a29be48091
本次提交对项目代码进行了广泛的重构，主要集中在以下几个方面：
1. **类型提示现代化**:
- 将 `typing` 模块中的 `Optional[T]`、`List[T]`、`Dict[K, V]` 等旧式类型提示更新为现代的 `T | None`、`list[T]`、`dict[K, V]` 语法。
- 这提高了代码的可读性，并与较新 Python 版本的风格保持一致。
2. **代码风格统一**:
- 移除了多余的空行和不必要的空格，使代码更加紧凑和规范。
- 统一了部分日志输出的格式，增强了日志的可读性。
3. **导入语句优化**:
- 调整了部分模块的 `import` 语句顺序，使其符合 PEP 8 规范。
这些更改不涉及任何功能性变动，旨在提升代码库的整体质量、可维护性和开发体验。
624 lines
26 KiB
Python
624 lines
26 KiB
Python
import time
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import traceback
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from typing import Any
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import orjson
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from json_repair import repair_json
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from src.chat.utils.prompt import Prompt, global_prompt_manager
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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from src.person_info.person_info import get_person_info_manager
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logger = get_logger("relationship_fetcher")
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def init_real_time_info_prompts():
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"""初始化实时信息提取相关的提示词"""
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relationship_prompt = """
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<聊天记录>
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{chat_observe_info}
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</聊天记录>
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{name_block}
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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：
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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。
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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：
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{info_cache_block}
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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"}}
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请以json格式输出，例如：
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{{
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"info_type": "信息类型",
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}}
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请严格按照json输出格式，不要输出多余内容：
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"""
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Prompt(relationship_prompt, "real_time_info_identify_prompt")
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fetch_info_prompt = """
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{name_block}
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以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解：
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{person_impression_block}
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{points_text_block}
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请从中提取用户"{person_name}"的有关"{info_type}"信息
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请以json格式输出，例如：
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{{
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{info_json_str}
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}}
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请严格按照json输出格式，不要输出多余内容：
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"""
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Prompt(fetch_info_prompt, "real_time_fetch_person_info_prompt")
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class RelationshipFetcher:
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def __init__(self, chat_id):
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self.chat_id = chat_id
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# 信息获取缓存：记录正在获取的信息请求
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self.info_fetching_cache: list[dict[str, Any]] = []
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# 信息结果缓存：存储已获取的信息结果，带TTL
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self.info_fetched_cache: dict[str, dict[str, Any]] = {}
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# 结构：{person_id: {info_type: {"info": str, "ttl": int, "start_time": float, "person_name": str, "unknown": bool}}}
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# LLM模型配置
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self.llm_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small, request_type="relation.fetcher"
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)
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# 小模型用于即时信息提取
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self.instant_llm_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small, request_type="relation.fetch"
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)
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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
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self._log_prefix_initialized = False
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async def _initialize_log_prefix(self):
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"""异步初始化log_prefix"""
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if not self._log_prefix_initialized:
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from src.chat.message_receive.chat_stream import get_chat_manager
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name = await get_chat_manager().get_stream_name(self.chat_id)
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self.log_prefix = f"[{name}] 实时信息"
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self._log_prefix_initialized = True
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def _cleanup_expired_cache(self):
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"""清理过期的信息缓存"""
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for person_id in list(self.info_fetched_cache.keys()):
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for info_type in list(self.info_fetched_cache[person_id].keys()):
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self.info_fetched_cache[person_id][info_type]["ttl"] -= 1
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if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0:
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del self.info_fetched_cache[person_id][info_type]
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if not self.info_fetched_cache[person_id]:
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del self.info_fetched_cache[person_id]
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async def build_relation_info(self, person_id, points_num=5):
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"""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"""
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# 初始化log_prefix
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await self._initialize_log_prefix()
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# 清理过期的信息缓存
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self._cleanup_expired_cache()
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person_info_manager = get_person_info_manager()
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person_name = await person_info_manager.get_value(person_id, "person_name")
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short_impression = await person_info_manager.get_value(person_id, "short_impression")
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full_impression = await person_info_manager.get_value(person_id, "impression")
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attitude = await person_info_manager.get_value(person_id, "attitude") or 50
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nickname_str = await person_info_manager.get_value(person_id, "nickname")
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platform = await person_info_manager.get_value(person_id, "platform")
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know_times = await person_info_manager.get_value(person_id, "know_times") or 0
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know_since = await person_info_manager.get_value(person_id, "know_since")
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last_know = await person_info_manager.get_value(person_id, "last_know")
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# 获取用户特征点
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current_points = await person_info_manager.get_value(person_id, "points") or []
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forgotten_points = await person_info_manager.get_value(person_id, "forgotten_points") or []
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# 确保 points 是列表类型（可能从数据库返回字符串）
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if not isinstance(current_points, list):
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current_points = []
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if not isinstance(forgotten_points, list):
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forgotten_points = []
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# 按时间排序并选择最有代表性的特征点
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all_points = current_points + forgotten_points
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if all_points:
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# 按权重和时效性综合排序
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all_points.sort(
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key=lambda x: (float(x[1]) if len(x) > 1 else 0, float(x[2]) if len(x) > 2 else 0), reverse=True
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)
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selected_points = all_points[:points_num]
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points_text = "\n".join([f"- {point[0]}（{point[2]}）" for point in selected_points if len(point) > 2])
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else:
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points_text = ""
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# 构建详细的关系描述
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relation_parts = []
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# 1. 基本信息
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if nickname_str and person_name != nickname_str:
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relation_parts.append(f"用户{person_name}在{platform}平台的昵称是{nickname_str}")
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# 2. 认识时间和频率
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if know_since:
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from datetime import datetime
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know_time = datetime.fromtimestamp(know_since).strftime("%Y年%m月%d日")
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relation_parts.append(f"你从{know_time}开始认识{person_name}")
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if know_times > 0:
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relation_parts.append(f"你们已经交流过{int(know_times)}次")
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if last_know:
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from datetime import datetime
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last_time = datetime.fromtimestamp(last_know).strftime("%m月%d日")
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relation_parts.append(f"最近一次交流是在{last_time}")
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# 3. 态度和印象
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attitude_desc = self._get_attitude_description(attitude)
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relation_parts.append(f"你对{person_name}的态度是{attitude_desc}")
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if short_impression:
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relation_parts.append(f"你对ta的总体印象：{short_impression}")
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if full_impression:
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relation_parts.append(f"更详细的了解：{full_impression}")
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# 4. 特征点和记忆
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if points_text:
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relation_parts.append(f"你记得关于{person_name}的一些事情：\n{points_text}")
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# 5. 从UserRelationships表获取完整关系信息（新系统）
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try:
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from src.common.database.sqlalchemy_database_api import db_query
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from src.common.database.sqlalchemy_models import UserRelationships
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# 查询用户关系数据（修复：添加 await）
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user_id = str(await person_info_manager.get_value(person_id, "user_id"))
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relationships = await db_query(
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UserRelationships,
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filters={"user_id": user_id},
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limit=1,
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)
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if relationships:
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# db_query 返回字典列表，使用字典访问方式
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rel_data = relationships[0]
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# 5.1 用户别名
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if rel_data.get("user_aliases"):
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aliases_list = [alias.strip() for alias in rel_data["user_aliases"].split(",") if alias.strip()]
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if aliases_list:
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aliases_str = "、".join(aliases_list)
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relation_parts.append(f"{person_name}的别名有：{aliases_str}")
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# 5.2 关系印象文本（主观认知）
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if rel_data.get("relationship_text"):
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relation_parts.append(f"你对{person_name}的整体认知：{rel_data['relationship_text']}")
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# 5.3 用户偏好关键词
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if rel_data.get("preference_keywords"):
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keywords_list = [kw.strip() for kw in rel_data["preference_keywords"].split(",") if kw.strip()]
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if keywords_list:
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keywords_str = "、".join(keywords_list)
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relation_parts.append(f"{person_name}的偏好和兴趣：{keywords_str}")
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# 5.4 关系亲密程度（好感分数）
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if rel_data.get("relationship_score") is not None:
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score_desc = self._get_relationship_score_description(rel_data["relationship_score"])
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relation_parts.append(f"你们的关系程度：{score_desc}（{rel_data['relationship_score']:.2f}）")
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except Exception as e:
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logger.error(f"查询UserRelationships表失败: {e}", exc_info=True)
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# 构建最终的关系信息字符串
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if relation_parts:
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relation_info = f"关于{person_name}，你知道以下信息：\n" + "\n".join(
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[f"• {part}" for part in relation_parts]
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)
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else:
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# 只有当所有数据源都没有信息时才返回默认文本
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relation_info = f"你完全不认识{person_name}，这是你们第一次交流。"
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return relation_info
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async def build_chat_stream_impression(self, stream_id: str) -> str:
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"""构建聊天流的印象信息
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Args:
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stream_id: 聊天流ID
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Returns:
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str: 格式化后的聊天流印象字符串
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"""
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try:
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from src.common.database.sqlalchemy_database_api import db_query
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from src.common.database.sqlalchemy_models import ChatStreams
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# 查询聊天流数据
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streams = await db_query(
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ChatStreams,
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filters={"stream_id": stream_id},
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limit=1,
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)
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if not streams:
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return ""
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# db_query 返回字典列表，使用字典访问方式
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stream_data = streams[0]
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impression_parts = []
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# 1. 聊天环境基本信息
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if stream_data.get("group_name"):
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impression_parts.append(f"这是一个名为「{stream_data['group_name']}」的群聊")
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else:
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impression_parts.append("这是一个私聊对话")
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# 2. 聊天流的主观印象
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if stream_data.get("stream_impression_text"):
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impression_parts.append(f"你对这个聊天环境的印象：{stream_data['stream_impression_text']}")
