from datetime import datetime

from pgvector.sqlalchemy import Vector
from sqlalchemy import Column, DateTime, String
from sqlalchemy.orm import declared_attr, deferred

from app.config import settings


class Embeddable:
    """Retrieval columns shared by every searchable corpus.

    `embedding_model` is what makes a model change detectable: vectors from two
    models share no space, so a mixed corpus returns meaningless distances.
    Deferred because a vector is large and never wanted in a list query.
    """

    # Mixin columns must be declared attributes, one per mapped class.
    @declared_attr
    def embedding(cls):
        return deferred(Column(Vector(settings.EMBEDDING_DIMENSIONS), nullable=True))

    @declared_attr
    def embedding_model(cls):
        return Column(String(120), nullable=True, index=True)

    @declared_attr
    def embedded_at(cls):
        return Column(DateTime, nullable=True)