chat/app.py

@@ -16,6 +16,7 @@ from chat.db.migrate import apply_migrations
 from chat.eventlog.log import read_events
 from chat.eventlog.projector import apply_event
 from chat.services.background import BackgroundWorker
+from chat.services.embedding_worker import EmbeddingWorker
 from chat.services.snapshot import latest_snapshot_path, restore_from_snapshot
 
 # Trigger handler registration:
@@ -85,9 +86,23 @@ async def lifespan(app: FastAPI):
     await worker.start()
     app.state.background_worker = worker
 
+    # T97: separate worker for the async embedding pass. Each
+    # ``memory_written`` enqueues an EmbeddingJob; the worker drains the
+    # queue, calls ``generate_embedding``, and emits ``embedding_indexed``.
+    # 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.
+    embedding_worker = EmbeddingWorker(
+        conn_factory=lambda: open_db(settings.db_path),
+        client=_factory(),
+    )
+    await embedding_worker.start()
+    app.state.embedding_worker = embedding_worker
+
     try:
         yield
     finally:
+        await embedding_worker.stop()
         await worker.stop()
 
 

chat/services/embedding_worker.py

@@ -0,0 +1,137 @@
+"""Embedding worker (T97, Phase 4).
+
+Drains a queue of embedding jobs. Each job carries a memory id and the
+narrative text to embed; the worker calls
+:func:`chat.services.embeddings.generate_embedding` and emits an
+``embedding_indexed`` event so the projector lands the vector in the
+``embeddings`` table.
+
+Mirrors the :class:`chat.services.background.BackgroundWorker` pattern:
+single asyncio task, sentinel-based shutdown, exceptions are caught and
+logged so a flaky embedding call doesn't take down the worker. Each job
+opens its own SQLite connection via ``conn_factory`` — the request path
+and the worker do not share connections.
+
+Featherless concurrency (the 2-conn cap) is respected by virtue of the
+single-task design: jobs run strictly serially. Phase 4's pseudo-embedding
+path is local and synchronous so this is largely moot, but the pattern
+is in place for the Phase 4.5+ real-embedding swap.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from dataclasses import dataclass
+from sqlite3 import Connection
+from typing import Callable
+
+from chat.eventlog.log import append_and_apply
+from chat.services.embeddings import (
+    DEFAULT_EMBEDDING_DIM,
+    DEFAULT_EMBEDDING_MODEL,
+    FALLBACK_EMBEDDING_MODEL,
+    generate_embedding,
+)
+
+
+log = logging.getLogger(__name__)
+
+
+@dataclass
+class EmbeddingJob:
+    """One unit of work for the embedding worker.
+
+    ``memory_id`` is the row to attach the vector to; ``text`` is the
+    narrative text to embed (typically ``memories.pov_summary``).
+    """
+
+    memory_id: int
+    text: str
+
+
+class EmbeddingWorker:
+    """asyncio.Queue-backed single-worker task for embedding generation.
+
+    Started on app startup; ``stop()`` enqueues a sentinel and awaits
+    the task so any in-flight job has a chance to finish. Pending jobs
+    after the sentinel are dropped on shutdown.
+    """
+
+    def __init__(
+        self,
+        *,
+        conn_factory: Callable[[], Connection],
+        client,  # LLMClient | None — unused on the pseudo path.
+        model: str = DEFAULT_EMBEDDING_MODEL,
+        dim: int = DEFAULT_EMBEDDING_DIM,
+        enabled: bool = True,
+    ) -> None:
+        self._queue: asyncio.Queue[EmbeddingJob | None] = asyncio.Queue()
+        self._conn_factory = conn_factory
+        self._client = client
+        self._model = model
+        self._dim = dim
+        self._task: asyncio.Task | None = None
+        self.enabled = enabled
+
+    def enqueue(self, job: EmbeddingJob) -> None:
+        if not self.enabled:
+            return
+        self._queue.put_nowait(job)
+
+    async def start(self) -> None:
+        if self._task is None:
+            self._task = asyncio.create_task(self._run())
+
+    async def stop(self) -> None:
+        if self._task is None:
+            return
+        self._queue.put_nowait(None)  # sentinel
+        await self._task
+        self._task = None
+
+    async def _run(self) -> None:
+        while True:
+            job = await self._queue.get()
+            if job is None:
+                return
+            try:
+                await self._process(job)
+            except Exception as exc:  # noqa: BLE001 — worker must not die
+                log.warning(
+                    "embedding worker failed for memory_id=%s: %s",
+                    job.memory_id,
+                    exc,
+                    exc_info=True,
+                )
+
+    async def _process(self, job: EmbeddingJob) -> None:
+        result = await generate_embedding(
+            self._client,
+            text=job.text,
+            model=self._model,
+            dim=self._dim,
+        )
+        if result.model == FALLBACK_EMBEDDING_MODEL:
+            # Don't index a fallback (zero) vector — the backfill script
+            # can retry later once a real embedding is available.
+            log.debug(
+                "embedding worker skipping fallback result for memory_id=%s",
+                job.memory_id,
+            )
+            return
+        with self._conn_factory() as conn:
+            append_and_apply(
+                conn,
+                kind="embedding_indexed",
+                payload={
+                    "memory_id": job.memory_id,
+                    "model": result.model,
+                    "dim": result.dim,
+                    "vector": result.vector,
+                },
+            )
+
+
+__all__ = ["EmbeddingJob", "EmbeddingWorker"]

