初始化

2025-08-11 19:34:18 +08:00
parent ff7d1177fa
commit 2d4745cd58
257 changed files with 69069 additions and 0 deletions
--- a/src/person_info/relationship_fetcher.py
+++ b/src/person_info/relationship_fetcher.py
@@ -0,0 +1,451 @@
+import time
+import traceback
+import json
+import random
+
+from typing import List, Dict, Any
+from json_repair import repair_json
+
+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.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.message_receive.chat_stream import get_chat_manager
+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}
+</聊天记录>
+
+{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"
+        )
+
+        name = get_chat_manager().get_stream_name(self.chat_id)
+        self.log_prefix = f"[{name}] 实时信息"
+
+    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=3):
+        # 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")
+
+        nickname_str = await person_info_manager.get_value(person_id, "nickname")
+        platform = await person_info_manager.get_value(person_id, "platform")
+
+        if person_name == nickname_str and not short_impression:
+            return ""
+
+        current_points = await person_info_manager.get_value(person_id, "points") or []
+
+        # 按时间排序forgotten_points
+        current_points.sort(key=lambda x: x[2])
+        # 按权重加权随机抽取最多3个不重复的points，point[1]的值在1-10之间，权重越高被抽到概率越大
+        if len(current_points) > points_num:
+            # point[1] 取值范围1-10，直接作为权重
+            weights = [max(1, min(10, int(point[1]))) for point in current_points]
+            # 使用加权采样不放回，保证不重复
+            indices = list(range(len(current_points)))
+            points = []
+            for _ in range(points_num):
+                if not indices:
+                    break
+                sub_weights = [weights[i] for i in indices]
+                chosen_idx = random.choices(indices, weights=sub_weights, k=1)[0]
+                points.append(current_points[chosen_idx])
+                indices.remove(chosen_idx)
+        else:
+            points = current_points
+
+        # 构建points文本
+        points_text = "\n".join([f"{point[2]}：{point[0]}" for point in points])
+
+        nickname_str = ""
+        if person_name != nickname_str:
+            nickname_str = f"(ta在{platform}上的昵称是{nickname_str})"
+
+        relation_info = ""
+
+        if short_impression and relation_info:
+            if points_text:
+                relation_info = f"你对{person_name}的印象是{nickname_str}：{short_impression}。具体来说：{relation_info}。你还记得ta最近做的事：{points_text}"
+            else:
+                relation_info = (
+                    f"你对{person_name}的印象是{nickname_str}：{short_impression}。具体来说：{relation_info}"
+                )
+        elif short_impression:
+            if points_text:
+                relation_info = (
+                    f"你对{person_name}的印象是{nickname_str}：{short_impression}。你还记得ta最近做的事：{points_text}"
+                )
+            else:
+                relation_info = f"你对{person_name}的印象是{nickname_str}：{short_impression}"
+        elif relation_info:
+            if points_text:
+                relation_info = (
+                    f"你对{person_name}的了解{nickname_str}：{relation_info}。你还记得ta最近做的事：{points_text}"
+                )
+            else:
+                relation_info = f"你对{person_name}的了解{nickname_str}：{relation_info}"
+        elif points_text:
+            relation_info = f"你记得{person_name}{nickname_str}最近做的事：{points_text}"
+        else:
+            relation_info = ""
+
+        return relation_info
+
+    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 = json.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": 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</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 = json.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()