merge: T112 real embedding model swap (Protocol + Mock + routing + backfill)

chat/app.py

@@ -94,9 +94,15 @@ async def lifespan(app: FastAPI):
     # Phase 4's pseudo-embedding path is local so the worker doesn't need
     # an LLM client; we still pass one so the Phase 4.5 swap to a real
     # model is a one-line change.
+    # T112 (Phase 4.5): the embedding model is now configurable via
+    # ``Settings.embedding_model``. Default ``"pseudo-sha256-384"``
+    # keeps the local-only path; swapping to a real model routes
+    # through ``client.embed(...)`` and falls back to a zero vector
+    # plus warning if the provider doesn't support embeddings.
     embedding_worker = EmbeddingWorker(
         conn_factory=lambda: open_db(settings.db_path),
         client=_factory(),
+        model=settings.embedding_model,
     )
     await embedding_worker.start()
     app.state.embedding_worker = embedding_worker

chat/config.py

@@ -39,6 +39,14 @@ class Settings(BaseModel):
     data_dir: Path = REPO_ROOT / "data"
     bind_host: str = "127.0.0.1"
     bind_port: int = 8000
+    # T112 (Phase 4.5): embedding model identifier. Default is the
+    # deterministic local pseudo (semantically meaningless but keeps the
+    # vector pipeline structurally valid). Swap to a real model name
+    # (e.g. "bge-small-en-v1.5") once the LLMClient implementation
+    # supports embed() — currently FeatherlessClient does NOT, so a
+    # non-default value will trigger the zero-vector fallback path
+    # plus a T107 warning until a different provider is wired in.
+    embedding_model: str = "pseudo-sha256-384"
 
 def load_settings() -> Settings:
     config_path = Path(os.environ.get("CHAT_CONFIG_PATH", DEFAULT_CONFIG))

chat/llm/client.py

@@ -12,3 +12,11 @@ class Message:
 class LLMClient(Protocol):
     async def generate(self, messages: Sequence[Message], *, model: str, **params) -> str: ...
     def stream(self, messages: Sequence[Message], *, model: str, **params) -> AsyncIterator[str]: ...
+    # T112 (Phase 4.5): real-embedding seam. Implementations either call a
+    # provider's ``/v1/embeddings`` endpoint or, when the provider doesn't
+    # expose embeddings (e.g. Featherless today), raise ``NotImplementedError``
+    # so ``generate_embedding`` can catch it and degrade to the zero-vector
+    # fallback. The Protocol is structural, so this method only needs to
+    # exist on implementations; existing callers that don't use it are
+    # unaffected.
+    async def embed(self, text: str, *, model: str) -> list[float]: ...

chat/llm/featherless.py

@@ -53,3 +53,26 @@ class FeatherlessClient:
                 delta = chunk.choices[0].delta.content or ""
                 if delta:
                     yield delta
+
+    async def embed(self, text: str, *, model: str) -> list[float]:
+        """Embeddings via Featherless — currently unsupported.
+
+        T112 (Phase 4.5) extends the LLMClient Protocol with ``embed()``
+        for a future real-embedding swap. Featherless's OpenAI-compatible
+        surface does NOT expose ``/v1/embeddings`` at the time of writing,
+        so this implementation raises ``NotImplementedError`` rather than
+        attempting a request that would 404. The
+        :func:`chat.services.embeddings.generate_embedding` wrapper
+        catches this and degrades to the existing zero-vector fallback
+        (with the T107 warning), so misconfigured callers fail loudly in
+        logs but the request path keeps working.
+
+        If Featherless ships embeddings, swap the body for an
+        ``self._client.embeddings.create(model=..., input=...)`` call
+        guarded by ``self._sem()`` (mirrors ``generate``/``stream``).
+        """
+        raise NotImplementedError(
+            "Featherless does not expose /v1/embeddings; "
+            "configure a different embedding provider or stick with "
+            "the default pseudo-sha256-384 model."
+        )

chat/llm/mock.py

@@ -4,8 +4,23 @@ from .client import Message
 
 
 class MockLLMClient:
-    def __init__(self, canned: list[str]):
+    """In-memory LLMClient for tests.
+
+    ``canned`` feeds ``generate``/``stream`` (one entry per call, popped
+    from the front). ``canned_embeddings`` (T112, Phase 4.5) feeds
+    ``embed`` the same way — each call pops the next vector. An empty
+    queue raises ``IndexError`` so misconfigured tests fail loudly
+    rather than returning ``None`` or hanging.
+    """
+
+    def __init__(
+        self,
+        canned: list[str],
+        *,
+        canned_embeddings: list[list[float]] | None = None,
+    ):
         self._canned = list(canned)
+        self._canned_embeddings: list[list[float]] = list(canned_embeddings or [])
 
