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models.py

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+from pydantic import BaseModel, Field
+from typing import Optional
+from enum import Enum
+
+
+class ZaiModel(str, Enum):
+    GLM_5 = "glm-5"
+    GLM_47 = "glm-4.7"
+    GLM_45_AIR = "glm-4.5-air"
+
+
+class SamplerType(str, Enum):
+    CATEGORY = "CATEGORY"
+    UNIFORM = "UNIFORM"
+    GAUSSIAN = "GAUSSIAN"
+    UUID = "UUID"
+    DATETIME = "DATETIME"
+
+
+class ColumnConfig(BaseModel):
+    name: str = Field(..., description="Column name")
+    type: str = Field(..., description="Column type: sampler, llm_text, llm_code, llm_structured")
+    params: dict = Field(default_factory=dict, description="Column-specific parameters")
+
+
+class GenerateRequest(BaseModel):
+    num_records: int = Field(default=10, ge=1, le=1000, description="Number of records to generate")
+    model: ZaiModel = Field(default=ZaiModel.GLM_47, description="z.ai model to use")
+    columns: list[ColumnConfig] = Field(..., description="Column configurations")
+    temperature: float = Field(default=0.7, ge=0.0, le=2.0)
+    max_tokens: int = Field(default=512, ge=64, le=4096)
+
+
+class PreviewRequest(BaseModel):
+    model: ZaiModel = Field(default=ZaiModel.GLM_47)
+    columns: list[ColumnConfig] = Field(..., description="Column configurations")
+    temperature: float = Field(default=0.7)
+    max_tokens: int = Field(default=512)
+
+
+class GenerateResponse(BaseModel):
+    success: bool
+    data: Optional[list[dict]] = None
+    record_count: int = 0
+    error: Optional[str] = None
+
+
+class PreviewResponse(BaseModel):
+    success: bool
+    sample: Optional[dict] = None
+    error: Optional[str] = None
+
+
+class HealthResponse(BaseModel):
+    status: str
+    model: str
+    api_configured: bool