better：优化表达方式和侧面人格

bot.py

@@ -1,18 +1,21 @@
 import asyncio
 import hashlib
 import os
+from dotenv import load_dotenv
+if os.path.exists(".env"):
+    load_dotenv(".env", override=True)
+    print("成功加载环境变量配置")
+else:
+    print("未找到.env文件，请确保程序所需的环境变量被正确设置")
 import sys
 import time
 import platform
 import traceback
 from pathlib import Path
-from dotenv import load_dotenv
 from rich.traceback import install
-
 # maim_message imports for console input
 from maim_message import Seg, UserInfo, BaseMessageInfo, MessageBase
 from src.chat.message_receive.bot import chat_bot
-
 # 最早期初始化日志系统，确保所有后续模块都使用正确的日志格式
 from src.common.logger import initialize_logging, get_logger, shutdown_logging
 from src.main import MainSystem
@@ -22,12 +25,7 @@ initialize_logging()
 
 logger = get_logger("main")
 
-# 直接加载生产环境变量配置
-if os.path.exists(".env"):
-    load_dotenv(".env", override=True)
-    logger.info("成功加载环境变量配置")
-else:
-    logger.warning("未找到.env文件，请确保程序所需的环境变量被正确设置")
+
 
 install(extra_lines=3)
 

