm.qq.com/q/VQ3XZrWgMs) |
|
||||
[麦麦要当VTB](https://qm.qq.com/q/wGePTl1UyY)
|
||||
[麦麦要当VTB](https://qm.qq.com/q/wGePTl1UyY) |
|
||||
|
||||
为了维持技术交流和互帮互助的氛围，请不要在技术交流群讨论过多无关内容~
|
||||
|
||||
**聊天吹水群：**
|
||||
- [麦麦之闲聊群](https://qm.qq.com/q/JxvHZnxyec)
|
||||
|
||||
麦麦相关闲聊群
|
||||
|
||||
**插件开发/测试版讨论群：**
|
||||
- [插件开发群](https://qm.qq.com/q/1036092828)
|
||||
|
||||
进阶内容，包括插件开发，测试版使用等等
|
||||
|
||||
## 📚 文档
|
||||
|
||||
**部分内容可能更新不够及时，请注意版本对应**
|
||||
|
|
|
|||
|
|
@ -1,17 +1,52 @@
|
|||
# Changelog
|
||||
|
||||
## [0.11.5] - 2025-11-26
|
||||
### 主要功能更改
|
||||
|
||||
|
||||
## [0.11.6] - 2025-12-2
|
||||
### 🌟 重大更新
|
||||
- 大幅提高记忆检索能力，略微提高token消耗
|
||||
- 重构历史消息概括器，更好的主题记忆
|
||||
- 日志查看器性能革命性优化
|
||||
- 支持可视化查看麦麦LPMM知识图谱
|
||||
- 支持根据不同的模型提供商/模板/URL自动获取模型，可以不用手动输入模型了
|
||||
- 新增Baka引导系统，使用React-JoyTour实现很棒的用户引导系统（让Baka也能看懂！）
|
||||
- 本地聊天室功能！！你可以直接在WebUI网页和麦麦聊天！！
|
||||
- 使用cookie模式替换原有的LocalStorage Token存储，可能需要重新手动输入一遍Token
|
||||
- WebUI本地聊天室支持用户模拟和平台模拟的功能！
|
||||
- WebUI新增黑话管理 & 编辑界面
|
||||
|
||||
### 细节功能更改
|
||||
- 可选记忆识别中是否启用jargon
|
||||
- 解耦表情包识别和图片识别
|
||||
- 修复部分破损json的解析问题
|
||||
- 黑话更高的提取效率，增加提取准确性
|
||||
- 升级jargon，更快更精准
|
||||
- 新增Lpmm可视化
|
||||
|
||||
### webui细节更新
|
||||
- 修复侧边栏收起、UI及表格横向滚动等问题，优化Toast动画
|
||||
- 修复适配器配置、插件克隆、表情包注册等相关BUG
|
||||
- 新增适配器/模型预设模式及模板，自动填写URL和类型
|
||||
- 支持模型任务列表拖拽排序
|
||||
- 更新重启弹窗和首次引导内容
|
||||
- 多处界面命名及标题优化，如模型配置相关菜单重命名和描述更新
|
||||
- 修复聊天配置“提及回复”相关开关命名错误
|
||||
- 调试配置新增“显示记忆/Planner/LPMM Prompt”选项
|
||||
- 新增卡片尺寸、排序、字号、行间距等个性化功能
|
||||
- 聊天ID及群聊选择优化，显示可读名称
|
||||
- 聊天编辑界面精简字段，新增后端聊天列表API支持
|
||||
- 默认行间距减小，显示更紧凑
|
||||
- 修复页面滚动、表情包排序、发言频率为0等问题
|
||||
- 新增React异常Traceback界面及模型列表搜索
|
||||
- 更新WebUI Icon，修复适配器docker路径等问题
|
||||
- 插件配置可视化编辑，表单控件/元数据/布局类型扩展
|
||||
- 新增插件API与开发文档
|
||||
- 新增机器人状态卡片和快速操作按钮
|
||||
- 调整饼图显示、颜色算法，修复部分统计及解析错误
|
||||
- 新增缓存、WebSocket配置
|
||||
- 表情包支持上传和缩略图
|
||||
- 修复首页极端加载、重启后CtrlC失效、主程序配置移动端适配等问题
|
||||
- 新增表达反思设置和WebUI聊天室“思考中”占位组件
|
||||
- 细节如移除部分字段或UI控件、优化按钮/弹窗/编辑逻辑等
|
||||
|
||||
## [0.11.5] - 2025-11-21
|
||||
### 🌟 重大更新
|
||||
- WebUI 现支持手动重启麦麦，曲线救国版“热重载”
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
from datetime import datetime
|
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import time
|
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import asyncio
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from typing import Dict
|
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|
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from src.chat.utils.chat_message_builder import (
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|
|
@ -46,6 +47,8 @@ class FrequencyControl:
|
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self.frequency_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small, request_type="frequency.adjust"
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)
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# 频率调整锁，防止并发执行
|
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self._adjust_lock = asyncio.Lock()
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|
||||
def get_talk_frequency_adjust(self) -> float:
|
||||
"""获取发言频率调整值"""
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|
|
@ -56,68 +59,78 @@ class FrequencyControl:
|
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self.talk_frequency_adjust = max(0.1, min(5.0, value))
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|
||||
async def trigger_frequency_adjust(self) -> None:
|
||||
msg_list = get_raw_msg_by_timestamp_with_chat(
|
||||
chat_id=self.chat_id,
|
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timestamp_start=self.last_frequency_adjust_time,
|
||||
timestamp_end=time.time(),
|
||||
)
|
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|
||||
if time.time() - self.last_frequency_adjust_time < 160 or len(msg_list) <= 20:
|
||||
return
|
||||
else:
|
||||
new_msg_list = get_raw_msg_by_timestamp_with_chat(
|
||||
# 使用异步锁防止并发执行
|
||||
async with self._adjust_lock:
|
||||
# 在锁内检查，避免并发触发
|
||||
current_time = time.time()
|
||||
previous_adjust_time = self.last_frequency_adjust_time
|
||||
|
||||
msg_list = get_raw_msg_by_timestamp_with_chat(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=self.last_frequency_adjust_time,
|
||||
timestamp_end=time.time(),
|
||||
limit=20,
|
||||
limit_mode="latest",
|
||||
timestamp_start=previous_adjust_time,
|
||||
timestamp_end=current_time,
|
||||
)
|
||||
|
||||
message_str = build_readable_messages(
|
||||
new_msg_list,
|
||||
replace_bot_name=True,
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||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
show_actions=False,
|
||||
)
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||||
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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bot_name = global_config.bot.nickname
|
||||
bot_nickname = (
|
||||
f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
|
||||
)
|
||||
name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
|
||||
if current_time - previous_adjust_time < 160 or len(msg_list) <= 20:
|
||||
return
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"frequency_adjust_prompt",
|
||||
name_block=name_block,
|
||||
time_block=time_block,
|
||||
message_str=message_str,
|
||||
)
|
||||
response, (reasoning_content, _, _) = await self.frequency_model.generate_response_async(
|
||||
prompt,
|
||||
)
|
||||
# 立即更新调整时间，防止并发触发
|
||||
self.last_frequency_adjust_time = current_time
|
||||
|
||||
# logger.info(f"频率调整 prompt: {prompt}")
|
||||
# logger.info(f"频率调整 response: {response}")
|
||||
try:
|
||||
new_msg_list = get_raw_msg_by_timestamp_with_chat(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=previous_adjust_time,
|
||||
timestamp_end=current_time,
|
||||
limit=20,
|
||||
limit_mode="latest",
|
||||
)
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"频率调整 prompt: {prompt}")
|
||||
logger.info(f"频率调整 response: {response}")
|
||||
logger.info(f"频率调整 reasoning_content: {reasoning_content}")
|
||||
message_str = build_readable_messages(
|
||||
new_msg_list,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
show_actions=False,
|
||||
)
|
||||
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||||
bot_name = global_config.bot.nickname
|
||||
bot_nickname = (
|
||||
f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
|
||||
)
|
||||
name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
|
||||
|
||||
final_value_by_api = frequency_api.get_current_talk_value(self.chat_id)
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"frequency_adjust_prompt",
|
||||
name_block=name_block,
|
||||
time_block=time_block,
|
||||
message_str=message_str,
|
||||
)
|
||||
response, (reasoning_content, _, _) = await self.frequency_model.generate_response_async(
|
||||
prompt,
|
||||
)
|
||||
|
||||
# LLM依然输出过多内容时取消本次调整。合法最多4个字，但有的模型可能会输出一些markdown换行符等，需要长度宽限
|
||||
if len(response) < 20:
|
||||
if "过于频繁" in response:
|
||||
logger.info(f"频率调整: 过于频繁，调整值到{final_value_by_api}")
|
||||
self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 0.8))
|
||||
elif "过少" in response:
|
||||
logger.info(f"频率调整: 过少，调整值到{final_value_by_api}")
|
||||
self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 1.2))
|
||||
self.last_frequency_adjust_time = time.time()
|
||||
else:
|
||||
logger.info("频率调整：response不符合要求，取消本次调整")
|
||||
# logger.info(f"频率调整 prompt: {prompt}")
|
||||
# logger.info(f"频率调整 response: {response}")
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"频率调整 prompt: {prompt}")
|
||||
logger.info(f"频率调整 response: {response}")
|
||||
logger.info(f"频率调整 reasoning_content: {reasoning_content}")
|
||||
|
||||
final_value_by_api = frequency_api.get_current_talk_value(self.chat_id)
|
||||
|
||||
# LLM依然输出过多内容时取消本次调整。合法最多4个字，但有的模型可能会输出一些markdown换行符等，需要长度宽限
|
||||
if len(response) < 20:
|
||||
if "过于频繁" in response:
|
||||
logger.info(f"频率调整: 过于频繁，调整值到{final_value_by_api}")
|
||||
self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 0.8))
|
||||
elif "过少" in response:
|
||||
logger.info(f"频率调整: 过少，调整值到{final_value_by_api}")
|
||||
self.talk_frequency_adjust = max(0.1, min(1.5, self.talk_frequency_adjust * 1.2))
|
||||
except Exception as e:
|
||||
logger.error(f"频率调整失败: {e}")
|
||||
# 即使失败也保持时间戳更新，避免频繁重试
|
||||
|
||||
|
||||
class FrequencyControlManager:
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import time
|
|||
import json
|
||||
import os
|
||||
import re
|
||||
import asyncio
|
||||
from typing import List, Optional, Tuple
|
||||
import traceback
|
||||
from src.common.logger import get_logger
|
||||
|
|
@ -91,6 +92,9 @@ class ExpressionLearner:
|
|||
# 维护每个chat的上次学习时间
|
||||
self.last_learning_time: float = time.time()
|
||||
|
||||
# 学习锁，防止并发执行学习任务
|
||||
self._learning_lock = asyncio.Lock()
|
||||
|
||||
# 学习参数
|
||||
_, self.enable_learning, self.learning_intensity = global_config.expression.get_expression_config_for_chat(
|
||||
self.chat_id
|
||||
