feat：重构的关系构建，关系处理器，更加精准

scripts/analyze_expressions.py

@@ -198,4 +198,4 @@ def analyze_expressions():
     print(f"各群组详细报告位于: {output_dir}")
 
 if __name__ == "__main__":
-    analyze_expressions() 
+    analyze_expressions() 

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -29,13 +29,13 @@ def init_prompt() -> None:
 4. 思考有没有特殊的梗，一并总结成语言风格
 5. 例子仅供参考，请严格根据群聊内容总结!!!
 注意：总结成如下格式的规律，总结的内容要详细，但具有概括性：
-当"xxx"时，可以"xxx", xxx不超过10个字
+当"xxxxxx"时，可以"xxxxxx", xxxxxx不超过20个字
 
 例如：
-当"表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
+当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
 当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
-当"想说明某个观点，但懒得明说，或者不便明说"，使用"懂的都懂"
-当"表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
+当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
+当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
 
 注意不要总结你自己（SELF）的发言
 现在请你概括

src/chat/focus_chat/info_processors/relationship_processor.py

@@ -13,27 +13,39 @@ from typing import List, Optional
 from typing import Dict
 from src.chat.focus_chat.info.info_base import InfoBase
 from src.chat.focus_chat.info.relation_info import RelationInfo
+from json_repair import repair_json
+from src.person_info.person_info import person_info_manager
+import json
 
 logger = get_logger("processor")
 
 
 def init_prompt():
     relationship_prompt = """
-{name_block}
-
-你和别人的关系信息是，请从这些信息中提取出你和别人的关系的原文：
-{relation_prompt}
-请只从上面这些信息中提取出内容。
-
+<聊天记录>
 {chat_observe_info}
+</聊天记录>
 
-现在请你根据现有的信息，总结你和群里的人的关系
-1. 根据聊天记录的需要，精简你和其他人的关系并输出
-2. 根据聊天记录，如果需要提及你和某个人的关系，请输出你和这个人之间的关系
-3. 如果没有特别需要提及的关系，就不用输出这个人的关系
+<人物信息>
+{relation_prompt}
+</人物信息>
 
-输出内容平淡一些，说中文。
-请注意不要输出多余内容(包括前后缀，括号()，表情包，at或 @等 )。只输出关系内容，记得明确说明这是你的关系。
+请区分聊天记录的内容和你之前对人的了解，聊天记录是现在发生的事情，人物信息是之前对某个人的持久的了解。
+
+{name_block}
+现在请你总结提取某人的信息，提取成一串文本
+1. 根据聊天记录的需求，如果需要你和某个人的信息，请输出你和这个人之间精简的信息
+2. 如果没有特别需要提及的信息，就不用输出这个人的信息
+3. 如果有人问你对他的看法或者关系，请输出你和这个人之间的信息
+
+请从这些信息中提取出你对某人的了解信息，信息提取成一串文本：
+
+请严格按照以下输出格式，不要输出多余内容，person_name可以有多个：
+{{
+    "person_name": "信息",
+    "person_name2": "信息",
+    "person_name3": "信息",
+}}
 
 """
     Prompt(relationship_prompt, "relationship_prompt")
@@ -122,8 +134,10 @@ class RelationshipProcessor(BaseProcessor):
             relation_prompt_init = "你对对方的印象是：\n"
         
         relation_prompt = ""
+        person_name_list = []
         for person in person_list:
-            relation_prompt += f"{await relationship_manager.build_relationship_info(person, is_id=True)}\n"
+            relation_prompt += f"{await relationship_manager.build_relationship_info(person, is_id=True)}\n\n"
+            person_name_list.append(await person_info_manager.get_value(person, "person_name"))
             
         if relation_prompt:
             relation_prompt = relation_prompt_init + relation_prompt
@@ -141,22 +155,41 @@ class RelationshipProcessor(BaseProcessor):
 
         content = ""
         try:
+            logger.info(f"{self.log_prefix} 关系识别prompt: \n{prompt}\n")
             content, _ = await self.llm_model.generate_response_async(prompt=prompt)
             if not content:
                 logger.warning(f"{self.log_prefix} LLM返回空结果，关系识别失败。")
+            
+            print(f"content: {content}")
+            
+            content = repair_json(content)
+            content = json.loads(content)
+            
+            person_info_str = ""
+            
+            for person_name, person_info in content.items():
+                # print(f"person_name: {person_name}, person_info: {person_info}")
+                # print(f"person_list: {person_name_list}")
+                if person_name not in person_name_list:
+                    continue
+                person_str = f"你对 {person_name} 的了解：{person_info}\n"
+                person_info_str += person_str
+                
+                
         except Exception as e:
             # 处理总体异常
             logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
             logger.error(traceback.format_exc())
-            content = "关系识别过程中出现错误"
+            person_info_str = "关系识别过程中出现错误"
 
-        if content == "None":
-            content = ""
+        if person_info_str == "None":
+            person_info_str = ""
+            
         # 记录初步思考结果
-        logger.info(f"{self.log_prefix} 关系识别prompt: \n{prompt}\n")
-        logger.info(f"{self.log_prefix} 关系识别: {content}")
+        
+        logger.info(f"{self.log_prefix} 关系识别: {person_info_str}")
 
