added a index for the VEctor DB
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+54
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"""add 1024 ebook embedding cosine index.
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Revision ID: c460105682d2
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Revises: 2db132cace1a
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Create Date: 2026-06-13 19:53:45.680289
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from alembic import op
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from python.orm import RichieBase
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if TYPE_CHECKING:
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from collections.abc import Sequence
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# revision identifiers, used by Alembic.
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revision: str = "c460105682d2"
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down_revision: str | None = "2db132cace1a"
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branch_labels: str | Sequence[str] | None = None
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depends_on: str | Sequence[str] | None = None
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schema = RichieBase.schema_name
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def upgrade() -> None:
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"""Upgrade."""
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# ### commands auto generated by Alembic - please adjust! ###
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op.create_index(
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"ix_ebook_chunk_embedding_1024_embedding_cosine",
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"ebook_chunk_embedding_1024",
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["embedding"],
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unique=False,
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schema=schema,
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postgresql_using="hnsw",
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postgresql_ops={"embedding": "vector_cosine_ops"},
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)
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# ### end Alembic commands ###
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def downgrade() -> None:
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"""Downgrade."""
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# ### commands auto generated by Alembic - please adjust! ###
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op.drop_index(
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"ix_ebook_chunk_embedding_1024_embedding_cosine",
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table_name="ebook_chunk_embedding_1024",
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schema=schema,
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postgresql_using="hnsw",
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postgresql_ops={"embedding": "vector_cosine_ops"},
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)
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# ### end Alembic commands ###
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@@ -5,7 +5,7 @@ from __future__ import annotations
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from datetime import datetime
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from pgvector.sqlalchemy import Vector
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from sqlalchemy import BigInteger, Boolean, DateTime, ForeignKey, String, UniqueConstraint
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from sqlalchemy import BigInteger, Boolean, DateTime, ForeignKey, Index, String, UniqueConstraint
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from python.orm.richie.base import TableBase, TableBaseBig
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@@ -101,7 +101,15 @@ class EbookChunkEmbedding1024(TableBaseBig):
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"""1024-dimensional chunk embedding."""
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__tablename__ = "ebook_chunk_embedding_1024"
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__table_args__ = (UniqueConstraint("chunk_id", "model_id"),)
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__table_args__ = (
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UniqueConstraint("chunk_id", "model_id"),
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Index(
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"ix_ebook_chunk_embedding_1024_embedding_cosine",
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"embedding",
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postgresql_using="hnsw",
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postgresql_ops={"embedding": "vector_cosine_ops"},
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),
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)
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chunk_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_chunk.id", ondelete="CASCADE"))
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model_id: Mapped[int] = mapped_column(ForeignKey("main.ebook_embedding_model.id", ondelete="CASCADE"))
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