src/chat/express/expression_selector.py

@@ -0,0 +1,243 @@
+from .exprssion_learner import get_expression_learner
+import random
+from typing import List, Dict, Tuple
+from json_repair import repair_json
+import json
+import os
+import time
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.common.logger import get_logger
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+
+logger = get_logger("expression_selector")
+
+def init_prompt():
+    expression_evaluation_prompt = """
+你的名字是{bot_name}
+
+以下是正在进行的聊天内容：
+{chat_observe_info}
+
+以下是可选的表达情境：
+{all_situations}
+
+请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的5-10个情境。
+考虑因素包括：
+1. 聊天的情绪氛围（轻松、严肃、幽默等）
+2. 话题类型（日常、技术、游戏、情感等）
+3. 情境与当前语境的匹配度
+
+请以JSON格式输出，只需要输出选中的情境编号：
+例如：
+{{
+    "selected_situations": [2, 3, 5, 7, 9, 12, 15, 18, 21, 25]
+}}
+例如：
+{{
+    "selected_situations": [1, 4, 7, 9, 13, 18, 24]
+}}
+
+请严格按照JSON格式输出，不要包含其他内容：
+"""
+    Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
+
+def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]:
+    """按权重随机抽样"""
+    if not population or not weights or k <= 0:
+        return []
+    
+    if len(population) <= k:
+        return population.copy()
+    
+    # 使用累积权重的方法进行加权抽样
+    selected = []
+    population_copy = population.copy()
+    weights_copy = weights.copy()
+    
+    for _ in range(k):
+        if not population_copy:
+            break
+            
+        # 选择一个元素
+        chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0]
+        selected.append(population_copy.pop(chosen_idx))
+        weights_copy.pop(chosen_idx)
+    
+    return selected
+
+class ExpressionSelector:
+    def __init__(self):
+        self.expression_learner = get_expression_learner()
+        # TODO: API-Adapter修改标记
+        self.llm_model = LLMRequest(
+            model=global_config.model.utils_small,
+            request_type="expression.selector",
+        )
+
+    def get_random_expressions(self, chat_id: str, style_num: int, grammar_num: int, personality_num: int) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
+        (
+            learnt_style_expressions,
+            learnt_grammar_expressions,
+            personality_expressions,
+        ) = self.expression_learner.get_expression_by_chat_id(chat_id)
+
+        # 按权重抽样（使用count作为权重）
+        if learnt_style_expressions:
+            style_weights = [expr.get("count", 1) for expr in learnt_style_expressions]
+            selected_style = weighted_sample(learnt_style_expressions, style_weights, style_num)
+        else:
+            selected_style = []
+            
+        if learnt_grammar_expressions:
+            grammar_weights = [expr.get("count", 1) for expr in learnt_grammar_expressions]
+            selected_grammar = weighted_sample(learnt_grammar_expressions, grammar_weights, grammar_num)
+        else:
+            selected_grammar = []
+            
+        if personality_expressions:
+            personality_weights = [expr.get("count", 1) for expr in personality_expressions]
+            selected_personality = weighted_sample(personality_expressions, personality_weights, personality_num)
+        else:
+            selected_personality = []
+
+        return selected_style, selected_grammar, selected_personality
+    
+    def update_expression_count(self, chat_id: str, expression: Dict[str, str], multiplier: float = 1.5):
+        """更新表达方式的count值"""
+        if expression.get("type") == "style_personality":
+            # personality表达方式存储在全局文件中
+            file_path = os.path.join("data", "expression", "personality", "expressions.json")
+        else:
+            # style和grammar表达方式存储在对应chat_id目录中
+            expr_type = expression.get("type", "style")
+            if expr_type == "style":
+                file_path = os.path.join("data", "expression", "learnt_style", str(chat_id), "expressions.json")
+            elif expr_type == "grammar":
+                file_path = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
+            else:
+                return
+        
+        if not os.path.exists(file_path):
+            return
+            
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                expressions = json.load(f)
+            
+            # 找到匹配的表达方式并更新count
+            for expr in expressions:
+                if (expr.get("situation") == expression.get("situation") and 
+                    expr.get("style") == expression.get("style")):
+                    expr["count"] = expr.get("count", 1) * multiplier
+                    expr["last_active_time"] = time.time()
+                    break
+            
+            # 保存更新后的文件
+            with open(file_path, "w", encoding="utf-8") as f:
+                json.dump(expressions, f, ensure_ascii=False, indent=2)
+                
+        except Exception as e:
+            logger.error(f"更新表达方式count失败: {e}")
+    
+    async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str) -> List[Dict[str, str]]:
+        """使用LLM选择适合的表达方式"""
+
+        # 1. 获取35个随机表达方式（现在按权重抽取）
+        style_exprs, grammar_exprs, personality_exprs = self.get_random_expressions(chat_id, 25, 25, 10)
+
+        # 2. 构建所有表达方式的索引和情境列表
+        all_expressions = []
+        all_situations = []
+
+        # 添加style表达方式
+        for expr in style_exprs:
+            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
+                expr_with_type = expr.copy()
+                expr_with_type["type"] = "style"
+                all_expressions.append(expr_with_type)
+                all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
+
+        # 添加grammar表达方式
+        for expr in grammar_exprs:
+            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
+                expr_with_type = expr.copy()
+                expr_with_type["type"] = "grammar"
+                all_expressions.append(expr_with_type)
+                all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
+
+        # 添加personality表达方式
+        for expr in personality_exprs:
+            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
+                expr_with_type = expr.copy()
+                expr_with_type["type"] = "style_personality"
+                all_expressions.append(expr_with_type)
+                all_situations.append(f"{len(all_expressions)}.{expr['situation']}")
+
+        if not all_expressions:
+            logger.warning("没有找到可用的表达方式")
+            return []
+
+        all_situations_str = "\n".join(all_situations)
+
+        # 3. 构建prompt（只包含情境，不包含完整的表达方式）
+        prompt = (await global_prompt_manager.get_prompt_async("expression_evaluation_prompt")).format(
+            bot_name=global_config.bot.nickname,
+            chat_observe_info=chat_info,
+            all_situations=all_situations_str,
+        )
+
+        print(prompt)
+        
+        # 4. 调用LLM
+        try:
+            content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt)
+
+            # logger.info(f"{self.log_prefix} LLM返回结果: {content}")
+
+            if not content:
+                logger.warning("LLM返回空结果")
+                return []
+
+            # 5. 解析结果
+            result = repair_json(content)
+            if isinstance(result, str):
+                result = json.loads(result)
+
+            if not isinstance(result, dict) or "selected_situations" not in result:
+                logger.error("LLM返回格式错误")
+                return []
+
+            selected_indices = result["selected_situations"]
+
+            # 根据索引获取完整的表达方式
+            valid_expressions = []
+            for idx in selected_indices:
+                if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
+                    expression = all_expressions[idx - 1]  # 索引从1开始
+                    valid_expressions.append(expression)
+                    
+                    # 对选中的表达方式count数*1.5
+                    self.update_expression_count(chat_id, expression, 1.5)
+
+            # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
+            return valid_expressions
+
+        except Exception as e:
+            logger.error(f"LLM处理表达方式选择时出错: {e}")
+            return []
+    
+
+init_prompt()
+
+try:
+    expression_selector = ExpressionSelector()
+except Exception as e:
+    print(f"ExpressionSelector初始化失败: {e}")
+
+
+
+
+
+
+

