import time import traceback from typing import Any import orjson from json_repair import repair_json from src.chat.utils.prompt import Prompt, global_prompt_manager from src.common.logger import get_logger from src.config.config import global_config, model_config from src.llm_models.utils_model import LLMRequest from src.person_info.person_info import get_person_info_manager logger = get_logger("relationship_fetcher") def init_real_time_info_prompts(): """初始化实时信息提取相关的提示词""" relationship_prompt = """ <聊天记录> {chat_observe_info} {name_block} 现在，你想要回复{person_name}的消息，消息内容是：{target_message}。请根据聊天记录和你要回复的消息，从你对{person_name}的了解中提取有关的信息： 1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，你们之间的交流方式，最近发生的事等等。 2.请注意，请不要重复调取相同的信息，已经调取的信息如下： {info_cache_block} 3.如果当前聊天记录中没有需要查询的信息，或者现有信息已经足够回复，请返回{{"none": "不需要查询"}} 请以json格式输出，例如： {{ "info_type": "信息类型", }} 请严格按照json输出格式，不要输出多余内容： """ Prompt(relationship_prompt, "real_time_info_identify_prompt") fetch_info_prompt = """ {name_block} 以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解： {person_impression_block} {points_text_block} 请从中提取用户"{person_name}"的有关"{info_type}"信息 请以json格式输出，例如： {{ {info_json_str} }} 请严格按照json输出格式，不要输出多余内容： """ Prompt(fetch_info_prompt, "real_time_fetch_person_info_prompt") class RelationshipFetcher: def __init__(self, chat_id): self.chat_id = chat_id # 信息获取缓存：记录正在获取的信息请求 self.info_fetching_cache: list[dict[str, Any]] = [] # 信息结果缓存：存储已获取的信息结果，带TTL self.info_fetched_cache: dict[str, dict[str, Any]] = {} # 结构：{person_id: {info_type: {"info": str, "ttl": int, "start_time": float, "person_name": str, "unknown": bool}}} # LLM模型配置 self.llm_model = LLMRequest( model_set=model_config.model_task_config.utils_small, request_type="relation.fetcher" ) # 小模型用于即时信息提取 self.instant_llm_model = LLMRequest( model_set=model_config.model_task_config.utils_small, request_type="relation.fetch" ) self.log_prefix = f"[{self.chat_id}] 实时信息" # 初始化时使用chat_id，稍后异步更新 self._log_prefix_initialized = False async def _initialize_log_prefix(self): """异步初始化log_prefix""" if not self._log_prefix_initialized: from src.chat.message_receive.chat_stream import get_chat_manager name = await get_chat_manager().get_stream_name(self.chat_id) self.log_prefix = f"[{name}] 实时信息" self._log_prefix_initialized = True def _cleanup_expired_cache(self): """清理过期的信息缓存""" for person_id in list(self.info_fetched_cache.keys()): for info_type in list(self.info_fetched_cache[person_id].keys()): self.info_fetched_cache[person_id][info_type]["ttl"] -= 1 if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0: del self.info_fetched_cache[person_id][info_type] if not self.info_fetched_cache[person_id]: del self.info_fetched_cache[person_id] async def build_relation_info(self, person_id, points_num=5): """构建详细的人物关系信息，包含从数据库中查询的丰富关系描述""" # 初始化log_prefix await self._initialize_log_prefix() # 清理过期的信息缓存 self._cleanup_expired_cache() person_info_manager = get_person_info_manager() person_name = await person_info_manager.get_value(person_id, "person_name") short_impression = await person_info_manager.get_value(person_id, "short_impression") full_impression = await person_info_manager.get_value(person_id, "impression") attitude = await person_info_manager.get_value(person_id, "attitude") or 50 nickname_str = await person_info_manager.get_value(person_id, "nickname") platform = await person_info_manager.get_value(person_id, "platform") know_times = await person_info_manager.get_value(person_id, "know_times") or 0 know_since = await person_info_manager.get_value(person_id, "know_since") last_know = await person_info_manager.get_value(person_id, "last_know") # 获取用户特征点 current_points = await person_info_manager.get_value(person_id, "points") or [] forgotten_points = await person_info_manager.get_value(person_id, "forgotten_points") or [] # 确保 points 是列表类型（可能从数据库返回字符串） if not isinstance(current_points, list): current_points = [] if not isinstance(forgotten_points, list): forgotten_points = [] # 按时间排序并选择最有代表性的特征点 all_points = current_points + forgotten_points if all_points: # 按权重和时效性综合排序 all_points.sort( key=lambda x: (float(x[1]) if len(x) > 1 else 0, float(x[2]) if len(x) > 2 else 0), reverse=True ) selected_points = all_points[:points_num] points_text = "\n".join([f"- {point[0]}（{point[2]}）" for point in selected_points if len(point) > 2]) else: points_text = "" # 构建详细的关系描述 relation_parts = [] # 1. 