chat/services/memory_write.py

@@ -13,6 +13,14 @@ Phase 1 simplifications (per plan §11.1, T27 will refine):
   pass overwrites via a follow-up event.
 - Witness flags are hard-coded ``[you=1, host=1, guest=0]``. Phase 2 will
   derive them from ``chat.guest_bot_id`` once a guest can be present.
+
+T97 (Phase 4): each successful memory write also enqueues an
+:class:`~chat.services.embedding_worker.EmbeddingJob` on the
+lifespan-managed embedding worker, so the just-written memory gets a
+vector indexed out-of-band. The hook is opt-in via the ``app`` kwarg —
+callers without a FastAPI app handle (e.g. one-off scripts, isolated
+unit tests) simply don't enqueue, and the backfill script can pick up
+those rows later.
 """
 
 from __future__ import annotations
@@ -20,6 +28,7 @@ from __future__ import annotations
 from sqlite3 import Connection
 
 from chat.eventlog.log import append_and_apply
+from chat.services.embedding_worker import EmbeddingJob
 
 
 def _write_one_memory(
@@ -35,9 +44,16 @@ def _write_one_memory(
     chat_clock_at: str | None,
     source: str,
     significance: int,
+    app=None,
 ) -> tuple[int, int | None]:
     """Append a single ``memory_written`` event for ``owner_id`` and return
-    ``(event_id, memory_id)`` for the projected row."""
+    ``(event_id, memory_id)`` for the projected row.
+
+    When ``app`` is provided and ``app.state.embedding_worker`` exists,
+    enqueue an :class:`EmbeddingJob` for the freshly-projected memory id
+    (T97). Skipped silently if the worker is absent or the projected row
+    can't be located — the backfill script handles missing-vector rows.
+    """
     payload: dict = {
         "owner_id": owner_id,
         "chat_id": chat_id,
@@ -64,6 +80,23 @@ def _write_one_memory(
         (owner_id, chat_id),
     ).fetchone()
     memory_id = row[0] if row else None
+
+    # T97: enqueue an embedding job for the just-written memory. The
+    # worker drains the queue out-of-band and emits an
+    # ``embedding_indexed`` event when the vector is ready. ``getattr``
+    # keeps this a no-op for callers without a wired-up app (scripts,
+    # tests) — the backfill script handles those rows.
+    if memory_id is not None and narrative_text and narrative_text.strip():
+        worker = (
+            getattr(app.state, "embedding_worker", None)
+            if app is not None
+            else None
+        )
+        if worker is not None:
+            worker.enqueue(
+                EmbeddingJob(memory_id=memory_id, text=narrative_text)
+            )
+
     return event_id, memory_id
 
 
@@ -79,6 +112,7 @@ def record_turn_memory_for_present(
     source: str = "direct",
     significance: int = 1,
     you_present: bool = True,
+    app=None,
 ) -> dict[str, tuple[int, int | None]]:
     """Single entry-point for per-turn memory writes (T84).
 
