106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
import datetime
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import uuid
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from enum import StrEnum
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from typing import Optional, List
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from sqlalchemy import Column, String, Boolean, DateTime, Text, ForeignKey, Enum as SQLEnum
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from sqlalchemy.dialects.postgresql import UUID, JSON
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from sqlalchemy.orm import relationship
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from app.db import Base
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class ModelType(StrEnum):
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"""模型类型枚举"""
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LLM = "llm"
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CHAT = "chat"
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EMBEDDING = "embedding"
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RERANK = "rerank"
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class ModelProvider(StrEnum):
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"""模型提供商枚举"""
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OPENAI = "openai"
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# ANTHROPIC = "anthropic"
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# GOOGLE = "google"
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# BAIDU = "baidu"
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DASHSCOPE = "dashscope"
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# ZHIPU = "zhipu"
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# MOONSHOT = "moonshot"
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# DEEPSEEK = "deepseek"
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OLLAMA = "ollama"
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XINFERENCE = "xinference"
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GPUSTACK = "gpustack"
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BEDROCK = "bedrock"
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class ModelConfig(Base):
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"""模型配置表"""
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__tablename__ = "model_configs"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
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tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, index=True, comment="租户ID")
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name = Column(String, nullable=False, comment="模型显示名称")
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type = Column(String, nullable=False, index=True, comment="模型类型")
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description = Column(String, comment="模型描述")
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# 模型配置参数
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config = Column(JSON, comment="模型配置参数")
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# - temperature : 控制生成文本的随机性。值越高，输出越随机、越有创造性；值越低，输出越确定、越保守。
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# - top_p : 一种替代 temperature 的采样方法，控制模型从概率最高的词中选择的范围。
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# - presence_penalty : 对新出现的主题进行惩罚，鼓励模型谈论已经提到过的话题。
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# - frequency_penalty : 对高频词进行惩罚，降低重复相同词语的可能性。
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# - stop 或 stop_sequences : 一个或多个字符串序列，当模型生成这些序列时会停止输出。
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# - 特定于提供商的参数 : 比如某些模型可能支持的 stream (流式输出) 开关、 seed (随机种子) 等。
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# # 模型能力参数
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# max_tokens = Column(String, comment="最大token数")
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# context_length = Column(String, comment="上下文长度")
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# 状态管理
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is_active = Column(Boolean, default=True, nullable=False, comment="是否激活")
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is_public = Column(Boolean, default=False, nullable=False, comment="是否公开")
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# 时间戳
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created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间")
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updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间")
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# 关联关系
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api_keys = relationship("ModelApiKey", back_populates="model_config", cascade="all, delete-orphan")
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def __repr__(self):
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return f"<ModelConfig(id={self.id}, name={self.name}, type={self.type})>"
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class ModelApiKey(Base):
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"""模型API密钥表"""
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__tablename__ = "model_api_keys"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
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model_config_id = Column(UUID(as_uuid=True), ForeignKey("model_configs.id"), nullable=False, comment="模型配置ID")
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# API Key 信息
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model_name = Column(String, nullable=False, comment="模型实际名称")
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provider = Column(String, nullable=False, comment="API Key提供商")
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api_key = Column(String, nullable=False, comment="API密钥")
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api_base = Column(String, comment="API基础URL")
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# 配置参数
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config = Column(JSON, comment="API Key特定配置")
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# 使用统计
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usage_count = Column(String, default="0", comment="使用次数")
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last_used_at = Column(DateTime, comment="最后使用时间")
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# 状态管理
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is_active = Column(Boolean, default=True, nullable=False, comment="是否激活")
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priority = Column(String, default="1", comment="优先级")
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# 时间戳
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created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间")
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updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间")
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# 关联关系
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model_config = relationship("ModelConfig", back_populates="api_keys")
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def __repr__(self):
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return f"<ModelApiKey(id={self.id}, model_name={self.model_name}, provider={self.provider}, model_config_id={self.model_config_id})>"
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