src/chat/express/exprssion_learner.py

@@ -29,7 +29,7 @@ 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"xxxxxx"时，可以"xxxxxx", xxxxxx不超过20个字
+当"xxxxxx"时，可以"xxxxxx", xxxxxx不超过20个字，为特定句式或表达
 
 例如：
 当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
@@ -73,7 +73,7 @@ class ExpressionLearner:
             request_type="expressor.learner",
         )
 
-    async def get_expression_by_chat_id(self, chat_id: str) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
+    def get_expression_by_chat_id(self, chat_id: str) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
         """
         读取/data/expression/learnt/{chat_id}/expressions.json和/data/expression/personality/expressions.json
         返回(learnt_expressions, personality_expressions)

src/chat/focus_chat/info_processors/expression_selector_processor.py

@@ -1,98 +1,18 @@
 import time
 import random
-from typing import List, Dict
+from typing import List
 from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
 from src.chat.heart_flow.observation.observation import Observation
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config
 from src.common.logger import get_logger
-from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
 from .base_processor import BaseProcessor
 from src.chat.focus_chat.info.info_base import InfoBase
 from src.chat.focus_chat.info.expression_selection_info import ExpressionSelectionInfo
-from src.chat.express.exprssion_learner import get_expression_learner
-from json_repair import repair_json
-import json
+from src.chat.express.expression_selector import expression_selector
 
 logger = get_logger("processor")
 
 
-def weighted_sample_no_replacement(items, weights, k) -> list:
-    """
-    加权随机抽样，不允许重复
-
-    Args:
-        items: 待抽样的项目列表
-        weights: 对应项目的权重列表
-        k: 抽样数量
-
-    Returns:
-        抽样结果列表
-    """
-    if not items or k <= 0:
-        return []
-
-    k = min(k, len(items))
-    selected = []
-    remaining_items = list(items)
-    remaining_weights = list(weights)
-
-    for _ in range(k):
-        if not remaining_items:
-            break
-
-        # 计算累积权重
-        total_weight = sum(remaining_weights)
-        if total_weight <= 0:
-            # 如果权重都为0或负数，则随机选择
-            selected_index = random.randint(0, len(remaining_items) - 1)
-        else:
-            # 加权随机选择
-            rand_val = random.uniform(0, total_weight)
-            cumulative_weight = 0
-            selected_index = 0
-            for i, weight in enumerate(remaining_weights):
-                cumulative_weight += weight
-                if rand_val <= cumulative_weight:
-                    selected_index = i
-                    break
-
-        # 添加选中的项目
-        selected.append(remaining_items[selected_index])
-        # 移除已选中的项目
-        remaining_items.pop(selected_index)
-        remaining_weights.pop(selected_index)
-
-    return selected
-
-
-def init_prompt():
-    expression_evaluation_prompt = """
-你的名字是{bot_name}
-
-以下是正在进行的聊天内容：
-{chat_observe_info}
-
-以下是可选的表达情境：
-{all_situations}
-
-请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的10个情境。
-考虑因素包括：
-1. 聊天的情绪氛围（轻松、严肃、幽默等）
-2. 话题类型（日常、技术、游戏、情感等）
-3. 情境与当前语境的匹配度
-
-请以JSON格式输出，只需要输出选中的情境编号：
-{{
-    "selected_situations": [1, 3, 5, 7, 9, 12, 15, 18, 21, 25]
-}}
-
-请严格按照JSON格式输出，不要包含其他内容：
-"""
-    Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
-
-
 class ExpressionSelectorProcessor(BaseProcessor):
     log_prefix = "表达选择器"
 
