From 268b428e8f7d465bed06317d61c5bb54c2578658 Mon Sep 17 00:00:00 2001
From: SengokuCola <1026294844@qq.com>
Date: Mon, 11 Aug 2025 21:51:59 +0800
Subject: [PATCH] =?UTF-8?q?feat:=20llm=E7=BB=9F=E8=AE=A1=E7=8E=B0=E5=B7=B2?=
=?UTF-8?q?=E8=AE=B0=E5=BD=95=E6=A8=A1=E5=9E=8B=E5=8F=8D=E5=BA=94=E6=97=B6?=
=?UTF-8?q?=E9=97=B4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
src/chat/chat_loop/heartFC_chat.py | 2 +-
src/chat/express/expression_learner.py | 4 +-
src/chat/express/expression_selector.py | 16 ++--
src/chat/memory_system/Hippocampus.py | 2 +-
src/chat/replyer/default_generator.py | 6 +-
src/chat/utils/statistic.py | 78 +++++++++++++++++--
src/common/database/database_model.py | 3 +
src/config/config.py | 17 +++-
src/llm_models/utils.py | 5 +-
src/llm_models/utils_model.py | 7 +-
src/mais4u/mais4u_chat/s4u_prompt.py | 2 +-
src/person_info/group_relationship_manager.py | 2 +-
src/person_info/relationship_manager.py | 2 +-
13 files changed, 117 insertions(+), 29 deletions(-)
diff --git a/src/chat/chat_loop/heartFC_chat.py b/src/chat/chat_loop/heartFC_chat.py
index db42dfac8..38674ee97 100644
--- a/src/chat/chat_loop/heartFC_chat.py
+++ b/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,
)
diff --git a/src/chat/express/expression_learner.py b/src/chat/express/expression_learner.py
index 8bcf75f1a..a45305203 100644
--- a/src/chat/express/expression_learner.py
+++ b/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
diff --git a/src/chat/express/expression_selector.py b/src/chat/express/expression_selector.py
index c5d08b61d..bf85d6cbd 100644
--- a/src/chat/express/expression_selector.py
+++ b/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）
- style_exprs = self.get_random_expressions(chat_id, 30)
+ # 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, 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:
diff --git a/src/chat/memory_system/Hippocampus.py b/src/chat/memory_system/Hippocampus.py
index c14acd116..b1832f41a 100644
--- a/src/chat/memory_system/Hippocampus.py
+++ b/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"""
diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py
index 0c0cb47fa..270f09065 100644
--- a/src/chat/replyer/default_generator.py
+++ b/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}
-请你根据聊天内容，组织一条新回复。
+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。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:
diff --git a/src/chat/utils/statistic.py b/src/chat/utils/statistic.py
index aa000df7a..d272a3005 100644
--- a/src/chat/utils/statistic.py
+++ b/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"| {stat_data[OUT_TOK_BY_MODEL][model_name]} | "
f"{stat_data[TOTAL_TOK_BY_MODEL][model_name]} | "
f"{stat_data[COST_BY_MODEL][model_name]:.4f} ¥ | "
+ f"{stat_data[AVG_TIME_COST_BY_MODEL][model_name]:.3f} 秒 | "
+ f"{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.3f} 秒 | "
f""
for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
]
@@ -677,6 +739,8 @@ class StatisticOutputTask(AsyncTask):
f"{stat_data[OUT_TOK_BY_TYPE][req_type]} | "
f"{stat_data[TOTAL_TOK_BY_TYPE][req_type]} | "
f"{stat_data[COST_BY_TYPE][req_type]:.4f} ¥ | "
+ f"{stat_data[AVG_TIME_COST_BY_TYPE][req_type]:.3f} 秒 | "
+ f"{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.3f} 秒 | "
f""
for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
]
@@ -691,6 +755,8 @@ class StatisticOutputTask(AsyncTask):
f"{stat_data[OUT_TOK_BY_MODULE][module_name]} | "
f"{stat_data[TOTAL_TOK_BY_MODULE][module_name]} | "
f"{stat_data[COST_BY_MODULE][module_name]:.4f} ¥ | "
+ f"{stat_data[AVG_TIME_COST_BY_MODULE][module_name]:.3f} 秒 | "
+ f"{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.3f} 秒 | "
f""
for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
]
@@ -717,7 +783,7 @@ class StatisticOutputTask(AsyncTask):
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) |
|---|
{model_rows}
@@ -726,7 +792,7 @@ class StatisticOutputTask(AsyncTask):
按模块分类统计
- | 模块名称 | 调用次数 | 输入Token | 输出Token | Token总量 | 累计花费 |
|---|
+ | 模块名称 | 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) |
|---|
{module_rows}
@@ -736,7 +802,7 @@ class StatisticOutputTask(AsyncTask):
按请求类型分类统计
- | 请求类型 | 调用次数 | 输入Token | 输出Token | Token总量 | 累计花费 |
|---|
+ | 请求类型 | 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) |
|---|
{type_rows}
diff --git a/src/common/database/database_model.py b/src/common/database/database_model.py
index 6be53521e..3c09b611e 100644
--- a/src/common/database/database_model.py
+++ b/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
diff --git a/src/config/config.py b/src/config/config.py
index c25320cca..7d2c6bcea 100644
--- a/src/config/config.py
+++ b/src/config/config.py
@@ -109,11 +109,18 @@ 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]
- d[path[-1]] = value
+
+ # 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
def compare_default_values(new, old, path=None, logs=None, changes=None):
@@ -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")
diff --git a/src/llm_models/utils.py b/src/llm_models/utils.py
index 52a6120c2..cf0476540 100644
--- a/src/llm_models/utils.py
+++ b/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
)
diff --git a/src/llm_models/utils_model.py b/src/llm_models/utils_model.py
index 683595124..e8e4db5f7 100644
--- a/src/llm_models/utils_model.py
+++ b/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:
diff --git a/src/mais4u/mais4u_chat/s4u_prompt.py b/src/mais4u/mais4u_chat/s4u_prompt.py
index 009eed985..7c629092f 100644
--- a/src/mais4u/mais4u_chat/s4u_prompt.py
+++ b/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:
diff --git a/src/person_info/group_relationship_manager.py b/src/person_info/group_relationship_manager.py
index 5a6f99950..e7e22eb73 100644
--- a/src/person_info/group_relationship_manager.py
+++ b/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
diff --git a/src/person_info/relationship_manager.py b/src/person_info/relationship_manager.py
index 9d7a48b97..d96425fcc 100644
--- a/src/person_info/relationship_manager.py
+++ b/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