116 lines
2.9 KiB
Python
116 lines
2.9 KiB
Python
"""LLM客户端基类 - 统一接口定义"""
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import List, Dict, Optional, Any
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from enum import Enum
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class LLMProvider(Enum):
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"""LLM提供商"""
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DEEPSEEK = "deepseek"
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QWEN = "qwen"
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QWEN_VL = "qwen_vl"
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@dataclass
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class LLMResponse:
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"""LLM响应结果"""
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content: str
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model: str
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usage: Dict[str, int] = field(default_factory=dict)
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finish_reason: str = "stop"
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latency_ms: int = 0
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error: Optional[str] = None
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@property
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def is_success(self) -> bool:
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return self.error is None
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@dataclass
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class LLMConfig:
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"""LLM配置"""
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provider: LLMProvider
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model: str
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api_key: str
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base_url: str
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max_tokens: int = 4096
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temperature: float = 0.7
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top_p: float = 0.9
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timeout: int = 300 # 默认超时300秒（摘要/Skills生成可能需要较长时间）
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class BaseLLMClient(ABC):
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"""LLM客户端基类"""
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def __init__(self, config: LLMConfig):
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self.config = config
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self._client = None
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@abstractmethod
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def _init_client(self):
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"""初始化客户端"""
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pass
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@abstractmethod
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def chat(
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self,
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messages: List[Dict[str, str]],
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max_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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**kwargs
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) -> LLMResponse:
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"""
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对话补全
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Args:
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messages: 对话消息列表 [{"role": "user/assistant/system", "content": "..."}]
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max_tokens: 最大输出token数
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temperature: 温度参数
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**kwargs: 其他参数
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Returns:
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LLMResponse: 响应结果
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"""
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pass
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def complete(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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max_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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**kwargs
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) -> LLMResponse:
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"""
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单轮补全（便捷方法）
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Args:
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prompt: 用户输入
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system_prompt: 系统提示词
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max_tokens: 最大输出token数
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temperature: 温度参数
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Returns:
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LLMResponse: 响应结果
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"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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return self.chat(messages, max_tokens, temperature, **kwargs)
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@abstractmethod
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def get_available_models(self) -> List[str]:
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"""获取可用模型列表"""
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pass
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def estimate_tokens(self, text: str) -> int:
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"""估算文本token数（粗略估计）"""
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# 中文字符约1.5 token，英文约0.25 token
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chinese_chars = sum(1 for c in text if '一' <= c <= '鿿')
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other_chars = len(text) - chinese_chars
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return int(chinese_chars * 1.5 + other_chars * 0.25)
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