-        return content
+        return person_info_str
 
 
 init_prompt()

src/chat/focus_chat/planners/planner_simple.py

@@ -31,8 +31,6 @@ def init_prompt():
 {self_info_block}
 请记住你的性格，身份和特点。
 
-{relation_info_block}
-
 {extra_info_block}
 {memory_str}
 
@@ -42,6 +40,8 @@ def init_prompt():
 
 {chat_content_block}
 
+{relation_info_block}
+
 {cycle_info_block}
 
 {moderation_prompt}
@@ -181,7 +181,7 @@ class ActionPlanner(BasePlanner):
                 prompt = f"{prompt}"
                 llm_content, (reasoning_content, _) = await self.planner_llm.generate_response_async(prompt=prompt)
                 
-                logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
+                logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
                 logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
                 logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
                 
@@ -225,7 +225,10 @@ class ActionPlanner(BasePlanner):
                         extra_info_block = ""
 
                     action_data["extra_info_block"] = extra_info_block
-
+                    
+                    if relation_info:
+                        action_data["relation_info_block"] = relation_info
+                    
                     # 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data
 
                     if extracted_action not in current_available_actions:

src/chat/focus_chat/replyer/default_replyer.py

@@ -41,6 +41,8 @@ def init_prompt():
 你现在正在群里聊天，以下是群里正在进行的聊天内容：
 {chat_info}
 
+{relation_info_block}
+
 以上是聊天内容，你需要了解聊天记录中的内容
 
 {chat_target}
@@ -262,6 +264,7 @@ class DefaultReplyer:
             target_message = action_data.get("target", "")
             identity = action_data.get("identity", "")
             extra_info_block = action_data.get("extra_info_block", "")
+            relation_info_block = action_data.get("relation_info_block", "")
 
             # 3. 构建 Prompt
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
@@ -270,6 +273,7 @@ class DefaultReplyer:
                     # in_mind_reply=in_mind_reply,
                     identity=identity,
                     extra_info_block=extra_info_block,
+                    relation_info_block=relation_info_block,
                     reason=reason,
                     sender_name=sender_name_for_prompt,  # Pass determined name
                     target_message=target_message,
@@ -286,8 +290,7 @@ class DefaultReplyer:
 
             try:
                 with Timer("LLM生成", {}):  # 内部计时器，可选保留
-                    # TODO: API-Adapter修改标记
-                    # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
+                    logger.info(f"{self.log_prefix}Prompt:\n{prompt}\n")
                     content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt)
 
                     # logger.info(f"prompt: {prompt}")
@@ -331,6 +334,7 @@ class DefaultReplyer:
         sender_name,
         # in_mind_reply,
         extra_info_block,
+        relation_info_block,
         identity,
         target_message,
         config_expression_style,
@@ -428,6 +432,7 @@ class DefaultReplyer:
                 chat_target=chat_target_1,
                 chat_info=chat_talking_prompt,
                 extra_info_block=extra_info_block,
+                relation_info_block=relation_info_block,
                 time_block=time_block,
                 # bot_name=global_config.bot.nickname,
                 # prompt_personality="",
@@ -448,6 +453,7 @@ class DefaultReplyer:
                 chat_target=chat_target_1,
                 chat_info=chat_talking_prompt,
                 extra_info_block=extra_info_block,
+                relation_info_block=relation_info_block,
                 time_block=time_block,
                 # bot_name=global_config.bot.nickname,
                 # prompt_personality="",

src/person_info/fix_points_format.py

@@ -0,0 +1,70 @@
+import os
+import sys  
+# 添加项目根目录到Python路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(os.path.dirname(current_dir))
+sys.path.append(project_root)
+
+from loguru import logger
+import json
+from src.common.database.database_model import PersonInfo
+
+def fix_points_format():
+    """修复数据库中的points和forgotten_points格式"""
+    fixed_count = 0
+    error_count = 0
+    
+    try:
+        # 获取所有用户
+        all_persons = PersonInfo.select()
+        
+        for person in all_persons:
+            try:
+                # 修复points
+                if person.points:
+                    try:
+                        # 尝试解析JSON
+                        points_data = json.loads(person.points)
+                    except json.JSONDecodeError:
+                        logger.error(f"无法解析points数据: {person.points}")
+                        points_data = []
+                    
+                    # 确保数据是列表格式
+                    if not isinstance(points_data, list):
+                        points_data = []
+                    
+                    # 直接更新数据库
+                    person.points = json.dumps(points_data, ensure_ascii=False)
+                    person.save()
+                    fixed_count += 1
+                
+                # 修复forgotten_points
+                if person.forgotten_points:
+                    try:
+                        # 尝试解析JSON
+                        forgotten_data = json.loads(person.forgotten_points)
+                    except json.JSONDecodeError:
+                        logger.error(f"无法解析forgotten_points数据: {person.forgotten_points}")
+                        forgotten_data = []
+                    
+                    # 确保数据是列表格式
+                    if not isinstance(forgotten_data, list):
+                        forgotten_data = []
+                    
+                    # 直接更新数据库
+                    person.forgotten_points = json.dumps(forgotten_data, ensure_ascii=False)
+                    person.save()
+                    fixed_count += 1
+                    
+            except Exception as e:
+                logger.error(f"处理用户 {person.person_id} 时出错: {str(e)}")
+                error_count += 1
+                continue
+        
+        logger.info(f"修复完成！成功修复 {fixed_count} 条记录，失败 {error_count} 条记录")
+        
+    except Exception as e:
+        logger.error(f"数据库操作出错: {str(e)}")
+
+if __name__ == "__main__":
+    fix_points_format() 

src/person_info/person_info.py

@@ -8,7 +8,6 @@ import datetime
 import asyncio
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config
-from src.individuality.individuality import individuality
 
 import json  # 新增导入
 from json_repair import repair_json
@@ -29,7 +28,7 @@ PersonInfoManager 类方法功能摘要：
 
 logger = get_logger("person_info")
 
