"""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()