@@ -97,6 +131,9 @@ def record_turn_memory_for_present(
       with ``you_present=False`` is a programming error and raises
       :class:`ValueError`.
 
+    When ``app`` is provided, each per-witness write also enqueues an
+    :class:`EmbeddingJob` on ``app.state.embedding_worker`` (T97).
+
     Returns a mapping ``{bot_id: (event_id, memory_id)}`` so callers can
     look up the freshly-projected memory id per owner without re-querying
     the database.
@@ -121,6 +158,7 @@ def record_turn_memory_for_present(
         chat_clock_at=chat_clock_at,
         source=source,
         significance=significance,
+        app=app,
     )
     if guest_bot_id is not None:
         result[guest_bot_id] = _write_one_memory(
@@ -135,6 +173,7 @@ def record_turn_memory_for_present(
             chat_clock_at=chat_clock_at,
             source=source,
             significance=significance,
+            app=app,
         )
     return result
 
@@ -150,6 +189,7 @@ def record_meanwhile_memory(
     chat_clock_at: str | None = None,
     source: str = "direct",
     significance: int = 1,
+    app=None,
 ) -> dict[str, tuple[int, int | None]]:
     """Backward-compat thin wrapper for meanwhile memory writes (T64, T84).
 
@@ -169,4 +209,5 @@ def record_meanwhile_memory(
         source=source,
         significance=significance,
         you_present=False,
+        app=app,
     )

chat/services/regenerate.py

@@ -103,6 +103,7 @@ async def regenerate_assistant_turn(
     chat_id: str,
     original_assistant_event_id: int,
     edited_user_prose: str | None = None,
+    app=None,
 ) -> str:
     """Regenerate the assistant turn linked to ``original_assistant_event_id``.
 
@@ -414,6 +415,7 @@ async def regenerate_assistant_turn(
         narrative_text=new_text,
         scene_id=scene["id"] if scene else None,
         chat_clock_at=chat.get("time"),
+        app=app,
     )
 
     last_at = chat.get("time")
@@ -648,6 +650,7 @@ async def regenerate_assistant_turn(
                 narrative_text=interject_text,
                 scene_id=scene["id"] if scene else None,
                 chat_clock_at=chat.get("time"),
+                app=app,
             )
 
             # Re-run the multi-pair state-update with the post-interjection

chat/web/drawer.py

@@ -993,6 +993,7 @@ async def skip_elision(
             chat_id=chat_id,
             new_time=new_time,
             landing_state_hint=landing_state_hint,
+            app=request.app,
         )
     except ChatNotFoundError as exc:
         # Missing chat row: typed exception (T81) replaces the prior
@@ -1036,6 +1037,7 @@ async def skip_jump(
             new_time=new_time,
             notable_prose=notable_prose,
             reset_activity=reset_flag,
+            app=request.app,
         )
     except ChatNotFoundError as exc:
         # Missing chat row: typed exception (T81) replaces the prior

chat/web/meanwhile.py

@@ -131,6 +131,7 @@ async def process_meanwhile_turn(
     *,
     chat_id: str,
     prose: str,
+    app=None,
 ) -> dict:
     """Run one meanwhile turn end-to-end.
 
@@ -314,6 +315,7 @@ async def process_meanwhile_turn(
             narrative_text=text,
             scene_id=scene_id,
             chat_clock_at=chat.get("time"),
+            app=app,
         )
 
         # 9. Post-turn state-update — exactly 2 directed pairs over the

chat/web/skip.py

@@ -91,6 +91,7 @@ async def process_elision_skip(
     chat_id: str,
     new_time: str,
     landing_state_hint: str = "",
+    app=None,
 ) -> dict:
     """Run an elision skip end-to-end.
 
@@ -175,6 +176,7 @@ async def process_jump_skip(
     new_time: str,
     notable_prose: str = "",
     reset_activity: bool = False,
+    app=None,
 ) -> dict:
     """Run a jump skip end-to-end.
 