-JSON_SERIALIZED_FIELDS = ["hobby", "hates", "meme", "relationship_others", "interaction"]
+JSON_SERIALIZED_FIELDS = ["points", "forgotten_points"]
 
 person_info_default = {
     "person_id": None,
@@ -169,9 +168,9 @@ class PersonInfoManager:
         processed_value = value
         if field_name in JSON_SERIALIZED_FIELDS:
             if isinstance(value, (list, dict)):
-                processed_value = json.dumps(value, ensure_ascii=False)
+                processed_value = json.dumps(value, ensure_ascii=False, indent=None)
             elif value is None: # Store None as "[]" for JSON list fields
-                processed_value = json.dumps([], ensure_ascii=False)
+                processed_value = json.dumps([], ensure_ascii=False, indent=None)
             # If value is already a string, assume it's pre-serialized or a non-JSON string.
 
         def _db_update_sync(p_id: str, f_name: str, val_to_set):
@@ -280,7 +279,7 @@ class PersonInfoManager:
 
         while current_try < max_retries:
             # prompt_personality = individuality.get_prompt(x_person=2, level=1)
-            bot_name = individuality.personality.bot_nickname
+            bot_name = global_config.bot.nickname
 
             qv_name_prompt = f"你是{bot_name}，一个伪装成人类的AI，你不能让别人发现这一点，"
             qv_name_prompt += f"现在你想给一个用户取一个昵称，用户的qq昵称是{user_nickname}，"

src/person_info/relationship_manager.py

@@ -134,7 +134,7 @@ class RelationshipManager:
             except (SyntaxError, ValueError):
                 points = []
         
-        random_points = random.sample(points, min(3, len(points))) if points else []
+        random_points = random.sample(points, min(5, len(points))) if points else []
         
         nickname_str = await person_info_manager.get_value(person_id, "nickname")
         platform = await person_info_manager.get_value(person_id, "platform")
@@ -312,13 +312,14 @@ class RelationshipManager:
         current_points = await person_info_manager.get_value(person_id, "points") or []
         if isinstance(current_points, str):
             try:
-                current_points = ast.literal_eval(current_points)
-            except (SyntaxError, ValueError):
+                current_points = json.loads(current_points)
+            except json.JSONDecodeError:
+                logger.error(f"解析points JSON失败: {current_points}")
                 current_points = []
         elif not isinstance(current_points, list):
             current_points = []
         current_points.extend(points_list)
-        await person_info_manager.update_one_field(person_id, "points", str(current_points).replace("(", "[").replace(")", "]"))
+        await person_info_manager.update_one_field(person_id, "points", json.dumps(current_points, ensure_ascii=False, indent=None))
 
         # 将新记录添加到现有记录中
         if isinstance(current_points, list):
@@ -365,8 +366,9 @@ class RelationshipManager:
             forgotten_points = await person_info_manager.get_value(person_id, "forgotten_points") or []
             if isinstance(forgotten_points, str):
                 try:
-                    forgotten_points = ast.literal_eval(forgotten_points)
-                except (SyntaxError, ValueError):
+                    forgotten_points = json.loads(forgotten_points)
+                except json.JSONDecodeError:
+                    logger.error(f"解析forgotten_points JSON失败: {forgotten_points}")
                     forgotten_points = []
             elif not isinstance(forgotten_points, list):
                 forgotten_points = []
@@ -487,10 +489,12 @@ class RelationshipManager:
                     return
 
             # 更新数据库
-            await person_info_manager.update_one_field(person_id, "forgotten_points", str(forgotten_points).replace("(", "[").replace(")", "]"))
+            await person_info_manager.update_one_field(person_id, "forgotten_points", json.dumps(forgotten_points, ensure_ascii=False, indent=None))
         
         # 更新数据库
-        await person_info_manager.update_one_field(person_id, "points", str(current_points).replace("(", "[").replace(")", "]"))
+        await person_info_manager.update_one_field(person_id, "points", json.dumps(current_points, ensure_ascii=False, indent=None))
+        know_times = await person_info_manager.get_value(person_id, "know_times") or 0
+        await person_info_manager.update_one_field(person_id, "know_times", know_times + 1)
         await person_info_manager.update_one_field(person_id, "last_know", timestamp)
 