     async def generate(self, messages: Sequence[Message], *, model: str, **params) -> str:
         return self._canned.pop(0)
@@ -14,3 +29,8 @@ class MockLLMClient:
         text = self._canned.pop(0)
         for ch in text:
             yield ch
+
+    async def embed(self, text: str, *, model: str) -> list[float]:
+        # Mirrors the canned-queue pattern; empty queue raises so
+        # misconfigured tests surface clearly instead of returning None.
+        return self._canned_embeddings.pop(0)

chat/services/embeddings.py

@@ -95,19 +95,27 @@ async def generate_embedding(
         # Pure-local pseudo path — no LLMClient call.
         return EmbeddingResult(vector=_pseudo_embed(text, dim), model=model, dim=dim)
 
-    # Future: real embedding via client.embed(...). Phase 4.5 work.
-    # For Phase 4, any non-default model falls through to fallback —
-    # warn so misconfigured callers (e.g., a real-model swap that isn't
-    # wired up yet) don't silently degrade to a zero vector.
-    _log.warning(
-        "generate_embedding: non-default model %r returned fallback "
-        "(model client.embed() not yet implemented in Phase 4.5+); "
-        "downstream search will degrade silently. Configure a supported model.",
-        model,
-    )
-    return EmbeddingResult(
-        vector=[0.0] * dim, model=FALLBACK_EMBEDDING_MODEL, dim=dim
-    )
+    # T112 (Phase 4.5): non-default model — route through the client's
+    # ``embed()`` method. On any failure (including ``NotImplementedError``
+    # from providers that don't expose embeddings, e.g. Featherless today),
+    # fall back to the zero vector and re-fire the T107 warning so
+    # misconfigured callers see the issue in logs rather than silently
+    # producing useless cosine results.
+    try:
+        vector = await client.embed(text, model=model)
+        return EmbeddingResult(vector=list(vector), model=model, dim=len(vector))
+    except Exception as exc:  # noqa: BLE001 — any failure must degrade gracefully
+        _log.warning(
+            "generate_embedding: non-default model %r returned fallback "
+            "(client.embed() raised %s: %s); "
+            "downstream search will degrade silently. Configure a supported model.",
+            model,
+            type(exc).__name__,
+            exc,
+        )
+        return EmbeddingResult(
+            vector=[0.0] * dim, model=FALLBACK_EMBEDDING_MODEL, dim=dim
+        )
 
 
 __all__ = [

scripts/backfill_embeddings.py

@@ -8,8 +8,21 @@ Phase 4 ships the deterministic local pseudo-embedding so this script
 runs synchronously without a network round-trip — the LLMClient argument
 is not needed on the pseudo path. Phase 4.5+ will need a real client.
 
+T112 (Phase 4.5) adds two flags:
+
+* ``--re-embed-all`` walks **every** memory regardless of whether it
+  already has an ``embeddings`` row. Useful when swapping embedding
+  models — the projector is INSERT OR REPLACE, so re-emitting an event
+  for an existing memory replaces the prior vector. Without this flag,
+  the script keeps the Phase 4 behavior of only filling in gaps.
+* ``--model M`` overrides ``Settings.embedding_model`` for this run.
+  Defaults to the configured model (which itself defaults to
+  ``"pseudo-sha256-384"``).
+
 Run from the repo root:
     .venv/bin/python scripts/backfill_embeddings.py [--limit N] [--dry-run]
+    .venv/bin/python scripts/backfill_embeddings.py --re-embed-all
+    .venv/bin/python scripts/backfill_embeddings.py --re-embed-all --model bge-small-en-v1.5
 """
 
 from __future__ import annotations
@@ -17,11 +30,12 @@ from __future__ import annotations
 import argparse
 import asyncio
 
-from chat.config import load_settings
+from chat.config import Settings, load_settings
 from chat.db.connection import open_db
 from chat.db.migrate import apply_migrations
 from chat.eventlog.log import append_and_apply
 from chat.services.embeddings import (
+    DEFAULT_EMBEDDING_MODEL,
     FALLBACK_EMBEDDING_MODEL,
     generate_embedding,
 )
@@ -34,6 +48,24 @@ import chat.state.memory  # noqa: F401
 import chat.state.world  # noqa: F401
 
