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feat：新的记忆系统，deepthink插件，修复平行动作

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This commit is contained in:
SengokuCola
2025-09-28 02:03:43 +08:00
parent 5cc1e56904
commit 0cd39476d8
15 changed files with 667 additions and 72 deletions
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2
src/chat/express/expression_learner.py
View File

@@ -427,7 +427,7 @@ class ExpressionLearner:
chat_str=random_msg_str,
)
print(f"random_msg_str:{random_msg_str}")
# print(f"random_msg_str:{random_msg_str}")
logger.info(f"学习{type_str}的prompt: {prompt}")
try:

6
src/chat/heart_flow/heartFC_chat.py
View File

@@ -25,6 +25,7 @@ from src.plugin_system.core import events_manager
from src.plugin_system.apis import generator_api, send_api, message_api, database_api
from src.mais4u.mai_think import mai_thinking_manager
from src.mais4u.s4u_config import s4u_config
from src.chat.memory_system.Memory_chest import global_memory_chest
from src.chat.utils.chat_message_builder import (
build_readable_messages_with_id,
get_raw_msg_before_timestamp_with_chat,
@@ -102,6 +103,7 @@ class HeartFChatting:
self.talk_threshold = global_config.chat.talk_value
self.no_reply_until_call = False
async def start(self):
"""检查是否需要启动主循环，如果未激活则启动。"""
@@ -284,6 +286,10 @@ class HeartFChatting:
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
await self.expression_learner.trigger_learning_for_chat()
await global_memory_chest.build_running_content(chat_id=self.stream_id)
cycle_timers, thinking_id = self.start_cycle()
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")