@@ -101,16 +21,9 @@ class ExpressionSelectorProcessor(BaseProcessor):
 
         self.subheartflow_id = subheartflow_id
         self.last_selection_time = 0
-        self.selection_interval = 40  # 1分钟间隔
+        self.selection_interval = 10  # 40秒间隔
         self.cached_expressions = []  # 缓存上一次选择的表达方式
 
-        # 表达方式选择模式
-        self.selection_mode = getattr(global_config.expression, "selection_mode", "llm")  # "llm" 或 "random"
-
-        self.llm_model = LLMRequest(
-            model=global_config.model.utils_small,
-            request_type="focus.processor.expression_selector",
-        )
 
         name = get_chat_manager().get_stream_name(self.subheartflow_id)
         self.log_prefix = f"[{name}] 表达选择器"
@@ -158,26 +71,20 @@ class ExpressionSelectorProcessor(BaseProcessor):
             return []
 
         try:
-            # 根据模式选择表达方式
-            # LLM模式：调用LLM选择15个，然后随机选5个
-            selected_expressions = await self._select_suitable_expressions_llm(chat_info)
+            # LLM模式：调用LLM选择5-10个，然后随机选5个
+            selected_expressions = await expression_selector.select_suitable_expressions_llm(self.subheartflow_id, chat_info)
             cache_size = len(selected_expressions) if selected_expressions else 0
             mode_desc = f"LLM模式（已缓存{cache_size}个）"
 
             if selected_expressions:
-                # 缓存选择的表达方式
                 self.cached_expressions = selected_expressions
-                # 更新最后选择时间
                 self.last_selection_time = current_time
 
-                # 从选择的表达方式中随机选5个
-                final_expressions = random.sample(selected_expressions, min(4, len(selected_expressions)))
-
                 # 创建表达选择信息
                 expression_info = ExpressionSelectionInfo()
-                expression_info.set_selected_expressions(final_expressions)
+                expression_info.set_selected_expressions(selected_expressions)
 
-                logger.info(f"{self.log_prefix} 为当前聊天选择了{len(final_expressions)}个表达方式（{mode_desc}）")
+                logger.info(f"{self.log_prefix} 为当前聊天选择了{len(selected_expressions)}个表达方式（{mode_desc}）")
                 return [expression_info]
             else:
                 logger.debug(f"{self.log_prefix} 未选择任何表达方式")
@@ -187,104 +94,3 @@ class ExpressionSelectorProcessor(BaseProcessor):
             logger.error(f"{self.log_prefix} 处理表达方式选择时出错: {e}")
             return []
 