 
+def _build_client(settings: Settings):
+    """Construct an LLMClient for the backfill run.
+
+    Default-model runs (the pseudo path) don't need a client, so we
+    return ``None`` and ``generate_embedding`` skips the call. Non-default
+    models route through the real client; injectable via monkeypatch in
+    tests.
+    """
+    if settings.embedding_model == DEFAULT_EMBEDDING_MODEL:
+        return None
+    from chat.llm.featherless import FeatherlessClient
+
+    return FeatherlessClient(
+        api_key=settings.featherless_api_key,
+        base_url=settings.featherless_base_url,
+    )
+
+
 async def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument(
@@ -47,23 +79,51 @@ async def main() -> None:
         action="store_true",
         help="Print the count of memories needing embeddings, then exit.",
     )
+    parser.add_argument(
+        "--re-embed-all",
+        action="store_true",
+        help=(
+            "Walk every memory (not just those without an embeddings row) "
+            "and re-emit embedding_indexed events. Use this when swapping "
+            "embedding models so the existing rows get replaced."
+        ),
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help=(
+            "Embedding model identifier. Overrides Settings.embedding_model "
+            "for this run; default uses the configured model."
+        ),
+    )
     args = parser.parse_args()
 
     settings = load_settings()
     settings.db_path.parent.mkdir(parents=True, exist_ok=True)
     apply_migrations(settings.db_path)
 
+    model = args.model or settings.embedding_model
+    # Override the settings instance so ``_build_client`` sees the
+    # effective model when deciding whether to construct a real client.
+    settings = settings.model_copy(update={"embedding_model": model})
+    client = _build_client(settings)
+
     with open_db(settings.db_path) as conn:
-        sql = (
-            "SELECT m.id, m.pov_summary FROM memories m "
-            "LEFT JOIN embeddings e ON e.memory_id = m.id "
-            "WHERE e.memory_id IS NULL "
-            "ORDER BY m.id"
-        )
+        if args.re_embed_all:
+            sql = "SELECT m.id, m.pov_summary FROM memories m ORDER BY m.id"
+        else:
+            sql = (
+                "SELECT m.id, m.pov_summary FROM memories m "
+                "LEFT JOIN embeddings e ON e.memory_id = m.id "
+                "WHERE e.memory_id IS NULL "
+                "ORDER BY m.id"
+            )
         if args.limit is not None:
             sql += f" LIMIT {int(args.limit)}"
         rows = conn.execute(sql).fetchall()
-        print(f"Found {len(rows)} memories needing embeddings.")
+        mode = "re-embedding" if args.re_embed_all else "needing embeddings"
+        print(f"Found {len(rows)} memories {mode} (model={model}).")
         if args.dry_run:
             return
 
@@ -71,11 +131,12 @@ async def main() -> None:
         skipped = 0
         for memory_id, text in rows:
             result = await generate_embedding(
-                client=None,  # pseudo path: no client needed
+                client=client,
                 text=text or "",
+                model=model,
             )
             if result.model == FALLBACK_EMBEDDING_MODEL:
-                print(f"  Skipping memory_id={memory_id} (empty text)")
+                print(f"  Skipping memory_id={memory_id} (empty text or fallback)")
                 skipped += 1
                 continue
             append_and_apply(