|
|
@ -139,32 +143,45 @@ class ExpressionLearner:
|
|||
Returns:
|
||||
bool: 是否成功触发学习
|
||||
"""
|
||||
if not self.should_trigger_learning():
|
||||
return
|
||||
# 使用异步锁防止并发执行
|
||||
async with self._learning_lock:
|
||||
# 在锁内检查，避免并发触发
|
||||
# 如果锁被持有，其他协程会等待，但等待期间条件可能已变化，所以需要再次检查
|
||||
if not self.should_trigger_learning():
|
||||
return
|
||||
|
||||
try:
|
||||
logger.info(f"在聊天流 {self.chat_name} 学习表达方式")
|
||||
# 学习语言风格
|
||||
learnt_style = await self.learn_and_store(num=25)
|
||||
# 保存学习开始前的时间戳，用于获取消息范围
|
||||
learning_start_timestamp = time.time()
|
||||
previous_learning_time = self.last_learning_time
|
||||
|
||||
# 立即更新学习时间，防止并发触发
|
||||
self.last_learning_time = learning_start_timestamp
|
||||
|
||||
# 更新学习时间
|
||||
self.last_learning_time = time.time()
|
||||
try:
|
||||
logger.info(f"在聊天流 {self.chat_name} 学习表达方式")
|
||||
# 学习语言风格，传递学习开始前的时间戳
|
||||
learnt_style = await self.learn_and_store(num=25, timestamp_start=previous_learning_time)
|
||||
|
||||
if learnt_style:
|
||||
logger.info(f"聊天流 {self.chat_name} 表达学习完成")
|
||||
else:
|
||||
logger.warning(f"聊天流 {self.chat_name} 表达学习未获得有效结果")
|
||||
if learnt_style:
|
||||
logger.info(f"聊天流 {self.chat_name} 表达学习完成")
|
||||
else:
|
||||
logger.warning(f"聊天流 {self.chat_name} 表达学习未获得有效结果")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}")
|
||||
traceback.print_exc()
|
||||
return
|
||||
except Exception as e:
|
||||
logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}")
|
||||
traceback.print_exc()
|
||||
# 即使失败也保持时间戳更新，避免频繁重试
|
||||
return
|
||||
|
||||
async def learn_and_store(self, num: int = 10) -> List[Tuple[str, str, str]]:
|
||||
async def learn_and_store(self, num: int = 10, timestamp_start: Optional[float] = None) -> List[Tuple[str, str, str]]:
|
||||
"""
|
||||
学习并存储表达方式
|
||||
|
||||
Args:
|
||||
num: 学习数量
|
||||
timestamp_start: 学习开始的时间戳，如果为None则使用self.last_learning_time
|
||||
"""
|
||||
learnt_expressions = await self.learn_expression(num)
|
||||
learnt_expressions = await self.learn_expression(num, timestamp_start=timestamp_start)
|
||||
|
||||
if learnt_expressions is None:
|
||||
logger.info("没有学习到表达风格")
|
||||
|
|
@ -374,18 +391,22 @@ class ExpressionLearner:
|
|||
|
||||
return matched_expressions
|
||||
|
||||
async def learn_expression(self, num: int = 10) -> Optional[List[Tuple[str, str, str, str]]]:
|
||||
async def learn_expression(self, num: int = 10, timestamp_start: Optional[float] = None) -> Optional[List[Tuple[str, str, str, str]]]:
|
||||
"""从指定聊天流学习表达方式
|
||||
|
||||
Args:
|
||||
num: 学习数量
|
||||
timestamp_start: 学习开始的时间戳，如果为None则使用self.last_learning_time
|
||||
"""
|
||||
current_time = time.time()
|
||||
|
||||
# 使用传入的时间戳，如果没有则使用self.last_learning_time
|
||||
start_timestamp = timestamp_start if timestamp_start is not None else self.last_learning_time
|
||||
|
||||
# 获取上次学习之后的消息
|
||||
random_msg = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=self.last_learning_time,
|
||||
timestamp_start=start_timestamp,
|
||||
timestamp_end=current_time,
|
||||
limit=num,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -249,3 +249,112 @@ async def explain_jargon_in_context(chat_id: str, messages: List[Any], chat_cont
|
|||
"""
|
||||
explainer = JargonExplainer(chat_id)
|
||||
return await explainer.explain_jargon(messages, chat_context)
|
||||
|
||||
|
||||
def match_jargon_from_text(chat_text: str, chat_id: str) -> List[str]:
|
||||
"""直接在聊天文本中匹配已知的jargon，返回出现过的黑话列表
|
||||
|
||||
Args:
|
||||
chat_text: 要匹配的聊天文本
|
||||
chat_id: 聊天ID
|
||||
|
||||
Returns:
|
||||
List[str]: 匹配到的黑话列表
|
||||
"""
|
||||
if not chat_text or not chat_text.strip():
|
||||
return []
|
||||
|
||||
query = Jargon.select().where((Jargon.meaning.is_null(False)) & (Jargon.meaning != ""))
|
||||
if global_config.jargon.all_global:
|
||||
query = query.where(Jargon.is_global)
|
||||
|
||||
query = query.order_by(Jargon.count.desc())
|
||||
|
||||
matched: Dict[str, None] = {}
|
||||
|
||||
for jargon in query:
|
||||
content = (jargon.content or "").strip()
|
||||
if not content:
|
||||
continue
|
||||
|
||||
if not global_config.jargon.all_global and not jargon.is_global:
|
||||
chat_id_list = parse_chat_id_list(jargon.chat_id)
|
||||
if not chat_id_list_contains(chat_id_list, chat_id):
|
||||
continue
|
||||
|
||||
pattern = re.escape(content)
|
||||
if re.search(r"[\u4e00-\u9fff]", content):
|
||||
search_pattern = pattern
|
||||
else:
|
||||
search_pattern = r"\b" + pattern + r"\b"
|
||||
|
||||
if re.search(search_pattern, chat_text, re.IGNORECASE):
|
||||
matched[content] = None
|
||||
|
||||
logger.info(f"匹配到 {len(matched)} 个黑话")
|
||||
|
||||
return list(matched.keys())
|
||||
|
||||
|
||||
async def retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> str:
|
||||
"""对概念列表进行jargon检索
|
||||
|
||||
Args:
|
||||
concepts: 概念列表
|
||||
chat_id: 聊天ID
|
||||
|
||||
Returns:
|
||||
str: 检索结果字符串
|
||||
"""
|
||||
if not concepts:
|
||||
return ""
|
||||
|
||||
results = []
|
||||
exact_matches = [] # 收集所有精确匹配的概念
|
||||
for concept in concepts:
|
||||
concept = concept.strip()
|
||||
if not concept:
|
||||
continue
|
||||
|
||||
# 先尝试精确匹配
|
||||
jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=False)
|
||||
|
||||
is_fuzzy_match = False
|
||||
|
||||
# 如果精确匹配未找到，尝试模糊搜索
|
||||
if not jargon_results:
|
||||
jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=True)
|
||||
is_fuzzy_match = True
|
||||
|
||||
if jargon_results:
|
||||
# 找到结果
|
||||
if is_fuzzy_match:
|
||||
# 模糊匹配
|
||||
output_parts = [f"未精确匹配到'{concept}'"]
|
||||
for result in jargon_results:
|
||||
found_content = result.get("content", "").strip()
|
||||
meaning = result.get("meaning", "").strip()
|
||||
if found_content and meaning:
|
||||
output_parts.append(f"找到 '{found_content}' 的含义为：{meaning}")
|
||||
results.append("，".join(output_parts))
|
||||
logger.info(f"在jargon库中找到匹配（模糊搜索）: {concept}，找到{len(jargon_results)}条结果")
|
||||
else:
|
||||
# 精确匹配
|
||||
output_parts = []
|
||||
for result in jargon_results:
|
||||
meaning = result.get("meaning", "").strip()
|
||||
if meaning:
|
||||
output_parts.append(f"'{concept}' 为黑话或者网络简写，含义为：{meaning}")
|
||||
results.append("；".join(output_parts) if len(output_parts) > 1 else output_parts[0])
|
||||
exact_matches.append(concept) # 收集精确匹配的概念，稍后统一打印
|
||||
else:
|
||||
# 未找到，不返回占位信息，只记录日志
|
||||
logger.info(f"在jargon库中未找到匹配: {concept}")
|
||||
|
||||
# 合并所有精确匹配的日志
|
||||
if exact_matches:
|
||||
logger.info(f"找到黑话: {', '.join(exact_matches)}，共找到{len(exact_matches)}条结果")
|
||||
|
||||
if results:
|
||||
return "【概念检索结果】\n" + "\n".join(results) + "\n"
|
||||
return ""
|
||||
|
|
@ -182,6 +182,9 @@ class JargonMiner:
|
|||
self.stream_name = stream_name if stream_name else self.chat_id
|
||||
self.cache_limit = 100
|
||||
self.cache: OrderedDict[str, None] = OrderedDict()
|
||||
|
||||
# 黑话提取锁，防止并发执行
|
||||
self._extraction_lock = asyncio.Lock()
|
||||
|
||||
def _add_to_cache(self, content: str) -> None:
|
||||
"""将提取到的黑话加入缓存，保持LRU语义"""
|
||||
|
|
@ -436,261 +439,265 @@ class JargonMiner:
|
|||
return bool(recent_messages and len(recent_messages) >= self.min_messages_for_learning)
|
||||
|
||||
async def run_once(self) -> None:
|
||||
try:
|
||||
if not self.should_trigger():
|
||||
return
|
||||
|
||||
chat_stream = get_chat_manager().get_stream(self.chat_id)
|
||||
if not chat_stream:
|
||||
return
|
||||
|
||||
# 记录本次提取的时间窗口，避免重复提取
|
||||
extraction_start_time = self.last_learning_time
|
||||
extraction_end_time = time.time()
|
||||
|
||||
# 拉取学习窗口内的消息
|
||||
messages = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=extraction_start_time,
|
||||
timestamp_end=extraction_end_time,
|
||||
limit=20,
|
||||
)
|
||||
if not messages:
|
||||
return
|
||||
|
||||
# 按时间排序，确保编号与上下文一致
|
||||
messages = sorted(messages, key=lambda msg: msg.time or 0)
|
||||
|
||||
chat_str, message_id_list = build_readable_messages_with_id(
|
||||
messages=messages,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="relative",
|
||||
truncate=False,
|
||||
show_actions=False,
|
||||
show_pic=True,
|
||||
pic_single=True,
|
||||
)
|
||||
if not chat_str.strip():
|
||||
return
|
||||
|
||||
msg_id_to_index: Dict[str, int] = {}
|
||||
for idx, (msg_id, _msg) in enumerate(message_id_list or []):
|
||||
if not msg_id:
|
||||
continue
|
||||
msg_id_to_index[msg_id] = idx
|
||||
if not msg_id_to_index:
|
||||
logger.warning("未能生成消息ID映射，跳过本次提取")
|
||||
return
|
||||
|
||||
prompt: str = await global_prompt_manager.format_prompt(
|
||||
"extract_jargon_prompt",
|
||||
bot_name=global_config.bot.nickname,
|
||||
chat_str=chat_str,
|
||||
)
|
||||
|
||||
response, _ = await self.llm.generate_response_async(prompt, temperature=0.2)
|
||||
if not response:
|
||||
return
|
||||
|
||||
if global_config.debug.show_jargon_prompt:
|
||||
logger.info(f"jargon提取提示词: {prompt}")
|
||||
logger.info(f"jargon提取结果: {response}")
|
||||
|
||||
# 解析为JSON
|
||||
entries: List[dict] = []
|
||||
# 使用异步锁防止并发执行
|
||||