321
src/chat/memory_system/Memory_chest.py Normal file
View File

@@ -0,0 +1,321 @@
from src.llm_models.utils_model import LLMRequest
from src.config.config import model_config
from src.common.database.database_model import MemoryChest as MemoryChestModel
from src.common.logger import get_logger
from src.config.config import global_config
from src.plugin_system.apis.message_api import build_readable_messages
import time
from src.plugin_system.apis.message_api import get_raw_msg_by_timestamp_with_chat
logger = get_logger("memory_chest")
class MemoryChest:
def __init__(self):
self.LLMRequest = LLMRequest(
model_set=model_config.model_task_config.utils_small,
request_type="memory_chest",
)
self.memory_build_threshold = 20
self.memory_size_limit = 300
self.running_content_list = {} # {chat_id: {"content": running_content, "last_update_time": timestamp}}
self.fetched_memory_list = [] # [(chat_id, (question, answer, timestamp)), ...]
async def build_running_content(self, chat_id: str = None) -> str:
"""
构建记忆仓库的运行内容
Args:
message_str: 消息内容
chat_id: 聊天ID，用于提取对应的运行内容
Returns:
str: 构建后的运行内容
"""
# 检查是否需要更新：上次更新时间和现在时间的消息数量大于30
if chat_id not in self.running_content_list:
self.running_content_list[chat_id] = {
"content": "",
"last_update_time": time.time()
}
should_update = True
if chat_id and chat_id in self.running_content_list:
last_update_time = self.running_content_list[chat_id]["last_update_time"]
current_time = time.time()
# 使用message_api获取消息数量
message_list = get_raw_msg_by_timestamp_with_chat(
timestamp_start=last_update_time,
timestamp_end=current_time,
chat_id=chat_id,
limit=global_config.chat.max_context_size * 2,
)
new_messages_count = len(message_list)
should_update = new_messages_count > self.memory_build_threshold
logger.info(f"chat_id {chat_id} 自上次更新后有 {new_messages_count} 条新消息，{'需要' if should_update else '不需要'}更新")
if should_update:
# 如果有chat_id，先提取对应的running_content
message_str = build_readable_messages(
message_list,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
show_actions=True,
)
current_running_content = ""
if chat_id and chat_id in self.running_content_list:
current_running_content = self.running_content_list[chat_id]["content"]
prompt = f"""
以下是你的记忆内容：
{current_running_content}
请将下面的新聊天记录内的有用的信息，添加到你的记忆中
请主要关注概念和知识，而不是聊天的琐事
记忆为一段纯文本，逻辑清晰，指出事件，概念的含义，并说明关系
请输出添加后的记忆内容，不要输出其他内容：
{message_str}
"""
if global_config.debug.show_prompt:
logger.info(f"记忆仓库构建运行内容 prompt: {prompt}")
else:
logger.debug(f"记忆仓库构建运行内容 prompt: {prompt}")
running_content, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(prompt)
print(f"记忆仓库构建运行内容: {running_content}")
# 如果有chat_id，更新对应的running_content
if chat_id and running_content:
self.running_content_list[chat_id] = {
"content": running_content,
"last_update_time": time.time()
}
# 检查running_content长度是否大于500
if len(running_content) > self.memory_size_limit:
await self._save_to_database_and_clear(chat_id, running_content)
return running_content
def get_all_titles(self) -> list[str]:
"""
获取记忆仓库中的所有标题
Returns:
list: 包含所有标题的列表
"""
try:
# 查询所有记忆记录的标题
titles = []
for memory in MemoryChestModel.select():
if memory.title:
titles.append(memory.title)
return titles
except Exception as e:
print(f"获取记忆标题时出错: {e}")
return []
async def get_answer_by_question(self, chat_id: str = "", question: str = "") -> str:
"""
根据问题获取答案
"""
title = await self.select_title_by_question(question)
if not title:
return ""
for memory in MemoryChestModel.select():
if memory.title == title:
content = memory.content
prompt = f"""
{content}
请根据问题：{question}
在上方内容中，提取相关信息的原文并输出，请务必提取上面原文，不要输出其他内容：
"""
if global_config.debug.show_prompt:
logger.info(f"记忆仓库获取答案 prompt: {prompt}")
else:
logger.debug(f"记忆仓库获取答案 prompt: {prompt}")
answer, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(prompt)
logger.info(f"记忆仓库获取答案: {answer}")
# 将问题和答案存到fetched_memory_list
if chat_id and answer:
self.fetched_memory_list.append((chat_id, (question, answer, time.time())))
# 清理fetched_memory_list
self._cleanup_fetched_memory_list()
return answer
def get_chat_memories_as_string(self, chat_id: str) -> str:
"""
获取某个chat_id的所有记忆，并构建成字符串
Args:
chat_id: 聊天ID
Returns:
str: 格式化的记忆字符串，格式：问题：xxx,答案:xxxxx\n问题：xxx,答案:xxxxx\n...
"""
try:
memories = []
# 从fetched_memory_list中获取该chat_id的所有记忆
for cid, (question, answer, timestamp) in self.fetched_memory_list:
if cid == chat_id:
memories.append(f"问题：{question},答案:{answer}")
# 按时间戳排序（最新的在后面）
memories.sort()
# 用换行符连接所有记忆
result = "\n".join(memories)
logger.info(f"chat_id {chat_id} 共有 {len(memories)} 条记忆")
return result
except Exception as e:
logger.error(f"获取chat_id {chat_id} 的记忆时出错: {e}")
return ""
async def select_title_by_question(self, question: str) -> str:
"""
根据消息内容选择最匹配的标题
Args:
question: 问题
Returns:
str: 选择的标题
"""
# 获取所有标题并构建格式化字符串
titles = self.get_all_titles()
formatted_titles = ""
for title in titles:
formatted_titles += f"{title}\n"
prompt = f"""
所有主题：
{formatted_titles}
请根据以下问题，选择一个能够回答问题的主题：
问题：{question}
请你输出主题，不要输出其他内容，完整输出主题名：
"""
if global_config.debug.show_prompt:
logger.info(f"记忆仓库选择标题 prompt: {prompt}")
else:
logger.debug(f"记忆仓库选择标题 prompt: {prompt}")
title, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(prompt)
# 根据 title 获取 titles 里的对应项
titles = self.get_all_titles()
selected_title = None
# 查找完全匹配的标题
for t in titles:
if t == title:
selected_title = t
break
logger.info(f"记忆仓库选择标题: {selected_title}")
return selected_title
def _cleanup_fetched_memory_list(self):
"""
清理fetched_memory_list，移除超过10分钟的记忆和超过10条的最旧记忆
"""
try:
current_time = time.time()
ten_minutes_ago = current_time - 600 # 10分钟 = 600秒
# 移除超过10分钟的记忆
self.fetched_memory_list = [
(chat_id, (question, answer, timestamp))
for chat_id, (question, answer, timestamp) in self.fetched_memory_list
if timestamp > ten_minutes_ago
]
# 如果记忆条数超过10条，移除最旧的5条
if len(self.fetched_memory_list) > 10:
# 按时间戳排序，移除最旧的5条
self.fetched_memory_list.sort(key=lambda x: x[1][2]) # 按timestamp排序
self.fetched_memory_list = self.fetched_memory_list[5:] # 保留最新的5条
logger.debug(f"fetched_memory_list清理后，当前有 {len(self.fetched_memory_list)} 条记忆")
except Exception as e:
logger.error(f"清理fetched_memory_list时出错: {e}")
async def _save_to_database_and_clear(self, chat_id: str, content: str):
"""
生成标题，保存到数据库，并清空对应chat_id的running_content
Args:
chat_id: 聊天ID
content: 要保存的内容
"""
try:
# 生成标题
title_prompt = f"""
请为以下内容生成一个描述全面的标题，要求描述内容的主要概念和事件：
{content}
请只输出标题，不要输出其他内容：
"""
if global_config.debug.show_prompt:
logger.info(f"记忆仓库生成标题 prompt: {title_prompt}")
else:
logger.debug(f"记忆仓库生成标题 prompt: {title_prompt}")
title, (reasoning_content, model_name, tool_calls) = await self.LLMRequest.generate_response_async(title_prompt)
if title:
# 保存到数据库
MemoryChestModel.create(
title=title.strip(),
content=content
)
logger.info(f"已保存记忆仓库内容，标题: {title.strip()}, chat_id: {chat_id}")
# 清空对应chat_id的running_content
if chat_id in self.running_content_list:
del self.running_content_list[chat_id]
logger.info(f"已清空chat_id {chat_id} 的running_content")
else:
logger.warning(f"生成标题失败，chat_id: {chat_id}")
except Exception as e:
logger.error(f"保存记忆仓库内容时出错: {e}")
global_memory_chest = MemoryChest()

8
src/chat/planner_actions/planner.py
View File

@@ -109,7 +109,7 @@ no_reply_until_call
"""
{action_name}
动作描述：{action_description}
使用条件：
使用条件{parallel_text}：
{action_require}
{{
"action": "{action_name}",{action_parameters},
@@ -421,6 +421,11 @@ class ActionPlanner:
for require_item in action_info.action_require:
require_text += f"- {require_item}\n"
require_text = require_text.rstrip("\n")
if not action_info.parallel_action:
parallel_text = "(当选择这个动作时，请不要选择其他动作)"
else:
parallel_text = ""
# 获取动作提示模板并填充
using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
@@ -429,6 +434,7 @@ class ActionPlanner:
action_description=action_info.description,
action_parameters=param_text,
action_require=require_text,
parallel_text=parallel_text,
)
action_options_block += using_action_prompt

19
src/chat/replyer/group_generator.py
View File

@@ -6,6 +6,7 @@ import re
from typing import List, Optional, Dict, Any, Tuple
from datetime import datetime
from src.chat.memory_system.Memory_chest import global_memory_chest
from src.mais4u.mai_think import mai_thinking_manager
from src.common.logger import get_logger
from src.common.data_models.database_data_model import DatabaseMessages
@@ -315,6 +316,17 @@ class DefaultReplyer:
# memory_str += f"- {instant_memory}\n"
# return memory_str
async def build_memory_block(self) -> str:
"""构建记忆块
"""
# if not global_config.memory.enable_memory:
# return ""
if global_memory_chest.get_chat_memories_as_string(self.chat_stream.stream_id):
return f"你有以下记忆：\n{global_memory_chest.get_chat_memories_as_string(self.chat_stream.stream_id)}"
else:
return ""
async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
"""构建工具信息块
@@ -701,6 +713,7 @@ class DefaultReplyer:
# self.build_relation_info(chat_talking_prompt_short, sender, person_list_short), "relation_info"
# ),
# self._time_and_run_task(self.build_memory_block(message_list_before_short, target), "memory_block"),
self._time_and_run_task(self.build_memory_block(), "memory_block"),
self._time_and_run_task(
self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool), "tool_info"
),
@@ -714,6 +727,7 @@ class DefaultReplyer:
"expression_habits": "选取表达方式",
"relation_info": "感受关系",
# "memory_block": "回忆",
"memory_block": "记忆",
"tool_info": "使用工具",
"prompt_info": "获取知识",
"actions_info": "动作信息",
@@ -742,6 +756,7 @@ class DefaultReplyer:
selected_expressions: List[int]
# relation_info: str = results_dict["relation_info"]
# memory_block: str = results_dict["memory_block"]
memory_block: str = results_dict["memory_block"]
tool_info: str = results_dict["tool_info"]
prompt_info: str = results_dict["prompt_info"] # 直接使用格式化后的结果
actions_info: str = results_dict["actions_info"]
@@ -779,6 +794,7 @@ class DefaultReplyer:
"replyer_self_prompt",
expression_habits_block=expression_habits_block,
tool_info_block=tool_info,
memory_block=memory_block,
knowledge_prompt=prompt_info,
# memory_block=memory_block,
# relation_info_block=relation_info,
@@ -798,6 +814,7 @@ class DefaultReplyer:
"replyer_prompt",
expression_habits_block=expression_habits_block,
tool_info_block=tool_info,
memory_block=memory_block,
knowledge_prompt=prompt_info,
# memory_block=memory_block,
# relation_info_block=relation_info,
@@ -946,7 +963,7 @@ class DefaultReplyer:
async def llm_generate_content(self, prompt: str):
with Timer("LLM生成", {}): # 内部计时器，可选保留
# 直接使用已初始化的模型实例
# logger.info(f"\n{prompt}\n")
logger.info(f"\n{prompt}\n")
if global_config.debug.show_prompt:
logger.info(f"\n{prompt}\n")