@@ -254,6 +256,7 @@ async def process_jump_skip(
                     chat_clock_at=new_time,
                     source="synthesized",
                     significance=mem.significance,
+                    app=app,
                 )
 
     narration = await narrate_skip(

chat/web/turns.py

@@ -248,6 +248,7 @@ async def post_turn(
                 settings,
                 chat_id=chat_id,
                 prose=prose,
+                app=request.app,
             )
         except ValueError as exc:
             raise HTTPException(status_code=400, detail=str(exc))
@@ -352,6 +353,7 @@ async def post_turn(
                 new_time=new_time,
                 landing_state_hint=getattr(parsed, "landing_state_hint", "")
                 or "",
+                app=request.app,
             )
         except ChatNotFoundError as exc:
             # Defensive: chat existence is checked above, so this only
@@ -512,6 +514,7 @@ async def post_turn(
         narrative_text=primary_text,
         scene_id=scene["id"] if scene else None,
         chat_clock_at=chat.get("time"),
+        app=request.app,
     )
 
     # 7b. Post-turn state-update pass (Requirements §3.4 / T40). All
@@ -746,6 +749,7 @@ async def post_turn(
                     narrative_text=interjection_text,
                     scene_id=scene["id"] if scene else None,
                     chat_clock_at=chat.get("time"),
+                    app=request.app,
                 )
 
                 # T74.2: enqueue a significance pass for the interjection
@@ -1092,6 +1096,7 @@ async def regenerate_turn(
             chat_id=chat_id,
             original_assistant_event_id=event_id,
             edited_user_prose=edited_prose,
+            app=request.app,
         )
     except ValueError as e:
         raise HTTPException(status_code=404, detail=str(e))

scripts/backfill_embeddings.py

@@ -0,0 +1,97 @@
+"""Backfill embeddings for memories that lack them (T97, Phase 4).
+
+Walks all memories where no row exists in the ``embeddings`` table. For
+each, calls :func:`chat.services.embeddings.generate_embedding` and emits
+an ``embedding_indexed`` event so the projector lands the vector.
+
+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.
+
+Run from the repo root:
+    .venv/bin/python scripts/backfill_embeddings.py [--limit N] [--dry-run]
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+
+from chat.config import 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 (
+    FALLBACK_EMBEDDING_MODEL,
+    generate_embedding,
+)
+
+# Trigger projector handler registration so ``append_and_apply`` lands
+# the embedding rows correctly.
+import chat.state.embeddings  # noqa: F401
+import chat.state.entities  # noqa: F401
+import chat.state.memory  # noqa: F401
+import chat.state.world  # noqa: F401
+
+
+async def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=None,
+        help="Cap the number of memories backfilled in this run.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print the count of memories needing embeddings, then exit.",
+    )
+    args = parser.parse_args()
+
+    settings = load_settings()
+    settings.db_path.parent.mkdir(parents=True, exist_ok=True)
+    apply_migrations(settings.db_path)
+
+    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.limit is not None:
+            sql += f" LIMIT {int(args.limit)}"
+        rows = conn.execute(sql).fetchall()
+        print(f"Found {len(rows)} memories needing embeddings.")
+        if args.dry_run:
+            return
+
+        indexed = 0
+        skipped = 0
+        for memory_id, text in rows:
+            result = await generate_embedding(
+                client=None,  # pseudo path: no client needed
+                text=text or "",
+            )
+            if result.model == FALLBACK_EMBEDDING_MODEL:
+                print(f"  Skipping memory_id={memory_id} (empty text)")
+                skipped += 1
+                continue
+            append_and_apply(
+                conn,
+                kind="embedding_indexed",
+                payload={
+                    "memory_id": memory_id,
+                    "model": result.model,
+                    "dim": result.dim,
+                    "vector": result.vector,
+                },
+            )
+            indexed += 1
+            print(f"  Indexed memory_id={memory_id}")
+        print(f"Done. Indexed {indexed}, skipped {skipped}.")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

