from datetime import datetime, timezone

from pgvector.sqlalchemy import Vector
from sqlalchemy import DateTime, ForeignKey, Integer
from sqlalchemy.orm import Mapped, mapped_column

from scribe.models import Base

# bge-small-en-v1.5 produces 384-dim unit-normalized vectors. The column is a
# native pgvector `vector(384)` (see migration 0067) so similarity search runs
# as an indexed `ORDER BY embedding <=> :q LIMIT k` in Postgres rather than a
# full-table Python cosine scan.
EMBEDDING_DIM = 384


class NoteEmbedding(Base):
    """Stores the embedding vector for a note, used for semantic search."""

    __tablename__ = "note_embeddings"

    note_id: Mapped[int] = mapped_column(
        Integer,
        ForeignKey("notes.id", ondelete="CASCADE"),
        primary_key=True,
    )
    user_id: Mapped[int] = mapped_column(Integer, nullable=False, index=True)
    embedding: Mapped[list] = mapped_column(Vector(EMBEDDING_DIM), nullable=False)
    updated_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True),
        default=lambda: datetime.now(timezone.utc),
    )