 

tests/test_relationship_processor.py

@@ -0,0 +1,609 @@
+import os
+import sys
+import asyncio
+import random
+import time
+import traceback
+from typing import List, Dict, Any, Tuple, Optional
+from datetime import datetime
+
+# 添加项目根目录到Python路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_dir)
+sys.path.append(project_root)
+
+from src.common.message_repository import find_messages
+from src.common.database.database_model import ActionRecords, ChatStreams
+from src.config.config import global_config
+from src.person_info.person_info import person_info_manager
+from src.chat.utils.utils import translate_timestamp_to_human_readable
+from src.chat.heart_flow.observation.observation import Observation
+from src.llm_models.utils_model import LLMRequest
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.person_info.relationship_manager import relationship_manager
+from src.common.logger_manager import get_logger
+from src.chat.focus_chat.info.info_base import InfoBase
+from src.chat.focus_chat.info.relation_info import RelationInfo
+
+logger = get_logger("processor")
+
+async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
+    """
+    从消息列表中提取不重复的 person_id 列表 (忽略机器人自身)。
+
+    Args:
+        messages: 消息字典列表。
+
+    Returns:
+        一个包含唯一 person_id 的列表。
+    """
+    person_ids_set = set()  # 使用集合来自动去重
+
+    for msg in messages:
+        platform = msg.get("user_platform")
+        user_id = msg.get("user_id")
+
+        # 检查必要信息是否存在 且 不是机器人自己
+        if not all([platform, user_id]) or user_id == global_config.bot.qq_account:
+            continue
+
+        person_id = person_info_manager.get_person_id(platform, user_id)
+
+        # 只有当获取到有效 person_id 时才添加
+        if person_id:
+            person_ids_set.add(person_id)
+
+    return list(person_ids_set)  # 将集合转换为列表返回
+
+class ChattingObservation(Observation):
+    def __init__(self, chat_id):
+        super().__init__(chat_id)
+        self.chat_id = chat_id
+        self.platform = "qq"
+        
+        # 从数据库获取聊天类型和目标信息
+        chat_info = ChatStreams.select().where(ChatStreams.stream_id == chat_id).first()
+        self.is_group_chat = True
+        self.chat_target_info = {
+            "person_name": chat_info.group_name if chat_info else None,
+            "user_nickname": chat_info.group_name if chat_info else None
+        }
+
+        # 初始化其他属性
+        self.talking_message = []
+        self.talking_message_str = ""
+        self.talking_message_str_truncate = ""
+        self.name = global_config.bot.nickname
+        self.nick_name = global_config.bot.alias_names
+        self.max_now_obs_len = global_config.focus_chat.observation_context_size
+        self.overlap_len = global_config.focus_chat.compressed_length
+        self.mid_memories = []
+        self.max_mid_memory_len = global_config.focus_chat.compress_length_limit
+        self.mid_memory_info = ""
+        self.person_list = []
+        self.oldest_messages = []
+        self.oldest_messages_str = ""
+        self.compressor_prompt = ""
+        self.last_observe_time = 0
+
+    def get_observe_info(self, ids=None):
+        """获取观察信息"""
+        return self.talking_message_str
+
+def init_prompt():
+    relationship_prompt = """
+<聊天记录>
+{chat_observe_info}
+</聊天记录>
+
+<人物信息>
+{relation_prompt}
+</人物信息>
+
+请区分聊天记录的内容和你之前对人的了解，聊天记录是现在发生的事情，人物信息是之前对某个人的持久的了解。
+
+{name_block}
+现在请你总结提取某人的信息，提取成一串文本
+1. 根据聊天记录的需求，如果需要你和某个人的信息，请输出你和这个人之间精简的信息
+2. 如果没有特别需要提及的信息，就不用输出这个人的信息
+3. 如果有人问你对他的看法或者关系，请输出你和这个人之间的信息
+
+请从这些信息中提取出你对某人的了解信息，信息提取成一串文本：
+
+请严格按照以下输出格式，不要输出多余内容，person_name可以有多个：
+{{
+    "person_name": "信息",
+    "person_name2": "信息",
+    "person_name3": "信息",
+}}
+
+"""
+    Prompt(relationship_prompt, "relationship_prompt")
+
+class RelationshipProcessor:
+    log_prefix = "关系"
+
+    def __init__(self, subheartflow_id: str):
+        self.subheartflow_id = subheartflow_id
+
+        self.llm_model = LLMRequest(
+            model=global_config.model.relation,
+            max_tokens=800,
+            request_type="relation",
+        )
+
+        # 直接从数据库获取名称
+        chat_info = ChatStreams.select().where(ChatStreams.stream_id == subheartflow_id).first()
+        name = chat_info.group_name if chat_info else "未知"
+        self.log_prefix = f"[{name}] "
+
+    async def process_info(
+        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
+    ) -> List[InfoBase]:
+        """处理信息对象
+
+        Args:
+            *infos: 可变数量的InfoBase类型的信息对象
+
+        Returns:
+            List[InfoBase]: 处理后的结构化信息列表
+        """
+        relation_info_str = await self.relation_identify(observations)
+
+        if relation_info_str:
+            relation_info = RelationInfo()
+            relation_info.set_relation_info(relation_info_str)
+        else:
+            relation_info = None
+            return None
+
+        return [relation_info]
+
+    async def relation_identify(
+        self, observations: Optional[List[Observation]] = None,
+    ):
+        """
+        在回复前进行思考，生成内心想法并收集工具调用结果
+
+        参数:
+            observations: 观察信息
+
+        返回:
+            如果return_prompt为False:
+                tuple: (current_mind, past_mind) 当前想法和过去的想法列表
+            如果return_prompt为True:
+                tuple: (current_mind, past_mind, prompt) 当前想法、过去的想法列表和使用的prompt
+        """
+
+        if observations is None:
+            observations = []
+        for observation in observations:
+            if isinstance(observation, ChattingObservation):
+                # 获取聊天元信息
