From 3bf476c6103d2af7afd8f9ebdf540f2857dccc71 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Thu, 14 Aug 2025 00:02:50 +0800 Subject: [PATCH] =?UTF-8?q?fix=EF=BC=9A=E6=97=B6=E8=AE=B0=E5=BF=86?= =?UTF-8?q?=E6=8F=90=E5=8F=96=E6=9B=B4=E7=B2=BE=E7=A1=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/chat/memory_system/memory_activator.py | 181 +++++++++++++++++++-- src/chat/replyer/default_generator.py | 9 - src/config/official_configs.py | 8 +- template/bot_config_template.toml | 10 +- 4 files changed, 177 insertions(+), 31 deletions(-) diff --git a/src/chat/memory_system/memory_activator.py b/src/chat/memory_system/memory_activator.py index 1565a42c..0529c4b3 100644 --- a/src/chat/memory_system/memory_activator.py +++ b/src/chat/memory_system/memory_activator.py @@ -1,15 +1,17 @@ import json from json_repair import repair_json -from typing import List, Dict +from typing import List, Tuple from src.llm_models.utils_model import LLMRequest from src.config.config import global_config, model_config from src.common.logger import get_logger -from src.chat.utils.prompt_builder import Prompt +from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.chat.memory_system.Hippocampus import hippocampus_manager from src.chat.utils.utils import parse_keywords_string +from src.chat.utils.chat_message_builder import build_readable_messages +import random logger = get_logger("memory_activator") @@ -40,20 +42,20 @@ def get_keywords_from_json(json_str) -> List: def init_prompt(): # --- Group Chat Prompt --- memory_activator_prompt = """ - 你是一个记忆分析器，你需要根据以下信息来进行回忆 - 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 + 你需要根据以下信息来挑选合适的记忆编号 + 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 聊天记录: {obs_info_text} 你想要回复的消息: {target_message} - 历史关键词（请避免重复提取这些关键词）： - {cached_keywords} + 记忆： + {memory_info} 请输出一个json格式，包含以下字段： {{ - "keywords": ["关键词1", "关键词2", "关键词3",......] + "memory_ids": "记忆1编号,记忆2编号,记忆3编号,......" }} 不要输出其他多余内容，只输出json格式就好 """ @@ -67,9 +69,14 @@ class MemoryActivator: model_set=model_config.model_task_config.utils_small, request_type="memory.activator", ) + # 用于记忆选择的 LLM 模型 + self.memory_selection_model = LLMRequest( + model_set=model_config.model_task_config.utils_small, + request_type="memory.selection", + ) - async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Dict]: + async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Tuple[str, str]]: """ 激活记忆 """ @@ -83,24 +90,172 @@ class MemoryActivator: keywords = parse_keywords_string(msg.get("key_words", "")) if keywords: if len(keywords_list) < 30: - # 最多容纳30个关键词 + # 最多容纳30个关键词 keywords_list.update(keywords) - print(keywords_list) + logger.debug(f"提取关键词: {keywords_list}") else: break if not keywords_list: + logger.debug("没有提取到关键词，返回空记忆列表") return [] + # 从海马体获取相关记忆 related_memory = await hippocampus_manager.get_memory_from_topic( valid_keywords=list(keywords_list), max_memory_num=5, max_memory_length=3, max_depth=3 ) - - logger.info(f"当前记忆关键词: {keywords_list} ") + logger.info(f"当前记忆关键词: {keywords_list}") logger.info(f"获取到的记忆: {related_memory}") + + if not related_memory: + logger.debug("海马体没有返回相关记忆") + return [] + + + + used_ids = set() + candidate_memories = [] + + # 为每个记忆分配随机ID并过滤相关记忆 + for memory in related_memory: + keyword, content = memory + found = False + for kw in keywords_list: + if kw in content: + found = True + break + + if found: + # 随机分配一个不重复的2位数id + while True: + random_id = "{:02d}".format(random.randint(0, 99)) + if random_id not in used_ids: + used_ids.add(random_id) + break + candidate_memories.append({"memory_id": random_id, "keyword": keyword, "content": content}) + + if not candidate_memories: + logger.info("没有找到相关的候选记忆") + return [] + + # 如果只有少量记忆，直接返回 + if len(candidate_memories) <= 2: + logger.info(f"候选记忆较少({len(candidate_memories)}个)，直接返回") + # 转换为 (keyword, content) 格式 + return [(mem["keyword"], mem["content"]) for mem in candidate_memories] + + # 使用 LLM 选择合适的记忆 + selected_memories = await self._select_memories_with_llm(target_message, chat_history_prompt, candidate_memories) + + return selected_memories + + async def _select_memories_with_llm(self, target_message, chat_history_prompt, candidate_memories) -> List[Tuple[str, str]]: + """ + 使用 LLM 选择合适的记忆 + + Args: + target_message: 目标消息 + chat_history_prompt: 聊天历史 + candidate_memories: 候选记忆列表，每个记忆包含 memory_id、keyword、content + + Returns: + List[Tuple[str, str]]: 选择的记忆列表，格式为 (keyword, content) + """ + try: + # 构建聊天历史字符串 + obs_info_text = build_readable_messages( + chat_history_prompt, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + show_actions=True, + ) + + + # 构建记忆信息字符串 + memory_lines = [] + for memory in candidate_memories: + memory_id = memory["memory_id"] + keyword = memory["keyword"] + content = memory["content"] + + # 将 content 列表转换为字符串 + if isinstance(content, list): + content_str = " | ".join(str(item) for item in content) + else: + content_str = str(content) + + memory_lines.append(f"记忆编号 {memory_id}: [关键词: {keyword}] {content_str}") + + memory_info = "\n".join(memory_lines) + + # 获取并格式化 prompt + prompt_template = await global_prompt_manager.get_prompt_async("memory_activator_prompt") + formatted_prompt = prompt_template.format( + obs_info_text=obs_info_text, + target_message=target_message, + memory_info=memory_info + ) + + + + # 调用 LLM + response, (reasoning_content, model_name, _) = await self.memory_selection_model.generate_response_async( + formatted_prompt, + temperature=0.3, + max_tokens=150 + ) + + if global_config.debug.show_prompt: + logger.info(f"记忆选择 prompt: {formatted_prompt}") + logger.info(f"LLM 记忆选择响应: {response}") + else: + logger.debug(f"记忆选择 prompt: {formatted_prompt}") + logger.debug(f"LLM 记忆选择响应: {response}") + + # 解析响应获取选择的记忆编号 + try: + fixed_json = repair_json(response) + + # 解析为 Python 对象 + result = json.loads(fixed_json) if isinstance(fixed_json, str) else fixed_json + + # 提取 memory_ids 字段 + memory_ids_str = result.get("memory_ids", "") + + # 解析逗号分隔的编号 + if memory_ids_str: + memory_ids = [mid.strip() for mid in str(memory_ids_str).split(",") if mid.strip()] + # 过滤掉空字符串和无效编号 + valid_memory_ids = [mid for mid in memory_ids if mid and len(mid) <= 3] + selected_memory_ids = valid_memory_ids + else: + selected_memory_ids = [] + except Exception as e: + logger.error(f"解析记忆选择响应失败: {e}", exc_info=True) + selected_memory_ids = [] + + # 根据编号筛选记忆 + selected_memories = [] + memory_id_to_memory = {mem["memory_id"]: mem for mem in candidate_memories} + + for memory_id in selected_memory_ids: + if memory_id in memory_id_to_memory: + selected_memories.append(memory_id_to_memory[memory_id]) + + logger.info(f"LLM 选择的记忆编号: {selected_memory_ids}") + logger.info(f"最终选择的记忆数量: {len(selected_memories)}") + + # 转换为 (keyword, content) 格式 + return [(mem["keyword"], mem["content"]) for mem in selected_memories] + + except Exception as e: + logger.error(f"LLM 选择记忆时出错: {e}", exc_info=True) + # 出错时返回前3个候选记忆作为备选，转换为 (keyword, content) 格式 + return [(mem["keyword"], mem["content"]) for mem in candidate_memories[:3]] - return related_memory init_prompt() diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index f3be85a9..756826ca 100644 --- a/src/chat/replyer/default_generator.py +++ b/src/chat/replyer/default_generator.py @@ -356,15 +356,6 @@ class DefaultReplyer: Returns: str: 记忆信息字符串 """ - chat_talking_prompt_short = build_readable_messages( - chat_history, - replace_bot_name=True, - merge_messages=False, - timestamp_mode="relative", - read_mark=0.0, - show_actions=True, - ) - if not global_config.memory.enable_memory: return "" diff --git a/src/config/official_configs.py b/src/config/official_configs.py index 40bba56b..981e09f3 100644 --- a/src/config/official_configs.py +++ b/src/config/official_configs.py @@ -399,7 +399,7 @@ class MessageReceiveConfig(ConfigBase): class ExpressionConfig(ConfigBase): """表达配置类""" - expression_learning: list[list] = field(default_factory=lambda: []) + learning_list: list[list] = field(default_factory=lambda: []) """ 表达学习配置列表，支持按聊天流配置 格式: [["chat_stream_id", "use_expression", "enable_learning", learning_intensity], ...] @@ -469,7 +469,7 @@ class ExpressionConfig(ConfigBase): Returns: tuple: (是否使用表达, 是否学习表达, 学习间隔) """ - if not self.expression_learning: + if not self.learning_list: # 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔 return True, True, 300 @@ -497,7 +497,7 @@ class ExpressionConfig(ConfigBase): Returns: 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 """ - for config_item in self.expression_learning: + for config_item in self.learning_list: if not config_item or len(config_item) < 4: continue @@ -534,7 +534,7 @@ class ExpressionConfig(ConfigBase): Returns: 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 """ - for config_item in self.expression_learning: + for config_item in self.learning_list: if not config_item or len(config_item) < 4: continue diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index e66a5f60..660c8459 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "6.4.0" +version = "6.4.2" #----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---- #如果你想要修改配置文件，请递增version的值 @@ -34,10 +34,10 @@ compress_identity = true # 是否压缩身份，压缩后会精简身份信息 [expression] # 表达学习配置 -expression_learning = [ # 表达学习配置列表，支持按聊天流配置 - ["", "enable", "enable", 1.0], # 全局配置：使用表达，启用学习，学习强度1.0 - ["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 - ["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 +learning_list = [ # 表达学习配置列表，支持按聊天流配置 + ["", "enable", "enable", "1.0"], # 全局配置：使用表达，启用学习，学习强度1.0 + ["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 + ["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 # 格式说明： # 第一位: chat_stream_id，空字符串表示全局配置 # 第二位: 是否使用学到的表达 ("enable"/"disable")