mirror of https://github.com/Mai-with-u/MaiBot.git
fix：更改一些参数
SengokuCola
parent
6e7e9be82a
commit
6b25c0295d
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@ -24,7 +24,7 @@ class MemoryChest:
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)
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)
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self.memory_build_threshold = 30
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self.memory_build_threshold = 30
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self.memory_size_limit = 800
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self.memory_size_limit = 1024
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self.running_content_list = {} # {chat_id: {"content": running_content, "last_update_time": timestamp}}
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self.running_content_list = {} # {chat_id: {"content": running_content, "last_update_time": timestamp}}
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self.fetched_memory_list = [] # [(chat_id, (question, answer, timestamp)), ...]
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self.fetched_memory_list = [] # [(chat_id, (question, answer, timestamp)), ...]
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@ -20,7 +20,7 @@ class HippocampusToMemoryChestTask(AsyncTask):
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def __init__(self):
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def __init__(self):
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super().__init__(
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super().__init__(
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task_name="Hippocampus to Memory Chest Task",
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task_name="Hippocampus to Memory Chest Task",
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wait_before_start=60, # 启动后等待60秒再开始
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wait_before_start=10, # 启动后等待60秒再开始
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run_interval=60 # 每60秒运行一次
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run_interval=60 # 每60秒运行一次
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)
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)
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@ -41,13 +41,14 @@ class HippocampusToMemoryChestTask(AsyncTask):
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# 获取所有节点
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# 获取所有节点
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all_nodes = list(memory_graph.nodes())
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all_nodes = list(memory_graph.nodes())
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if len(all_nodes) < 5:
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if len(all_nodes) < 10:
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selected_nodes = all_nodes
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logger.info(f"[海马体转换] 当前只有 {len(all_nodes)} 个节点，少于5个，跳过本次转换")
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logger.info(f"[海马体转换] 当前只有 {len(all_nodes)} 个节点，少于5个，跳过本次转换")
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return
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else:
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# 随机选择5个节点
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# 随机选择5个节点
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selected_nodes = random.sample(all_nodes, 5)
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selected_nodes = random.sample(all_nodes, 10)
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logger.info(f"[海马体转换] 随机选择了 {len(selected_nodes)} 个节点: {selected_nodes}")
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logger.info(f"[海马体转换] 随机选择了 {len(selected_nodes)} 个节点: {selected_nodes}")
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# 拼接节点内容
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# 拼接节点内容
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content_parts = []
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content_parts = []
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@ -1,241 +0,0 @@
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import json
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import random
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from json_repair import repair_json
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from typing import List, Tuple
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from src.config.config import global_config, model_config
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from src.common.logger import get_logger
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.utils.utils import parse_keywords_string
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from src.chat.utils.chat_message_builder import build_readable_messages
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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from src.llm_models.utils_model import LLMRequest
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logger = get_logger("memory_activator")
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def get_keywords_from_json(json_str) -> List:
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"""
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从JSON字符串中提取关键词列表
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Args:
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json_str: JSON格式的字符串
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Returns:
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List[str]: 关键词列表
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"""
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try:
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# 使用repair_json修复JSON格式
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fixed_json = repair_json(json_str)
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# 如果repair_json返回的是字符串，需要解析为Python对象
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result = json.loads(fixed_json) if isinstance(fixed_json, str) else fixed_json
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return result.get("keywords", [])
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except Exception as e:
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logger.error(f"解析关键词JSON失败: {e}")
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return []
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def init_prompt():
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# --- Group Chat Prompt ---
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memory_activator_prompt = """
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你需要根据以下信息来挑选合适的记忆编号
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以下是一段聊天记录，请根据这些信息，和下方的记忆，挑选和群聊内容有关的记忆编号
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聊天记录:
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{obs_info_text}
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你想要回复的消息:
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{target_message}
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记忆：
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{memory_info}
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请输出一个json格式，包含以下字段：
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{{
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"memory_ids": "记忆1编号,记忆2编号,记忆3编号,......"