tests/test_embedding_worker.py

@@ -0,0 +1,185 @@
+"""Embedding worker (T97, Phase 4).
+
+The worker drains a queue of EmbeddingJobs and emits ``embedding_indexed``
+events. Mirrors test_significance.py's BackgroundWorker tests in shape:
+seed a memory, enqueue jobs, call ``stop()`` to drain via sentinel, then
+assert on the projected ``embeddings`` table and the underlying event_log.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from chat.db.connection import open_db
+from chat.db.migrate import apply_migrations
+from chat.eventlog.log import append_event
+from chat.eventlog.projector import project
+from chat.services.embedding_worker import EmbeddingJob, EmbeddingWorker
+from chat.services.embeddings import (
+    DEFAULT_EMBEDDING_MODEL,
+    EmbeddingResult,
+    FALLBACK_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
+
+
+def _seed_memories(db_path: Path, count: int) -> list[int]:
+    """Seed ``count`` memory rows for ``bot_a`` and 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()
+        ]
+
+
+async def test_worker_drains_jobs_and_emits_indexed_events(tmp_path):
+    """Three jobs in -> three ``embedding_indexed`` events out, all
+    projected into the ``embeddings`` table."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    memory_ids = _seed_memories(db, count=3)
+
+    worker = EmbeddingWorker(
+        conn_factory=lambda: open_db(db),
+        client=None,  # pseudo path — no client needed
+    )
+    await worker.start()
+    for mid in memory_ids:
+        worker.enqueue(EmbeddingJob(memory_id=mid, text=f"text-{mid}"))
+    await worker.stop()
+
+    with open_db(db) as conn:
+        # Three embedding_indexed events landed.
+        cur = conn.execute(
+            "SELECT COUNT(*) FROM event_log WHERE kind = 'embedding_indexed'"
+        )
+        assert cur.fetchone()[0] == 3
+        # Three rows in the embeddings table — one per memory.
+        cur = conn.execute(
+            "SELECT memory_id, model, dim FROM embeddings ORDER BY memory_id"
+        )
+        rows = cur.fetchall()
+        assert len(rows) == 3
+        for (mid, model, dim), expected_mid in zip(rows, memory_ids):
+            assert mid == expected_mid
+            assert model == DEFAULT_EMBEDDING_MODEL
+            assert dim > 0
+
+
+async def test_worker_skips_fallback_results(tmp_path, monkeypatch):
+    """A fallback EmbeddingResult must NOT produce an embedding_indexed
+    event — backfill can retry later when a real embedding is available."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    memory_ids = _seed_memories(db, count=1)
+
+    async def _fake_generate(client, *, text, model, dim, timeout_s=30.0):
+        return EmbeddingResult(
+            vector=[0.0] * dim, model=FALLBACK_EMBEDDING_MODEL, dim=dim
+        )
+
+    # Patch the symbol the worker resolved at import time.
+    import chat.services.embedding_worker as worker_mod
+
+    monkeypatch.setattr(worker_mod, "generate_embedding", _fake_generate)
+
+    worker = EmbeddingWorker(
+        conn_factory=lambda: open_db(db),
+        client=None,
+    )
+    await worker.start()
+    worker.enqueue(EmbeddingJob(memory_id=memory_ids[0], text="anything"))
+    await worker.stop()
+
+    with open_db(db) as conn:
+        cur = conn.execute(
+            "SELECT COUNT(*) FROM event_log WHERE kind = 'embedding_indexed'"
+        )
+        assert cur.fetchone()[0] == 0
+        cur = conn.execute("SELECT COUNT(*) FROM embeddings")
+        assert cur.fetchone()[0] == 0
+
+
+async def test_worker_handles_concurrent_jobs_serially(tmp_path):
+    """Five jobs queued back-to-back must process in FIFO order — the
+    single-task design respects the Featherless 2-conn cap (and keeps
+    event_log ordering deterministic)."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    memory_ids = _seed_memories(db, count=5)
+
+    worker = EmbeddingWorker(
+        conn_factory=lambda: open_db(db),
+        client=None,
+    )
+    await worker.start()
+    # Enqueue all five before yielding to the loop — exercises the queue
+    # rather than a one-at-a-time drain.
+    for mid in memory_ids:
+        worker.enqueue(EmbeddingJob(memory_id=mid, text=f"text-{mid}"))
+    await worker.stop()
+
+    with open_db(db) as conn:
+        # Events landed in enqueue order (FIFO).
+        cur = conn.execute(
+            "SELECT json_extract(payload_json, '$.memory_id') "
+            "FROM event_log WHERE kind = 'embedding_indexed' "
+            "ORDER BY id"
+        )
+        seen = [r[0] for r in cur.fetchall()]
+        assert seen == memory_ids
+
+        # All five embeddings projected.
+        cur = conn.execute("SELECT COUNT(*) FROM embeddings")
+        assert cur.fetchone()[0] == 5