async with self._extraction_lock:
|
||||
try:
|
||||
resp = response.strip()
|
||||
parsed = None
|
||||
if resp.startswith("[") and resp.endswith("]"):
|
||||
parsed = json.loads(resp)
|
||||
else:
|
||||
repaired = repair_json(resp)
|
||||
if isinstance(repaired, str):
|
||||
parsed = json.loads(repaired)
|
||||
else:
|
||||
parsed = repaired
|
||||
|
||||
if isinstance(parsed, dict):
|
||||
parsed = [parsed]
|
||||
|
||||
if not isinstance(parsed, list):
|
||||
# 在锁内检查，避免并发触发
|
||||
if not self.should_trigger():
|
||||
return
|
||||
|
||||
for item in parsed:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
chat_stream = get_chat_manager().get_stream(self.chat_id)
|
||||
if not chat_stream:
|
||||
return
|
||||
|
||||
content = str(item.get("content", "")).strip()
|
||||
msg_id_value = item.get("msg_id")
|
||||
|
||||
if not content:
|
||||
continue
|
||||
|
||||
if contains_bot_self_name(content):
|
||||
logger.info(f"解析阶段跳过包含机器人昵称/别名的词条: {content}")
|
||||
continue
|
||||
|
||||
msg_id_str = str(msg_id_value or "").strip()
|
||||
if not msg_id_str:
|
||||
logger.warning(f"解析jargon失败：msg_id缺失，content={content}")
|
||||
continue
|
||||
|
||||
msg_index = msg_id_to_index.get(msg_id_str)
|
||||
if msg_index is None:
|
||||
logger.warning(f"解析jargon失败：msg_id未找到，content={content}, msg_id={msg_id_str}")
|
||||
continue
|
||||
|
||||
target_msg = messages[msg_index]
|
||||
if is_bot_message(target_msg):
|
||||
logger.info(f"解析阶段跳过引用机器人自身消息的词条: content={content}, msg_id={msg_id_str}")
|
||||
continue
|
||||
|
||||
context_paragraph = build_context_paragraph(messages, msg_index)
|
||||
if not context_paragraph:
|
||||
logger.warning(f"解析jargon失败：上下文为空，content={content}, msg_id={msg_id_str}")
|
||||
continue
|
||||
|
||||
entries.append({"content": content, "raw_content": [context_paragraph]})
|
||||
cached_entries = self._collect_cached_entries(messages)
|
||||
if cached_entries:
|
||||
entries.extend(cached_entries)
|
||||
except Exception as e:
|
||||
logger.error(f"解析jargon JSON失败: {e}; 原始: {response}")
|
||||
return
|
||||
|
||||
if not entries:
|
||||
return
|
||||
|
||||
# 去重并合并raw_content（按 content 聚合）
|
||||
merged_entries: OrderedDict[str, Dict[str, List[str]]] = OrderedDict()
|
||||
for entry in entries:
|
||||
content_key = entry["content"]
|
||||
raw_list = entry.get("raw_content", []) or []
|
||||
if content_key in merged_entries:
|
||||
merged_entries[content_key]["raw_content"].extend(raw_list)
|
||||
else:
|
||||
merged_entries[content_key] = {
|
||||
"content": content_key,
|
||||
"raw_content": list(raw_list),
|
||||
}
|
||||
|
||||
uniq_entries = []
|
||||
for merged_entry in merged_entries.values():
|
||||
raw_content_list = merged_entry["raw_content"]
|
||||
if raw_content_list:
|
||||
merged_entry["raw_content"] = list(dict.fromkeys(raw_content_list))
|
||||
uniq_entries.append(merged_entry)
|
||||
|
||||
saved = 0
|
||||
updated = 0
|
||||
for entry in uniq_entries:
|
||||
content = entry["content"]
|
||||
raw_content_list = entry["raw_content"] # 已经是列表
|
||||
|
||||
try:
|
||||
# 查询所有content匹配的记录
|
||||
query = Jargon.select().where(Jargon.content == content)
|
||||
|
||||
# 查找匹配的记录
|
||||
matched_obj = None
|
||||
for obj in query:
|
||||
if global_config.jargon.all_global:
|
||||
# 开启all_global：所有content匹配的记录都可以
|
||||
matched_obj = obj
|
||||
break
|
||||
else:
|
||||
# 关闭all_global：需要检查chat_id列表是否包含目标chat_id
|
||||
chat_id_list = parse_chat_id_list(obj.chat_id)
|
||||
if chat_id_list_contains(chat_id_list, self.chat_id):
|
||||
matched_obj = obj
|
||||
break
|
||||
|
||||
if matched_obj:
|
||||
obj = matched_obj
|
||||
try:
|
||||
obj.count = (obj.count or 0) + 1
|
||||
except Exception:
|
||||
obj.count = 1
|
||||
|
||||
# 合并raw_content列表：读取现有列表，追加新值，去重
|
||||
existing_raw_content = []
|
||||
if obj.raw_content:
|
||||
try:
|
||||
existing_raw_content = (
|
||||
json.loads(obj.raw_content) if isinstance(obj.raw_content, str) else obj.raw_content
|
||||
)
|
||||
if not isinstance(existing_raw_content, list):
|
||||
existing_raw_content = [existing_raw_content] if existing_raw_content else []
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
existing_raw_content = [obj.raw_content] if obj.raw_content else []
|
||||
|
||||
# 合并并去重
|
||||
merged_list = list(dict.fromkeys(existing_raw_content + raw_content_list))
|
||||
obj.raw_content = json.dumps(merged_list, ensure_ascii=False)
|
||||
|
||||
# 更新chat_id列表：增加当前chat_id的计数
|
||||
chat_id_list = parse_chat_id_list(obj.chat_id)
|
||||
updated_chat_id_list = update_chat_id_list(chat_id_list, self.chat_id, increment=1)
|
||||
obj.chat_id = json.dumps(updated_chat_id_list, ensure_ascii=False)
|
||||
|
||||
# 开启all_global时，确保记录标记为is_global=True
|
||||
if global_config.jargon.all_global:
|
||||
obj.is_global = True
|
||||
# 关闭all_global时，保持原有is_global不变（不修改）
|
||||
|
||||
obj.save()
|
||||
|
||||
# 检查是否需要推断（达到阈值且超过上次判定值）
|
||||
if _should_infer_meaning(obj):
|
||||
# 异步触发推断，不阻塞主流程
|
||||
# 重新加载对象以确保数据最新
|
||||
jargon_id = obj.id
|
||||
asyncio.create_task(self._infer_meaning_by_id(jargon_id))
|
||||
|
||||
updated += 1
|
||||
else:
|
||||
# 没找到匹配记录，创建新记录
|
||||
if global_config.jargon.all_global:
|
||||
# 开启all_global：新记录默认为is_global=True
|
||||
is_global_new = True
|
||||
else:
|
||||
# 关闭all_global：新记录is_global=False
|
||||
is_global_new = False
|
||||
|
||||
# 使用新格式创建chat_id列表：[[chat_id, count]]
|
||||
chat_id_list = [[self.chat_id, 1]]
|
||||
chat_id_json = json.dumps(chat_id_list, ensure_ascii=False)
|
||||
|
||||
Jargon.create(
|
||||
content=content,
|
||||
raw_content=json.dumps(raw_content_list, ensure_ascii=False),
|
||||
chat_id=chat_id_json,
|
||||
is_global=is_global_new,
|
||||
count=1,
|
||||
)
|
||||
saved += 1
|
||||
except Exception as e:
|
||||
logger.error(f"保存jargon失败: chat_id={self.chat_id}, content={content}, err={e}")
|
||||
continue
|
||||
finally:
|
||||
self._add_to_cache(content)
|
||||
|
||||
# 固定输出提取的jargon结果，格式化为可读形式（只要有提取结果就输出）
|
||||
if uniq_entries:
|
||||
# 收集所有提取的jargon内容
|
||||
jargon_list = [entry["content"] for entry in uniq_entries]
|
||||
jargon_str = ",".join(jargon_list)
|
||||
|
||||
# 输出格式化的结果（使用logger.info会自动应用jargon模块的颜色）
|
||||
logger.info(f"[{self.stream_name}]疑似黑话: {jargon_str}")
|
||||
|
||||
# 更新为本次提取的结束时间，确保不会重复提取相同的消息窗口
|
||||
# 记录本次提取的时间窗口，避免重复提取
|
||||
extraction_start_time = self.last_learning_time
|
||||
extraction_end_time = time.time()
|
||||
|
||||
# 立即更新学习时间，防止并发触发
|
||||
self.last_learning_time = extraction_end_time
|
||||
|
||||
if saved or updated:
|
||||
logger.info(f"jargon写入: 新增 {saved} 条，更新 {updated} 条，chat_id={self.chat_id}")
|
||||
except Exception as e:
|
||||
logger.error(f"JargonMiner 运行失败: {e}")
|
||||
# 拉取学习窗口内的消息
|
||||
messages = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=extraction_start_time,
|
||||
timestamp_end=extraction_end_time,
|
||||
limit=20,
|
||||
)
|
||||
if not messages:
|
||||
return
|
||||
|
||||
# 按时间排序，确保编号与上下文一致
|
||||
messages = sorted(messages, key=lambda msg: msg.time or 0)
|
||||
|
||||
chat_str, message_id_list = build_readable_messages_with_id(
|
||||
messages=messages,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="relative",
|
||||
truncate=False,
|
||||
show_actions=False,
|
||||
show_pic=True,
|
||||
pic_single=True,
|
||||
)
|
||||
if not chat_str.strip():
|
||||
return
|
||||
|
||||
msg_id_to_index: Dict[str, int] = {}
|
||||
for idx, (msg_id, _msg) in enumerate(message_id_list or []):
|
||||
if not msg_id:
|
||||
continue
|
||||
msg_id_to_index[msg_id] = idx
|
||||
if not msg_id_to_index:
|
||||
logger.warning("未能生成消息ID映射，跳过本次提取")
|
||||
return
|
||||
|
||||
prompt: str = await global_prompt_manager.format_prompt(
|
||||
"extract_jargon_prompt",
|
||||
bot_name=global_config.bot.nickname,
|
||||
chat_str=chat_str,
|
||||
)
|
||||
|
||||
response, _ = await self.llm.generate_response_async(prompt, temperature=0.2)
|
||||
if not response:
|
||||
return
|
||||
|
||||
if global_config.debug.show_jargon_prompt:
|
||||
logger.info(f"jargon提取提示词: {prompt}")
|
||||
logger.info(f"jargon提取结果: {response}")
|
||||
|
||||
# 解析为JSON
|
||||
entries: List[dict] = []
|
||||
try:
|
||||
resp = response.strip()
|
||||
parsed = None
|
||||
if resp.startswith("[") and resp.endswith("]"):
|
||||
parsed = json.loads(resp)
|
||||
else:
|
||||
repaired = repair_json(resp)
|
||||
if isinstance(repaired, str):
|
||||
parsed = json.loads(repaired)
|
||||
else:
|
||||
parsed = repaired
|
||||
|
||||
if isinstance(parsed, dict):
|
||||
parsed = [parsed]
|
||||
|
||||
if not isinstance(parsed, list):
|
||||
return
|
||||
|
||||
for item in parsed:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
|
||||
content = str(item.get("content", "")).strip()
|
||||
msg_id_value = item.get("msg_id")
|
||||
|
||||
if not content:
|
||||
continue
|
||||
|
||||
if contains_bot_self_name(content):
|
||||
logger.info(f"解析阶段跳过包含机器人昵称/别名的词条: {content}")
|
||||
continue
|
||||
|
||||
msg_id_str = str(msg_id_value or "").strip()