-    async def _get_random_expressions(self) -> tuple[List[Dict], List[Dict], List[Dict]]:
-        """随机获取表达方式：20个style，20个grammar，20个personality"""
-        expression_learner = get_expression_learner()
-
-        # 获取所有表达方式
-        (
-            learnt_style_expressions,
-            learnt_grammar_expressions,
-            personality_expressions,
-        ) = await expression_learner.get_expression_by_chat_id(self.subheartflow_id)
-
-        # 随机选择
-        selected_style = random.sample(learnt_style_expressions, min(15, len(learnt_style_expressions)))
-        selected_grammar = random.sample(learnt_grammar_expressions, min(15, len(learnt_grammar_expressions)))
-        selected_personality = random.sample(personality_expressions, min(5, len(personality_expressions)))
-
-        return selected_style, selected_grammar, selected_personality
-
-    async def _select_suitable_expressions_llm(self, chat_info: str) -> List[Dict[str, str]]:
-        """使用LLM选择适合的表达方式"""
-
-        # 1. 获取35个随机表达方式
-        style_exprs, grammar_exprs, personality_exprs = await self._get_random_expressions()
-
-        # 2. 构建所有表达方式的索引和情境列表
-        all_expressions = []
-        all_situations = []
-
-        # 添加style表达方式
-        for expr in style_exprs:
-            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                expr_with_type = expr.copy()
-                expr_with_type["type"] = "style"
-                all_expressions.append(expr_with_type)
-                all_situations.append(f"{len(all_expressions)}. [语言风格] {expr['situation']}")
-
-        # 添加grammar表达方式
-        for expr in grammar_exprs:
-            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                expr_with_type = expr.copy()
-                expr_with_type["type"] = "grammar"
-                all_expressions.append(expr_with_type)
-                all_situations.append(f"{len(all_expressions)}. [句法语法] {expr['situation']}")
-
-        # 添加personality表达方式
-        for expr in personality_exprs:
-            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                expr_with_type = expr.copy()
-                expr_with_type["type"] = "personality"
-                all_expressions.append(expr_with_type)
-                all_situations.append(f"{len(all_expressions)}. [个性表达] {expr['situation']}")
-
-        if not all_expressions:
-            logger.warning(f"{self.log_prefix} 没有找到可用的表达方式")
-            return []
-
-        all_situations_str = "\n".join(all_situations)
-
-        # 3. 构建prompt（只包含情境，不包含完整的表达方式）
-        prompt = (await global_prompt_manager.get_prompt_async("expression_evaluation_prompt")).format(
-            bot_name=global_config.bot.nickname,
-            chat_observe_info=chat_info,
-            all_situations=all_situations_str,
-        )
-
-        # 4. 调用LLM
-        try:
-            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
-
-            # logger.info(f"{self.log_prefix} LLM返回结果: {content}")
-
-            if not content:
-                logger.warning(f"{self.log_prefix} LLM返回空结果")
-                return []
-
-            # 5. 解析结果
-            result = repair_json(content)
-            if isinstance(result, str):
-                result = json.loads(result)
-
-            if not isinstance(result, dict) or "selected_situations" not in result:
-                logger.error(f"{self.log_prefix} LLM返回格式错误")
-                return []
-
-            selected_indices = result["selected_situations"]
-
-            # 根据索引获取完整的表达方式
-            valid_expressions = []
-            for idx in selected_indices:
-                if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
-                    valid_expressions.append(all_expressions[idx - 1])  # 索引从1开始
-
-            logger.info(f"{self.log_prefix} LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
-            return valid_expressions
-
-        except Exception as e:
-            logger.error(f"{self.log_prefix} LLM处理表达方式选择时出错: {e}")
-            return []
-
-
-init_prompt()

src/chat/focus_chat/replyer/default_generator.py

@@ -490,7 +490,7 @@ class DefaultReplyer:
             learnt_style_expressions,
             learnt_grammar_expressions,
             personality_expressions,
-        ) = await expression_learner.get_expression_by_chat_id(chat_stream.stream_id)
+        ) = expression_learner.get_expression_by_chat_id(chat_stream.stream_id)
 
         style_habbits = []
         grammar_habbits = []

src/chat/normal_chat/normal_prompt.py

@@ -1,7 +1,5 @@
-from src.chat.express.exprssion_learner import get_expression_learner
 from src.config.config import global_config
 from src.common.logger import get_logger
-from src.individuality.individuality import get_individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
 import time
@@ -10,6 +8,8 @@ from src.manager.mood_manager import mood_manager
 from src.chat.memory_system.Hippocampus import hippocampus_manager
 from src.chat.knowledge.knowledge_lib import qa_manager
 import random
+from src.person_info.person_info import get_person_info_manager
+from src.chat.express.expression_selector import expression_selector
 import re
 
 from src.person_info.relationship_manager import get_relationship_manager
@@ -27,7 +27,7 @@ def init_prompt():
         """
 你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中：
 {style_habbits}
-请你根据情景使用以下句法，不要盲目使用,不要生硬使用，而是结合到表达中：
+请你根据情景使用以下，不要盲目使用,不要生硬使用，而是结合到表达中：
 {grammar_habbits}
 