tests/test_backfill_embeddings.py

@@ -0,0 +1,231 @@
+"""Tests for the backfill_embeddings script (T112, Phase 4.5).
+
+Phase 4 shipped a backfill that walked memories *without* an embedding
+row and produced a vector for each (deterministic pseudo path). T112
+adds a ``--re-embed-all`` flag that walks **every** memory regardless
+of whether it already has an embeddings row, so operators can swap
+embedding models and have the existing rows replaced (the
+``embedding_indexed`` projector is INSERT OR REPLACE).
+
+These tests exercise the script's ``main()`` directly via asyncio —
+shell-out via subprocess would also work but importing keeps the
+fixture surface small and the failure mode clearer.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from chat.db.connection import open_db
+from chat.db.migrate import apply_migrations
+from chat.eventlog.log import append_and_apply, append_event
+from chat.eventlog.projector import project
+from chat.services.embeddings import DEFAULT_EMBEDDING_MODEL
+
+# Trigger handler registration for projection.
+import chat.state.embeddings  # noqa: F401
+import chat.state.entities  # noqa: F401
+import chat.state.memory  # noqa: F401
+import chat.state.world  # noqa: F401
+
+import scripts.backfill_embeddings as backfill
+
+
+def _seed(db_path: Path, count: int) -> list[int]:
+    """Seed ``count`` memory rows for ``bot_a``; return their ids."""
+    with open_db(db_path) as conn:
+        append_event(
+            conn,
+            kind="bot_authored",
+            payload={
+                "id": "bot_a",
+                "name": "BotA",
+                "persona": "...",
+                "voice_samples": [],
+                "traits": [],
+                "backstory": "",
+                "initial_relationship_to_you": "",
+                "kickoff_prose": "",
+            },
+        )
+        append_event(
+            conn,
+            kind="chat_created",
+            payload={
+                "id": "chat_bot_a",
+                "host_bot_id": "bot_a",
+                "initial_time": "2026-04-26T20:00:00+00:00",
+                "narrative_anchor": "Day 1",
+                "weather": "",
+            },
+        )
+        for i in range(count):
+            append_event(
+                conn,
+                kind="memory_written",
+                payload={
+                    "owner_id": "bot_a",
+                    "chat_id": "chat_bot_a",
+                    "pov_summary": f"memory text {i}",
+                    "witness_you": 1,
+                    "witness_host": 1,
+                    "witness_guest": 0,
+                    "source": "direct",
+                    "reliability": 1.0,
+                    "significance": 1,
+                    "pinned": 0,
+                    "auto_pinned": 0,
+                },
+            )
+        project(conn)
+        return [
+            r[0]
+            for r in conn.execute(
+                "SELECT id FROM memories WHERE owner_id = 'bot_a' ORDER BY id"
+            ).fetchall()
+        ]
+
+
+def _seed_embedding(db_path: Path, memory_id: int, model: str = "stale-model") -> None:
+    """Insert a stale ``embedding_indexed`` event so the row already
+    exists in ``embeddings`` (and the default backfill would skip it)."""
+    with open_db(db_path) as conn:
+        append_and_apply(
+            conn,
+            kind="embedding_indexed",
+            payload={
+                "memory_id": memory_id,
+                "model": model,
+                "dim": 3,
+                "vector": [0.0, 0.0, 0.0],
+            },
+        )
+
+
+@pytest.mark.asyncio
+async def test_re_embed_all_walks_every_memory(tmp_path, monkeypatch, capsys):
+    """``--re-embed-all`` re-embeds memories that already have rows in
+    ``embeddings`` (default mode skips them). After the run, every
+    memory should have an updated embedding tagged with the configured
+    model (the projector replaces stale rows in place)."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    memory_ids = _seed(db, count=3)
+    # Pre-seed stale embeddings on two of the three memories so the
+    # default path would skip them and only ``--re-embed-all`` covers
+    # everything.
+    _seed_embedding(db, memory_ids[0])
+    _seed_embedding(db, memory_ids[1])
+
+    cfg = tmp_path / "config.toml"
+    cfg.write_text(
+        f'featherless_api_key = "x"\n'
+        f'db_path = "{db}"\n'
+        f'data_dir = "{tmp_path}"\n'
+    )
+    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
+    monkeypatch.setenv("CHAT_DB_PATH", str(db))
+
+    with patch("sys.argv", ["backfill_embeddings.py", "--re-embed-all"]):
+        await backfill.main()
+
+    # All three memories now have a fresh embedding tagged with the
+    # default pseudo model (replacing the stale rows).
+    with open_db(db) as conn:
+        rows = conn.execute(
+            "SELECT memory_id, model FROM embeddings ORDER BY memory_id"
+        ).fetchall()
+        assert len(rows) == 3
+        for mid, model in rows:
+            assert mid in memory_ids
+            assert model == DEFAULT_EMBEDDING_MODEL
+
+
+@pytest.mark.asyncio
+async def test_default_backfill_only_walks_missing(tmp_path, monkeypatch):
+    """Without ``--re-embed-all``, the script keeps the Phase 4
+    behavior — memories with an existing embedding row are left
+    alone (their stale-model tag survives)."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    memory_ids = _seed(db, count=2)
+    _seed_embedding(db, memory_ids[0], model="stale-model")
+    # memory_ids[1] has no embedding yet.
+
+    cfg = tmp_path / "config.toml"
+    cfg.write_text(
+        f'featherless_api_key = "x"\n'
+        f'db_path = "{db}"\n'
+        f'data_dir = "{tmp_path}"\n'
+    )
+    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
+    monkeypatch.setenv("CHAT_DB_PATH", str(db))
+
+    with patch("sys.argv", ["backfill_embeddings.py"]):
+        await backfill.main()
+
+    with open_db(db) as conn:
+        rows = dict(
+            conn.execute(
+                "SELECT memory_id, model FROM embeddings ORDER BY memory_id"
+            ).fetchall()
+        )
+        # Stale row preserved; only the missing one was filled.
+        assert rows[memory_ids[0]] == "stale-model"
+        assert rows[memory_ids[1]] == DEFAULT_EMBEDDING_MODEL
+
+
+@pytest.mark.asyncio
+async def test_re_embed_all_respects_model_arg(tmp_path, monkeypatch):
+    """The ``--model`` flag overrides ``Settings.embedding_model``.
+    With a non-default model and a client that returns canned vectors,
+    every memory is re-embedded with the supplied model tag."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    memory_ids = _seed(db, count=2)
+    _seed_embedding(db, memory_ids[0])
+
+    cfg = tmp_path / "config.toml"
+    cfg.write_text(
+        f'featherless_api_key = "x"\n'
+        f'db_path = "{db}"\n'
+        f'data_dir = "{tmp_path}"\n'
+    )
+    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
+    monkeypatch.setenv("CHAT_DB_PATH", str(db))
+
+    # Patch the client factory the script uses to produce a Mock with
+    # canned embeddings — one per memory.
+    from chat.llm.mock import MockLLMClient
+
+    canned_vec = [0.1] * 384
+
+    def _factory(_settings):
+        return MockLLMClient(
+            canned=[],
+            canned_embeddings=[list(canned_vec) for _ in memory_ids],
+        )
+
+    monkeypatch.setattr(backfill, "_build_client", _factory)
+
+    with patch(
+        "sys.argv",
+        [
+            "backfill_embeddings.py",
+            "--re-embed-all",
+            "--model",
+            "bge-small-en-v1.5",
+        ],
+    ):
+        await backfill.main()
+
+    with open_db(db) as conn:
+        rows = conn.execute(
+            "SELECT memory_id, model FROM embeddings ORDER BY memory_id"
+        ).fetchall()
+        assert len(rows) == 2
+        for _, model in rows:
+            assert model == "bge-small-en-v1.5"