基本信息 if nickname_str and person_name != nickname_str: relation_parts.append(f"用户{person_name}在{platform}平台的昵称是{nickname_str}") # 2. 认识时间和频率 if know_since: from datetime import datetime know_time = datetime.fromtimestamp(know_since).strftime("%Y年%m月%d日") relation_parts.append(f"你从{know_time}开始认识{person_name}") if know_times > 0: relation_parts.append(f"你们已经交流过{int(know_times)}次") if last_know: from datetime import datetime last_time = datetime.fromtimestamp(last_know).strftime("%m月%d日") relation_parts.append(f"最近一次交流是在{last_time}") # 3. 态度和印象 attitude_desc = self._get_attitude_description(attitude) relation_parts.append(f"你对{person_name}的态度是{attitude_desc}") if short_impression: relation_parts.append(f"你对ta的总体印象：{short_impression}") if full_impression: relation_parts.append(f"更详细的了解：{full_impression}") # 4. 特征点和记忆 if points_text: relation_parts.append(f"你记得关于{person_name}的一些事情：\n{points_text}") # 5. 从UserRelationships表获取完整关系信息（新系统） try: from src.common.database.sqlalchemy_database_api import db_query from src.common.database.sqlalchemy_models import UserRelationships # 查询用户关系数据（修复：添加 await） user_id = str(await person_info_manager.get_value(person_id, "user_id")) relationships = await db_query( UserRelationships, filters={"user_id": user_id}, limit=1, ) if relationships: # db_query 返回字典列表，使用字典访问方式 rel_data = relationships[0] # 5.1 用户别名 if rel_data.get("user_aliases"): aliases_list = [alias.strip() for alias in rel_data["user_aliases"].split(",") if alias.strip()] if aliases_list: aliases_str = "、".join(aliases_list) relation_parts.append(f"{person_name}的别名有：{aliases_str}") # 5.2 关系印象文本（主观认知） if rel_data.get("relationship_text"): relation_parts.append(f"你对{person_name}的整体认知：{rel_data['relationship_text']}") # 5.3 用户偏好关键词 if rel_data.get("preference_keywords"): keywords_list = [kw.strip() for kw in rel_data["preference_keywords"].split(",") if kw.strip()] if keywords_list: keywords_str = "、".join(keywords_list) relation_parts.append(f"{person_name}的偏好和兴趣：{keywords_str}") # 5.4 关系亲密程度（好感分数） if rel_data.get("relationship_score") is not None: score_desc = self._get_relationship_score_description(rel_data["relationship_score"]) relation_parts.append(f"你们的关系程度：{score_desc}（{rel_data['relationship_score']:.2f}）") except Exception as e: logger.error(f"查询UserRelationships表失败: {e}", exc_info=True) # 构建最终的关系信息字符串 if relation_parts: relation_info = f"关于{person_name}，你知道以下信息：\n" + "\n".join( [f"• {part}" for part in relation_parts] ) else: # 只有当所有数据源都没有信息时才返回默认文本 relation_info = f"你完全不认识{person_name}，这是你们第一次交流。" return relation_info async def build_chat_stream_impression(self, stream_id: str) -> str: """构建聊天流的印象信息 Args: stream_id: 聊天流ID Returns: str: 格式化后的聊天流印象字符串 """ try: from src.common.database.sqlalchemy_database_api import db_query from src.common.database.sqlalchemy_models import ChatStreams # 查询聊天流数据 streams = await db_query( ChatStreams, filters={"stream_id": stream_id}, limit=1, ) if not streams: return "" # db_query 返回字典列表，使用字典访问方式 stream_data = streams[0] impression_parts = [] # 1. 聊天环境基本信息 if stream_data.get("group_name"): impression_parts.append(f"这是一个名为「{stream_data['group_name']}」的群聊") else: impression_parts.append("这是一个私聊对话") # 2. 聊天流的主观印象 if stream_data.get("stream_impression_text"): impression_parts.append(f"你对这个聊天环境的印象：{stream_data['stream_impression_text']}") # 3. 聊天风格 if stream_data.get("stream_chat_style"): impression_parts.append(f"这里的聊天风格：{stream_data['stream_chat_style']}") # 4. 常见话题 if stream_data.get("stream_topic_keywords"): topics_list = [topic.strip() for topic in stream_data["stream_topic_keywords"].split(",") if topic.strip()] if topics_list: topics_str = "、".join(topics_list) impression_parts.append(f"这里常讨论的话题：{topics_str}") # 5. 