+                is_group_chat = observation.is_group_chat
+                chat_target_info = observation.chat_target_info
+                chat_target_name = "对方"  # 私聊默认名称
+                if not is_group_chat and chat_target_info:
+                    # 优先使用person_name，其次user_nickname，最后回退到默认值
+                    chat_target_name = (
+                        chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or chat_target_name
+                    )
+                # 获取聊天内容
+                chat_observe_info = observation.get_observe_info()
+                person_list = observation.person_list
+
+        nickname_str = ""
+        for nicknames in global_config.bot.alias_names:
+            nickname_str += f"{nicknames},"
+        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。"
+
+        if is_group_chat:
+            relation_prompt_init = "你对群聊里的人的印象是：\n"
+        else:
+            relation_prompt_init = "你对对方的印象是：\n"
+        
+        relation_prompt = ""
+        for person in person_list:
+            relation_prompt += f"{await relationship_manager.build_relationship_info(person, is_id=True)}\n"
+            
+        if relation_prompt:
+            relation_prompt = relation_prompt_init + relation_prompt
+        else:
+            relation_prompt = relation_prompt_init + "没有特别在意的人\n"
+
+        prompt = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
+            name_block=name_block,
+            relation_prompt=relation_prompt,
+            time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            chat_observe_info=chat_observe_info,
+        )
+        # The above code is a Python script that is attempting to print the variable `prompt`.
+        # However, the code is not complete as the content of the `prompt` variable is missing.
+        # print(prompt)
+
+        content = ""
+        try:
+            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
+            if not content:
+                logger.warning(f"{self.log_prefix} LLM返回空结果，关系识别失败。")
+        except Exception as e:
+            # 处理总体异常
+            logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
+            logger.error(traceback.format_exc())
+            content = "关系识别过程中出现错误"
+
+        if content == "None":
+            content = ""
+        # 记录初步思考结果
+        logger.info(f"{self.log_prefix} 关系识别prompt: \n{prompt}\n")
+        logger.info(f"{self.log_prefix} 关系识别: {content}")
+
+        return content
+
+init_prompt()
+
+# ==== 只复制最小依赖的relationship_manager ====
+class SimpleRelationshipManager:
+    async def build_relationship_info(self, person, is_id: bool = False) -> str:
+        if is_id:
+            person_id = person
+        else:
+            person_id = person_info_manager.get_person_id(person[0], person[1])
+
+        person_name = await person_info_manager.get_value(person_id, "person_name")
+        if not person_name or person_name == "none":
+            return ""
+        impression = await person_info_manager.get_value(person_id, "impression")
+        interaction = await person_info_manager.get_value(person_id, "interaction")
+        points = await person_info_manager.get_value(person_id, "points") or []
+        
+        if isinstance(points, str):
+            try:
+                import ast
+                points = ast.literal_eval(points)
+            except (SyntaxError, ValueError):
+                points = []
+        
+        import random
+        random_points = random.sample(points, min(3, len(points))) if points else []
+        
+        nickname_str = await person_info_manager.get_value(person_id, "nickname")
+        platform = await person_info_manager.get_value(person_id, "platform")
+        relation_prompt = f"'{person_name}' ，ta在{platform}上的昵称是{nickname_str}。"
+        
+        if impression:
+            relation_prompt += f"你对ta的印象是：{impression}。"
+        if interaction:
+            relation_prompt += f"你与ta的关系是：{interaction}。"
+        if random_points:
+            for point in random_points:
+                point_str = f"时间：{point[2]}。内容：{point[0]}"
+            relation_prompt += f"你记得{person_name}最近的点是：{point_str}。"
+        return relation_prompt
+
+# 用于替换原有的relationship_manager
+relationship_manager = SimpleRelationshipManager()
+
+def get_raw_msg_by_timestamp_random(
+    timestamp_start: float, timestamp_end: float, limit: int = 0, limit_mode: str = "latest"
+) -> List[Dict[str, Any]]:
+    """先在范围时间戳内随机选择一条消息，取得消息的chat_id，然后根据chat_id获取该聊天在指定时间戳范围内的消息"""
+    # 获取所有消息，只取chat_id字段
+    filter_query = {"time": {"$gt": timestamp_start, "$lt": timestamp_end}}
+    all_msgs = find_messages(message_filter=filter_query)
+    if not all_msgs:
+        return []
+    # 随机选一条
+    msg = random.choice(all_msgs)
+    chat_id = msg["chat_id"]
+    timestamp_start = msg["time"]
+    # 用 chat_id 获取该聊天在指定时间戳范围内的消息
+    filter_query = {"chat_id": chat_id, "time": {"$gt": timestamp_start, "$lt": timestamp_end}}
+    sort_order = [("time", 1)] if limit == 0 else None
+    return find_messages(message_filter=filter_query, sort=sort_order, limit=limit, limit_mode="earliest")
+
+def _build_readable_messages_internal(
+    messages: List[Dict[str, Any]],
+    replace_bot_name: bool = True,
+    merge_messages: bool = False,
+    timestamp_mode: str = "relative",
+    truncate: bool = False,
+) -> Tuple[str, List[Tuple[float, str, str]]]:
+    """内部辅助函数，构建可读消息字符串和原始消息详情列表"""
+    if not messages:
+        return "", []
+
+    message_details_raw: List[Tuple[float, str, str]] = []
+
+    # 1 & 2: 获取发送者信息并提取消息组件
