update

backend/app/services/llm/base_client.py

@@ -0,0 +1,116 @@
+# src/services/llm/base_client.py
+"""LLM客户端基类 - 统一接口定义"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import List, Dict, Optional, Any
+from enum import Enum
+
+
+class LLMProvider(Enum):
+    """LLM提供商"""
+    DEEPSEEK = "deepseek"
+    QWEN = "qwen"
+    QWEN_VL = "qwen_vl"
+
+
+@dataclass
+class LLMResponse:
+    """LLM响应结果"""
+    content: str
+    model: str
+    usage: Dict[str, int] = field(default_factory=dict)
+    finish_reason: str = "stop"
+    latency_ms: int = 0
+    error: Optional[str] = None
+
+    @property
+    def is_success(self) -> bool:
+        return self.error is None
+
+
+@dataclass
+class LLMConfig:
+    """LLM配置"""
+    provider: LLMProvider
+    model: str
+    api_key: str
+    base_url: str
+    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）
+
+
+class BaseLLMClient(ABC):
+    """LLM客户端基类"""
+
+    def __init__(self, config: LLMConfig):
+        self.config = config
+        self._client = None
+
+    @abstractmethod
+    def _init_client(self):
+        """初始化客户端"""
+        pass
+
+    @abstractmethod
+    def chat(
+        self,
+        messages: List[Dict[str, str]],
+        max_tokens: Optional[int] = None,
+        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
+        """
+        pass
+
+    def complete(
+        self,
+        prompt: str,
+        system_prompt: Optional[str] = None,
+        max_tokens: Optional[int] = None,
+        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
+        """
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": prompt})
+
+        return self.chat(messages, max_tokens, temperature, **kwargs)
+
+    @abstractmethod
+    def get_available_models(self) -> List[str]:
+        """获取可用模型列表"""
+        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
+        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)