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}}
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不要输出其他多余内容，只输出json格式就好
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"""
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Prompt(memory_activator_prompt, "memory_activator_prompt")
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class MemoryActivator:
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def __init__(self):
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self.key_words_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small,
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request_type="memory.activator",
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)
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# 用于记忆选择的 LLM 模型
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self.memory_selection_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small,
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request_type="memory.selection",
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)
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async def activate_memory_with_chat_history(
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self, target_message, chat_history: List[DatabaseMessages]
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) -> List[Tuple[str, str]]:
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"""
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激活记忆
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"""
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# 如果记忆系统被禁用，直接返回空列表
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if not global_config.memory.enable_memory:
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return []
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keywords_list = set()
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for msg in chat_history:
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keywords = parse_keywords_string(msg.key_words)
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if keywords:
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if len(keywords_list) < 30:
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# 最多容纳30个关键词
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keywords_list.update(keywords)
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logger.debug(f"提取关键词: {keywords_list}")
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else:
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break
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if not keywords_list:
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logger.debug("没有提取到关键词，返回空记忆列表")
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return []
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# 从海马体获取相关记忆
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related_memory = await hippocampus_manager.get_memory_from_topic(
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valid_keywords=list(keywords_list), max_memory_num=5, max_memory_length=3, max_depth=3
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)
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# logger.info(f"当前记忆关键词: {keywords_list}")
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logger.debug(f"获取到的记忆: {related_memory}")
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if not related_memory:
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logger.debug("海马体没有返回相关记忆")
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return []
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used_ids = set()
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candidate_memories = []
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# 为每个记忆分配随机ID并过滤相关记忆
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for memory in related_memory:
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keyword, content = memory
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found = any(kw in content for kw in keywords_list)
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if found:
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# 随机分配一个不重复的2位数id
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while True:
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random_id = "{:02d}".format(random.randint(0, 99))
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if random_id not in used_ids:
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used_ids.add(random_id)
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break
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candidate_memories.append({"memory_id": random_id, "keyword": keyword, "content": content})
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if not candidate_memories:
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logger.info("没有找到相关的候选记忆")
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return []
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# 如果只有少量记忆，直接返回
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if len(candidate_memories) <= 2:
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logger.debug(f"候选记忆较少({len(candidate_memories)}个)，直接返回")
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# 转换为 (keyword, content) 格式
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return [(mem["keyword"], mem["content"]) for mem in candidate_memories]
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return await self._select_memories_with_llm(target_message, chat_history, candidate_memories)
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async def _select_memories_with_llm(
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self, target_message, chat_history: List[DatabaseMessages], candidate_memories
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) -> List[Tuple[str, str]]:
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"""
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使用 LLM 选择合适的记忆
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Args:
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target_message: 目标消息
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chat_history_prompt: 聊天历史
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candidate_memories: 候选记忆列表，每个记忆包含 memory_id、keyword、content
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Returns:
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List[Tuple[str, str]]: 选择的记忆列表，格式为 (keyword, content)
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"""
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try:
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# 构建聊天历史字符串
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obs_info_text = build_readable_messages(
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chat_history,
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replace_bot_name=True,