 {memory_prompt}
@@ -91,7 +91,14 @@ class PromptBuilder:
         enable_planner: bool = False,
         available_actions=None,
     ) -> str:
-        prompt_personality = get_individuality().get_prompt(x_person=2, level=2)
+        core_personality = global_config.personality.personality_core
+        person_info_manager = get_person_info_manager()
+        bot_person_id = person_info_manager.get_person_id("system", "bot_id")
+        short_impression = await person_info_manager.get_value(bot_person_id, "short_impression")
+        prompt_personality = core_personality
+        if short_impression:
+            prompt_personality += short_impression
+
         is_group_chat = bool(chat_stream.group_info)
 
         who_chat_in_group = []
@@ -113,40 +120,8 @@ class PromptBuilder:
                 relation_prompt += await relationship_manager.build_relationship_info(person)
 
         mood_prompt = mood_manager.get_mood_prompt()
-        expression_learner = get_expression_learner()
-        (
-            learnt_style_expressions,
-            learnt_grammar_expressions,
-            personality_expressions,
-        ) = await expression_learner.get_expression_by_chat_id(chat_stream.stream_id)
-
-        style_habbits = []
-        grammar_habbits = []
-        # 1. learnt_expressions加权随机选2条
-        if learnt_style_expressions:
-            weights = [expr["count"] for expr in learnt_style_expressions]
-            selected_learnt = weighted_sample_no_replacement(learnt_style_expressions, weights, 2)
-            for expr in selected_learnt:
-                if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                    style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-        # 2. learnt_grammar_expressions加权随机选2条
-        if learnt_grammar_expressions:
-            weights = [expr["count"] for expr in learnt_grammar_expressions]
-            selected_learnt = weighted_sample_no_replacement(learnt_grammar_expressions, weights, 2)
-            for expr in selected_learnt:
-                if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                    grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-        # 3. personality_expressions随机选1条
-        if personality_expressions:
-            expr = random.choice(personality_expressions)
-            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-
-        style_habbits_str = "\n".join(style_habbits)
-        grammar_habbits_str = "\n".join(grammar_habbits)
 
         memory_prompt = ""
-
         if global_config.memory.enable_memory:
             related_memory = await hippocampus_manager.get_memory_from_text(
                 text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
@@ -173,6 +148,37 @@ class PromptBuilder:
             read_mark=0.0,
             show_actions=True,
         )
+        
+        message_list_before_now_half = get_raw_msg_before_timestamp_with_chat(
+            chat_id=chat_stream.stream_id,
+            timestamp=time.time(),
+            limit=global_config.focus_chat.observation_context_size * 0.5,
+        )
+        chat_talking_prompt_half = build_readable_messages(
+            message_list_before_now_half,
+            replace_bot_name=True,
+            merge_messages=False,
+            timestamp_mode="relative",
+            read_mark=0.0,
+            show_actions=True,
+        )
+        
+        expressions = expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half)
+        style_habbits = []
+        grammar_habbits = []
+        if expressions:
+            for expr in expressions:
+                if isinstance(expr, dict) and "situation" in expr and "style" in expr:
+                    expr_type = expr.get("type", "style")
+                    if expr_type == "grammar":
+                        grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+                    else:
+                        style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+        else:
+            logger.debug("没有从处理器获得表达方式，将使用空的表达方式")
+
+        style_habbits_str = "\n".join(style_habbits)
+        grammar_habbits_str = "\n".join(grammar_habbits)
 
         # 关键词检测与反应
         keywords_reaction_prompt = ""

src/chat/utils/chat_message_builder.py

@@ -599,7 +599,6 @@ def build_readable_messages(
         copy_messages.sort(key=lambda x: x.get("time", 0))
 
     if read_mark <= 0:
-        print(f"read_mark: {read_mark}")
         # 没有有效的 read_mark，直接格式化所有消息
         formatted_string, _, pic_id_mapping, _ = _build_readable_messages_internal(
             copy_messages, replace_bot_name, merge_messages, timestamp_mode, truncate

src/individuality/individuality.py

@@ -1,5 +1,7 @@
 from typing import Optional
 import asyncio
+
+from src.llm_models.utils_model import LLMRequest
 from .personality import Personality
 from .identity import Identity
 from .expression_style import PersonalityExpression
@@ -10,6 +12,7 @@ import hashlib
 from rich.traceback import install
 from src.common.logger import get_logger
 from src.person_info.person_info import get_person_info_manager
+from src.config.config import global_config
 
 install(extra_lines=3)
 
