Drop the JSON vector column, and fix what was hiding behind it

Migration 38 left memories.embedding in place so a rollback could still find
the vectors. Production has since been verified reading from embedding_blob,
so migration 42 drops it: 4 MB of a 99.6 MB database holding nothing anyone
reads.

Removing it surfaced a live bug. Changing your embedding model is supposed to
throw the bank's vectors away and let the post-turn pass rebuild them, because
two models' vectors are not comparable. The settings route did that by nulling
memories.embedding -- correct until 38 moved the vectors, after which it
cleared the dead column and left the blob intact with `embedded` still true.
_embed_pending filters on `embedded IS FALSE`, so it never saw those rows and
the bank went on ranking against the old model's vectors permanently.

Nothing would have reported it. cosine returns 0.0 on a width mismatch, so a
different-width model scores every memory zero and retrieval returns whichever
rows happen to sort first; a same-width model scores plausible garbage.

The bulk clear now sets both columns. It stays a bulk UPDATE rather than going
through set_vector -- loading the rows is the cost that whole path exists to
avoid -- so set_vector's docstring now names it as the one caller that
legitimately writes those columns by hand. No cache invalidation is added:
clearing `embedded` drops the rows out of the catalogue query, and set_vector
evicts each entry as the re-embed puts it back.

test_embedding_blob.py now rebuilds the pre-38 schema by hand where it tests
the backfill, since create_all no longer produces the column it converts from,
and asserts 42 removes it at the end of a full bootstrap -- 38 reads that
column and 42 drops it, so an upgrade that reordered them would arrive with an
empty bank.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017Dvvqn9ZDR4ixeFPHNbww7

backend/app/models.py

@@ -158,10 +158,6 @@ class Memory(Base):
     id: Mapped[int] = mapped_column(primary_key=True)
     adventure_id: Mapped[int] = mapped_column(ForeignKey("adventures.id", ondelete="CASCADE"))
     text: Mapped[str] = mapped_column(Text, default="")
-    # Superseded by embedding_blob and still written alongside it, so a
-    # rollback finds the vectors, until a follow-up migration drops it. Nothing
-    # reads it.
-    embedding: Mapped[list | None] = mapped_column(JSON, nullable=True, deferred=True)
     # The vector, little-endian float32. Deferred because it is wider than the
     # rest of the row put together and exactly one code path wants it: anything
     # bulk-loading memories (the Memories drawer, eviction, the embed queue)