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timestamp_mode="relative",
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read_mark=0.0,
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show_actions=True,
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)
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# 构建记忆信息字符串
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memory_lines = []
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for memory in candidate_memories:
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memory_id = memory["memory_id"]
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keyword = memory["keyword"]
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content = memory["content"]
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# 将 content 列表转换为字符串
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if isinstance(content, list):
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content_str = " | ".join(str(item) for item in content)
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else:
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content_str = str(content)
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memory_lines.append(f"记忆编号 {memory_id}: [关键词: {keyword}] {content_str}")
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memory_info = "\n".join(memory_lines)
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# 获取并格式化 prompt
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prompt_template = await global_prompt_manager.get_prompt_async("memory_activator_prompt")
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formatted_prompt = prompt_template.format(
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obs_info_text=obs_info_text, target_message=target_message, memory_info=memory_info
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)
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# 调用 LLM
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response, (reasoning_content, model_name, _) = await self.memory_selection_model.generate_response_async(
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formatted_prompt, temperature=0.3, max_tokens=150
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)
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if global_config.debug.show_prompt:
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logger.info(f"记忆选择 prompt: {formatted_prompt}")
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logger.info(f"LLM 记忆选择响应: {response}")
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else:
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logger.debug(f"记忆选择 prompt: {formatted_prompt}")
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logger.debug(f"LLM 记忆选择响应: {response}")
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# 解析响应获取选择的记忆编号
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try:
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fixed_json = repair_json(response)
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# 解析为 Python 对象
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result = json.loads(fixed_json) if isinstance(fixed_json, str) else fixed_json
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# 提取 memory_ids 字段并解析逗号分隔的编号
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if memory_ids_str := result.get("memory_ids", ""):
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memory_ids = [mid.strip() for mid in str(memory_ids_str).split(",") if mid.strip()]
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# 过滤掉空字符串和无效编号
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valid_memory_ids = [mid for mid in memory_ids if mid and len(mid) <= 3]
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selected_memory_ids = valid_memory_ids
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else:
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selected_memory_ids = []
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except Exception as e:
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logger.error(f"解析记忆选择响应失败: {e}", exc_info=True)
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selected_memory_ids = []
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# 根据编号筛选记忆
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selected_memories = []
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memory_id_to_memory = {mem["memory_id"]: mem for mem in candidate_memories}
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selected_memories = [
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memory_id_to_memory[memory_id] for memory_id in selected_memory_ids if memory_id in memory_id_to_memory
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]
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logger.info(f"LLM 选择的记忆编号: {selected_memory_ids}")
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logger.info(f"最终选择的记忆数量: {len(selected_memories)}")
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# 转换为 (keyword, content) 格式
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return [(mem["keyword"], mem["content"]) for mem in selected_memories]
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except Exception as e:
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logger.error(f"LLM 选择记忆时出错: {e}", exc_info=True)
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# 出错时返回前3个候选记忆作为备选，转换为 (keyword, content) 格式
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return [(mem["keyword"], mem["content"]) for mem in candidate_memories[:3]]
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init_prompt()
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@ -10,7 +10,7 @@
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"license": "GPL-v3.0-or-later",
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"license": "GPL-v3.0-or-later",
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"host_application": {
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"host_application": {
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"min_version": "0.10.1"
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"min_version": "0.10.4"
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},
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},
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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@ -12,132 +12,6 @@ from src.plugin_system.base.base_tool import BaseTool
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from typing import Any
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from typing import Any
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logger = get_logger("memory")
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logger = get_logger("memory")
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def init_prompt():