tests/test_config.py

@@ -24,3 +24,25 @@ def test_chat_db_path_env_overrides_default(tmp_path, monkeypatch):
     (tmp_path / "config.toml").write_text('featherless_api_key = "x"\n')
     s = load_settings()
     assert s.db_path == tmp_path / "alt.db"
+
+
+def test_embedding_model_defaults_to_pseudo(tmp_path, monkeypatch):
+    """T112: ``embedding_model`` defaults to the deterministic pseudo
+    so existing zero-config installs keep the Phase 4 behavior."""
+    monkeypatch.setenv("CHAT_CONFIG_PATH", str(tmp_path / "config.toml"))
+    (tmp_path / "config.toml").write_text('featherless_api_key = "x"\n')
+    s = load_settings()
+    assert s.embedding_model == "pseudo-sha256-384"
+
+
+def test_embedding_model_overridable_via_toml(tmp_path, monkeypatch):
+    """T112: operators swap the embedding model by editing config.toml.
+    The new value flows through to the embedding worker at startup."""
+    cfg = tmp_path / "config.toml"
+    cfg.write_text(
+        'featherless_api_key = "x"\n'
+        'embedding_model = "bge-small-en-v1.5"\n'
+    )
+    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
+    s = load_settings()
+    assert s.embedding_model == "bge-small-en-v1.5"

tests/test_embeddings.py

@@ -120,3 +120,51 @@ async def test_generate_embedding_default_model_does_not_warn(caplog):
     await generate_embedding(_client(), text="hello")
     warnings = [r for r in caplog.records if r.levelno == logging.WARNING]
     assert warnings == []
+
+
+@pytest.mark.asyncio
+async def test_embed_routes_to_client_when_non_default_model():
+    """T112: when a non-default ``model`` is requested, generate_embedding
+    routes through ``client.embed(text, model=...)`` and wraps the
+    returned vector in an EmbeddingResult tagged with the requested
+    model (NOT the fallback sentinel)."""
+    canned = [0.1, 0.2, 0.3, 0.4]
+    client = MockLLMClient(canned=[], canned_embeddings=[canned])
+
+    result = await generate_embedding(
+        client, text="hello world", model="bge-small-en-v1.5"
+    )
+    assert result.vector == canned
+    assert result.model == "bge-small-en-v1.5"
+    assert result.dim == len(canned)
+
+
+@pytest.mark.asyncio
+async def test_embed_falls_back_on_client_failure(caplog):
+    """T112: when ``client.embed`` raises (e.g. NotImplementedError on
+    Featherless, or a transient network error), generate_embedding logs
+    the existing T107 warning and returns the zero-vector fallback so
+    callers detect the sentinel and skip indexing."""
+
+    class _FailingClient:
+        async def generate(self, messages, *, model, **params):  # pragma: no cover
+            raise AssertionError("generate must not be called")
+
+        def stream(self, messages, *, model, **params):  # pragma: no cover
+            raise AssertionError("stream must not be called")
+
+        async def embed(self, text, *, model):
+            raise NotImplementedError("provider does not expose embeddings")
+
+    caplog.set_level(logging.WARNING, logger="chat.services.embeddings")
+    result = await generate_embedding(
+        _FailingClient(), text="hello", model="bge-small-en-v1.5"
+    )
+
+    assert result.model == FALLBACK_EMBEDDING_MODEL == "fallback"
+    assert len(result.vector) == DEFAULT_EMBEDDING_DIM
+    assert all(x == 0.0 for x in result.vector)
+
+    # Existing T107 warning fires (re-used from the new exception branch).
+    warnings = [r for r in caplog.records if r.levelno == logging.WARNING]
+    assert any("bge-small-en-v1.5" in r.getMessage() for r in warnings)

tests/test_featherless.py

@@ -0,0 +1,32 @@
+"""Tests for FeatherlessClient (Phase 4.5+).
+
+Phase 4.5 adds an ``embed()`` method to the LLMClient Protocol (T112).
+Featherless does not expose an OpenAI-compatible ``/v1/embeddings``
+endpoint, so its implementation deliberately raises
+``NotImplementedError`` to surface the gap clearly. The
+``generate_embedding`` wrapper catches this and degrades to the
+zero-vector fallback (the existing T107 warning path).
+
+If/when Featherless ships embeddings, swap the body for a real call to
+``/v1/embeddings`` and update this test to mock the HTTP layer.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from chat.llm.featherless import FeatherlessClient
+
+
+@pytest.mark.asyncio
+async def test_featherless_embed_raises_not_implemented():
+    """Featherless does not expose ``/v1/embeddings`` — embed() must
+    raise ``NotImplementedError`` so callers (``generate_embedding``)
+    can degrade to the fallback zero vector + warning rather than
+    silently producing useless output."""
+    client = FeatherlessClient(api_key="test-key")
+    with pytest.raises(NotImplementedError) as excinfo:
+        await client.embed("hello world", model="bge-small-en-v1.5")
+    # Message should hint at the cause so operators see why their
+    # real-model swap fell back.
+    assert "embeddings" in str(excinfo.value).lower()

tests/test_llm_mock.py

@@ -19,3 +19,28 @@ async def test_mock_streams_tokens():
     async for chunk in client.stream(msgs, model="any"):
         chunks.append(chunk)
     assert "".join(chunks) == "abcd"
+
+
+@pytest.mark.asyncio
+async def test_mock_llm_client_embed_pops_canned():
+    """T112: MockLLMClient.embed() pops a canned vector from the front
+    of ``canned_embeddings`` (mirrors the existing ``canned`` queue
+    pattern for generate/stream)."""
+    v1 = [0.1, 0.2, 0.3]
+    v2 = [0.4, 0.5, 0.6]
+    client = MockLLMClient(canned=[], canned_embeddings=[v1, v2])
+
+    out1 = await client.embed("first", model="bge-small-en-v1.5")
+    out2 = await client.embed("second", model="bge-small-en-v1.5")
+    assert out1 == v1
+    assert out2 == v2
+
+
+@pytest.mark.asyncio
+async def test_mock_llm_client_embed_empty_queue_raises():
+    """When the canned_embeddings queue is empty, ``embed`` must raise
+    a clear failure (IndexError) so misconfigured tests don't silently
+    return None or hang."""
+    client = MockLLMClient(canned=[])
+    with pytest.raises(IndexError):
+        await client.embed("text", model="any")