165 lines
6.6 KiB
Python
165 lines
6.6 KiB
Python
"""LLM Configuration model for managing multiple AI models."""
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from sqlalchemy import Column, String, Text, Boolean, Integer, Float, JSON
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from sqlalchemy.orm import relationship
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from typing import Dict, Any, Optional
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import json
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from ..db.base import BaseModel
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class LLMConfig(BaseModel):
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"""LLM Configuration model for managing AI model settings."""
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__tablename__ = "llm_configs"
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name = Column(String(100), nullable=False, index=True) # 配置名称
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provider = Column(String(50), nullable=False, index=True) # 服务商：openai, deepseek, doubao, zhipu, moonshot, baidu
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model_name = Column(String(100), nullable=False) # 模型名称
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api_key = Column(String(500), nullable=False) # API密钥（加密存储）
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base_url = Column(String(200), nullable=True) # API基础URL
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# 模型参数
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max_tokens = Column(Integer, default=2048, nullable=False)
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temperature = Column(Float, default=0.7, nullable=False)
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top_p = Column(Float, default=1.0, nullable=False)
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frequency_penalty = Column(Float, default=0.0, nullable=False)
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presence_penalty = Column(Float, default=0.0, nullable=False)
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# 配置信息
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description = Column(Text, nullable=True) # 配置描述
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is_active = Column(Boolean, default=True, nullable=False) # 是否启用
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is_default = Column(Boolean, default=False, nullable=False) # 是否为默认配置
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is_embedding = Column(Boolean, default=False, nullable=False) # 是否为嵌入模型
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# 扩展配置（JSON格式）
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extra_config = Column(JSON, nullable=True) # 额外配置参数
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# 使用统计
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usage_count = Column(Integer, default=0, nullable=False) # 使用次数
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last_used_at = Column(String(50), nullable=True) # 最后使用时间
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def __repr__(self):
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return f"<LLMConfig(id={self.id}, name='{self.name}', provider='{self.provider}', model='{self.model_name}')>"
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def to_dict(self, include_sensitive=False):
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"""Convert to dictionary, optionally excluding sensitive data."""
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data = super().to_dict()
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data.update({
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'name': self.name,
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'provider': self.provider,
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'model_name': self.model_name,
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'base_url': self.base_url,
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'max_tokens': self.max_tokens,
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'temperature': self.temperature,
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'top_p': self.top_p,
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'frequency_penalty': self.frequency_penalty,
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'presence_penalty': self.presence_penalty,
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'description': self.description,
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'is_active': self.is_active,
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'is_default': self.is_default,
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'is_embedding': self.is_embedding,
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'extra_config': self.extra_config,
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'usage_count': self.usage_count,
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'last_used_at': self.last_used_at
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})
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if include_sensitive:
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data['api_key'] = self.api_key
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else:
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# 只显示API密钥的前几位和后几位
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if self.api_key:
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key_len = len(self.api_key)
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if key_len > 8:
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data['api_key_masked'] = f"{self.api_key[:4]}...{self.api_key[-4:]}"
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else:
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data['api_key_masked'] = "***"
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else:
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data['api_key_masked'] = None
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return data
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def get_client_config(self) -> Dict[str, Any]:
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"""获取用于创建客户端的配置."""
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config = {
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'api_key': self.api_key,
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'base_url': self.base_url,
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'model': self.model_name,
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'max_tokens': self.max_tokens,
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'temperature': self.temperature,
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'top_p': self.top_p,
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'frequency_penalty': self.frequency_penalty,
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'presence_penalty': self.presence_penalty
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}
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# 添加额外配置
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if self.extra_config:
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config.update(self.extra_config)
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return config
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def validate_config(self) -> Dict[str, Any]:
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"""验证配置是否有效."""
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if not self.name or not self.name.strip():
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return {"valid": False, "error": "配置名称不能为空"}
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if not self.provider or self.provider not in ['openai', 'deepseek', 'doubao', 'zhipu', 'moonshot', 'baidu']:
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return {"valid": False, "error": "不支持的服务商"}
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if not self.model_name or not self.model_name.strip():
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return {"valid": False, "error": "模型名称不能为空"}
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if not self.api_key or not self.api_key.strip():
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return {"valid": False, "error": "API密钥不能为空"}
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if self.max_tokens <= 0 or self.max_tokens > 32000:
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return {"valid": False, "error": "最大令牌数必须在1-32000之间"}
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if self.temperature < 0 or self.temperature > 2:
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return {"valid": False, "error": "温度参数必须在0-2之间"}
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return {"valid": True, "error": None}
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def increment_usage(self):
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"""增加使用次数."""
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from datetime import datetime
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self.usage_count += 1
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self.last_used_at = datetime.now().isoformat()
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@classmethod
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def get_default_config(cls, provider: str, is_embedding: bool = False):
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"""获取服务商的默认配置模板."""
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templates = {
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'openai': {
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'base_url': 'https://api.openai.com/v1',
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'model_name': 'gpt-3.5-turbo' if not is_embedding else 'text-embedding-ada-002',
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'max_tokens': 2048,
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'temperature': 0.7
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},
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'deepseek': {
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'base_url': 'https://api.deepseek.com/v1',
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'model_name': 'deepseek-chat' if not is_embedding else 'deepseek-embedding',
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'max_tokens': 2048,
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'temperature': 0.7
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},
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'doubao': {
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'base_url': 'https://ark.cn-beijing.volces.com/api/v3',
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'model_name': 'doubao-lite-4k' if not is_embedding else 'doubao-embedding',
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'max_tokens': 2048,
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'temperature': 0.7
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},
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'zhipu': {
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'base_url': 'https://open.bigmodel.cn/api/paas/v4',
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'model_name': 'glm-4' if not is_embedding else 'embedding-3',
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'max_tokens': 2048,
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'temperature': 0.7
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},
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'moonshot': {
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'base_url': 'https://api.moonshot.cn/v1',
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'model_name': 'moonshot-v1-8k' if not is_embedding else 'moonshot-embedding',
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'max_tokens': 2048,
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'temperature': 0.7
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}
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}
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return templates.get(provider, {}) |