Fix SSE route dependency and align architecture docs

backend/app/services/llm/base_client.py

@@ -1,13 +1,15 @@
-"""LLM客户端基类 - 统一接口定义"""
+"""Provide service-layer logic for base client."""
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import List, Dict, Optional, Any
 from enum import Enum
+# Keep provider-specific behavior explicit so debugging stays straightforward.
+
 
 
 class LLMProvider(Enum):
-    """LLM提供商"""
+    """Define the L L M Provider enumeration."""
     DEEPSEEK = "deepseek"
     QWEN = "qwen"
     QWEN_VL = "qwen_vl"
@@ -15,7 +17,7 @@ class LLMProvider(Enum):
 
 @dataclass
 class LLMResponse:
-    """LLM响应结果"""
+    """Represent the L L M Response type."""
     content: str
     model: str
     usage: Dict[str, int] = field(default_factory=dict)
@@ -25,12 +27,13 @@ class LLMResponse:
 
     @property
     def is_success(self) -> bool:
+        """Return whether success for the L L M Response instance."""
         return self.error is None
 
 
 @dataclass
 class LLMConfig:
-    """LLM配置"""
+    """Define configuration for l l m config."""
     provider: LLMProvider
     model: str
     api_key: str
@@ -38,19 +41,20 @@ class LLMConfig:
     max_tokens: int = 4096
     temperature: float = 0.7
     top_p: float = 0.9
-    timeout: int = 300  # 默认超时300秒（摘要/Skills生成可能需要较长时间）
+    timeout: int = 300  # Keep provider-specific behavior explicit so debugging stays straightforward.
 
 
 class BaseLLMClient(ABC):
-    """LLM客户端基类"""
+    """Represent the Base L L M Client type."""
 
     def __init__(self, config: LLMConfig):
+        """Initialize the Base L L M Client instance."""
         self.config = config
         self._client = None
 
     @abstractmethod
     def _init_client(self):
-        """初始化客户端"""
+        """Handle init client for this module for the Base L L M Client instance."""
         pass
 
     @abstractmethod
@@ -61,18 +65,7 @@ class BaseLLMClient(ABC):
         temperature: Optional[float] = None,
         **kwargs
     ) -> LLMResponse:
-        """
-        对话补全
-
-        Args:
-            messages: 对话消息列表 [{"role": "user/assistant/system", "content": "..."}]
-            max_tokens: 最大输出token数
-            temperature: 温度参数
-            **kwargs: 其他参数
-
-        Returns:
-            LLMResponse: 响应结果
-        """
+        """Handle chat for the Base L L M Client instance."""
         pass
 
     def complete(
@@ -83,18 +76,7 @@ class BaseLLMClient(ABC):
         temperature: Optional[float] = None,
         **kwargs
     ) -> LLMResponse:
-        """
-        单轮补全（便捷方法）
-
-        Args:
-            prompt: 用户输入
-            system_prompt: 系统提示词
-            max_tokens: 最大输出token数
-            temperature: 温度参数
-
-        Returns:
-            LLMResponse: 响应结果
-        """
+        """Handle complete for the Base L L M Client instance."""
         messages = []
         if system_prompt:
             messages.append({"role": "system", "content": system_prompt})
@@ -104,12 +86,12 @@ class BaseLLMClient(ABC):
 
     @abstractmethod
     def get_available_models(self) -> List[str]:
-        """获取可用模型列表"""
+        """Return available models for the Base L L M Client instance."""
         pass
 
     def estimate_tokens(self, text: str) -> int:
-        """估算文本token数（粗略估计）"""
-        # 中文字符约1.5 token，英文约0.25 token
+        """Handle estimate tokens for the Base L L M Client instance."""
+        # Keep provider-specific behavior explicit so debugging stays straightforward.
         chinese_chars = sum(1 for c in text if '一' <= c <= '鿿')
         other_chars = len(text) - chinese_chars
         return int(chinese_chars * 1.5 + other_chars * 0.25)