+    for msg in messages:
+        # 检查是否是动作记录
+        if msg.get("is_action_record", False):
+            is_action = True
+            timestamp = msg.get("time")
+            content = msg.get("display_message", "")
+            message_details_raw.append((timestamp, global_config.bot.nickname, content, is_action))
+            continue
+
+        # 检查并修复缺少的user_info字段
+        if "user_info" not in msg:
+            msg["user_info"] = {
+                "platform": msg.get("user_platform", ""),
+                "user_id": msg.get("user_id", ""),
+                "user_nickname": msg.get("user_nickname", ""),
+                "user_cardname": msg.get("user_cardname", ""),
+            }
+
+        user_info = msg.get("user_info", {})
+        platform = user_info.get("platform")
+        user_id = user_info.get("user_id")
+        user_nickname = user_info.get("user_nickname")
+        user_cardname = user_info.get("user_cardname")
+        timestamp = msg.get("time")
+        
+        if msg.get("display_message"):
+            content = msg.get("display_message")
+        else:
+            content = msg.get("processed_plain_text", "")
+
+        if "ᶠ" in content:
+            content = content.replace("ᶠ", "")
+        if "ⁿ" in content:
+            content = content.replace("ⁿ", "")
+
+        if not all([platform, user_id, timestamp is not None]):
+            continue
+
+        person_id = person_info_manager.get_person_id(platform, user_id)
+        if replace_bot_name and user_id == global_config.bot.qq_account:
+            person_name = f"{global_config.bot.nickname}(你)"
+        else:
+            person_name = person_info_manager.get_value_sync(person_id, "person_name")
+
+        if not person_name:
+            if user_cardname:
+                person_name = f"昵称：{user_cardname}"
+            elif user_nickname:
+                person_name = f"{user_nickname}"
+            else:
+                person_name = "某人"
+
+        if content != "":
+            message_details_raw.append((timestamp, person_name, content, False))
+
+    if not message_details_raw:
+        return "", []
+
+    message_details_raw.sort(key=lambda x: x[0])
+
+    # 为每条消息添加一个标记，指示它是否是动作记录
+    message_details_with_flags = []
+    for timestamp, name, content, is_action in message_details_raw:
+        message_details_with_flags.append((timestamp, name, content, is_action))
+
+    # 应用截断逻辑
+    message_details: List[Tuple[float, str, str, bool]] = []
+    n_messages = len(message_details_with_flags)
+    if truncate and n_messages > 0:
+        for i, (timestamp, name, content, is_action) in enumerate(message_details_with_flags):
+            if is_action:
+                message_details.append((timestamp, name, content, is_action))
+                continue
+
+            percentile = i / n_messages
+            original_len = len(content)
+            limit = -1
+
+            if percentile < 0.2:
+                limit = 50
+                replace_content = "......（记不清了）"
+            elif percentile < 0.5:
+                limit = 100
+                replace_content = "......（有点记不清了）"
+            elif percentile < 0.7:
+                limit = 200
+                replace_content = "......（内容太长了）"
+            elif percentile < 1.0:
+                limit = 300
+                replace_content = "......（太长了）"
+
+            truncated_content = content
+            if 0 < limit < original_len:
+                truncated_content = f"{content[:limit]}{replace_content}"
+
+            message_details.append((timestamp, name, truncated_content, is_action))
+    else:
+        message_details = message_details_with_flags
+
+    # 合并连续消息
+    merged_messages = []
+    if merge_messages and message_details:
+        current_merge = {
+            "name": message_details[0][1],
+            "start_time": message_details[0][0],
+            "end_time": message_details[0][0],
+            "content": [message_details[0][2]],
+            "is_action": message_details[0][3]
+        }
+
+        for i in range(1, len(message_details)):
+            timestamp, name, content, is_action = message_details[i]
+            
+            if is_action or current_merge["is_action"]:
+                merged_messages.append(current_merge)
+                current_merge = {
+                    "name": name,
+                    "start_time": timestamp,
+                    "end_time": timestamp,
+                    "content": [content],
+                    "is_action": is_action
+                }
+                continue
+
+            if name == current_merge["name"] and (timestamp - current_merge["end_time"] <= 60):
+                current_merge["content"].append(content)
+                current_merge["end_time"] = timestamp
+            else:
+                merged_messages.append(current_merge)
+                current_merge = {
+                    "name": name, 
+                    "start_time": timestamp, 
+                    "end_time": timestamp, 
+                    "content": [content],
+                    "is_action": is_action
+                }
+        merged_messages.append(current_merge)
+    elif message_details:
+        for timestamp, name, content, is_action in message_details:
+            merged_messages.append(
+                {
+                    "name": name,
+                    "start_time": timestamp,
+                    "end_time": timestamp,
+                    "content": [content],
+                    "is_action": is_action
+                }
+            )
+
+    # 格式化为字符串
+    output_lines = []
+    for merged in merged_messages:
+        readable_time = translate_timestamp_to_human_readable(merged["start_time"], mode=timestamp_mode)
+
+        if merged["is_action"]:
+            output_lines.append(f"{readable_time}, {merged['content'][0]}")
+        else:
+            header = f"{readable_time}, {merged['name']} :"
+            output_lines.append(header)
+            for line in merged["content"]:
+                stripped_line = line.strip()
+                if stripped_line:
+                    if stripped_line.endswith("。"):
+                        stripped_line = stripped_line[:-1]
+                    if not stripped_line.endswith("（内容太长）"):
+                        output_lines.append(f"{stripped_line}")
+                    else:
+                        output_lines.append(stripped_line)
+        output_lines.append("\n")
+
+    formatted_string = "".join(output_lines).strip()
+    return formatted_string, [(t, n, c) for t, n, c, is_action in message_details if not is_action]
+
+def build_readable_messages(
+    messages: List[Dict[str, Any]],
+    replace_bot_name: bool = True,
+    merge_messages: bool = False,
+    timestamp_mode: str = "relative",
+    read_mark: float = 0.0,
+    truncate: bool = False,
+    show_actions: bool = False,
+) -> str:
+    """将消息列表转换为可读的文本格式"""
+    copy_messages = [msg.copy() for msg in messages]
+    
+    if show_actions and copy_messages:
+        min_time = min(msg.get("time", 0) for msg in copy_messages)
+        max_time = max(msg.get("time", 0) for msg in copy_messages)
+        chat_id = copy_messages[0].get("chat_id") if copy_messages else None
+        
+        actions = ActionRecords.select().where(
+            (ActionRecords.time >= min_time) & 
+            (ActionRecords.time <= max_time) &
+            (ActionRecords.chat_id == chat_id)
+        ).order_by(ActionRecords.time)
+        
+        for action in actions:
+            if action.action_build_into_prompt:
+                action_msg = {
+                    "time": action.time,
+                    "user_id": global_config.bot.qq_account,
+                    "user_nickname": global_config.bot.nickname,
+                    "user_cardname": "",
+                    "processed_plain_text": f"{action.action_prompt_display}",
+                    "display_message": f"{action.action_prompt_display}",
+                    "chat_info_platform": action.chat_info_platform,
+                    "is_action_record": True,
+                    "action_name": action.action_name,
+                }
+                copy_messages.append(action_msg)
+        
+        copy_messages.sort(key=lambda x: x.get("time", 0))
+
+    if read_mark <= 0:
+        formatted_string, _ = _build_readable_messages_internal(
+            copy_messages, replace_bot_name, merge_messages, timestamp_mode, truncate
+        )
+        return formatted_string
+    else:
+        messages_before_mark = [msg for msg in copy_messages if msg.get("time", 0) <= read_mark]
+        messages_after_mark = [msg for msg in copy_messages if msg.get("time", 0) > read_mark]
+
+        formatted_before, _ = _build_readable_messages_internal(
+            messages_before_mark, replace_bot_name, merge_messages, timestamp_mode, truncate
+        )
+        formatted_after, _ = _build_readable_messages_internal(
+            messages_after_mark,
+            replace_bot_name,
+            merge_messages,
+            timestamp_mode,
+        )
+
+        read_mark_line = "\n--- 以上消息是你已经看过---\n--- 请关注以下未读的新消息---\n"
+
+        if formatted_before and formatted_after:
+            return f"{formatted_before}{read_mark_line}{formatted_after}"
+        elif formatted_before:
+            return f"{formatted_before}{read_mark_line}"
+        elif formatted_after:
+            return f"{read_mark_line}{formatted_after}"
+        else:
+            return read_mark_line.strip()
+
+async def test_relationship_processor():
+    """测试关系处理器的功能"""
+    
+    # 测试10次
+    for i in range(10):
+        print(f"\n=== 测试 {i+1} ===")
+        
+        # 获取随机消息
+        current_time = time.time()
+        start_time = current_time - 864000  # 10天前
+        messages = get_raw_msg_by_timestamp_random(start_time, current_time, limit=25)
+        
+        if not messages:
+            print("没有找到消息，跳过此次测试")
+            continue
+            
+        chat_id = messages[0]["chat_id"]
+        
+        # 构建可读消息
+        chat_observe_info = build_readable_messages(
+            messages,
+            replace_bot_name=True,
+            timestamp_mode="normal_no_YMD",
+            truncate=True,
+            show_actions=True,
+        )
+        # print(chat_observe_info)
+        # 创建观察对象
+        processor = RelationshipProcessor(chat_id)
+        observation = ChattingObservation(chat_id)
+        observation.talking_message_str = chat_observe_info
+        observation.talking_message = messages  # 设置消息列表
+        observation.person_list = await get_person_id_list(messages)  # 使用get_person_id_list获取person_list
+        
+        # 处理关系
+        result = await processor.process_info([observation])
+        
+        if result:
+            print("\n关系识别结果:")
+            print(result[0].get_processed_info())
+        else:
+            print("关系识别失败")
+            
+        # 等待一下，避免请求过快
+        await asyncio.sleep(1)
+
+if __name__ == "__main__":
+    asyncio.run(test_relationship_processor()) 

tests/test_sentence_similarity.py

@@ -1,156 +0,0 @@
-import time
-import unittest
-import jieba
-from difflib import SequenceMatcher
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-
-def tfidf_similarity(s1, s2):
-    """
-    使用 TF-IDF 和余弦相似度计算两个句子的相似性。
-    """
-    # 1. 使用 jieba 进行分词
-    s1_words = " ".join(jieba.cut(s1))
-    s2_words = " ".join(jieba.cut(s2))
-    
-    # 2. 将两句话放入一个列表中
-    corpus = [s1_words, s2_words]
-    
-    # 3. 创建 TF-IDF 向量化器并进行计算
-    try:
-        vectorizer = TfidfVectorizer()
-        tfidf_matrix = vectorizer.fit_transform(corpus)
-    except ValueError:
-        # 如果句子完全由停用词组成，或者为空，可能会报错
-        return 0.0
-
-    # 4. 计算余弦相似度
-    similarity_matrix = cosine_similarity(tfidf_matrix)
-    
-    # 返回 s1 和 s2 的相似度
-    return similarity_matrix[0, 1]
-
-def sequence_similarity(s1, s2):
-    """
-    使用 SequenceMatcher 计算两个句子的相似性。
-    """
-    return SequenceMatcher(None, s1, s2).ratio()
-
-class TestSentenceSimilarity(unittest.TestCase):
-    def test_similarity_comparison(self):
-        """比较不同相似度计算方法的结果"""
-        test_cases = [
-            {
-                "sentence1": "今天天气怎么样",
-                "sentence2": "今天气候如何",
-                "expected_similar": True
-            },
-            {
-                "sentence1": "今天天气怎么样",
-                "sentence2": "我今天要去吃麦当劳",
-                "expected_similar": False
-            },
-            {
-                "sentence1": "我今天要去吃麦当劳",
-                "sentence2": "肯德基和麦当劳哪家好吃",
-                "expected_similar": True
-            },
-            {
-                "sentence1": "Vindemiatrix提到昨天三个无赖杀穿交界地",
-                "sentence2": "Vindemiatrix昨天用三个无赖角色杀穿了游戏中的交界地",
-                "expected_similar": True
-            },
-            {
-                "sentence1": "tc_魔法士解释了之前templateinfo的with用法和现在的单独逻辑发送的区别",
-                "sentence2": "tc_魔法士解释了templateinfo的用法，包括它是一个字典，key是prompt的名字，value是prompt的内容，格式是只支持大括号的fstring",
-                "expected_similar": False
-            },
-            {
-                "sentence1": "YXH_XianYu分享了一张舰娘街机游戏的图片，并提到'玩舰娘街机的董不懂'",
-                "sentence2": "YXH_XianYu对街机游戏表现出兴趣，并分享了玩舰娘街机的经历",
-                "expected_similar": True
-            },
-            {
-                "sentence1": "YXH_XianYu在考虑入坑明日方舟，犹豫是否要从零开荒或使用初始号",
-                "sentence2": "YXH_XianYu考虑入坑明日方舟，倾向于从零开荒或初始号开荒",
-                "expected_similar": True
-            },
-            {
-                "sentence1": "YXH_XianYu提到秋叶原好多人在玩maimai",
-                "sentence2": "YXH_XianYu对学园偶像的付费石头机制表示惊讶",
-                "expected_similar": False
-            }
-        ]
-
-        print("\n相似度计算方法比较:")
-        for i, case in enumerate(test_cases, 1):
-            print(f"\n测试用例 {i}:")
-            print(f"句子1: {case['sentence1']}")
-            print(f"句子2: {case['sentence2']}")
-
-            # TF-IDF 相似度
-            start_time = time.time()
-            tfidf_sim = tfidf_similarity(case['sentence1'], case['sentence2'])
-            tfidf_time = time.time() - start_time
-
-            # SequenceMatcher 相似度
-            start_time = time.time()
-            seq_sim = sequence_similarity(case['sentence1'], case['sentence2'])
-            seq_time = time.time() - start_time
-
-            print(f"TF-IDF相似度: {tfidf_sim:.4f} (耗时: {tfidf_time:.4f}秒)")
-            print(f"SequenceMatcher相似度: {seq_sim:.4f} (耗时: {seq_time:.4f}秒)")
-
-    def test_batch_processing(self):
-        """测试批量处理性能"""
-        sentences = [
-            "人工智能正在改变世界",
-            "AI技术发展迅速",
-            "机器学习是人工智能的一个分支",
-            "深度学习在图像识别领域取得了突破",
-            "自然语言处理技术越来越成熟"
-        ]
-
-        print("\n批量处理测试:")
-        
-        # TF-IDF 批量处理
-        start_time = time.time()
-        tfidf_matrix = []
-        for i in range(len(sentences)):
-            row = []
-            for j in range(len(sentences)):
-                similarity = tfidf_similarity(sentences[i], sentences[j])
-                row.append(similarity)
-            tfidf_matrix.append(row)
-        tfidf_time = time.time() - start_time
-
-        # SequenceMatcher 批量处理
-        start_time = time.time()
-        seq_matrix = []
-        for i in range(len(sentences)):
-            row = []
-            for j in range(len(sentences)):
-                similarity = sequence_similarity(sentences[i], sentences[j])
-                row.append(similarity)
-            seq_matrix.append(row)
-        seq_time = time.time() - start_time
-
-        print(f"TF-IDF批量处理 {len(sentences)} 个句子耗时: {tfidf_time:.4f}秒")
-        print(f"SequenceMatcher批量处理 {len(sentences)} 个句子耗时: {seq_time:.4f}秒")
-
-        # 打印TF-IDF相似度矩阵
-        print("\nTF-IDF相似度矩阵:")
-        for row in tfidf_matrix:
-            for similarity in row:
-                print(f"{similarity:.4f}", end="\t")
-            print()
-
-        # 打印SequenceMatcher相似度矩阵
-        print("\nSequenceMatcher相似度矩阵:")
-        for row in seq_matrix:
-            for similarity in row:
-                print(f"{similarity:.4f}", end="\t")
-            print()
-
-if __name__ == '__main__':
-    unittest.main(verbosity=2)