tests/test_memory_write.py

@@ -540,3 +540,49 @@ def test_record_turn_memory_you_present_false_requires_guest(tmp_path):
                 narrative_text="invalid",
                 you_present=False,
             )
+
+
+# ---------------------------------------------------------------------------
+# T97: embedding-worker enqueue hook.
+# ---------------------------------------------------------------------------
+
+
+def test_record_turn_memory_enqueues_embedding_job(tmp_path):
+    """When ``app.state.embedding_worker`` is wired, every per-witness
+    write enqueues an :class:`EmbeddingJob` carrying the freshly-projected
+    memory id and the narrative text. Two-bot turn -> two jobs."""
+    from types import SimpleNamespace
+
+    from chat.services.embedding_worker import EmbeddingJob
+
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    _seed_two_bots(db)
+
+    captured: list[EmbeddingJob] = []
+
+    class _StubWorker:
+        def enqueue(self, job: EmbeddingJob) -> None:
+            captured.append(job)
+
+    fake_app = SimpleNamespace(
+        state=SimpleNamespace(embedding_worker=_StubWorker())
+    )
+
+    with open_db(db) as conn:
+        result = record_turn_memory_for_present(
+            conn,
+            chat_id="chat_ab",
+            host_bot_id="bot_a",
+            guest_bot_id="bot_b",
+            narrative_text="Both bots witness this beat.",
+            app=fake_app,
+        )
+
+    # One job per witness — host first, then guest (matches result dict
+    # insertion order in record_turn_memory_for_present).
+    assert len(captured) == 2
+    expected_ids = {result["bot_a"][1], result["bot_b"][1]}
+    assert {job.memory_id for job in captured} == expected_ids
+    for job in captured:
+        assert job.text == "Both bots witness this beat."

