feat: llm统计现已记录模型反应时间

src/chat/chat_loop/heartFC_chat.py

@@ -487,7 +487,7 @@ class HeartFChatting:
                                 available_actions=available_actions,
                                 reply_reason=action_info.get("reasoning", ""),
                                 enable_tool=global_config.tool.enable_tool,
-                                request_type="chat.replyer",
+                                request_type="replyer",
                                 from_plugin=False,
                             )
 

src/chat/express/expression_learner.py

@@ -38,7 +38,7 @@ def init_prompt() -> None:
 
 请从上面这段群聊中概括除了人名为"SELF"之外的人的语言风格
 1. 只考虑文字，不要考虑表情包和图片
-2. 不要涉及具体的人名，只考虑语言风格，特殊的梗，不要总结自己
+2. 不要涉及具体的人名，但是可以涉及具体名词
 3. 思考有没有特殊的梗，一并总结成语言风格
 4. 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：
@@ -59,7 +59,7 @@ def init_prompt() -> None:
 class ExpressionLearner:
     def __init__(self, chat_id: str) -> None:
         self.express_learn_model: LLMRequest = LLMRequest(
-            model_set=model_config.model_task_config.replyer, request_type="expressor.learner"
+            model_set=model_config.model_task_config.replyer, request_type="expression.learner"
         )
         self.chat_id = chat_id
         self.chat_name = get_chat_manager().get_stream_name(chat_id) or chat_id

src/chat/express/expression_selector.py

@@ -25,7 +25,7 @@ def init_prompt():
 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{min_num}-{max_num}个情境。
+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，最多{max_num}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）
@@ -35,7 +35,7 @@ def init_prompt():
 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, 19, 22, 25, 38, 39, 45, 48, 64]
+    "selected_situations": [2, 3, 5, 7, 19]
 }}
 
 请严格按照JSON格式输出，不要包含其他内容：
@@ -195,7 +195,6 @@ class ExpressionSelector:
         chat_id: str,
         chat_info: str,
         max_num: int = 10,
-        min_num: int = 5,
         target_message: Optional[str] = None,
     ) -> List[Dict[str, Any]]:
         # sourcery skip: inline-variable, list-comprehension
@@ -206,8 +205,8 @@ class ExpressionSelector:
             logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
             return []
 
-        # 1. 获取35个随机表达方式（现在按权重抽取）
+        # 1. 获取20个随机表达方式（现在按权重抽取）
-        style_exprs = self.get_random_expressions(chat_id, 30)
+        style_exprs = self.get_random_expressions(chat_id, 10)
 
         # 2. 构建所有表达方式的索引和情境列表
         all_expressions = []
@@ -219,7 +218,7 @@ class ExpressionSelector:
                 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']}")
+                all_situations.append(f"{len(all_expressions)}.当 {expr['situation']} 时，使用 {expr['style']}")
 
         if not all_expressions:
             logger.warning("没有找到可用的表达方式")
@@ -239,13 +238,12 @@ class ExpressionSelector:
             bot_name=global_config.bot.nickname,
             chat_observe_info=chat_info,
             all_situations=all_situations_str,
-            min_num=min_num,
             max_num=max_num,
             target_message=target_message_str,
             target_message_extra_block=target_message_extra_block,
         )
 
-        # print(prompt)
+        print(prompt)
 
         # 4. 调用LLM
         try:
@@ -255,7 +253,7 @@ class ExpressionSelector:
             # logger.info(f"LLM请求时间: {model_name}  {time.time() - start_time} \n{prompt}")
 
             # logger.info(f"模型名称: {model_name}")
-            # logger.info(f"LLM返回结果: {content}")
+            logger.info(f"LLM返回结果: {content}")
             # if reasoning_content:
             #     logger.info(f"LLM推理: {reasoning_content}")
             # else:

src/chat/memory_system/Hippocampus.py

@@ -200,7 +200,7 @@ class Hippocampus:
         self.parahippocampal_gyrus = ParahippocampalGyrus(self)
         # 从数据库加载记忆图
         self.entorhinal_cortex.sync_memory_from_db()
-        self.model_small = LLMRequest(model_set=model_config.model_task_config.utils_small, request_type="memory.small")
+        self.model_small = LLMRequest(model_set=model_config.model_task_config.utils_small, request_type="memory.modify")
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""

src/chat/replyer/default_generator.py

@@ -117,8 +117,8 @@ def init_prompt():
 你现在正在一个QQ群里聊天，以下是正在进行的聊天内容：
 {background_dialogue_prompt}
 
