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/tests/test_embedding_model_switch.py

@@ -0,0 +1,169 @@
+"""Switching embedding models must re-embed the bank.
+
+Vectors from two different models are not comparable — different space, often
+different width — so changing the model has to throw the stored ones away and
+let the post-turn pass rebuild them.
+
+That worked while the vectors lived in `memories.embedding`: the settings
+route nulled that column and the embed queue picked the rows up. Migration 38
+moved the vectors to `embedding_blob` with an `embedded` flag beside them, and
+the bulk clear kept nulling the old column alone. The blob survived, the flag
+stayed true, `_embed_pending` (which looks for `embedded IS FALSE`) never saw
+the rows, and the bank went on ranking against the previous model's vectors
+for good.
+
+Nothing reports this. `cosine` returns 0.0 on a width mismatch, so a
+different-width model scores every memory zero and retrieval quietly returns
+whichever rows sort first; a same-width model scores plausible-looking
+garbage.
+
+    python -m pytest tests/test_embedding_model_switch.py -v
+"""
+import os
+import tempfile
+
+_tmp = tempfile.NamedTemporaryFile(suffix=".db", delete=False)
+_tmp.close()
+os.environ["AIDND_DB_PATH"] = _tmp.name
+os.environ.pop("AIDND_DATABASE_URL", None)
+os.environ.pop("DATABASE_URL", None)
+
+import asyncio
+
+import pytest
+from fastapi import Depends
+from fastapi.testclient import TestClient
+
+from app import auth, limits, memorybank, models
+from app.database import Base, SessionLocal, engine, get_db
+from app.main import app
+
+DIMS = 8
+
+
+@pytest.fixture()
+def client(monkeypatch):
+    Base.metadata.create_all(bind=engine)
+    memorybank._vector_cache.clear()
+    setup = SessionLocal()
+    user = models.User(is_guest=False, email="switch@example.com")
+    setup.add(user)
+    setup.flush()
+    setup.add(models.Settings(
+        user_id=user.id, api_key="enc:dummy", model="test-model",
+        embedding_model="model-a",
+    ))
+    adventure = models.Adventure(
+        user_id=user.id, title="Cave", script_state={}, memory_bank_enabled=True
+    )
+    setup.add(adventure)
+    setup.flush()
+    for i in range(5):
+        memory = models.Memory(adventure_id=adventure.id, text=f"Memory {i}")
+        memorybank.set_vector(memory, [float(i)] + [0.0] * (DIMS - 1))
+        setup.add(memory)
+    setup.commit()
+    adv_id, user_id = adventure.id, user.id
+    setup.close()
+
+    monkeypatch.setattr(limits, "rate_limit", lambda *a, **k: None)
+    monkeypatch.setattr(limits, "check_row_cap", lambda *a, **k: None)
+
+    def _current_user(db=Depends(get_db)):
+        return db.get(models.User, user_id)
+
+    app.dependency_overrides[auth.get_current_user] = _current_user
+    c = TestClient(app)
+    c.adv_id = adv_id
+    try:
+        yield c
+    finally:
+        app.dependency_overrides.clear()
+        memorybank._vector_cache.clear()
+        Base.metadata.drop_all(bind=engine)
+
+
+def memories(db):
+    return db.query(models.Memory).order_by(models.Memory.id).all()
+
+
+def test_the_bank_starts_embedded(client):
+    db = SessionLocal()
+    try:
+        rows = memories(db)
+        assert len(rows) == 5
+        assert all(m.embedded for m in rows)
+        assert all(m.embedding_blob for m in rows)
+    finally:
+        db.close()
+
+
+def test_changing_the_model_clears_every_vector(client):
+    r = client.put("/api/settings", json={"embedding_model": "model-b"})
+    assert r.status_code == 200, r.text
+
+    db = SessionLocal()
+    try:
+        rows = memories(db)
+        assert [m.embedding_blob for m in rows] == [None] * 5, \
+            "the blob survived the model change"
+        assert not any(m.embedded for m in rows), \
+            "`embedded` stayed true, so nothing will ever re-embed these"
+    finally:
+        db.close()
+
+
+def test_cleared_memories_are_queued_for_re_embedding(client):
+    """The flag is not cosmetic: it is the only thing `_embed_pending` filters
+    on, so this is the assertion that the bank actually recovers."""
+    client.put("/api/settings", json={"embedding_model": "model-b"})
+
+    db = SessionLocal()
+    try:
+        pending = (
+            db.query(models.Memory)
+            .filter(models.Memory.embedded.is_(False),
+                    models.Memory.forgotten.is_(False))
+            .all()
+        )
+        assert len(pending) == 5
+    finally:
+        db.close()
+
+
+def test_retrieval_uses_no_stale_vector_after_the_switch(client, monkeypatch):
+    """Until the re-embed runs, the bank must return nothing rather than
+    ranking against the old model's vectors."""
+    client.put("/api/settings", json={"embedding_model": "model-b"})
+
+    class Embedder:
+        async def embed(self, texts):
+            return [[1.0] + [0.0] * (DIMS - 1) for _ in texts]
+
+    monkeypatch.setattr(memorybank, "embedding_provider", lambda s: Embedder())
+
+    db = SessionLocal()
+    try:
+        adventure = db.get(models.Adventure, client.adv_id)
+        settings = db.query(models.Settings).first()
+        result = asyncio.run(
+            memorybank.retrieve_memories(adventure, settings, update_stats=False)
+        )
+        assert result["used"] == []
+    finally:
+        db.close()
+
+
+def test_an_unrelated_settings_change_keeps_the_vectors(client):
+    """Only an embedding-model change may clear the bank — re-embedding costs
+    an API call per memory."""
+    r = client.put("/api/settings", json={"model": "some-other-chat-model"})
+    assert r.status_code == 200, r.text
+
+    db = SessionLocal()
+    try:
+        rows = memories(db)
+        assert all(m.embedded for m in rows)
+        assert all(m.embedding_blob for m in rows)
+    finally:
+        db.close()