tests/test_phase4_integration.py

@@ -0,0 +1,180 @@
+"""Phase 4 cross-feature integration tests (T97 follow-up).
+
+Wave 8 / T101 will populate this file with the full Phase 4 retrieval +
+embedding integration suite. For now this houses a single test pinning
+the T97.5 wiring: the production turn route plumbs ``app=request.app``
+all the way through ``record_turn_memory_for_present`` so the embedding
+worker actually receives jobs in production. Without this fix-up the
+plumbing added in T97 was dormant — every per-witness write took the
+no-app branch and silently dropped the embed enqueue.
+
+The test monkeypatches ``app.state.embedding_worker.enqueue`` to record
+jobs (rather than draining the worker mid-test) so the assertion is
+deterministic and free of asyncio-timing flakiness inside FastAPI's
+TestClient. The bug we're guarding against is "did the call site pass
+``app`` at all" — the worker's drain path is exercised in
+:mod:`tests.test_embedding_worker`, so duplicating that here would add
+no coverage.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+from fastapi.testclient import TestClient
+
+from chat.app import app
+from chat.db.connection import open_db
+from chat.eventlog.log import append_event
+from chat.eventlog.projector import project
+from chat.llm.mock import MockLLMClient
+
+
+def _zero_state() -> str:
+    return json.dumps(
+        {"affinity_delta": 0, "trust_delta": 0, "knowledge_facts": []}
+    )
+
+
+def _override_llm(canned: list[str]) -> MockLLMClient:
+    from chat.web.kickoff import get_llm_client
+
+    mock = MockLLMClient(canned=list(canned))
+    app.dependency_overrides[get_llm_client] = lambda: mock
+    return mock
+
+
+@pytest.fixture
+def app_state_setup(tmp_path, monkeypatch):
+    cfg = tmp_path / "config.toml"
+    cfg.write_text('featherless_api_key = "test"\n')
+    monkeypatch.setenv("CHAT_CONFIG_PATH", str(cfg))
+    db = tmp_path / "test.db"
+    monkeypatch.setenv("CHAT_DB_PATH", str(db))
+    with TestClient(app) as c:
+        # The background worker is disabled so the canned-response queue
+        # is consumed only by the request path. The embedding worker
+        # stays "started" but its loop won't observe the captured
+        # enqueues — we replace ``enqueue`` on the worker instance below.
+        app.state.background_worker.enabled = False
+        yield c
+    app.dependency_overrides.clear()
+
+
+def _seed(db_path: Path) -> None:
+    """Mirror of ``tests/test_turn_flow.py::_seed`` — single bot + chat
+    + edge + activities so the prompt assembler has something to render.
+    """
+    with open_db(db_path) as conn:
+        append_event(
+            conn,
+            kind="bot_authored",
+            payload={
+                "id": "bot_a",
+                "name": "BotA",
+                "persona": "thoughtful, observant",
+                "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": "",
+            },
+        )
+        append_event(
+            conn,
+            kind="edge_update",
+            payload={
+                "source_id": "bot_a",
+                "target_id": "you",
+                "chat_id": "chat_bot_a",
+                "knowledge_facts": ["coworker"],
+            },
+        )
+        for entity_id, verb in [("you", "talking"), ("bot_a", "listening")]:
+            append_event(
+                conn,
+                kind="activity_change",
+                payload={
+                    "entity_id": entity_id,
+                    "posture": "sitting",
+                    "action": {
+                        "verb": verb,
+                        "interruptible": True,
+                        "required_attention": "low",
+                        "expected_duration": "ongoing",
+                    },
+                    "attention": "",
+                    "holding": [],
+                    "status": {},
+                },
+            )
+        project(conn)
+
+
+def test_post_turn_embeddings_indexed_via_worker_hook(
+    app_state_setup, tmp_path
+):
+    """POST a turn; the route must pass ``app=request.app`` into
+    ``record_turn_memory_for_present`` so the per-witness write enqueues
+    an :class:`EmbeddingJob` on ``app.state.embedding_worker``.
+
+    Without the T97.5 wiring this test fails: the call site previously
+    omitted ``app=`` and the helper's ``app is None`` branch silently
+    skipped every enqueue. We monkeypatch ``enqueue`` on the live
+    embedding worker (rather than draining the queue mid-request) so the
+    assertion does not depend on asyncio scheduling inside the
+    TestClient — the bug is in the wiring, and the wiring is what we
+    pin. The drain path is covered separately in
+    :mod:`tests.test_embedding_worker`.
+    """
+    _seed(tmp_path / "test.db")
+
+    canned_parse = json.dumps(
+        {"segments": [{"kind": "dialogue", "text": "hello"}]}
+    )
+    _override_llm(
+        [canned_parse, "Hi there.", _zero_state(), _zero_state()]
+    )
+
+    captured: list = []
+    worker = app.state.embedding_worker
+    original_enqueue = worker.enqueue
+    worker.enqueue = captured.append  # type: ignore[assignment]
+    try:
+        response = app_state_setup.post(
+            "/chats/chat_bot_a/turns", data={"prose": "hello"}
+        )
+        assert response.status_code == 204
+    finally:
+        worker.enqueue = original_enqueue  # type: ignore[assignment]
+        app.dependency_overrides.clear()
+
+    # Single-bot turn -> one ``memory_written`` -> one EmbeddingJob.
+    # The job's ``memory_id`` should match the freshly-projected memory
+    # row, and its ``text`` should carry the assistant's narrative text.
+    assert len(captured) == 1
+    job = captured[0]
+    assert job.text == "Hi there."
+
+    with open_db(tmp_path / "test.db") as conn:
+        memory_ids = [
+            r[0]
+            for r in conn.execute(
+                "SELECT id FROM memories WHERE owner_id = ?",
+                ("bot_a",),
+            ).fetchall()
+        ]
+    assert job.memory_id in memory_ids