-你现在想补充说明你刚刚自己的发言内容：{target}
+你现在想补充说明你刚刚自己的发言内容：{target}，原因是{reason}
-请你根据聊天内容，组织一条新回复。
+请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。
 你现在的心情是：{mood_state}
 {reply_style}
 {keywords_reaction_prompt}
@@ -331,7 +331,7 @@ class DefaultReplyer:
         # 使用从处理器传来的选中表达方式
         # 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.chat_stream.stream_id, chat_history, max_num=8, min_num=2, target_message=target
+            self.chat_stream.stream_id, chat_history, max_num=8, target_message=target
         )
 
         if selected_expressions:

src/chat/utils/statistic.py

@@ -36,6 +36,18 @@ COST_BY_TYPE = "costs_by_type"
 COST_BY_USER = "costs_by_user"
 COST_BY_MODEL = "costs_by_model"
 COST_BY_MODULE = "costs_by_module"
+TIME_COST_BY_TYPE = "time_costs_by_type"
+TIME_COST_BY_USER = "time_costs_by_user"
+TIME_COST_BY_MODEL = "time_costs_by_model"
+TIME_COST_BY_MODULE = "time_costs_by_module"
+AVG_TIME_COST_BY_TYPE = "avg_time_costs_by_type"
+AVG_TIME_COST_BY_USER = "avg_time_costs_by_user"
+AVG_TIME_COST_BY_MODEL = "avg_time_costs_by_model"
+AVG_TIME_COST_BY_MODULE = "avg_time_costs_by_module"
+STD_TIME_COST_BY_TYPE = "std_time_costs_by_type"
+STD_TIME_COST_BY_USER = "std_time_costs_by_user"
+STD_TIME_COST_BY_MODEL = "std_time_costs_by_model"
+STD_TIME_COST_BY_MODULE = "std_time_costs_by_module"
 ONLINE_TIME = "online_time"
 TOTAL_MSG_CNT = "total_messages"
 MSG_CNT_BY_CHAT = "messages_by_chat"
@@ -293,6 +305,18 @@ class StatisticOutputTask(AsyncTask):
                 COST_BY_USER: defaultdict(float),
                 COST_BY_MODEL: defaultdict(float),
                 COST_BY_MODULE: defaultdict(float),
+                TIME_COST_BY_TYPE: defaultdict(list),
+                TIME_COST_BY_USER: defaultdict(list),
+                TIME_COST_BY_MODEL: defaultdict(list),
+                TIME_COST_BY_MODULE: defaultdict(list),
+                AVG_TIME_COST_BY_TYPE: defaultdict(float),
+                AVG_TIME_COST_BY_USER: defaultdict(float),
+                AVG_TIME_COST_BY_MODEL: defaultdict(float),
+                AVG_TIME_COST_BY_MODULE: defaultdict(float),
+                STD_TIME_COST_BY_TYPE: defaultdict(float),
+                STD_TIME_COST_BY_USER: defaultdict(float),
+                STD_TIME_COST_BY_MODEL: defaultdict(float),
+                STD_TIME_COST_BY_MODULE: defaultdict(float),
             }
             for period_key, _ in collect_period
         }
@@ -344,7 +368,41 @@ class StatisticOutputTask(AsyncTask):
                         stats[period_key][COST_BY_USER][user_id] += cost
                         stats[period_key][COST_BY_MODEL][model_name] += cost
                         stats[period_key][COST_BY_MODULE][module_name] += cost
+
+                        # 收集time_cost数据
+                        time_cost = record.time_cost or 0.0
+                        if time_cost > 0:  # 只记录有效的time_cost
+                            stats[period_key][TIME_COST_BY_TYPE][request_type].append(time_cost)
+                            stats[period_key][TIME_COST_BY_USER][user_id].append(time_cost)
+                            stats[period_key][TIME_COST_BY_MODEL][model_name].append(time_cost)
+                            stats[period_key][TIME_COST_BY_MODULE][module_name].append(time_cost)
                     break
+
+        # 计算平均耗时和标准差
+        for period_key in stats:
+            for category in [REQ_CNT_BY_TYPE, REQ_CNT_BY_USER, REQ_CNT_BY_MODEL, REQ_CNT_BY_MODULE]:
+                time_cost_key = f"time_costs_by_{category.split('_')[-1]}"
+                avg_key = f"avg_time_costs_by_{category.split('_')[-1]}"
+                std_key = f"std_time_costs_by_{category.split('_')[-1]}"
+                
+                for item_name in stats[period_key][category]:
+                    time_costs = stats[period_key][time_cost_key].get(item_name, [])
+                    if time_costs:
+                        # 计算平均耗时
+                        avg_time_cost = sum(time_costs) / len(time_costs)
+                        stats[period_key][avg_key][item_name] = round(avg_time_cost, 3)
+                        
+                        # 计算标准差
+                        if len(time_costs) > 1:
+                            variance = sum((x - avg_time_cost) ** 2 for x in time_costs) / len(time_costs)
+                            std_time_cost = variance ** 0.5
+                            stats[period_key][std_key][item_name] = round(std_time_cost, 3)
+                        else:
+                            stats[period_key][std_key][item_name] = 0.0
+                    else:
+                        stats[period_key][avg_key][item_name] = 0.0
+                        stats[period_key][std_key][item_name] = 0.0
+
         return stats
 