|
||||
if not msg_id_str:
|
||||
logger.warning(f"解析jargon失败：msg_id缺失，content={content}")
|
||||
continue
|
||||
|
||||
msg_index = msg_id_to_index.get(msg_id_str)
|
||||
if msg_index is None:
|
||||
logger.warning(f"解析jargon失败：msg_id未找到，content={content}, msg_id={msg_id_str}")
|
||||
continue
|
||||
|
||||
target_msg = messages[msg_index]
|
||||
if is_bot_message(target_msg):
|
||||
logger.info(f"解析阶段跳过引用机器人自身消息的词条: content={content}, msg_id={msg_id_str}")
|
||||
continue
|
||||
|
||||
context_paragraph = build_context_paragraph(messages, msg_index)
|
||||
if not context_paragraph:
|
||||
logger.warning(f"解析jargon失败：上下文为空，content={content}, msg_id={msg_id_str}")
|
||||
continue
|
||||
|
||||
entries.append({"content": content, "raw_content": [context_paragraph]})
|
||||
cached_entries = self._collect_cached_entries(messages)
|
||||
if cached_entries:
|
||||
entries.extend(cached_entries)
|
||||
except Exception as e:
|
||||
logger.error(f"解析jargon JSON失败: {e}; 原始: {response}")
|
||||
return
|
||||
|
||||
if not entries:
|
||||
return
|
||||
|
||||
# 去重并合并raw_content（按 content 聚合）
|
||||
merged_entries: OrderedDict[str, Dict[str, List[str]]] = OrderedDict()
|
||||
for entry in entries:
|
||||
content_key = entry["content"]
|
||||
raw_list = entry.get("raw_content", []) or []
|
||||
if content_key in merged_entries:
|
||||
merged_entries[content_key]["raw_content"].extend(raw_list)
|
||||
else:
|
||||
merged_entries[content_key] = {
|
||||
"content": content_key,
|
||||
"raw_content": list(raw_list),
|
||||
}
|
||||
|
||||
uniq_entries = []
|
||||
for merged_entry in merged_entries.values():
|
||||
raw_content_list = merged_entry["raw_content"]
|
||||
if raw_content_list:
|
||||
merged_entry["raw_content"] = list(dict.fromkeys(raw_content_list))
|
||||
uniq_entries.append(merged_entry)
|
||||
|
||||
saved = 0
|
||||
updated = 0
|
||||
for entry in uniq_entries:
|
||||
content = entry["content"]
|
||||
raw_content_list = entry["raw_content"] # 已经是列表
|
||||
|
||||
try:
|
||||
# 查询所有content匹配的记录
|
||||
query = Jargon.select().where(Jargon.content == content)
|
||||
|
||||
# 查找匹配的记录
|
||||
matched_obj = None
|
||||
for obj in query:
|
||||
if global_config.jargon.all_global:
|
||||
# 开启all_global：所有content匹配的记录都可以
|
||||
matched_obj = obj
|
||||
break
|
||||
else:
|
||||
# 关闭all_global：需要检查chat_id列表是否包含目标chat_id
|
||||
chat_id_list = parse_chat_id_list(obj.chat_id)
|
||||
if chat_id_list_contains(chat_id_list, self.chat_id):
|
||||
matched_obj = obj
|
||||
break
|
||||
|
||||
if matched_obj:
|
||||
obj = matched_obj
|
||||
try:
|
||||
obj.count = (obj.count or 0) + 1
|
||||
except Exception:
|
||||
obj.count = 1
|
||||
|
||||
# 合并raw_content列表：读取现有列表，追加新值，去重
|
||||
existing_raw_content = []
|
||||
if obj.raw_content:
|
||||
try:
|
||||
existing_raw_content = (
|
||||
json.loads(obj.raw_content) if isinstance(obj.raw_content, str) else obj.raw_content
|
||||
)
|
||||
if not isinstance(existing_raw_content, list):
|
||||
existing_raw_content = [existing_raw_content] if existing_raw_content else []
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
existing_raw_content = [obj.raw_content] if obj.raw_content else []
|
||||
|
||||
# 合并并去重
|
||||
merged_list = list(dict.fromkeys(existing_raw_content + raw_content_list))
|
||||
obj.raw_content = json.dumps(merged_list, ensure_ascii=False)
|
||||
|
||||
# 更新chat_id列表：增加当前chat_id的计数
|
||||
chat_id_list = parse_chat_id_list(obj.chat_id)
|
||||
updated_chat_id_list = update_chat_id_list(chat_id_list, self.chat_id, increment=1)
|
||||
obj.chat_id = json.dumps(updated_chat_id_list, ensure_ascii=False)
|
||||
|
||||
# 开启all_global时，确保记录标记为is_global=True
|
||||
if global_config.jargon.all_global:
|
||||
obj.is_global = True
|
||||
# 关闭all_global时，保持原有is_global不变（不修改）
|
||||
|
||||
obj.save()
|
||||
|
||||
# 检查是否需要推断（达到阈值且超过上次判定值）
|
||||
if _should_infer_meaning(obj):
|
||||
# 异步触发推断，不阻塞主流程
|
||||
# 重新加载对象以确保数据最新
|
||||
jargon_id = obj.id
|
||||
asyncio.create_task(self._infer_meaning_by_id(jargon_id))
|
||||
|
||||
updated += 1
|
||||
else:
|
||||
# 没找到匹配记录，创建新记录
|
||||
if global_config.jargon.all_global:
|
||||
# 开启all_global：新记录默认为is_global=True
|
||||
is_global_new = True
|
||||
else:
|
||||
# 关闭all_global：新记录is_global=False
|
||||
is_global_new = False
|
||||
|
||||
# 使用新格式创建chat_id列表：[[chat_id, count]]
|
||||
chat_id_list = [[self.chat_id, 1]]
|
||||
chat_id_json = json.dumps(chat_id_list, ensure_ascii=False)
|
||||
|
||||
Jargon.create(
|
||||
content=content,
|
||||
raw_content=json.dumps(raw_content_list, ensure_ascii=False),
|
||||
chat_id=chat_id_json,
|
||||
is_global=is_global_new,
|
||||
count=1,
|
||||
)
|
||||
saved += 1
|
||||
except Exception as e:
|
||||
logger.error(f"保存jargon失败: chat_id={self.chat_id}, content={content}, err={e}")
|
||||
continue
|
||||
finally:
|
||||
self._add_to_cache(content)
|
||||
|
||||
# 固定输出提取的jargon结果，格式化为可读形式（只要有提取结果就输出）
|
||||
if uniq_entries:
|
||||
# 收集所有提取的jargon内容
|
||||
jargon_list = [entry["content"] for entry in uniq_entries]
|
||||
jargon_str = ",".join(jargon_list)
|
||||
|
||||
# 输出格式化的结果（使用logger.info会自动应用jargon模块的颜色）
|
||||
logger.info(f"[{self.stream_name}]疑似黑话: {jargon_str}")
|
||||
|
||||
if saved or updated:
|
||||
logger.info(f"jargon写入: 新增 {saved} 条，更新 {updated} 条，chat_id={self.chat_id}")
|
||||
except Exception as e:
|
||||
logger.error(f"JargonMiner 运行失败: {e}")
|
||||
# 即使失败也保持时间戳更新，避免频繁重试
|
||||
|
||||
|
||||
class JargonMinerManager:
|
||||
|
|
|
|||
|
|
@ -1,17 +1,16 @@
|
|||
import time
|
||||
import json
|
||||
import re
|
||||
import asyncio
|
||||
from typing import List, Dict, Any, Optional, Tuple, Set
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
from src.plugin_system.apis import llm_api
|
||||
from src.common.database.database_model import ThinkingBack, Jargon
|
||||
from json_repair import repair_json
|
||||
from src.common.database.database_model import ThinkingBack
|
||||
from src.memory_system.retrieval_tools import get_tool_registry, init_all_tools
|
||||
from src.memory_system.memory_utils import parse_questions_json
|
||||
from src.llm_models.payload_content.message import MessageBuilder, RoleType, Message
|
||||
from src.jargon.jargon_utils import parse_chat_id_list, chat_id_list_contains
|
||||
from src.jargon.jargon_explainer import match_jargon_from_text, retrieve_concepts_with_jargon
|
||||
|
||||
logger = get_logger("memory_retrieval")
|
||||
|
||||
|
|
@ -101,10 +100,6 @@ def init_memory_retrieval_prompt():
|
|||
Prompt(
|
||||
"""你的名字是{bot_name}。现在是{time_now}。
|
||||
你正在参与聊天，你需要搜集信息来回答问题，帮助你参与聊天。
|
||||
|
||||
**重要限制：**
|
||||
- 思考要简短，直接切入要点
|
||||
|
||||
当前需要解答的问题：{question}
|
||||
已收集的信息：
|
||||
{collected_info}
|
||||
|
|
@ -113,11 +108,13 @@ def init_memory_retrieval_prompt():
|
|||
- 如果涉及过往事件，或者查询某个过去可能提到过的概念，或者某段时间发生的事件。可以使用聊天记录查询工具查询过往事件
|
||||
- 如果涉及人物，可以使用人物信息查询工具查询人物信息
|
||||
- 如果没有可靠信息，且查询时间充足，或者不确定查询类别，也可以使用lpmm知识库查询，作为辅助信息
|
||||
- 如果信息不足需要使用tool，说明需要查询什么，并输出为纯文本说明，然后调用相应工具查询（可并行调用多个工具）
|
||||
- **如果信息不足需要使用tool，说明需要查询什么，并输出为纯文本说明，然后调用相应工具查询（可并行调用多个工具）**
|
||||
- **如果当前已收集的信息足够回答问题，且能找到明确答案，调用found_answer工具标记已找到答案**
|
||||
|
||||
**思考**
|
||||
- 你可以对查询思路给出简短的思考
|
||||
- 你必须给出使用什么工具进行查询
|
||||
- 你可以对查询思路给出简短的思考：思考要简短，直接切入要点
|
||||
- 如果信息不足，你必须给出使用什么工具进行查询
|
||||
- 如果信息足够，你必须调用found_answer工具
|
||||
""",
|
||||
name="memory_retrieval_react_prompt_head",
|
||||
)
|
||||
|
|
@ -149,198 +146,40 @@ def init_memory_retrieval_prompt():
|
|||
)
|
||||
|
||||
|
||||
def _parse_react_response(response: str) -> Optional[Dict[str, Any]]:
|
||||
"""解析ReAct Agent的响应
|
||||
|
||||
Args:
|
||||
response: LLM返回的响应
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: 解析后的动作信息，如果解析失败返回None
|
||||
格式: {"thought": str, "actions": List[Dict[str, Any]]}
|
||||
每个action格式: {"action_type": str, "action_params": dict}
|
||||
"""
|
||||
try:
|
||||
# 尝试提取JSON（可能包含在```json代码块中）
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
matches = re.findall(json_pattern, response, re.DOTALL)
|
||||
|
||||
if matches:
|
||||
json_str = matches[0]
|
||||
else:
|
||||
# 尝试直接解析整个响应
|
||||
json_str = response.strip()
|
||||
|
||||
# 修复可能的JSON错误
|
||||
repaired_json = repair_json(json_str)
|
||||
|
||||
# 解析JSON
|
||||
action_info = json.loads(repaired_json)
|
||||
|
||||
if not isinstance(action_info, dict):
|
||||
logger.warning(f"解析的JSON不是对象格式: {action_info}")
|
||||
return None
|
||||
|
||||
# 确保actions字段存在且为列表
|
||||
if "actions" not in action_info:
|
||||
logger.warning(f"响应中缺少actions字段: {action_info}")
|
||||
return None
|
||||
|
||||
if not isinstance(action_info["actions"], list):
|
||||
logger.warning(f"actions字段不是数组格式: {action_info['actions']}")
|
||||
return None
|
||||
|
||||
# 确保actions不为空
|
||||
if len(action_info["actions"]) == 0:
|
||||
logger.warning("actions数组为空")
|
||||
return None
|
||||
|
||||
return action_info
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"解析ReAct响应失败: {e}, 响应内容: {response[:200]}...")