兴趣程度 if stream_data.get("stream_interest_score") is not None: interest_desc = self._get_interest_score_description(stream_data["stream_interest_score"]) impression_parts.append(f"你对这个聊天环境的兴趣程度：{interest_desc}（{stream_data['stream_interest_score']:.2f}）") # 构建最终的印象信息字符串 if impression_parts: impression_info = "关于当前的聊天环境：\n" + "\n".join( [f"• {part}" for part in impression_parts] ) return impression_info else: return "" except Exception as e: logger.debug(f"查询ChatStreams表失败: {e}") return "" def _get_interest_score_description(self, score: float) -> str: """根据兴趣分数返回描述性文字""" if score >= 0.8: return "非常感兴趣，很喜欢这里的氛围" elif score >= 0.6: return "比较感兴趣，愿意积极参与" elif score >= 0.4: return "一般兴趣，会适度参与" elif score >= 0.2: return "兴趣不大，较少主动参与" else: return "不太感兴趣，很少参与" def _get_attitude_description(self, attitude: int) -> str: """根据态度分数返回描述性文字""" if attitude >= 80: return "非常喜欢和欣赏" elif attitude >= 60: return "比较有好感" elif attitude >= 40: return "中立态度" elif attitude >= 20: return "有些反感" else: return "非常厌恶" def _get_relationship_score_description(self, score: float) -> str: """根据关系分数返回描述性文字""" if score >= 0.8: return "非常亲密的好友" elif score >= 0.6: return "关系不错的朋友" elif score >= 0.4: return "普通熟人" elif score >= 0.2: return "认识但不熟悉" else: return "陌生人" async def _build_fetch_query(self, person_id, target_message, chat_history): nickname_str = ",".join(global_config.bot.alias_names) name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" person_info_manager = get_person_info_manager() person_name: str = await person_info_manager.get_value(person_id, "person_name") # type: ignore info_cache_block = self._build_info_cache_block() prompt = (await global_prompt_manager.get_prompt_async("real_time_info_identify_prompt")).format( chat_observe_info=chat_history, name_block=name_block, info_cache_block=info_cache_block, person_name=person_name, target_message=target_message, ) try: logger.debug(f"{self.log_prefix} 信息识别prompt: \n{prompt}\n") content, _ = await self.llm_model.generate_response_async(prompt=prompt) if content: content_json = orjson.loads(repair_json(content)) # 检查是否返回了不需要查询的标志 if "none" in content_json: logger.debug(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}") return None if info_type := content_json.get("info_type"): # 记录信息获取请求 self.info_fetching_cache.append( { "person_id": await get_person_info_manager().get_person_id_by_person_name(person_name), "person_name": person_name, "info_type": info_type, "start_time": time.time(), "forget": False, } ) # 限制缓存大小 if len(self.info_fetching_cache) > 10: self.info_fetching_cache.pop(0) logger.info(f"{self.log_prefix} 识别到需要调取用户 {person_name} 的[{info_type}]信息") return info_type else: logger.warning(f"{self.log_prefix} LLM未返回有效的info_type。响应: {content}") except Exception as e: logger.error(f"{self.log_prefix} 执行信息识别LLM请求时出错: {e}") logger.error(traceback.format_exc()) return None def _build_info_cache_block(self) -> str: """构建已获取信息的缓存块""" info_cache_block = "" if self.info_fetching_cache: # 对于每个(person_id, info_type)组合，只保留最新的记录 latest_records = {} for info_fetching in self.info_fetching_cache: key = (info_fetching["person_id"], info_fetching["info_type"]) if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]: latest_records[key] = info_fetching # 按时间排序并生成显示文本 sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"]) for info_fetching in sorted_records: info_cache_block += ( f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n" ) return info_cache_block async def _extract_single_info(self, person_id: str, info_type: str, person_name: str): """提取单个信息类型 Args: person_id: 用户ID info_type: 信息类型 person_name: 用户名 """ start_time = time.time() person_info_manager = get_person_info_manager() # 首先检查 info_list 缓存 info_list = await person_info_manager.get_value(person_id, "info_list") or [] cached_info = None # 查找对应的 info_type for info_item in info_list: if info_item.get("info_type") == info_type: cached_info = info_item.get("info_content") logger.debug(f"{self.log_prefix} 在info_list中找到 {person_name} 的 {info_type} 信息: {cached_info}") break # 如果缓存中有信息，直接使用 if cached_info: if person_id not in self.info_fetched_cache: self.info_fetched_cache[person_id] = {} self.info_fetched_cache[person_id][info_type] = { "info": cached_info, "ttl": 2, "start_time": start_time, "person_name": person_name, "unknown": cached_info == "none", } logger.info(f"{self.log_prefix} 记得 {person_name} 的 {info_type}: {cached_info}") return # 如果缓存中没有，尝试从用户档案中提取 try: person_impression = await person_info_manager.get_value(person_id, "impression") points = await