123 lines
5.0 KiB
Python
123 lines
5.0 KiB
Python
"""Persist dimension-checked vector snapshots with composite scope constraints."""
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import sqlalchemy as sa
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from alembic import op
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revision = "0006_vectors"
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down_revision = "0005_chunk_indexes"
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branch_labels = None
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depends_on = None
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class ExtensionVector(sa.types.UserDefinedType):
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"""Qualify extension type without adding public business tables to search_path."""
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def get_col_spec(self, **kw):
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return "public.vector"
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def upgrade():
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schema = op.get_bind().scalar(
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sa.text("SELECT extnamespace::regnamespace::text FROM pg_extension WHERE extname='vector'")
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)
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if schema != "public":
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raise RuntimeError("vector 扩展须安装在 public；请由数据库管理员核对，迁移不会移动共享扩展")
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op.create_unique_constraint("uq_chunk_text_scope", "chunks", ["id", "index_version_id"])
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op.create_table(
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"embedding_spaces",
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sa.Column("id", sa.String(64), primary_key=True),
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sa.Column("dimension", sa.Integer(), nullable=False),
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sa.Column("spec", sa.JSON(), nullable=False),
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sa.UniqueConstraint("id", "dimension", name="uq_embedding_space_dimension"),
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sa.CheckConstraint("dimension BETWEEN 1 AND 2000", name="ck_embedding_space_dimension"),
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)
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op.create_table(
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"vector_versions",
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sa.Column("id", sa.String(36), primary_key=True),
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sa.Column(
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"text_index_version_id",
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sa.String(36),
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sa.ForeignKey("index_versions.id"),
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nullable=False,
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),
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sa.Column("space_id", sa.String(64), nullable=False),
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sa.Column("dimension", sa.Integer(), nullable=False),
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sa.Column("chunk_count", sa.Integer(), nullable=False),
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sa.Column("status", sa.String(20), nullable=False),
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sa.Column("created_by", sa.Integer(), sa.ForeignKey("users.id"), nullable=False),
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sa.Column(
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"created_at", sa.DateTime(timezone=True), server_default=sa.func.now(), nullable=False
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),
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sa.Column("published_at", sa.DateTime(timezone=True)),
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sa.ForeignKeyConstraint(
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["space_id", "dimension"],
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["embedding_spaces.id", "embedding_spaces.dimension"],
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name="fk_vector_version_space",
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),
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sa.UniqueConstraint(
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"id", "text_index_version_id", "space_id", "dimension", name="uq_vector_version_scope"
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),
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sa.CheckConstraint(
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"status IN ('ready', 'published', 'superseded')", name="ck_vector_version_status"
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),
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sa.CheckConstraint("chunk_count BETWEEN 0 AND 2000", name="ck_vector_version_count"),
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)
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op.create_table(
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"chunk_vectors",
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sa.Column("version_id", sa.String(36), primary_key=True),
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sa.Column("chunk_id", sa.String(36), primary_key=True),
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sa.Column("text_index_version_id", sa.String(36), nullable=False),
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sa.Column("space_id", sa.String(64), nullable=False),
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sa.Column("dimension", sa.Integer(), nullable=False),
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sa.Column("embedding", ExtensionVector(), nullable=False),
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sa.ForeignKeyConstraint(
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["version_id", "text_index_version_id", "space_id", "dimension"],
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[
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"vector_versions.id",
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"vector_versions.text_index_version_id",
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"vector_versions.space_id",
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"vector_versions.dimension",
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],
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name="fk_chunk_vector_scope",
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),
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sa.ForeignKeyConstraint(
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["chunk_id", "text_index_version_id"],
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["chunks.id", "chunks.index_version_id"],
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name="fk_chunk_vector_text",
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),
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sa.CheckConstraint("public.vector_dims(embedding) = dimension", name="ck_vector_dimension"),
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sa.CheckConstraint(
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"public.vector_norm(embedding) BETWEEN 0.9999 AND 1.0001", name="ck_vector_unit_norm"
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),
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)
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op.create_table(
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"vector_state",
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sa.Column("id", sa.Integer(), primary_key=True),
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sa.Column("active_version_id", sa.String(36), sa.ForeignKey("vector_versions.id")),
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sa.Column("generation", sa.Integer(), nullable=False),
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sa.CheckConstraint("id = 1", name="ck_vector_state_singleton"),
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)
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op.execute(sa.text("INSERT INTO vector_state (id, generation) VALUES (1, 0)"))
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op.create_table(
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"vector_publications",
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sa.Column("generation", sa.Integer(), primary_key=True),
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sa.Column("previous_version_id", sa.String(36), sa.ForeignKey("vector_versions.id")),
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sa.Column("version_id", sa.String(36), sa.ForeignKey("vector_versions.id"), nullable=False),
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sa.Column("actor_id", sa.Integer(), sa.ForeignKey("users.id"), nullable=False),
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sa.Column(
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"published_at", sa.DateTime(timezone=True), server_default=sa.func.now(), nullable=False
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),
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)
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def downgrade():
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for table in [
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"vector_publications",
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"vector_state",
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"chunk_vectors",
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"vector_versions",
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"embedding_spaces",
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]:
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op.drop_table(table)
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op.drop_constraint("uq_chunk_text_scope", "chunks", type_="unique")
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