|
||||
return None
|
||||
|
||||
|
||||
async def _retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> str:
|
||||
"""对概念列表进行jargon检索
|
||||
|
||||
Args:
|
||||
concepts: 概念列表
|
||||
chat_id: 聊天ID
|
||||
|
||||
Returns:
|
||||
str: 检索结果字符串
|
||||
"""
|
||||
if not concepts:
|
||||
return ""
|
||||
|
||||
from src.jargon.jargon_miner import search_jargon
|
||||
|
||||
results = []
|
||||
exact_matches = [] # 收集所有精确匹配的概念
|
||||
for concept in concepts:
|
||||
concept = concept.strip()
|
||||
if not concept:
|
||||
continue
|
||||
|
||||
# 先尝试精确匹配
|
||||
jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=False)
|
||||
|
||||
is_fuzzy_match = False
|
||||
|
||||
# 如果精确匹配未找到，尝试模糊搜索
|
||||
if not jargon_results:
|
||||
jargon_results = search_jargon(keyword=concept, chat_id=chat_id, limit=10, case_sensitive=False, fuzzy=True)
|
||||
is_fuzzy_match = True
|
||||
|
||||
if jargon_results:
|
||||
# 找到结果
|
||||
if is_fuzzy_match:
|
||||
# 模糊匹配
|
||||
output_parts = [f"未精确匹配到'{concept}'"]
|
||||
for result in jargon_results:
|
||||
found_content = result.get("content", "").strip()
|
||||
meaning = result.get("meaning", "").strip()
|
||||
if found_content and meaning:
|
||||
output_parts.append(f"找到 '{found_content}' 的含义为：{meaning}")
|
||||
results.append("，".join(output_parts))
|
||||
logger.info(f"在jargon库中找到匹配（模糊搜索）: {concept}，找到{len(jargon_results)}条结果")
|
||||
else:
|
||||
# 精确匹配
|
||||
output_parts = []
|
||||
for result in jargon_results:
|
||||
meaning = result.get("meaning", "").strip()
|
||||
if meaning:
|
||||
output_parts.append(f"'{concept}' 为黑话或者网络简写，含义为：{meaning}")
|
||||
results.append("；".join(output_parts) if len(output_parts) > 1 else output_parts[0])
|
||||
exact_matches.append(concept) # 收集精确匹配的概念，稍后统一打印
|
||||
else:
|
||||
# 未找到，不返回占位信息，只记录日志
|
||||
logger.info(f"在jargon库中未找到匹配: {concept}")
|
||||
|
||||
# 合并所有精确匹配的日志
|
||||
if exact_matches:
|
||||
logger.info(f"找到黑话: {', '.join(exact_matches)}，共找到{len(exact_matches)}条结果")
|
||||
|
||||
if results:
|
||||
return "【概念检索结果】\n" + "\n".join(results) + "\n"
|
||||
return ""
|
||||
|
||||
|
||||
def _match_jargon_from_text(chat_text: str, chat_id: str) -> List[str]:
|
||||
"""直接在聊天文本中匹配已知的jargon，返回出现过的黑话列表"""
|
||||
# print(chat_text)
|
||||
if not chat_text or not chat_text.strip():
|
||||
return []
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
query = Jargon.select().where((Jargon.meaning.is_null(False)) & (Jargon.meaning != ""))
|
||||
if global_config.jargon.all_global:
|
||||
query = query.where(Jargon.is_global)
|
||||
|
||||
query = query.order_by(Jargon.count.desc())
|
||||
|
||||
query_time = time.time()
|
||||
matched: Dict[str, None] = {}
|
||||
|
||||
for jargon in query:
|
||||
content = (jargon.content or "").strip()
|
||||
if not content:
|
||||
continue
|
||||
|
||||
if not global_config.jargon.all_global and not jargon.is_global:
|
||||
chat_id_list = parse_chat_id_list(jargon.chat_id)
|
||||
if not chat_id_list_contains(chat_id_list, chat_id):
|
||||
continue
|
||||
|
||||
pattern = re.escape(content)
|
||||
if re.search(r"[\u4e00-\u9fff]", content):
|
||||
search_pattern = pattern
|
||||
else:
|
||||
search_pattern = r"\b" + pattern + r"\b"
|
||||
|
||||
if re.search(search_pattern, chat_text, re.IGNORECASE):
|
||||
matched[content] = None
|
||||
|
||||
# end_time = time.time()
|
||||
logger.info(
|
||||
# f"记忆检索黑话匹配: 查询耗时 {(query_time - start_time):.3f}s, "
|
||||
# f"匹配耗时 {(end_time - query_time):.3f}s, 总耗时 {(end_time - start_time):.3f}s, "
|
||||
f"匹配到 {len(matched)} 个黑话"
|
||||
)
|
||||
|
||||
return list(matched.keys())
|
||||
|
||||
|
||||
def _log_conversation_messages(conversation_messages: List[Message], head_prompt: Optional[str] = None) -> None:
|
||||
def _log_conversation_messages(
|
||||
conversation_messages: List[Message],
|
||||
head_prompt: Optional[str] = None,
|
||||
final_status: Optional[str] = None,
|
||||
) -> None:
|
||||
"""输出对话消息列表的日志
|
||||
|
||||
|
||||
Args:
|
||||
conversation_messages: 对话消息列表
|
||||
head_prompt: 第一条系统消息（head_prompt）的内容，可选
|
||||
final_status: 最终结果状态描述（例如：找到答案/未找到答案），可选
|
||||
"""
|
||||
if not global_config.debug.show_memory_prompt:
|
||||
return
|
||||
|
||||
log_lines = []
|
||||
|
||||
|
||||
log_lines: List[str] = []
|
||||
|
||||
# 如果有head_prompt，先添加为第一条消息
|
||||
if head_prompt:
|
||||
msg_info = "\n[消息 1] 角色: System 内容类型: 文本\n========================================"
|
||||
msg_info = "========================================\n[消息 1] 角色: System 内容类型: 文本\n-----------------------------"
|
||||
msg_info += f"\n{head_prompt}"
|
||||
log_lines.append(msg_info)
|
||||
start_idx = 2
|
||||
else:
|
||||
start_idx = 1
|
||||
|
||||
|
||||
if not conversation_messages and not head_prompt:
|
||||
return
|
||||
|
||||
|
||||
for idx, msg in enumerate(conversation_messages, start_idx):
|
||||
role_name = msg.role.value if hasattr(msg.role, "value") else str(msg.role)
|
||||
|
||||
|
||||
# 处理内容 - 显示完整内容，不截断
|
||||
if isinstance(msg.content, str):
|
||||
full_content = msg.content
|
||||
|
|
@ -353,25 +192,28 @@ def _log_conversation_messages(conversation_messages: List[Message], head_prompt
|
|||
else:
|
||||
full_content = str(msg.content)
|
||||
content_type = "未知"
|
||||
|
||||
|
||||
# 构建单条消息的日志信息
|
||||
msg_info = f"\n[消息 {idx}] 角色: {role_name} 内容类型: {content_type}\n========================================"
|
||||
|
||||
msg_info = f"\n========================================\n[消息 {idx}] 角色: {role_name} 内容类型: {content_type}\n-----------------------------"
|
||||
|
||||
if full_content:
|
||||
msg_info += f"\n{full_content}"
|
||||
|
||||
|
||||
if msg.tool_calls:
|
||||
msg_info += f"\n 工具调用: {len(msg.tool_calls)}个"
|
||||
for tool_call in msg.tool_calls:
|
||||
msg_info += f"\n - {tool_call}"
|
||||
|
||||
|
||||
if msg.tool_call_id:
|
||||
msg_info += f"\n 工具调用ID: {msg.tool_call_id}"
|
||||
|
||||
|
||||
log_lines.append(msg_info)
|
||||
|
||||
|
||||
total_count = len(conversation_messages) + (1 if head_prompt else 0)
|
||||
logger.info(f"消息列表 (共{total_count}条):{''.join(log_lines)}")
|
||||
log_text = f"消息列表 (共{total_count}条):{''.join(log_lines)}"
|
||||
if final_status:
|
||||
log_text += f"\n\n[最终结果] {final_status}"
|
||||
logger.info(log_text)
|
||||
|
||||
|
||||
async def _react_agent_solve_question(
|
||||
|
|
@ -407,7 +249,7 @@ async def _react_agent_solve_question(
|
|||
thinking_steps = []
|
||||
is_timeout = False
|
||||
conversation_messages: List[Message] = []
|
||||
last_head_prompt: Optional[str] = None # 保存最后一次使用的head_prompt
|
||||
first_head_prompt: Optional[str] = None # 保存第一次使用的head_prompt（用于日志显示）
|
||||
|
||||
for iteration in range(max_iterations):
|
||||
# 检查超时
|
||||
|
|
@ -430,40 +272,6 @@ async def _react_agent_solve_question(
|
|||
remaining_iterations = max_iterations - current_iteration
|
||||
is_final_iteration = current_iteration >= max_iterations
|
||||
|
||||
# 每次迭代开始时，先评估当前信息是否足够回答问题
|
||||
evaluation_prompt = await global_prompt_manager.format_prompt(
|
||||
"memory_retrieval_react_final_prompt",
|
||||
bot_name=bot_name,
|
||||
time_now=time_now,
|
||||
question=question,
|
||||
collected_info=collected_info if collected_info else "暂无信息",
|
||||
current_iteration=current_iteration,
|
||||
remaining_iterations=remaining_iterations,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
|
||||
# if global_config.debug.show_memory_prompt:
|
||||
# logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 评估Prompt: {evaluation_prompt}")
|
||||
|
||||
eval_success, eval_response, eval_reasoning_content, eval_model_name, eval_tool_calls = await llm_api.generate_with_model_with_tools(
|
||||
evaluation_prompt,
|
||||
model_config=model_config.model_task_config.tool_use,
|
||||
tool_options=[], # 评估阶段不提供工具
|
||||
request_type="memory.react.eval",
|
||||
)
|
||||
|
||||
if not eval_success:
|
||||
logger.error(f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段 LLM调用失败: {eval_response}")
|
||||
# 评估失败，如果还有剩余迭代次数，尝试继续查询
|
||||
if not is_final_iteration:
|
||||
continue
|
||||
else:
|
||||
break
|
||||
|
||||
logger.info(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 评估响应: {eval_response}"
|
||||
)
|
||||
|
||||
# 提取函数调用中参数的值，支持单引号和双引号
|
||||
def extract_quoted_content(text, func_name, param_name):
|
||||
"""从文本中提取函数调用中参数的值，支持单引号和双引号
|
||||
|
|
@ -522,88 +330,132 @@ async def _react_agent_solve_question(
|
|||
|
||||
return None
|
||||
|
||||
# 从评估响应中提取found_answer或not_enough_info
|
||||
found_answer_content = None
|
||||
not_enough_info_reason = None
|
||||
# 如果是最后一次迭代，使用final_prompt进行总结
|
||||
if is_final_iteration:
|
||||
evaluation_prompt = await global_prompt_manager.format_prompt(
|
||||
"memory_retrieval_react_final_prompt",
|
||||
bot_name=bot_name,
|
||||
time_now=time_now,
|
||||
question=question,
|
||||
collected_info=collected_info if collected_info else "暂无信息",
|
||||
current_iteration=current_iteration,
|
||||
remaining_iterations=remaining_iterations,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
|
||||
if eval_response:
|
||||
found_answer_content = extract_quoted_content(eval_response, "found_answer", "answer")
|
||||
if not found_answer_content:
|
||||
not_enough_info_reason = extract_quoted_content(eval_response, "not_enough_info", "reason")
|
||||
if global_config.debug.show_memory_prompt:
|
||||
logger.info(f"ReAct Agent 最终评估Prompt: {evaluation_prompt}")
|
||||
|
||||
# 如果找到答案，直接返回
|
||||
if found_answer_content:
|
||||
eval_step = {
|
||||
"iteration": iteration + 1,
|
||||
"thought": f"[评估] {eval_response}",
|
||||
