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fix：通过计时定位LLM异常延时，移除memory模型

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This commit is contained in:
SengokuCola
2025-08-08 22:10:08 +08:00
parent d1f15a932a
commit 721546fff9
7 changed files with 29 additions and 21 deletions
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11
src/chat/express/expression_selector.py
View File

@@ -36,11 +36,7 @@ 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]
}}
例如：
{{
"selected_situations": [1, 4, 7, 9, 23, 38, 44]
"selected_situations": [2, 3, 5, 7, 19, 22, 25, 38, 39, 45, 48, 64]
}}
请严格按照JSON格式输出，不要包含其他内容：
@@ -214,7 +210,7 @@ class ExpressionSelector:
"""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 = self.get_random_expressions(chat_id, 50, 0.5, 0.5)
style_exprs, grammar_exprs = self.get_random_expressions(chat_id, 30, 0.5, 0.5)
# 2. 构建所有表达方式的索引和情境列表
all_expressions = []
@@ -264,7 +260,10 @@ class ExpressionSelector:
# 4. 调用LLM
try:
start_time = time.time()
content, (reasoning_content, model_name, _) = await self.llm_model.generate_response_async(prompt=prompt)
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}")

6
src/chat/memory_system/Hippocampus.py
View File

@@ -190,7 +190,7 @@ class MemoryGraph:
class Hippocampus:
def __init__(self):
self.memory_graph = MemoryGraph()
self.model_summary: LLMRequest = None # type: ignore
self.model_small: LLMRequest = None # type: ignore
self.entorhinal_cortex: EntorhinalCortex = None # type: ignore
self.parahippocampal_gyrus: ParahippocampalGyrus = None # type: ignore
@@ -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_summary = LLMRequest(model_set=model_config.model_task_config.memory, request_type="memory.builder")
self.model_small = LLMRequest(model_set=model_config.model_task_config.utils_small, request_type="memory.small")
def get_all_node_names(self) -> list:
"""获取记忆图中所有节点的名字列表"""
@@ -340,7 +340,7 @@ class Hippocampus:
else:
topic_num = 5 # 51+字符: 5个关键词 (其余长文本)
topics_response, _ = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
topics_response, _ = await self.model_small.generate_response_async(self.find_topic_llm(text, topic_num))
# 提取关键词
keywords = re.findall(r"<([^>]+)>", topics_response)

2
src/chat/memory_system/instant_memory.py
View File

@@ -38,7 +38,7 @@ class InstantMemory:
self.chat_id = chat_id
self.last_view_time = time.time()
self.summary_model = LLMRequest(
model_set=model_config.model_task_config.memory,
model_set=model_config.model_task_config.utils,
request_type="memory.summary",
)

3
src/config/api_ada_configs.py
View File

@@ -105,9 +105,6 @@ class ModelTaskConfig(ConfigBase):
replyer_2: TaskConfig
"""normal_chat次要回复模型配置"""
memory: TaskConfig
"""记忆模型配置"""
emotion: TaskConfig
"""情绪模型配置"""

6
src/llm_models/model_client/openai_client.py
View File

@@ -1,6 +1,7 @@
import asyncio
import io
import json
import time
import re
import base64
from collections.abc import Iterable
@@ -452,6 +453,7 @@ class OpenaiClient(BaseClient):
resp, usage_record = await stream_response_handler(req_task.result(), interrupt_flag)
else:
# 发送请求并获取响应
# start_time = time.time()
req_task = asyncio.create_task(
self.client.chat.completions.create(
model=model_info.model_identifier,
@@ -469,7 +471,9 @@ class OpenaiClient(BaseClient):
# 如果中断量存在且被设置，则取消任务并抛出异常
req_task.cancel()
raise ReqAbortException("请求被外部信号中断")
await asyncio.sleep(0.5) # 等待0.5秒后再次检查任务&中断信号量状态
await asyncio.sleep(0.1) # 等待0.5秒后再次检查任务&中断信号量状态
# logger.info(f"OpenAI请求时间: {model_info.model_identifier} {time.time() - start_time} \n{messages}")
resp, usage_record = async_response_parser(req_task.result())
except APIConnectionError as e:

13
src/llm_models/utils_model.py
View File

@@ -1,6 +1,7 @@
import re
import copy
import asyncio
import time
from enum import Enum
from rich.traceback import install
@@ -150,14 +151,22 @@ 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
"""
# 请求体构建
start_time = time.time()
message_builder = MessageBuilder()
message_builder.add_text_content(prompt)
messages = [message_builder.build()]
tool_built = self._build_tool_options(tools)
# 模型选择
model_info, api_provider, client = self._select_model()
# 请求并处理返回值
logger.info(f"LLM选择耗时: {model_info.name} {time.time() - start_time}")
response = await self._execute_request(
api_provider=api_provider,
client=client,
@@ -168,6 +177,8 @@ class LLMRequest:
max_tokens=max_tokens,
tool_options=tool_built,
)
content = response.content
reasoning_content = response.reasoning_content or ""
tool_calls = response.tool_calls
@@ -175,6 +186,7 @@ class LLMRequest:
if not reasoning_content and content:
content, extracted_reasoning = self._extract_reasoning(content)
reasoning_content = extracted_reasoning
if usage := response.usage:
llm_usage_recorder.record_usage_to_database(
model_info=model_info,
@@ -183,6 +195,7 @@ class LLMRequest:
request_type=self.request_type,
endpoint="/chat/completions",
)
if not content:
if raise_when_empty:
logger.warning("生成的响应为空")

7
template/model_config_template.toml
View File

@@ -1,5 +1,5 @@
[inner]
version = "1.1.1"
version = "1.2.0"
# 配置文件版本号迭代规则同bot_config.toml
@@ -132,11 +132,6 @@ model_list = ["siliconflow-deepseek-v3"]
temperature = 0.3
max_tokens = 800
[model_task_config.memory] # 记忆模型
model_list = ["qwen3-30b"]
temperature = 0.7
max_tokens = 800
[model_task_config.vlm] # 图像识别模型
model_list = ["qwen2.5-vl-72b"]
max_tokens = 800