mirror of https://github.com/Mai-with-u/MaiBot.git
fix：时记忆提取更精确
SengokuCola
parent
ef7a3aee23
commit
3bf476c610
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@ -1,15 +1,17 @@
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import json
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from json_repair import repair_json
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from typing import List, Dict
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from typing import List, Tuple
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from src.llm_models.utils_model import LLMRequest
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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.chat.utils.prompt_builder import Prompt
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.memory_system.Hippocampus import hippocampus_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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import random
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logger = get_logger("memory_activator")
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@ -40,20 +42,20 @@ def get_keywords_from_json(json_str) -> List:
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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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你是一个记忆分析器，你需要根据以下信息来进行回忆
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以下是一段聊天记录，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
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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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历史关键词（请避免重复提取这些关键词）：
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{cached_keywords}
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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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"keywords": ["关键词1", "关键词2", "关键词3",......]
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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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@ -67,9 +69,14 @@ class MemoryActivator:
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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(self, target_message, chat_history_prompt) -> List[Dict]:
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async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Tuple[str, str]]:
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"""
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激活记忆
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"""
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@ -83,24 +90,172 @@ class MemoryActivator:
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keywords = parse_keywords_string(msg.get("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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# 最多容纳30个关键词
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keywords_list.update(keywords)
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print(keywords_list)
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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.info(f"当前记忆关键词: {keywords_list}")
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logger.info(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 = False
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for kw in keywords_list:
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if kw in content:
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found = True
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break
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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.info(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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# 使用 LLM 选择合适的记忆
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selected_memories = await self._select_memories_with_llm(target_message, chat_history_prompt, candidate_memories)
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return selected_memories
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async def _select_memories_with_llm(self, target_message, chat_history_prompt, candidate_memories) -> 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_prompt,
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replace_bot_name=True,
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merge_messages=False,
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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,
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target_message=target_message,
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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,
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temperature=0.3,
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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
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memory_ids_str = result.get("memory_ids", "")
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# 解析逗号分隔的编号
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if memory_ids_str:
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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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for memory_id in selected_memory_ids:
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if memory_id in memory_id_to_memory:
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selected_memories.append(memory_id_to_memory[memory_id])
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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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return related_memory
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init_prompt()
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@ -356,15 +356,6 @@ class DefaultReplyer:
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Returns:
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str: 记忆信息字符串
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"""
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chat_talking_prompt_short = build_readable_messages(
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chat_history,
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replace_bot_name=True,
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merge_messages=False,
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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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if not global_config.memory.enable_memory:
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return ""
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@ -399,7 +399,7 @@ class MessageReceiveConfig(ConfigBase):
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class ExpressionConfig(ConfigBase):
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"""表达配置类"""
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expression_learning: list[list] = field(default_factory=lambda: [])
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learning_list: list[list] = field(default_factory=lambda: [])
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"""
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表达学习配置列表，支持按聊天流配置
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格式: [["chat_stream_id", "use_expression", "enable_learning", learning_intensity], ...]
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@ -469,7 +469,7 @@ class ExpressionConfig(ConfigBase):
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)
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"""
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if not self.expression_learning:
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if not self.learning_list:
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# 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔
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return True, True, 300
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@ -497,7 +497,7 @@ class ExpressionConfig(ConfigBase):
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None
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"""
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for config_item in self.expression_learning:
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for config_item in self.learning_list:
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if not config_item or len(config_item) < 4:
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continue
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@ -534,7 +534,7 @@ class ExpressionConfig(ConfigBase):
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None
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"""
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for config_item in self.expression_learning:
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for config_item in self.learning_list:
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if not config_item or len(config_item) < 4:
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continue
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@ -1,5 +1,5 @@
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[inner]
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version = "6.4.0"
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version = "6.4.2"
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#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
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#如果你想要修改配置文件，请递增version的值
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@ -34,10 +34,10 @@ compress_identity = true # 是否压缩身份，压缩后会精简身份信息
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[expression]
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# 表达学习配置
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expression_learning = [ # 表达学习配置列表，支持按聊天流配置
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["", "enable", "enable", 1.0], # 全局配置：使用表达，启用学习，学习强度1.0
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["qq:1919810:group", "enable", "enable", 1.5], # 特定群聊配置：使用表达，启用学习，学习强度1.5
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["qq:114514:private", "enable", "disable", 0.5], # 特定私聊配置：使用表达，禁用学习，学习强度0.5
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learning_list = [ # 表达学习配置列表，支持按聊天流配置
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["", "enable", "enable", "1.0"], # 全局配置：使用表达，启用学习，学习强度1.0
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["qq:1919810:group", "enable", "enable", "1.5"], # 特定群聊配置：使用表达，启用学习，学习强度1.5
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["qq:114514:private", "enable", "disable", "0.5"], # 特定私聊配置：使用表达，禁用学习，学习强度0.5
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# 格式说明：
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# 第一位: chat_stream_id，空字符串表示全局配置
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# 第二位: 是否使用学到的表达 ("enable"/"disable")
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