"actions": [{"action_type": "found_answer", "action_params": {"answer": found_answer_content}}],
|
||||
"observations": ["评估阶段检测到found_answer"]
|
||||
}
|
||||
thinking_steps.append(eval_step)
|
||||
logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段找到关于问题{question}的答案: {found_answer_content}")
|
||||
|
||||
# React完成时输出消息列表
|
||||
_log_conversation_messages(conversation_messages, last_head_prompt)
|
||||
|
||||
return True, found_answer_content, thinking_steps, False
|
||||
eval_success, eval_response, eval_reasoning_content, eval_model_name, eval_tool_calls = await llm_api.generate_with_model_with_tools(
|
||||
evaluation_prompt,
|
||||
model_config=model_config.model_task_config.tool_use,
|
||||
tool_options=[], # 最终评估阶段不提供工具
|
||||
request_type="memory.react.final",
|
||||
)
|
||||
|
||||
# 如果评估为not_enough_info，且是最终迭代，返回not_enough_info
|
||||
if not_enough_info_reason:
|
||||
if is_final_iteration:
|
||||
if not eval_success:
|
||||
logger.error(f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段 LLM调用失败: {eval_response}")
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status="未找到答案：最终评估阶段LLM调用失败",
|
||||
)
|
||||
return False, "最终评估阶段LLM调用失败", thinking_steps, False
|
||||
|
||||
logger.info(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估响应: {eval_response}"
|
||||
)
|
||||
|
||||
# 从最终评估响应中提取found_answer或not_enough_info
|
||||
found_answer_content = None
|
||||
not_enough_info_reason = None
|
||||
|
||||
if eval_response:
|
||||
found_answer_content = extract_quoted_content(eval_response, "found_answer", "answer")
|
||||
if not found_answer_content:
|
||||
not_enough_info_reason = extract_quoted_content(eval_response, "not_enough_info", "reason")
|
||||
|
||||
# 如果找到答案，返回
|
||||
if found_answer_content:
|
||||
eval_step = {
|
||||
"iteration": iteration + 1,
|
||||
"thought": f"[评估] {eval_response}",
|
||||
"thought": f"[最终评估] {eval_response}",
|
||||
"actions": [{"action_type": "found_answer", "action_params": {"answer": found_answer_content}}],
|
||||
"observations": ["最终评估阶段检测到found_answer"]
|
||||
}
|
||||
thinking_steps.append(eval_step)
|
||||
logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段找到关于问题{question}的答案: {found_answer_content}")
|
||||
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status=f"找到答案：{found_answer_content}",
|
||||
)
|
||||
|
||||
return True, found_answer_content, thinking_steps, False
|
||||
|
||||
# 如果评估为not_enough_info，返回空字符串（不返回任何信息）
|
||||
if not_enough_info_reason:
|
||||
eval_step = {
|
||||
"iteration": iteration + 1,
|
||||
"thought": f"[最终评估] {eval_response}",
|
||||
"actions": [{"action_type": "not_enough_info", "action_params": {"reason": not_enough_info_reason}}],
|
||||
"observations": ["评估阶段检测到not_enough_info"]
|
||||
"observations": ["最终评估阶段检测到not_enough_info"]
|
||||
}
|
||||
thinking_steps.append(eval_step)
|
||||
logger.info(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段判断信息不足: {not_enough_info_reason}"
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 最终评估阶段判断信息不足: {not_enough_info_reason}"
|
||||
)
|
||||
|
||||
# React完成时输出消息列表
|
||||
_log_conversation_messages(conversation_messages, last_head_prompt)
|
||||
|
||||
return False, not_enough_info_reason, thinking_steps, False
|
||||
else:
|
||||
# 非最终迭代，信息不足，继续搜集信息
|
||||
logger.info(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 评估阶段判断信息不足: {not_enough_info_reason}，继续查询"
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status=f"未找到答案：{not_enough_info_reason}",
|
||||
)
|
||||
|
||||
return False, "", thinking_steps, False
|
||||
|
||||
# 如果是最终迭代但没有明确判断，视为not_enough_info
|
||||
if is_final_iteration:
|
||||
# 如果没有明确判断，视为not_enough_info，返回空字符串（不返回任何信息）
|
||||
eval_step = {
|
||||
"iteration": iteration + 1,
|
||||
"thought": f"[评估] {eval_response}",
|
||||
"thought": f"[最终评估] {eval_response}",
|
||||
"actions": [{"action_type": "not_enough_info", "action_params": {"reason": "已到达最后一次迭代，无法找到答案"}}],
|
||||
"observations": ["已到达最后一次迭代，无法找到答案"]
|
||||
}
|
||||
thinking_steps.append(eval_step)
|
||||
logger.info(f"ReAct Agent 第 {iteration + 1} 次迭代 已到达最后一次迭代，无法找到答案")
|
||||
|
||||
# React完成时输出消息列表
|
||||
_log_conversation_messages(conversation_messages, last_head_prompt)
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status="未找到答案：已到达最后一次迭代，无法找到答案",
|
||||
)
|
||||
|
||||
return False, "已到达最后一次迭代，无法找到答案", thinking_steps, False
|
||||
return False, "", thinking_steps, False
|
||||
|
||||
# 非最终迭代且信息不足，使用head_prompt决定调用哪些工具
|
||||
# 前n-1次迭代，使用head_prompt决定调用哪些工具（包含found_answer工具）
|
||||
tool_definitions = tool_registry.get_tool_definitions()
|
||||
logger.info(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代，问题: {question}|可用工具数量: {len(tool_definitions)}"
|
||||
)
|
||||
|
||||
head_prompt = await global_prompt_manager.format_prompt(
|
||||
"memory_retrieval_react_prompt_head",
|
||||
bot_name=bot_name,
|
||||
time_now=time_now,
|
||||
question=question,
|
||||
collected_info=collected_info if collected_info else "",
|
||||
current_iteration=current_iteration,
|
||||
remaining_iterations=remaining_iterations,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
last_head_prompt = head_prompt # 保存最后一次使用的head_prompt
|
||||
# head_prompt应该只构建一次，使用初始的collected_info，后续迭代都复用同一个
|
||||
if first_head_prompt is None:
|
||||
# 第一次构建，使用初始的collected_info（即initial_info）
|
||||
initial_collected_info = initial_info if initial_info else ""
|
||||
first_head_prompt = await global_prompt_manager.format_prompt(
|
||||
"memory_retrieval_react_prompt_head",
|
||||
bot_name=bot_name,
|
||||
time_now=time_now,
|
||||
question=question,
|
||||
collected_info=initial_collected_info,
|
||||
current_iteration=current_iteration,
|
||||
remaining_iterations=remaining_iterations,
|
||||
max_iterations=max_iterations,
|
||||
)
|
||||
|
||||
# 后续迭代都复用第一次构建的head_prompt
|
||||
head_prompt = first_head_prompt
|
||||
|
||||
def message_factory(
|
||||
_client,
|
||||
|
|
@ -635,7 +487,7 @@ async def _react_agent_solve_question(
|
|||
request_type="memory.react",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
logger.debug(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 模型: {model_name} ，调用工具数量: {len(tool_calls) if tool_calls else 0} ，调用工具响应: {response}"
|
||||
)
|
||||
|
||||
|
|
@ -643,8 +495,7 @@ async def _react_agent_solve_question(
|
|||
logger.error(f"ReAct Agent LLM调用失败: {response}")
|
||||
break
|
||||
|
||||
# 注意：这里不检查found_answer或not_enough_info，这些只在评估阶段（memory_retrieval_react_final_prompt）检查
|
||||
# memory_retrieval_react_prompt_head只用于决定调用哪些工具来搜集信息
|
||||
# 注意：这里会检查found_answer工具调用，如果检测到found_answer工具，会直接返回答案
|
||||
|
||||
assistant_message: Optional[Message] = None
|
||||
if tool_calls:
|
||||
|
|
@ -686,16 +537,42 @@ async def _react_agent_solve_question(
|
|||
continue
|
||||
|
||||
# 处理工具调用
|
||||
# 首先检查是否有found_answer工具调用，如果有则立即返回，不再处理其他工具
|
||||
found_answer_from_tool = None
|
||||
for tool_call in tool_calls:
|
||||
tool_name = tool_call.func_name
|
||||
tool_args = tool_call.args or {}
|
||||
|
||||
if tool_name == "found_answer":
|
||||
found_answer_from_tool = tool_args.get("answer", "")
|
||||
if found_answer_from_tool:
|
||||
step["actions"].append({"action_type": "found_answer", "action_params": {"answer": found_answer_from_tool}})
|
||||
step["observations"] = ["检测到found_answer工具调用"]
|
||||
thinking_steps.append(step)
|
||||
logger.debug(f"ReAct Agent 第 {iteration + 1} 次迭代 通过found_answer工具找到关于问题{question}的答案: {found_answer_from_tool}")
|
||||
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status=f"找到答案：{found_answer_from_tool}",
|
||||
)
|
||||
|
||||
return True, found_answer_from_tool, thinking_steps, False
|
||||
|
||||
# 如果没有found_answer工具调用，或者found_answer工具调用没有答案，继续处理其他工具
|
||||
tool_tasks = []
|
||||
|
||||
for i, tool_call in enumerate(tool_calls):
|
||||
tool_name = tool_call.func_name
|
||||
tool_args = tool_call.args or {}
|
||||
|
||||
logger.info(
|
||||
logger.debug(
|
||||
f"ReAct Agent 第 {iteration + 1} 次迭代 工具调用 {i + 1}/{len(tool_calls)}: {tool_name}({tool_args})"
|
||||
)
|
||||
|
||||
# 跳过found_answer工具调用（已经在上面处理过了）
|
||||
if tool_name == "found_answer":
|
||||
continue
|
||||
|
||||
# 普通工具调用
|
||||
tool = tool_registry.get_tool(tool_name)
|
||||
if tool:
|
||||
|
|
@ -740,15 +617,10 @@ async def _react_agent_solve_question(
|
|||
step["observations"].append(observation_text)
|
||||
collected_info += f"\n{observation_text}\n"
|
||||
if stripped_observation:
|
||||
tool_builder = MessageBuilder()
|
||||
tool_builder.set_role(RoleType.Tool)
|
||||
tool_builder.add_text_content(observation_text)
|
||||
tool_builder.add_tool_call(tool_call_item.call_id)
|
||||
conversation_messages.append(tool_builder.build())
|
||||
# 检查工具输出中是否有新的jargon，如果有则追加到工具结果中
|
||||
if enable_jargon_detection:
|
||||
jargon_concepts = _match_jargon_from_text(stripped_observation, chat_id)
|
||||
jargon_concepts = match_jargon_from_text(stripped_observation, chat_id)
|
||||
if jargon_concepts:
|
||||
jargon_info = ""
|
||||
new_concepts = []
|
||||
for concept in jargon_concepts:
|
||||
normalized_concept = concept.strip()
|
||||
|
|
@ -756,10 +628,18 @@ async def _react_agent_solve_question(
|
|||
new_concepts.append(normalized_concept)
|
||||
seen_jargon_concepts.add(normalized_concept)
|
||||
if new_concepts:
|
||||
jargon_info = await _retrieve_concepts_with_jargon(new_concepts, chat_id)
|
||||
if jargon_info:
|
||||
collected_info += f"\n{jargon_info}\n"
|
||||
logger.info(f"工具输出触发黑话解析: {new_concepts}")
|
||||
jargon_info = await retrieve_concepts_with_jargon(new_concepts, chat_id)
|
||||
if jargon_info:
|
||||
# 将jargon查询结果追加到工具结果中
|
||||
observation_text += f"\n\n{jargon_info}"
|
||||
collected_info += f"\n{jargon_info}\n"
|
||||
logger.info(f"工具输出触发黑话解析: {new_concepts}")
|
||||
|
||||
tool_builder = MessageBuilder()
|
||||
tool_builder.set_role(RoleType.Tool)
|
||||
tool_builder.add_text_content(observation_text)
|
||||
tool_builder.add_tool_call(tool_call_item.call_id)
|
||||
conversation_messages.append(tool_builder.build())
|
||||
|
||||
thinking_steps.append(step)
|
||||
|
||||
|
|
@ -776,13 +656,18 @@ async def _react_agent_solve_question(
|
|||
logger.warning("ReAct Agent达到最大迭代次数，直接视为not_enough_info")
|
||||
|
||||
# React完成时输出消息列表
|
||||
_log_conversation_messages(conversation_messages, last_head_prompt)
|
||||
timeout_reason = "超时" if is_timeout else "达到最大迭代次数"
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status=f"未找到答案：{timeout_reason}",
|
||||
)
|
||||
|
||||
return False, "未找到相关信息", thinking_steps, is_timeout
|
||||
return False, "", thinking_steps, is_timeout
|
||||
|
||||
|
||||
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
|
||||
def _get_recent_query_history(chat_id: str, time_window_seconds: float = 600.0) -> str:
|
||||
"""获取最近一段时间内的查询历史（用于避免重复查询）
|
||||
|
||||
Args:
|
||||
chat_id: 聊天ID
|
||||
|
|
@ -832,6 +717,49 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 300.0)
|
|||
return ""
|
||||
|
||||
|
||||
def _get_recent_found_answers(chat_id: str, time_window_seconds: float = 600.0) -> List[str]:
|
||||
"""获取最近一段时间内已找到答案的查询记录（用于返回给 replyer）
|
||||
|
||||
Args:
|
||||
chat_id: 聊天ID
|
||||
time_window_seconds: 时间窗口（秒），默认10分钟
|
||||
|
||||
Returns:
|
||||
List[str]: 格式化的答案列表，每个元素格式为 "问题：xxx\n答案：xxx"
|
||||
"""
|
||||
try:
|
||||
current_time = time.time()
|
||||
start_time = current_time - time_window_seconds
|
||||
|
||||
# 查询最近时间窗口内已找到答案的记录，按更新时间倒序
|
||||
records = (
|
||||
ThinkingBack.select()
|
||||
.where(
|
||||
(ThinkingBack.chat_id == chat_id)
|
||||
& (ThinkingBack.update_time >= start_time)
|
||||
& (ThinkingBack.found_answer == 1)
|
||||
& (ThinkingBack.answer.is_null(False))
|
||||
& (ThinkingBack.answer != "")
|
||||
)
|
||||
.order_by(ThinkingBack.update_time.desc())
|
||||
.limit(3) # 最多返回5条最近的记录
|
||||
)
|
||||
|
||||
if not records.exists():
|
||||
return []
|
||||
|
||||
found_answers = []
|
||||
for record in records:
|
||||
if record.answer:
|
||||
found_answers.append(f"问题：{record.question}\n答案：{record.answer}")
|
||||
|
||||
return found_answers
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取最近已找到答案的记录失败: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def _store_thinking_back(
|
||||
chat_id: str, question: str, context: str, found_answer: bool, answer: str, thinking_steps: List[Dict[str, Any]]
|
||||
) -> None:
|
||||
|
|
@ -969,8 +897,8 @@ async def build_memory_retrieval_prompt(
|
|||
bot_name = global_config.bot.nickname
|
||||
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=300.0)
|
||||
# 获取最近查询历史（最近10分钟内的查询，用于避免重复查询）
|
||||
recent_query_history = _get_recent_query_history(chat_id, time_window_seconds=600.0)
|
||||
if not recent_query_history:
|
||||
recent_query_history = "最近没有查询记录。"
|
||||
|
||||
|
|
@ -1000,7 +928,7 @@ async def build_memory_retrieval_prompt(
|
|||
return ""
|
||||
|
||||
# 解析概念列表和问题列表
|
||||
_, questions = _parse_questions_json(response)
|
||||
_, questions = parse_questions_json(response)
|
||||
if questions:
|
||||
logger.info(f"解析到 {len(questions)} 个问题: {questions}")
|
||||
|
||||
|
|
@ -1009,7 +937,7 @@ async def build_memory_retrieval_prompt(
|
|||
|
||||
if enable_jargon_detection:
|
||||
# 使用匹配逻辑自动识别聊天中的黑话概念
|
||||
concepts = _match_jargon_from_text(message, chat_id)
|
||||
concepts = match_jargon_from_text(message, chat_id)
|
||||
if concepts:
|
||||
logger.info(f"黑话匹配命中 {len(concepts)} 个概念: {concepts}")
|
||||
else:
|
||||
|
|
@ -1020,12 +948,12 @@ async def build_memory_retrieval_prompt(
|
|||
# 对匹配到的概念进行jargon检索，作为初始信息
|
||||
initial_info = ""
|
||||
if enable_jargon_detection and concepts:
|
||||
concept_info = await _retrieve_concepts_with_jargon(concepts, chat_id)
|
||||
concept_info = await retrieve_concepts_with_jargon(concepts, chat_id)
|
||||
if concept_info:
|
||||
initial_info += concept_info
|
||||
logger.info(f"概念检索完成，结果: {concept_info}")
|
||||
logger.debug(f"概念检索完成，结果: {concept_info}")
|
||||
else:
|
||||
logger.info("概念检索未找到任何结果")
|
||||
logger.debug("概念检索未找到任何结果")
|
||||
|
||||
if not questions:
|
||||
logger.debug("模型认为不需要检索记忆或解析失败，不返回任何查询结果")
|
||||
|
|
@ -1060,67 +988,47 @@ async def build_memory_retrieval_prompt(
|
|||
elif result is not None:
|
||||
question_results.append(result)
|
||||
|
||||
# 获取最近10分钟内已找到答案的缓存记录
|
||||
cached_answers = _get_recent_found_answers(chat_id, time_window_seconds=600.0)
|
||||
|
||||
# 合并当前查询结果和缓存答案（去重：如果当前查询的问题在缓存中已存在，优先使用当前结果）
|
||||
all_results = []
|
||||
|
||||
# 先添加当前查询的结果
|
||||
current_questions = set()
|
||||
for result in question_results:
|
||||
# 提取问题（格式为 "问题：xxx\n答案：xxx"）
|
||||
if result.startswith("问题："):
|
||||
question_end = result.find("\n答案：")
|
||||
if question_end != -1:
|
||||
current_questions.add(result[4:question_end])
|
||||
all_results.append(result)
|
||||
|
||||
# 添加缓存答案（排除当前查询中已存在的问题）
|
||||
for cached_answer in cached_answers:
|
||||
if cached_answer.startswith("问题："):
|
||||
question_end = cached_answer.find("\n答案：")
|
||||
if question_end != -1:
|
||||
cached_question = cached_answer[4:question_end]
|
||||
if cached_question not in current_questions:
|
||||
all_results.append(cached_answer)
|
||||
|
||||
end_time = time.time()
|
||||
|
||||
if question_results:
|
||||
retrieved_memory = "\n\n".join(question_results)
|
||||
logger.info(f"记忆检索成功，耗时: {(end_time - start_time):.3f}秒，包含 {len(question_results)} 条记忆")
|
||||
if all_results:
|
||||
retrieved_memory = "\n\n".join(all_results)
|
||||
current_count = len(question_results)
|
||||
cached_count = len(all_results) - current_count
|
||||
logger.info(
|
||||
f"记忆检索成功，耗时: {(end_time - start_time):.3f}秒，"
|
||||
f"当前查询 {current_count} 条记忆，缓存 {cached_count} 条记忆，共 {len(all_results)} 条记忆"
|
||||
)
|
||||
return f"你回忆起了以下信息：\n{retrieved_memory}\n如果与回复内容相关，可以参考这些回忆的信息。\n"
|
||||
else:
|
||||
logger.debug("所有问题均未找到答案")
|
||||
logger.debug("所有问题均未找到答案，且无缓存答案")
|
||||
return ""
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"记忆检索时发生异常: {str(e)}")
|
||||
return ""
|
||||
|
||||
|
||||
def _parse_questions_json(response: str) -> Tuple[List[str], List[str]]:
|
||||
"""解析问题JSON，返回概念列表和问题列表
|
||||
|
||||
Args:
|
||||
response: LLM返回的响应
|
||||
|
||||
Returns:
|
||||
Tuple[List[str], List[str]]: (概念列表, 问题列表)
|
||||
"""
|
||||
try:
|
||||
# 尝试提取JSON（可能包含在```json代码块中）
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
matches = re.findall(json_pattern, response, re.DOTALL)
|
||||
|
||||
if matches:
|
||||
json_str = matches[0]
|
||||
else:
|
||||
# 尝试直接解析整个响应
|
||||
json_str = response.strip()
|
||||
|
||||
# 修复可能的JSON错误
|
||||
repaired_json = repair_json(json_str)
|
||||
|
||||
# 解析JSON
|
||||
parsed = json.loads(repaired_json)
|
||||
|
||||
# 只支持新格式：包含concepts和questions的对象
|
||||
if not isinstance(parsed, dict):
|
||||
logger.warning(f"解析的JSON不是对象格式: {parsed}")
|
||||
return [], []
|
||||
|
||||
concepts_raw = parsed.get("concepts", [])
|
||||
questions_raw = parsed.get("questions", [])
|
||||
|
||||
# 确保是列表
|
||||
if not isinstance(concepts_raw, list):
|
||||
concepts_raw = []
|
||||
if not isinstance(questions_raw, list):
|
||||
questions_raw = []
|
||||
|
||||
# 确保所有元素都是字符串
|
||||
concepts = [c for c in concepts_raw if isinstance(c, str) and c.strip()]
|
||||
questions = [q for q in questions_raw if isinstance(q, str) and q.strip()]
|
||||
|
||||
return concepts, questions
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"解析问题JSON失败: {e}, 响应内容: {response[:200]}...")
|
||||
return [], []
|
||||
|
|
|
|||
|
|
@ -8,7 +8,8 @@ import json
|
|||
import re
|
||||
from datetime import datetime
|
||||
from typing import Tuple
|
||||
from difflib import SequenceMatcher
|
||||
from typing import List
|
||||
from json_repair import repair_json
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
||||
|
|
@ -16,101 +17,56 @@ from src.common.logger import get_logger
|
|||
logger = get_logger("memory_utils")
|
||||
|
||||
|
||||
def parse_md_json(json_text: str) -> list[str]:
|
||||
"""从Markdown格式的内容中提取JSON对象和推理内容"""
|
||||
json_objects = []
|
||||
reasoning_content = ""
|
||||
|
||||
# 使用正则表达式查找```json包裹的JSON内容
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
matches = re.findall(json_pattern, json_text, re.DOTALL)
|
||||
|
||||
# 提取JSON之前的内容作为推理文本
|
||||
if matches:
|
||||
# 找到第一个```json的位置
|
||||
first_json_pos = json_text.find("```json")
|
||||
if first_json_pos > 0:
|
||||
reasoning_content = json_text[:first_json_pos].strip()
|
||||
# 清理推理内容中的注释标记
|
||||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||||
reasoning_content = reasoning_content.strip()
|
||||
|
||||
for match in matches:
|
||||
try:
|
||||
# 清理可能的注释和格式问题
|
||||
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
|
||||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
|
||||
if json_str := json_str.strip():
|
||||
json_obj = json.loads(json_str)
|
||||
if isinstance(json_obj, dict):
|
||||
json_objects.append(json_obj)
|
||||
elif isinstance(json_obj, list):
|
||||
for item in json_obj:
|
||||
if isinstance(item, dict):
|
||||
json_objects.append(item)
|
||||
except Exception as e:
|
||||
logger.warning(f"解析JSON块失败: {e}, 块内容: {match[:100]}...")