     @staticmethod
@@ -566,11 +624,11 @@ class StatisticOutputTask(AsyncTask):
         """
         if stats[TOTAL_REQ_CNT] <= 0:
             return ""
-        data_fmt = "{:<32}  {:>10}  {:>12}  {:>12}  {:>12}  {:>9.4f}¥"
+        data_fmt = "{:<32}  {:>10}  {:>12}  {:>12}  {:>12}  {:>9.4f}¥  {:>10}  {:>10}"
 
         output = [
             "按模型分类统计:",
-            " 模型名称                          调用次数    输入Token     输出Token     Token总量     累计花费",
+            " 模型名称                          调用次数    输入Token     输出Token     Token总量     累计花费    平均耗时(秒)  标准差(秒)",
         ]
         for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()):
             name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name
@@ -578,7 +636,9 @@ class StatisticOutputTask(AsyncTask):
             out_tokens = stats[OUT_TOK_BY_MODEL][model_name]
             tokens = stats[TOTAL_TOK_BY_MODEL][model_name]
             cost = stats[COST_BY_MODEL][model_name]
-            output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost))
+            avg_time_cost = stats[AVG_TIME_COST_BY_MODEL][model_name]
+            std_time_cost = stats[STD_TIME_COST_BY_MODEL][model_name]
+            output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost, avg_time_cost, std_time_cost))
 
         output.append("")
         return "\n".join(output)
@@ -663,6 +723,8 @@ class StatisticOutputTask(AsyncTask):
                     f"<td>{stat_data[OUT_TOK_BY_MODEL][model_name]}</td>"
                     f"<td>{stat_data[TOTAL_TOK_BY_MODEL][model_name]}</td>"
                     f"<td>{stat_data[COST_BY_MODEL][model_name]:.4f} ¥</td>"
+                    f"<td>{stat_data[AVG_TIME_COST_BY_MODEL][model_name]:.3f} 秒</td>"
+                    f"<td>{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.3f} 秒</td>"
                     f"</tr>"
                     for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
                 ]
@@ -677,6 +739,8 @@ class StatisticOutputTask(AsyncTask):
                     f"<td>{stat_data[OUT_TOK_BY_TYPE][req_type]}</td>"
                     f"<td>{stat_data[TOTAL_TOK_BY_TYPE][req_type]}</td>"
                     f"<td>{stat_data[COST_BY_TYPE][req_type]:.4f} ¥</td>"
+                    f"<td>{stat_data[AVG_TIME_COST_BY_TYPE][req_type]:.3f} 秒</td>"
+                    f"<td>{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.3f} 秒</td>"
                     f"</tr>"
                     for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
                 ]
@@ -691,6 +755,8 @@ class StatisticOutputTask(AsyncTask):
                     f"<td>{stat_data[OUT_TOK_BY_MODULE][module_name]}</td>"
                     f"<td>{stat_data[TOTAL_TOK_BY_MODULE][module_name]}</td>"
                     f"<td>{stat_data[COST_BY_MODULE][module_name]:.4f} ¥</td>"
+                    f"<td>{stat_data[AVG_TIME_COST_BY_MODULE][module_name]:.3f} 秒</td>"
+                    f"<td>{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.3f} 秒</td>"
                     f"</tr>"
                     for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
                 ]
@@ -717,7 +783,7 @@ class StatisticOutputTask(AsyncTask):
                 