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# 3. 聊天风格
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if stream_data.get("stream_chat_style"):
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impression_parts.append(f"这里的聊天风格：{stream_data['stream_chat_style']}")
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# 4. 常见话题
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if stream_data.get("stream_topic_keywords"):
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topics_list = [topic.strip() for topic in stream_data["stream_topic_keywords"].split(",") if topic.strip()]
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if topics_list:
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topics_str = "、".join(topics_list)
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impression_parts.append(f"这里常讨论的话题：{topics_str}")
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# 5. 兴趣程度
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if stream_data.get("stream_interest_score") is not None:
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interest_desc = self._get_interest_score_description(stream_data["stream_interest_score"])
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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}）")
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# 构建最终的印象信息字符串
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if impression_parts:
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impression_info = "关于当前的聊天环境：\n" + "\n".join(
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[f"• {part}" for part in impression_parts]
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)
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return impression_info
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else:
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return ""
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except Exception as e:
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logger.debug(f"查询ChatStreams表失败: {e}")
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return ""
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def _get_interest_score_description(self, score: float) -> str:
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"""根据兴趣分数返回描述性文字"""
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if score >= 0.8:
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return "非常感兴趣，很喜欢这里的氛围"
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elif score >= 0.6:
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return "比较感兴趣，愿意积极参与"
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elif score >= 0.4:
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return "一般兴趣，会适度参与"
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elif score >= 0.2:
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return "兴趣不大，较少主动参与"
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else:
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return "不太感兴趣，很少参与"
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def _get_attitude_description(self, attitude: int) -> str:
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"""根据态度分数返回描述性文字"""
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if attitude >= 80:
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return "非常喜欢和欣赏"
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elif attitude >= 60:
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return "比较有好感"
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elif attitude >= 40:
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return "中立态度"
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elif attitude >= 20:
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return "有些反感"
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else:
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return "非常厌恶"
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def _get_relationship_score_description(self, score: float) -> str:
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"""根据关系分数返回描述性文字"""
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if score >= 0.8:
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return "非常亲密的好友"
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elif score >= 0.6:
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return "关系不错的朋友"
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elif score >= 0.4:
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return "普通熟人"
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elif score >= 0.2:
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return "认识但不熟悉"
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else:
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return "陌生人"
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async def _build_fetch_query(self, person_id, target_message, chat_history):
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nickname_str = ",".join(global_config.bot.alias_names)
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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。"
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person_info_manager = get_person_info_manager()
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person_name: str = await person_info_manager.get_value(person_id, "person_name") # type: ignore
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info_cache_block = self._build_info_cache_block()
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prompt = (await global_prompt_manager.get_prompt_async("real_time_info_identify_prompt")).format(
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chat_observe_info=chat_history,
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name_block=name_block,
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info_cache_block=info_cache_block,
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person_name=person_name,
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target_message=target_message,
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)
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try:
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logger.debug(f"{self.log_prefix} 信息识别prompt: \n{prompt}\n")
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content, _ = await self.llm_model.generate_response_async(prompt=prompt)
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if content:
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content_json = orjson.loads(repair_json(content))
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# 检查是否返回了不需要查询的标志
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if "none" in content_json:
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logger.debug(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}")
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return None
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if info_type := content_json.get("info_type"):
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# 记录信息获取请求
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self.info_fetching_cache.append(
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{
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"person_id": await get_person_info_manager().get_person_id_by_person_name(person_name),
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"person_name": person_name,
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"info_type": info_type,
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"start_time": time.time(),
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"forget": False,
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}
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)
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# 限制缓存大小
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if len(self.info_fetching_cache) > 10:
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self.info_fetching_cache.pop(0)
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logger.info(f"{self.log_prefix} 识别到需要调取用户 {person_name} 的[{info_type}]信息")
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return info_type
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else:
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logger.warning(f"{self.log_prefix} LLM未返回有效的info_type。响应: {content}")
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except Exception as e:
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logger.error(f"{self.log_prefix} 执行信息识别LLM请求时出错: {e}")
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logger.error(traceback.format_exc())
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return None
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def _build_info_cache_block(self) -> str:
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"""构建已获取信息的缓存块"""
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info_cache_block = ""
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if self.info_fetching_cache:
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# 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</对{person_name}的总体了解>"
|
||
)
|
||
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</对{person_name}的近期了解>"
|
||
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()
|