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Prompt(
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"""
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以下是一些记忆条目的分类：
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----------------------
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{category_list}
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----------------------
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每一个分类条目类型代表了你对用户："{person_name}"的印象的一个类别
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现在，你有一条对 {person_name} 的新记忆内容：
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{memory_point}
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请判断该记忆内容是否属于上述分类，请给出分类的名称。
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如果不属于上述分类，请输出一个合适的分类名称，对新记忆内容进行概括。要求分类名具有概括性。
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注意分类数一般不超过5个
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请严格用json格式输出，不要输出任何其他内容：
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{{
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"category": "分类名称"
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}} """,
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"relation_category",
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)
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Prompt(
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"""
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以下是有关{category}的现有记忆：
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----------------------
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{memory_list}
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----------------------
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现在，你有一条对 {person_name} 的新记忆内容：
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{memory_point}
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请判断该新记忆内容是否已经存在于现有记忆中，你可以对现有进行进行以下修改：
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注意，一般来说记忆内容不超过5个，且记忆文本不应太长
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1.新增：当记忆内容不存在于现有记忆，且不存在矛盾，请用json格式输出：
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{{
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"new_memory": "需要新增的记忆内容"
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}}
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2.加深印象：如果这个新记忆已经存在于现有记忆中，在内容上与现有记忆类似，请用json格式输出：
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{{
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|
||||||
"memory_id": 1, #请输出你认为需要加深印象的，与新记忆内容类似的，已经存在的记忆的序号
|
|
||||||
"integrate_memory": "加深后的记忆内容，合并内容类似的新记忆和旧记忆"
|
|
||||||
}}
|
|
||||||
3.整合：如果这个新记忆与现有记忆产生矛盾，请你结合其他记忆进行整合，用json格式输出：
|
|
||||||
{{
|
|
||||||
"memory_id": 1, #请输出你认为需要整合的，与新记忆存在矛盾的，已经存在的记忆的序号
|
|
||||||
"integrate_memory": "整合后的记忆内容，合并内容矛盾的新记忆和旧记忆"
|
|
||||||
}}
|
|
||||||
|
|
||||||
现在，请你根据情况选出合适的修改方式，并输出json，不要输出其他内容：
|
|
||||||
""",
|
|
||||||
"relation_category_update",
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# class BuildMemoryAction(BaseAction):
|
|
||||||
# """关系动作 - 构建关系"""
|
|
||||||
|
|
||||||
# activation_type = ActionActivationType.LLM_JUDGE
|
|
||||||
# parallel_action = True
|
|
||||||
|
|
||||||
# # 动作基本信息
|
|
||||||
# action_name = "build_memory"
|
|
||||||
# action_description = (
|
|
||||||
# "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息"
|
|
||||||
# )
|
|
||||||
|
|
||||||
# # 动作参数定义
|
|
||||||
# action_parameters = {
|
|
||||||
# "concept_name": "需要了解或记忆的概念或事件的名称",
|
|
||||||
# "concept_description": "需要了解或记忆的概念或事件的描述，需要具体且明确",
|
|
||||||
# }
|
|
||||||
|
|
||||||
# # 动作使用场景
|
|
||||||
# action_require = [
|
|
||||||
# "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息",
|
|
||||||
# "有你不了解的概念",
|
|
||||||
# "有人要求你记住某个概念或者事件",
|
|
||||||
# "你对某件事或概念有新的理解，或产生了兴趣",
|
|
||||||
# ]
|
|
||||||
|
|
||||||
# # 关联类型
|
|
||||||
# associated_types = ["text"]
|
|
||||||
|
|
||||||
# async def execute(self) -> Tuple[bool, str]:
|
|
||||||
# """执行关系动作"""
|
|
||||||
|
|
||||||
# try:
|
|
||||||
# # 1. 获取构建关系的原因
|
|
||||||
# concept_description = self.action_data.get("concept_description", "")
|
|
||||||
# logger.info(f"{self.log_prefix} 添加记忆原因: {self.reasoning}")
|
|
||||||
# concept_name = self.action_data.get("concept_name", "")
|
|
||||||
# # 2. 获取目标用户信息
|
|
||||||
|
|
||||||
# # 对 concept_name 进行jieba分词
|
|
||||||
# concept_name_tokens = cut_key_words(concept_name)
|
|
||||||
# # logger.info(f"{self.log_prefix} 对 concept_name 进行分词结果: {concept_name_tokens}")
|
|
||||||
|
|
||||||
# filtered_concept_name_tokens = [
|
|
||||||
# token
|
|
||||||
# for token in concept_name_tokens
|
|
||||||
# if all(keyword not in token for keyword in global_config.memory.memory_ban_words)
|
|
||||||
# ]
|
|
||||||
|
|
||||||
# if not filtered_concept_name_tokens:
|
|
||||||
# logger.warning(f"{self.log_prefix} 过滤后的概念名称列表为空，跳过添加记忆")
|
|
||||||
# return False, "过滤后的概念名称列表为空，跳过添加记忆"
|
|
||||||
|
|
||||||
# similar_topics_dict = (
|
|
||||||
# hippocampus_manager.get_hippocampus().parahippocampal_gyrus.get_similar_topics_from_keywords(
|
|
||||||
# filtered_concept_name_tokens
|
|
||||||
# )
|
|
||||||
# )
|
|
||||||
# await hippocampus_manager.get_hippocampus().parahippocampal_gyrus.add_memory_with_similar(
|
|
||||||
# concept_description, similar_topics_dict
|
|
||||||
# )
|
|
||||||
|
|
||||||
# return True, f"成功添加记忆: {concept_name}"
|
|
||||||
|
|
||||||
# except Exception as e:
|
|
||||||
# logger.error(f"{self.log_prefix} 构建记忆时出错: {e}")
|
|
||||||
# return False, f"构建记忆时出错: {e}"
|
|
||||||
|
|
||||||
class GetMemoryTool(BaseTool):
|
class GetMemoryTool(BaseTool):
|
||||||
"""获取用户信息"""
|
"""获取用户信息"""
|
||||||
|
|
||||||
|
|
@ -166,8 +40,6 @@ class GetMemoryTool(BaseTool):
|
||||||
|
|
||||||
return {"content": f"问题：{question}，答案：{answer}"}
|
return {"content": f"问题：{question}，答案：{answer}"}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class GetMemoryAction(BaseAction):
|
class GetMemoryAction(BaseAction):
|
||||||
"""关系动作 - 获取记忆"""
|
"""关系动作 - 获取记忆"""
|
||||||
|
|
||||||
|
|
@ -217,7 +89,3 @@ class GetMemoryAction(BaseAction):
|
||||||
)
|
)
|
||||||
|
|
||||||
return True, f"成功获取记忆: {answer}"
|
return True, f"成功获取记忆: {answer}"
|
||||||
|
|
||||||
|
|
||||||
# 还缺一个关系的太多遗忘和对应的提取
|
|
||||||
init_prompt()
|
|
||||||
|
|
|
||||||
|
|
@ -39,7 +39,7 @@ class MemoryBuildPlugin(BasePlugin):
|
||||||
config_schema: dict = {
|
config_schema: dict = {
|
||||||
"plugin": {
|
"plugin": {
|
||||||
"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
|
"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
|
||||||
"config_version": ConfigField(type=str, default="1.1.0", description="配置文件版本"),
|
"config_version": ConfigField(type=str, default="1.1.1", description="配置文件版本"),
|
||||||
},
|
},
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue