AIRegulation-DocAnalysis/backend/app/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)