person_info_manager.get_value(person_id, "points") # 构建印象信息块 if person_impression: person_impression_block = ( f"<对{person_name}的总体了解>\n{person_impression}\n" ) else: person_impression_block = "" # 构建要点信息块 if points: points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points]) points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n" else: points_text_block = "" # 如果完全没有用户信息 if not points_text_block and not person_impression_block: if person_id not in self.info_fetched_cache: self.info_fetched_cache[person_id] = {} self.info_fetched_cache[person_id][info_type] = { "info": "none", "ttl": 2, "start_time": start_time, "person_name": person_name, "unknown": True, } logger.info(f"{self.log_prefix} 完全不认识 {person_name}") await self._save_info_to_cache(person_id, info_type, "none") return # 使用LLM提取信息 nickname_str = ",".join(global_config.bot.alias_names) name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" prompt = (await global_prompt_manager.get_prompt_async("real_time_fetch_person_info_prompt")).format( name_block=name_block, info_type=info_type, person_impression_block=person_impression_block, person_name=person_name, info_json_str=f'"{info_type}": "有关{info_type}的信息内容"', points_text_block=points_text_block, ) # 使用小模型进行即时提取 content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt) if content: content_json = orjson.loads(repair_json(content)) if info_type in content_json: info_content = content_json[info_type] is_unknown = info_content == "none" or not info_content # 保存到运行时缓存 if person_id not in self.info_fetched_cache: self.info_fetched_cache[person_id] = {} self.info_fetched_cache[person_id][info_type] = { "info": "unknown" if is_unknown else info_content, "ttl": 3, "start_time": start_time, "person_name": person_name, "unknown": is_unknown, } # 保存到持久化缓存 (info_list) await self._save_info_to_cache(person_id, info_type, "none" if is_unknown else info_content) if not is_unknown: logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {info_content}") else: logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息") else: logger.warning(f"{self.log_prefix} 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。") except Exception as e: logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}") logger.error(traceback.format_exc()) async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str): # sourcery skip: use-next """将提取到的信息保存到 person_info 的 info_list 字段中 Args: person_id: 用户ID info_type: 信息类型 info_content: 信息内容 """ try: person_info_manager = get_person_info_manager() # 获取现有的 info_list info_list = await person_info_manager.get_value(person_id, "info_list") or [] # 查找是否已存在相同 info_type 的记录 found_index = -1 for i, info_item in enumerate(info_list): if isinstance(info_item, dict) and info_item.get("info_type") == info_type: found_index = i break # 创建新的信息记录 new_info_item = { "info_type": info_type, "info_content": info_content, } if found_index >= 0: # 更新现有记录 info_list[found_index] = new_info_item logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存") else: # 添加新记录 info_list.append(new_info_item) logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存") # 保存更新后的 info_list await person_info_manager.update_one_field(person_id, "info_list", info_list) except Exception as e: logger.error(f"{self.log_prefix} [缓存保存] 保存信息到缓存失败: {e}") logger.error(traceback.format_exc()) class RelationshipFetcherManager: """关系提取器管理器 管理不同 chat_id 的 RelationshipFetcher 实例 """ def __init__(self): self._fetchers: dict[str, RelationshipFetcher] = {} def get_fetcher(self, chat_id: str) -> RelationshipFetcher: """获取或创建指定 chat_id 的 RelationshipFetcher Args: chat_id: 聊天ID Returns: RelationshipFetcher: 关系提取器实例 """ if chat_id not in self._fetchers: self._fetchers[chat_id] = RelationshipFetcher(chat_id) return self._fetchers[chat_id] def remove_fetcher(self, chat_id: str): """移除指定 chat_id 的 RelationshipFetcher Args: chat_id: 聊天ID """ if chat_id in self._fetchers: del self._fetchers[chat_id] def clear_all(self): """清空所有 RelationshipFetcher""" self._fetchers.clear() def get_active_chat_ids(self) -> list[str]: """获取所有活跃的 chat_id 列表""" return list(self._fetchers.keys()) # 全局管理器实例 relationship_fetcher_manager = RelationshipFetcherManager() init_real_time_info_prompts()