                 <h2>按模型分类统计</h2>
                 <table>
-                    <thead><tr><th>模型名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr></thead>
+                    <thead><tr><th>模型名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr></thead>
                     <tbody>
                         {model_rows}
                     </tbody>
@@ -726,7 +792,7 @@ class StatisticOutputTask(AsyncTask):
                 <h2>按模块分类统计</h2>
                 <table>
                     <thead>
-                        <tr><th>模块名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr>
+                        <tr><th>模块名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr>
                     </thead>
                     <tbody>
                     {module_rows}
@@ -736,7 +802,7 @@ class StatisticOutputTask(AsyncTask):
                 <h2>按请求类型分类统计</h2>
                 <table>
                     <thead>
-                        <tr><th>请求类型</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th></tr>
+                        <tr><th>请求类型</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr>
                     </thead>
                     <tbody>
                     {type_rows}

src/common/database/database_model.py

@@ -79,6 +79,8 @@ class LLMUsage(BaseModel):
     """
 
     model_name = TextField(index=True)  # 添加索引
+    model_assign_name = TextField(null=True)  # 添加索引
+    model_api_provider = TextField(null=True)  # 添加索引
     user_id = TextField(index=True)  # 添加索引
     request_type = TextField(index=True)  # 添加索引
     endpoint = TextField()
@@ -86,6 +88,7 @@ class LLMUsage(BaseModel):
     completion_tokens = IntegerField()
     total_tokens = IntegerField()
     cost = DoubleField()
+    time_cost = DoubleField(null=True)
     status = TextField()
     timestamp = DateTimeField(index=True)  # 更改为 DateTimeField 并添加索引
 

src/config/config.py

@@ -109,10 +109,17 @@ def get_value_by_path(d, path):
 
 
 def set_value_by_path(d, path, value):
+    """设置嵌套字典中指定路径的值"""
     for k in path[:-1]:
         if k not in d or not isinstance(d[k], dict):
             d[k] = {}
         d = d[k]
+    
+    # 使用 tomlkit.item 来保持 TOML 格式
+    try:
+        d[path[-1]] = tomlkit.item(value)
+    except (TypeError, ValueError):
+        # 如果转换失败，直接赋值
         d[path[-1]] = value
 
 
@@ -237,6 +244,7 @@ def _update_config_generic(config_name: str, template_name: str):
             for log in logs:
                 logger.info(log)
             # 检查旧配置是否等于旧默认值，如果是则更新为新默认值
+            config_updated = False
             for path, old_default, new_default in changes:
                 old_value = get_value_by_path(old_config, path)
                 if old_value == old_default:
@@ -244,6 +252,13 @@ def _update_config_generic(config_name: str, template_name: str):
                     logger.info(
                         f"已自动将{config_name}配置 {'.'.join(path)} 的值从旧默认值 {old_default} 更新为新默认值 {new_default}"
                     )
+                    config_updated = True
+            
+            # 如果配置有更新，立即保存到文件
+            if config_updated:
+                with open(old_config_path, "w", encoding="utf-8") as f:
+                    f.write(tomlkit.dumps(old_config))
+                logger.info(f"已保存更新后的{config_name}配置文件")
         else:
             logger.info(f"未检测到{config_name}模板默认值变动")
 