19
src/chat/replyer/private_generator.py
View File

@@ -6,6 +6,7 @@ import re
from typing import List, Optional, Dict, Any, Tuple
from datetime import datetime
from src.chat.memory_system.Memory_chest import global_memory_chest
from src.mais4u.mai_think import mai_thinking_manager
from src.common.logger import get_logger
from src.common.data_models.database_data_model import DatabaseMessages
@@ -312,6 +313,15 @@ class PrivateReplyer:
# return memory_str
async def build_memory_block(self) -> str:
"""构建记忆块
"""
if global_memory_chest.get_chat_memories_as_string(self.chat_stream.stream_id):
return f"你有以下记忆：\n{global_memory_chest.get_chat_memories_as_string(self.chat_stream.stream_id)}"
else:
return ""
async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
"""构建工具信息块
@@ -582,6 +592,7 @@ class PrivateReplyer:
self._time_and_run_task(
self.build_relation_info(chat_talking_prompt_short, sender), "relation_info"
),
self._time_and_run_task(self.build_memory_block(), "memory_block"),
# self._time_and_run_task(self.build_memory_block(message_list_before_short, target), "memory_block"),
self._time_and_run_task(
self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool), "tool_info"
@@ -595,7 +606,7 @@ class PrivateReplyer:
task_name_mapping = {
"expression_habits": "选取表达方式",
"relation_info": "感受关系",
# "memory_block": "回忆",
"memory_block": "回忆",
"tool_info": "使用工具",
"prompt_info": "获取知识",
"actions_info": "动作信息",
@@ -623,7 +634,7 @@ class PrivateReplyer:
expression_habits_block: str
selected_expressions: List[int]
relation_info: str = results_dict["relation_info"]
# memory_block: str = results_dict["memory_block"]
memory_block: str = results_dict["memory_block"]
tool_info: str = results_dict["tool_info"]
prompt_info: str = results_dict["prompt_info"] # 直接使用格式化后的结果
actions_info: str = results_dict["actions_info"]
@@ -649,7 +660,7 @@ class PrivateReplyer:
expression_habits_block=expression_habits_block,
tool_info_block=tool_info,
knowledge_prompt=prompt_info,
# memory_block=memory_block,
memory_block=memory_block,
relation_info_block=relation_info,
extra_info_block=extra_info_block,
identity=personality_prompt,
@@ -670,7 +681,7 @@ class PrivateReplyer:
expression_habits_block=expression_habits_block,
tool_info_block=tool_info,
knowledge_prompt=prompt_info,
# memory_block=memory_block,
memory_block=memory_block,
relation_info_block=relation_info,
extra_info_block=extra_info_block,
identity=personality_prompt,

8
src/chat/replyer/prompt/replyer_prompt.py
View File

@@ -13,7 +13,7 @@ def init_replyer_prompt():
Prompt(
"""{knowledge_prompt}{tool_info_block}{extra_info_block}
{expression_habits_block}
{expression_habits_block}{memory_block}
你正在qq群里聊天，下面是群里正在聊的内容:
{time_block}
@@ -34,7 +34,7 @@ def init_replyer_prompt():
Prompt(
"""{knowledge_prompt}{tool_info_block}{extra_info_block}
{expression_habits_block}
{expression_habits_block}{memory_block}
你正在qq群里聊天，下面是群里正在聊的内容:
{time_block}
@@ -55,7 +55,7 @@ def init_replyer_prompt():
Prompt(
"""{knowledge_prompt}{tool_info_block}{extra_info_block}
{expression_habits_block}
{expression_habits_block}{memory_block}
你正在和{sender_name}聊天，这是你们之前聊的内容:
{time_block}
@@ -74,7 +74,7 @@ def init_replyer_prompt():
Prompt(
"""{knowledge_prompt}{tool_info_block}{extra_info_block}
{expression_habits_block}
{expression_habits_block}{memory_block}
你正在和{sender_name}聊天，这是你们之前聊的内容:
{time_block}