Write up the embedding-cost fix and the story-tree design

Two plan documents from designing the branching story tree. The tree work
turned up that memory retrieval fetches the whole bank's embeddings every
turn -- 96% of a turn's database traffic -- and that is the one read a tree
cannot window, so it lands first.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CYEJKobJ2Re4Dv7qUoSA7

plan/13-memory-embedding-cost.md

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+# 13 — Memory-bank embedding cost (round three of the egress work)
+
+**Goal:** stop every turn fetching the entire memory bank's embeddings. Measured at
+**3,024 KB per turn** on adventure 25 against **129 KB** for everything else a turn
+reads — the memory bank is ~96% of a turn's database traffic, and it is fetched fresh
+every single turn to pick `memory_top_k = 5` memories.
+
+Found 2026-08-16 while designing the story tree (see `14-phase-story-tree.md`), because
+memory retrieval is the one read a tree **cannot** window — it is long-range recall by
+design, so it always spans the full path. That makes this the cost floor of a turn under
+the tree, which is why it lands first.
+
+## The measurement (production, Neon SQL editor)
+
+```sql
+SELECT count(*), avg(json_array_length(embedding))::int,
+       avg(length(embedding::text))::int,
+       pg_size_pretty(sum(length(embedding::text))::bigint)
+FROM memories WHERE embedding IS NOT NULL;
+--  134 memories | 1536 dims | 30,971 bytes each | 4,053 kB total
+```
+
+| adventure | active | fetched | per turn |
+|---|---|---|---|
+| 25 | 100 | 100 | **3,024 kB** |
+| 21 | 18 | 18 | 545 kB |
+| 12 | 12 | 12 | 363 kB |
+| 20 | 4 | 4 | 121 kB |
+
+Break-even against the rest of a turn is **4.2 memories**, i.e. about action 25. Every
+adventure past that is dominated by this.
+
+`active == fetched` everywhere: nothing has been evicted yet, so the Python-side
+`forgotten` filter currently costs nothing. It becomes a real leak the moment eviction
+starts.
+
+## Why round two missed it
+
+`retrieve_memories` needs `settings.embedding_model`, and embedding providers are
+**BYOK-only by construction** — they never touch the demo key. So the public demo never
+embeds anything, and the round-two stress harness (which had no embedding model
+configured) measured the turn loop with its heaviest read switched off. The 23.0 MB
+figure for a 200-action playthrough is the memory-bank-**off** number; with it on,
+adventure 25 is closer to 300 MB.
+
+**Rule going forward: any egress measurement must run with an embedding model set.**
+
+## Root cause
+
+`memorybank.py:208`
+
+```python
+candidates = [m for m in adventure.memories if not m.forgotten and m.embedding]
+```
+
+Walks the relationship, so every memory row for the adventure loads with its embedding.
+`embedding` is `Mapped[list]` on a `JSON` column — 1536 floats serialised as text is
+~31 KB. Cosine ranking happens in Python (a deliberate choice, documented at
+`models.py:144`), so all of it must cross the wire. The comment sized it by **count**
+("fine at a few hundred") rather than by **bytes**.
+
+Not the same bug as migration 36/37 — the column is not a repeating group and there is
+no denormalisation to fix. It is a *format* problem plus a *fetch-frequency* problem.
+
+## Decisions (settled 2026-08-16)
+
+- **Packed float32, keep 1536 dimensions.** 31 KB → 6 KB, a straight 5x, with **zero
+  retrieval-quality risk**. Explicitly rejected dropping to 512/768 dims: the size fix
+  plus the cache makes the extra 3x unnecessary, and it would have meant re-embedding.
+- **No re-embedding.** Dimensions are unchanged, so the migration is a pure format
+  conversion of the 134 existing rows — read the JSON, write packed bytes, no API calls.
+  One-time 4 MB read.
+- **In-process cache alongside the size fix**, not sequenced after it. Turns for one
+  adventure arrive back-to-back, so a dict keyed by adventure id takes steady-state cost
+  to ~0. 100 vectors as float32 is 600 KB of RAM — negligible.
+- **`memory_bank_capacity` 200 → ~80.** Taken on *quality* grounds as much as cost:
+  ranking 200 memories to pick 5 dilutes retrieval. Note this will start evicting on
+  adventure 25 immediately (it sits at 100).
+- **Not pgvector.** It is the structural answer and would keep vectors in the database
+  entirely, but it breaks SQLite dev parity — which the codebase protects deliberately
+  (`context/history.py:42`, the `replace()`/`trim()` dialect dance). Revisit only if the
+  bank grows past what Python cosine can handle.
+
+## Work, in order
+
+1. **Rebuild the byte-meter harness — in the repo this time.** `backend/tools/dbmeter.py`
+   + `stress_session.py`. The originals lived outside the repo and are gone. **Default it
+   to running with an embedding model configured**, since that omission is precisely what
+   hid this finding. Do this first so every item below is measured, not assumed.
+2. **Migration 38 — `memories.embedding_blob` (`LargeBinary`).** Backfill in Python
+   (`struct.pack(f"<{n}f", *vec)`); the conversion cannot be expressed in portable SQL, so
+   unlike migration 36/37 this one does pay a one-time 4 MB read. Drop the old JSON column
+   in a follow-up migration once verified, not in the same one.
+3. **Read path.** `retrieve_memories` queries `memories` directly with
+   `forgotten = false AND embedding_blob IS NOT NULL` in **SQL**, not Python. Unpack with
+   `struct`/`numpy`.
+4. **Vector cache.** Keyed by `adventure_id`, invalidated on memory create, evict and
+   delete. Must survive the retry/undo paths that prune memories.
+5. **Capacity default 200 → 80.** Existing adventures inherit it, so adventure 25 evicts
+   on its next turn — check that the eviction path is sane at scale before shipping.
+6. **Infinite scroll upward in `Play.jsx`** for the remaining 423 KB page load of a
+   finished adventure — the last open item from round two. Load the newest turns, fetch
+   older ones as the reader scrolls up.
+
+## Guardrails to add with this work
+
+- **Query-count / byte assertions per endpoint**, extending the `test_egress.py` idea:
+  assert an endpoint issues at most N queries and fetches under X KB against
+  production-sized fixtures. This class of bug is invisible at ten rows.
+- **Explicit column projections on read paths.** List endpoints name the fields they
+  need rather than loading whole entities, so the next heavy column is opt-**in**. This
+  is the structural version of what `deferred=True` does by hand.
+- **Row-width review rule.** Any new large column justifies itself or goes out-of-line.
+  `actions` now carries five JSON columns.
+
+Deliberately **not** taken: moving `context_snapshot` out of the database. It costs
+nothing on reads now that it is deferred, and storage is ~$0.02/mo. Revisit only if
+backups or storage start to hurt.
+
+## Verification
+
+- Harness: one turn on adventure 25, memory bank **on**, before and after. Target is
+  3,024 kB → ~600 kB cold, ~0 warm.
+- Re-run the round-two shapes with an embedding model configured, so the 200-action
+  playthrough number is finally honest.
+- The existing `test_egress.py` guard must still pass — nothing here should touch the
+  deferred action columns.