|
||||
continue
|
||||
|
||||
return json_objects, reasoning_content
|
||||
|
||||
|
||||
def calculate_similarity(text1: str, text2: str) -> float:
|
||||
"""
|
||||
计算两个文本的相似度
|
||||
def parse_questions_json(response: str) -> Tuple[List[str], List[str]]:
|
||||
"""解析问题JSON，返回概念列表和问题列表
|
||||
|
||||
Args:
|
||||
text1: 第一个文本
|
||||
text2: 第二个文本
|
||||
response: LLM返回的响应
|
||||
|
||||
Returns:
|
||||
float: 相似度分数 (0-1)
|
||||
Tuple[List[str], List[str]]: (概念列表, 问题列表)
|
||||
"""
|
||||
try:
|
||||
# 预处理文本
|
||||
text1 = preprocess_text(text1)
|
||||
text2 = preprocess_text(text2)
|
||||
# 尝试提取JSON（可能包含在```json代码块中）
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
matches = re.findall(json_pattern, response, re.DOTALL)
|
||||
|
||||
# 使用SequenceMatcher计算相似度
|
||||
similarity = SequenceMatcher(None, text1, text2).ratio()
|
||||
if matches:
|
||||
json_str = matches[0]
|
||||
else:
|
||||
# 尝试直接解析整个响应
|
||||
json_str = response.strip()
|
||||
|
||||
# 如果其中一个文本包含另一个，提高相似度
|
||||
if text1 in text2 or text2 in text1:
|
||||
similarity = max(similarity, 0.8)
|
||||
# 修复可能的JSON错误
|
||||
repaired_json = repair_json(json_str)
|
||||
|
||||
return similarity
|
||||
# 解析JSON
|
||||
parsed = json.loads(repaired_json)
|
||||
|
||||
# 只支持新格式：包含concepts和questions的对象
|
||||
if not isinstance(parsed, dict):
|
||||
logger.warning(f"解析的JSON不是对象格式: {parsed}")
|
||||
return [], []
|
||||
|
||||
concepts_raw = parsed.get("concepts", [])
|
||||
questions_raw = parsed.get("questions", [])
|
||||
|
||||
# 确保是列表
|
||||
if not isinstance(concepts_raw, list):
|
||||
concepts_raw = []
|
||||
if not isinstance(questions_raw, list):
|
||||
questions_raw = []
|
||||
|
||||
# 确保所有元素都是字符串
|
||||
concepts = [c for c in concepts_raw if isinstance(c, str) and c.strip()]
|
||||
questions = [q for q in questions_raw if isinstance(q, str) and q.strip()]
|
||||
|
||||
return concepts, questions
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算相似度时出错: {e}")
|
||||
return 0.0
|
||||
|
||||
|
||||
def preprocess_text(text: str) -> str:
|
||||
"""
|
||||
预处理文本，提高匹配准确性
|
||||
|
||||
Args:
|
||||
text: 原始文本
|
||||
|
||||
Returns:
|
||||
str: 预处理后的文本
|
||||
"""
|
||||
try:
|
||||
# 转换为小写
|
||||
text = text.lower()
|
||||
|
||||
# 移除标点符号和特殊字符
|
||||
text = re.sub(r"[^\w\s]", "", text)
|
||||
|
||||
# 移除多余空格
|
||||
text = re.sub(r"\s+", " ", text).strip()
|
||||
|
||||
return text
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"预处理文本时出错: {e}")
|
||||
return text
|
||||
|
||||
logger.error(f"解析问题JSON失败: {e}, 响应内容: {response[:200]}...")
|
||||
return [], []
|
||||
|
||||
def parse_datetime_to_timestamp(value: str) -> float:
|
||||
"""
|
||||
|
|
@ -140,29 +96,3 @@ def parse_datetime_to_timestamp(value: str) -> float:
|
|||
except Exception as e:
|
||||
last_err = e
|
||||
raise ValueError(f"无法解析时间: {value} ({last_err})")
|
||||
|
||||
|
||||
def parse_time_range(time_range: str) -> Tuple[float, float]:
|
||||
"""
|
||||
解析时间范围字符串，返回开始和结束时间戳
|
||||
|
||||
Args:
|
||||
time_range: 时间范围字符串，格式："YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS"
|
||||
|
||||
Returns:
|
||||
Tuple[float, float]: (开始时间戳, 结束时间戳)
|
||||
"""
|
||||
if " - " not in time_range:
|
||||
raise ValueError(f"时间范围格式错误，应为 '开始时间 - 结束时间': {time_range}")
|
||||
|
||||
parts = time_range.split(" - ", 1)
|
||||
if len(parts) != 2:
|
||||
raise ValueError(f"时间范围格式错误: {time_range}")
|
||||
|
||||
start_str = parts[0].strip()
|
||||
end_str = parts[1].strip()
|
||||
|
||||
start_timestamp = parse_datetime_to_timestamp(start_str)
|
||||
end_timestamp = parse_datetime_to_timestamp(end_str)
|
||||
|
||||
return start_timestamp, end_timestamp
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ from .tool_registry import (
|
|||
from .query_chat_history import register_tool as register_query_chat_history
|
||||
from .query_lpmm_knowledge import register_tool as register_lpmm_knowledge
|
||||
from .query_person_info import register_tool as register_query_person_info
|
||||
from .found_answer import register_tool as register_found_answer
|
||||
from src.config.config import global_config
|
||||
|
||||
|
||||
|
|
@ -21,6 +22,7 @@ def init_all_tools():
|
|||
"""初始化并注册所有记忆检索工具"""
|
||||
register_query_chat_history()
|
||||
register_query_person_info()
|
||||
register_found_answer() # 注册found_answer工具
|
||||
|
||||
if global_config.lpmm_knowledge.lpmm_mode == "agent":
|
||||
register_lpmm_knowledge()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,40 @@
|
|||
"""
|
||||
found_answer工具 - 用于在记忆检索过程中标记找到答案
|
||||
"""
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from .tool_registry import register_memory_retrieval_tool
|
||||
|
||||
logger = get_logger("memory_retrieval_tools")
|
||||
|
||||
|
||||
async def found_answer(answer: str) -> str:
|
||||
"""标记已找到问题的答案
|
||||
|
||||
Args:
|
||||
answer: 找到的答案内容
|
||||
|
||||
Returns:
|
||||
str: 确认信息
|
||||
"""
|
||||
# 这个工具主要用于标记，实际答案会通过返回值传递
|
||||
logger.info(f"找到答案: {answer}")
|
||||
return f"已确认找到答案: {answer}"
|
||||
|
||||
|
||||
def register_tool():
|
||||
"""注册found_answer工具"""
|
||||
register_memory_retrieval_tool(
|
||||
name="found_answer",
|
||||
description="当你在已收集的信息中找到了问题的明确答案时，调用此工具标记已找到答案。只有在检索到明确、具体的答案时才使用此工具，不要编造信息。",
|
||||
parameters=[
|
||||
{
|
||||
"name": "answer",
|
||||
"type": "string",
|
||||
"description": "找到的答案内容，必须基于已收集的信息，不要编造",
|
||||
"required": True,
|
||||
},
|
||||
],
|
||||
execute_func=found_answer,
|
||||
)
|
||||
|
||||
|
|
@ -275,10 +275,6 @@ async def get_chat_history_detail(chat_id: str, memory_ids: str) -> str:
|
|||
except (json.JSONDecodeError, TypeError, ValueError):
|
||||
pass
|
||||
|
||||
# 添加原文内容
|
||||
if record.original_text:
|
||||
result_parts.append(f"原文内容：\n{record.original_text}")
|
||||
|
||||
results.append("\n".join(result_parts))
|
||||
|
||||
if not results:
|
||||
|
|
@ -318,7 +314,7 @@ def register_tool():
|
|||
# 注册工具2：获取记忆详情
|
||||
register_memory_retrieval_tool(
|
||||
name="get_chat_history_detail",
|
||||
description="根据记忆ID，展示某条或某几条记忆的具体内容。包括主题、时间、参与人、关键词、概括、关键信息点和原文内容等详细信息。需要先使用search_chat_history工具获取记忆ID。",
|
||||
description="根据记忆ID，展示某条或某几条记忆的具体内容。包括主题、时间、参与人、关键词、概括和关键信息点等详细信息。需要先使用search_chat_history工具获取记忆ID。",
|
||||
parameters=[
|
||||
{
|
||||
"name": "memory_ids",
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ from pydantic import BaseModel
|
|||
from typing import Optional, List, Dict
|
||||
from src.common.logger import get_logger
|
||||
from src.common.database.database_model import Expression, ChatStreams
|
||||
from .token_manager import get_token_manager
|
||||
from .auth import verify_auth_token_from_cookie_or_header
|
||||
import time
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
"""黑话（俚语）管理路由"""
|
||||
|
||||
import json
|
||||
from typing import Optional, List
|
||||
from typing import Optional, List, Annotated
|
||||
from fastapi import APIRouter, HTTPException, Query
|
||||
from pydantic import BaseModel, Field
|
||||
from peewee import fn
|
||||
|
|
@ -331,19 +331,19 @@ async def get_jargon_stats():
|
|||
total = Jargon.select().count()
|
||||
|
||||
# 已确认是黑话的数量
|
||||
confirmed_jargon = Jargon.select().where(Jargon.is_jargon == True).count()
|
||||
confirmed_jargon = Jargon.select().where(Jargon.is_jargon).count()
|
||||
|
||||
# 已确认不是黑话的数量
|
||||
confirmed_not_jargon = Jargon.select().where(Jargon.is_jargon == False).count()
|
||||
confirmed_not_jargon = Jargon.select().where(~Jargon.is_jargon).count()
|
||||
|
||||
# 未判定的数量
|
||||
pending = Jargon.select().where(Jargon.is_jargon.is_null()).count()
|
||||
|
||||
# 全局黑话数量
|
||||
global_count = Jargon.select().where(Jargon.is_global == True).count()
|
||||
global_count = Jargon.select().where(Jargon.is_global).count()
|
||||
|
||||
# 已完成推断的数量
|
||||
complete_count = Jargon.select().where(Jargon.is_complete == True).count()
|
||||
complete_count = Jargon.select().where(Jargon.is_complete).count()
|
||||
|
||||
# 关联的聊天数量
|
||||
chat_count = (
|
||||
|
|
@ -519,8 +519,8 @@ async def batch_delete_jargons(request: BatchDeleteRequest):
|
|||
|
||||
@router.post("/batch/set-jargon", response_model=JargonUpdateResponse)
|
||||
async def batch_set_jargon_status(
|
||||
ids: List[int] = Query(..., description="黑话ID列表"),
|
||||
is_jargon: bool = Query(..., description="是否是黑话"),
|
||||
ids: Annotated[List[int], Query(description="黑话ID列表")],
|
||||
is_jargon: Annotated[bool, Query(description="是否是黑话")],
|
||||
):
|
||||
"""批量设置黑话状态"""
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ from pydantic import BaseModel
|
|||
from typing import Optional, List, Dict
|
||||
from src.common.logger import get_logger
|
||||
from src.common.database.database_model import PersonInfo
|
||||
from .token_manager import get_token_manager
|
||||
from .auth import verify_auth_token_from_cookie_or_header
|
||||
import json
|
||||
import time
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@
|
|||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime
|
||||
from fastapi import APIRouter, HTTPException
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -7,7 +7,7 @@
|
|||
<link rel="icon" type="image/x-icon" href="/maimai.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>MaiBot Dashboard</title>
|
||||
<script type="module" crossorigin src="/assets/index-DuFwC87p.js"></script>
|
||||
<script type="module" crossorigin src="/assets/index-DJb_iiTR.js"></script>
|
||||
<link rel="modulepreload" crossorigin href="/assets/react-vendor-Dtc2IqVY.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/router-CWhjJi2n.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/utils-CCeOswSm.js">
|
||||
|
|
|
|||
Loading…
Reference in New Issue