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"""
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MongoDB Models for Audience Segmentation
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Author: AI Generated
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Created: 2025-11-24
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Purpose: Define data models for storing audience segmentation results
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"""
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from datetime import datetime
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from typing import Optional, List, Dict
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from pydantic import BaseModel, Field
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from bson import ObjectId
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class PyObjectId(ObjectId):
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"""Custom ObjectId type for Pydantic v2"""
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@classmethod
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def __get_pydantic_core_schema__(cls, source_type, handler):
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from pydantic_core import core_schema
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return core_schema.union_schema([
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core_schema.is_instance_schema(ObjectId),
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core_schema.chain_schema([
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core_schema.str_schema(),
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core_schema.no_info_plain_validator_function(cls.validate),
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])
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],
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serialization=core_schema.plain_serializer_function_ser_schema(
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lambda x: str(x)
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))
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@classmethod
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def validate(cls, v):
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if isinstance(v, ObjectId):
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return v
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if isinstance(v, str):
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if not ObjectId.is_valid(v):
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raise ValueError(f"Invalid ObjectId: {v}")
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return ObjectId(v)
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raise ValueError(f"Expected ObjectId or string, got {type(v)}")
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class AudienceSegment(BaseModel):
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"""
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Defines the characteristics of an audience segment.
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This is the result of K-Means clustering on user behavior data.
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"""
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id: Optional[PyObjectId] = Field(default_factory=PyObjectId, alias="_id")
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segment_name: str = Field(..., description="Human-readable segment name, e.g., 'Big Spenders', 'Music Lovers'")
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description: Optional[str] = Field(None, description="Detailed description of this segment")
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criteria: Dict = Field(default_factory=dict, description="Statistical criteria: min_spend, max_spend, top_categories, etc.")
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user_count: int = Field(0, description="Number of users in this segment")
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last_updated: datetime = Field(default_factory=datetime.utcnow)
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marketing_content: Optional[Dict] = Field(
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None,
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description="AI-generated marketing content: { 'email_subject': str, 'email_body': str }"
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)
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class Config:
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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json_encoders = {ObjectId: str}
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class UserSegmentAssignment(BaseModel):
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"""
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Links a user to their assigned segment.
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Many-to-one relationship: many users belong to one segment.
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"""
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id: Optional[PyObjectId] = Field(default_factory=PyObjectId, alias="_id")
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user_id: PyObjectId = Field(..., description="Reference to User._id")
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segment_id: PyObjectId = Field(..., description="Reference to AudienceSegment._id")
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confidence_score: float = Field(..., description="Distance to cluster center (lower is better)")
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assigned_at: datetime = Field(default_factory=datetime.utcnow)
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class Config:
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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json_encoders = {ObjectId: str}
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