src/llm_models/utils.py

@@ -155,7 +155,7 @@ class LLMUsageRecorder:
             logger.error(f"创建 LLMUsage 表失败: {str(e)}")
 
     def record_usage_to_database(
-        self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str
+        self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str, time_cost: float = 0.0
     ):
         input_cost = (model_usage.prompt_tokens / 1000000) * model_info.price_in
         output_cost = (model_usage.completion_tokens / 1000000) * model_info.price_out
@@ -164,6 +164,8 @@ class LLMUsageRecorder:
             # 使用 Peewee 模型创建记录
             LLMUsage.create(
                 model_name=model_info.model_identifier,
+                model_assign_name=model_info.name,
+                model_api_provider=model_info.api_provider,
                 user_id=user_id,
                 request_type=request_type,
                 endpoint=endpoint,
@@ -171,6 +173,7 @@ class LLMUsageRecorder:
                 completion_tokens=model_usage.completion_tokens or 0,
                 total_tokens=model_usage.total_tokens or 0,
                 cost=total_cost or 0.0,
+                time_cost = round(time_cost or 0.0, 3),
                 status="success",
                 timestamp=datetime.now(),  # Peewee 会处理 DateTimeField
             )

src/llm_models/utils_model.py

@@ -71,6 +71,7 @@ class LLMRequest:
             (Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
         """
         # 模型选择
+        start_time = time.time()
         model_info, api_provider, client = self._select_model()
 
         # 请求体构建
@@ -105,6 +106,7 @@ class LLMRequest:
                 user_id="system",
                 request_type=self.request_type,
                 endpoint="/chat/completions",
+                time_cost=time.time() - start_time,
             )
         return content, (reasoning_content, model_info.name, tool_calls)
 
@@ -149,8 +151,6 @@ class LLMRequest:
         # 请求体构建
         start_time = time.time()
         
-        
-        
         message_builder = MessageBuilder()
         message_builder.add_text_content(prompt)
         messages = [message_builder.build()]
@@ -190,6 +190,7 @@ class LLMRequest:
                 user_id="system",
                 request_type=self.request_type,
                 endpoint="/chat/completions",
+                time_cost=time.time() - start_time,
             )
         
         if not content:
@@ -208,6 +209,7 @@ class LLMRequest:
             (Tuple[List[float], str]): (嵌入向量，使用的模型名称)
         """
         # 无需构建消息体，直接使用输入文本
+        start_time = time.time()
         model_info, api_provider, client = self._select_model()
 
         # 请求并处理返回值
@@ -228,6 +230,7 @@ class LLMRequest:
                 user_id="system",
                 request_type=self.request_type,
                 endpoint="/embeddings",
+                time_cost=time.time() - start_time,
             )
 
         if not embedding:

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -104,7 +104,7 @@ class PromptBuilder:
         # 使用从处理器传来的选中表达方式
         # 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(
-            chat_stream.stream_id, chat_history, max_num=12, min_num=5, target_message=target
+            chat_stream.stream_id, chat_history, max_num=12, target_message=target
         )
 
         if selected_expressions:

src/person_info/group_relationship_manager.py

@@ -22,7 +22,7 @@ logger = get_logger("group_relationship_manager")
 class GroupRelationshipManager:
     def __init__(self):
         self.group_llm = LLMRequest(
-            model_set=model_config.model_task_config.utils, request_type="group.relationship"
+            model_set=model_config.model_task_config.utils, request_type="relationship.group"
         )
         self.last_group_impression_time = 0.0
         self.last_group_impression_message_count = 0

src/person_info/relationship_manager.py

@@ -20,7 +20,7 @@ logger = get_logger("relation")
 class RelationshipManager:
     def __init__(self):
         self.relationship_llm = LLMRequest(
-            model_set=model_config.model_task_config.utils, request_type="relationship"
+            model_set=model_config.model_task_config.utils, request_type="relationship.person"
         )  # 用于动作规划
 
     @staticmethod