@@ -28,6 +31,11 @@ class Individuality:
         self.name = ""
         self.bot_person_id = ""
         self.meta_info_file_path = "data/personality/meta.json"
+        
+        self.model = LLMRequest(
+            model=global_config.model.utils,
+            request_type="individuality.compress",
+        )
 
     async def initialize(
         self,
@@ -90,6 +98,11 @@ class Individuality:
             )
             logger.info("已将完整人设更新到bot的impression中")
 
+        # 创建压缩版本的short_impression
+        asyncio.create_task(self._create_compressed_impression(
+            personality_core, personality_sides, identity_detail
+        ))
+
         asyncio.create_task(self.express_style.extract_and_store_personality_expressions())
 
     def to_dict(self) -> dict:
@@ -357,6 +370,71 @@ class Individuality:
                 logger.error(f"解析info_list失败: {info_list_json}")
         return keywords
 
+    async def _create_compressed_impression(
+        self, personality_core: str, personality_sides: list, identity_detail: list
+    ) -> str:
+        """使用LLM创建压缩版本的impression
+        
+        Args:
+            personality_core: 核心人格
+            personality_sides: 人格侧面列表
+            identity_detail: 身份细节列表
+            
+        Returns:
+            str: 压缩后的impression文本
+        """
+        # 核心人格保持不变
+        compressed_parts = []
+        if personality_core:
+            compressed_parts.append(f"{personality_core}")
+        
+        # 准备需要压缩的内容
+        content_to_compress = []
+        if personality_sides:
+            content_to_compress.append(f"人格特质: {'、'.join(personality_sides)}")
+        if identity_detail:
+            content_to_compress.append(f"身份背景: {'、'.join(identity_detail)}")
+            
+        if not content_to_compress:
+            # 如果没有需要压缩的内容，直接返回核心人格
+            result = "。".join(compressed_parts)
+            return result + "。" if result else ""
+            
+        # 使用LLM压缩其他内容
+        try:
+            compress_content = "、".join(content_to_compress)
+            
+            prompt = f"""请将以下人设信息进行简洁压缩，保留主要内容，用简练的中文表达：
+
+{compress_content}
+
+要求：
+1. 保持原意不变，尽量使用原文
+2. 尽量简洁，不超过30字
+3. 直接输出压缩后的内容，不要解释"""
+
+            response,(_,_) = await self.model.generate_response_async(
+                prompt=prompt,
+            )
+            
+            if response.strip():
+                compressed_parts.append(response.strip())
+                logger.info(f"精简人格侧面: {response.strip()}")
+            else:
+                logger.error(f"使用LLM压缩人设时出错: {response}")
+        except Exception as e:
+            logger.error(f"使用LLM压缩人设时出错: {e}")
+            
+        result = "。".join(compressed_parts)
+    
+        # 更新short_impression字段
+        if result:
+            person_info_manager = get_person_info_manager()
+            await person_info_manager.update_one_field(
+                self.bot_person_id, "short_impression", result
+            )
+            logger.info("已将压缩人设更新到bot的short_impression中")
+
 
 individuality = None
 

src/llm_models/utils_model.py

@@ -109,6 +109,7 @@ class LLMRequest:
     def __init__(self, model: dict, **kwargs):
         # 将大写的配置键转换为小写并从config中获取实际值
         try:
+            # print(f"model['provider']: {model['provider']}")
             self.api_key = os.environ[f"{model['provider']}_KEY"]
             self.base_url = os.environ[f"{model['provider']}_BASE_URL"]
         except AttributeError as e: