Wave 7 chunk B: remove the embeddings machinery — the ladder collapses

The owner's wave-7 ruling: embeddings die entirely (git history is the stash); clustering is birth-session derived (chunk A, 950ddfd), labels are session names, and the prober's graph tools replace injection. - settings: GraphSettings collapses to { enabled } — embedding, embeddingModel, clustering, labeling all die. Migration story: stale record keys are UNREAD and dropped by the sanitizer's reconstruction; the next graph-section PUT (replace-whole) persists the collapsed shape. The settings-panel GraphSection renders one master row. - deleted: src/server/embeddings.ts (adapters, resolveEmbeddingPortFor, embedNewNodes, embeddingPort, embeddingModelCatalog), the GET /api/models/embeddings route, scripts/embed-graph.ts, tests for all three, and the /api/models/embeddings picker surfaces in the UI. - prices: EMBEDDING_PRICES overlay + callCost fallback die. cost-summary: the embedding stage fold dies — back to three stages (extraction/ audit/naming) per the owner ruling; historical stages.embedding spend in old run records is simply not displayed. - ports: EmbeddingPort/EmbeddingResult/EmbeddingTask die. The embeddings LEDGER surface (readEmbeddings/writeEmbeddings + EmbeddingsLedger + EMBEDDINGS_FILE + graph-deletion's vector pruning) STAYS — inert data, kept only so graph-context.ts compiles until Teacher's pass removes it; the seam dies in the follow-up interlude. - chat-handler (approved cross-lane unwiring, wave-6 precedent): the extraction pipeline's embedder becomes a documented no-op (nothing embedded, nothing spent, ledger untouched) until Teacher's pass removes the phase; the graph-context injection dispatch gate, the graphContext deps wiring, the setContextRespawn seam, and turn-service's triggerGraphContext are gone — injection is dead code pending Teacher's file deletions. - scripts: backfill-graph/import-notes pass the same no-op embedder; replay-layout derives clusters from birthSessionPartition (the same partition the real settle uses) instead of the embeddings ledger. - settings-body: parseGraphSettingsBody reads the master switch only — old-shape keys in a body are ignored, not rejected; bad shapes 400. - next.config: the @huggingface tracing-exclusion guards die with the last reference. Cross-lane flags: extraction.ts/graph-context.ts keep stale embed comments + the embed phase until Teacher's full pass (their parallel interlude owns those files — zero overlap kept by design); semantic-clusters.ts + its test stay one more round (extraction.test.ts, Teacher's file, still imports it — they delete it with the namer cases); ChatSettings.graphContextTokens + the graph-context-control UI stay until Teacher+UI. Gates: tsc clean, eslint clean, 885/885 tests, build compiles. Live-verified on the port-3004 dev tree: PUT /api/settings/graph with dead keys collapses on write, bad shapes 400, GET reads {enabled: true}, /api/models/embeddings 404s.
2026-09-23 15:02:55 +03:00
parent 0fcffdffaf
commit c86430e8bf
26 changed files with 120 additions and 1823 deletions
@@ -1,17 +0,0 @@
 /**
 * GET /api/models/embeddings — the BYOK embedding-model picker's data
 * (wave 6, owner ruling 2026-09-23): every KEYED provider's embedding
 * catalog, federated LIVE (unlike /api/models — pi-ai's static catalog
 * carries no embedding models). Rows are provider-qualified slugs, the
 * exact shape graph.embeddingModel stores. Empty array when no provider
 * is keyed (or every catalog fetch failed — per-provider failures are
 * logged and skipped, never 500s).
 */
 import { embeddingModelCatalog } from "@/src/server";
 export const dynamic = "force-dynamic";
 export async function GET() {
 	const models = await embeddingModelCatalog();
 	return Response.json(models);
 }
@@ -279,7 +279,6 @@ function GraphCostPanel({ costs }: { costs: GraphCostSummary }) {
 		["Extraction", costs.stages.extraction],
 		["Audit", costs.stages.audit],
 		["Cluster naming", costs.stages.naming],
 		["Embeddings", costs.stages.embedding],
 	];
 	return (
 		<div className="w-80 max-w-[calc(100vw-1.5rem)] rounded-xl border bg-card/95 p-3 text-xs shadow-lg backdrop-blur">
@@ -16,39 +16,10 @@
 * persist but resolve inert — the switch shows stored, the dim effective).
 */
-import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import type { AppearanceSettings, GraphSettings } from "@/src/core";
 import { MenuShell } from "@/components/menu-shell";
 import { useSettingsSaved } from "@/components/settings-signal";
 import { EMBEDDING_PRICES, type AppearanceSettings, type GraphSettings } from "@/src/core";
 import { useAppearance } from "./appearance.tsx";
 import { useSettingsDialog } from "./settings-dialog.tsx";
 import { CommitInput, SettingsRow, ThinSelect, Toggle } from "./controls.tsx";
 /** One row of GET /api/models/embeddings (the live-federated BYOK
 *  catalog). The server twin is src/server/embeddings.ts's
 *  EmbeddingCatalogModel — components may not import the server layer,
 *  and the shape is exactly these two strings. */
 interface EmbeddingCatalogModel {
 	id: string;
 	name: string;
 }
 /** Pure: the price line for a catalog row — USD per 1M INPUT tokens
 *  (embeddings emit no output). Unlisted models stay honestly "—"
 *  (never zero). The label convention is the model menu's. */
 export function embeddingPriceLabel(id: string): string {
 	const price = EMBEDDING_PRICES[id];
 	if (!price) return "—";
 	const fmt = (n: number) => `$${n < 1 ? n.toFixed(2).replace(/0$/, "") : n.toFixed(2)}`;
 	return `${fmt(price.input)}/1M in`;
 }
 /** Pure: which ladder rungs need a configured embedding model to do
 *  anything — everything but the master switch. */
 export function needsEmbeddingModel(key: "enabled" | "embedding" | "clustering" | "labeling"): boolean {
 	return key !== "enabled";
 }
 export function AppearanceSection({
 	appearance,
 	onSectionChange,
@@ -167,17 +138,6 @@ export function AppearanceSection({
 	);
 }
 const LADDER: readonly {
 	key: "enabled" | "embedding" | "clustering" | "labeling";
 	label: string;
 	needs: "enabled" | "embedding" | "clustering" | "labeling" | null;
 }[] = [
 	{ key: "enabled", label: "Graph", needs: null },
 	{ key: "embedding", label: "Embedding", needs: "enabled" },
 	{ key: "clustering", label: "Clustering", needs: "embedding" },
 	{ key: "labeling", label: "Labeling", needs: "clustering" },
 ];
 export function GraphSection({
 	graph,
 	onSectionChange,
@@ -185,280 +145,19 @@ export function GraphSection({
 	graph: GraphSettings | undefined;
 	onSectionChange: (next: GraphSettings) => void;
 }) {
-	const { open: openSettingsSection } = useSettingsDialog();
+	// Wave 7 collapse (owner ruling 2026-09-23): the section is the MASTER
-	// The BYOK embedding catalog — null until the first fetch lands.
+	// SWITCH ONLY. The embedding/clustering/labeling rungs and the BYOK
-	const [catalog, setCatalog] = useState<EmbeddingCatalogModel[] | null>(null);
+	// embedding-model picker are gone — clustering is the free birth-session
-	const [menuOpen, setMenuOpen] = useState(false);
+	// partition derived on read, labels ARE session names, and the prober's
-	const anchorRef = useRef<HTMLButtonElement>(null);
+	// graph tools read the ledgers directly. There is nothing left to
-	// The re-embed gate: a pick that differs from the stored model, held
+	// configure but on/off.
 	// until the user answers the confirm strip (or the graph turns out to
 	// be empty — nothing to re-embed, the write lands directly).
 	const [pending, setPending] = useState<EmbeddingCatalogModel | null>(null);
 	// null while the count fetch is in flight; -1 when it failed (the
 	// confirm line then omits the number rather than guessing one).
 	const [pendingCount, setPendingCount] = useState<number | null>(null);
 	// Stored values (the switches show these) vs EFFECTIVE values (a
 	// stored-on rung under a broken chain is inert — the schema's ladder).
 	const stored = {
 		enabled: graph?.enabled ?? true,
 		embedding: graph?.embedding ?? true,
 		clustering: graph?.clustering ?? true,
 		labeling: graph?.labeling ?? true,
 	};
 	const effective = {
 		enabled: stored.enabled,
 		embedding: stored.enabled && stored.embedding,
 		clustering: stored.enabled && stored.embedding && stored.clustering,
 		labeling: stored.enabled && stored.embedding && stored.clustering && stored.labeling,
 	};
 	// No embedding model = the whole ladder under the master switch is
 	// inert (the embedding port refuses without one) — those rows gray
 	// out with a hint pointing at the picker below (owner, wave 6).
 	const modelUnset = !graph?.embeddingModel;
 	const commitPickRef = useRef(onSectionChange);
 	commitPickRef.current = onSectionChange;
 	const commitRef = useRef<(id: string | null) => void>(null);
 	commitRef.current = (id) => commitPickRef.current({ ...graph, embeddingModel: id });
 	// Catalog: fetch on mount and re-read on any settings save — keying a
 	// provider changes what this list can offer.
 	const refreshCatalog = useCallback(async () => {
 		try {
 			const resp = await fetch("/api/models/embeddings");
 			if (!resp.ok) return;
 			const list = (await resp.json()) as EmbeddingCatalogModel[];
 			if (Array.isArray(list)) setCatalog(list);
 		} catch {
 			// leave the last known list; the empty-add path stays clickable
 		}
 	}, []);
 	useEffect(() => {
 		void refreshCatalog();
 	}, [refreshCatalog]);
 	useSettingsSaved(() => {
 		void refreshCatalog();
 	});
 	// Dismissal: the agent-section contract — outside pointerdown closes
 	// unless it lands inside the menu or on the anchor; Escape is CLAIMED
 	// so the dialog behind stays put.
 	useEffect(() => {
 		if (!menuOpen) return;
 		const onPointer = (e: PointerEvent) => {
 			const t = e.target as HTMLElement;
 			if (t.closest("[data-lh-menu]") || anchorRef.current?.contains(t)) return;
 			setMenuOpen(false);
 		};
 		const onKey = (e: KeyboardEvent) => {
 			if (e.key === "Escape") {
 				e.stopPropagation();
 				setMenuOpen(false);
 			}
 		};
 		document.addEventListener("pointerdown", onPointer);
 		document.addEventListener("keydown", onKey, true);
 		return () => {
 		document.removeEventListener("pointerdown", onPointer);
 		document.removeEventListener("keydown", onKey, true);
 		};
 	}, [menuOpen]);
 	// A held pick: count the graph's propositions. Zero = nothing to
 	// re-embed, so the write lands without a confirm (the strip would be
 	// noise). Otherwise the strip shows the honest number.
 	useEffect(() => {
 		if (!pending) return;
 		let cancelled = false;
 		(async () => {
 			let n = -1;
 			try {
 				const resp = await fetch("/api/graph");
 				const snap = (await resp.json()) as { nodes?: unknown };
 				if (Array.isArray(snap.nodes)) n = snap.nodes.length;
 			} catch {
 				// count unknown — the strip reads "all propositions"
 			}
 			if (cancelled) return;
 			if (n === 0) {
 				setPending(null);
 				setPendingCount(null);
 				commitRef.current?.(pending.id);
 			} else {
 				setPendingCount(n);
 			}
 		})();
 		return () => {
 			cancelled = true;
 		};
 	}, [pending]);
 	const pick = (m: EmbeddingCatalogModel | null) => {
 		setMenuOpen(false);
 		if (m === null) {
 			// Unset never re-embeds — the port just stops resolving.
 			if (graph?.embeddingModel != null) commitRef.current?.(null);
 			return;
 		}
 		if (m.id === graph?.embeddingModel) return;
 		setPending(m);
 		setPendingCount(null);
 	};
 	// The trigger's label: the catalog row's name when known, else the raw
 	// slug (a key may have been removed after the model was set).
 	const currentName = useMemo(() => {
 		const id = graph?.embeddingModel ?? null;
 		if (!id) return null;
 		return catalog?.find((m) => m.id === id)?.name ?? id;
 	}, [graph?.embeddingModel, catalog]);
 	return (
-		<>
+		<SettingsRow label="Graph" testid="graph-row-enabled">
-			{LADDER.map(({ key, label, needs }) => {
+			<Toggle
-				// Touchable only when every ancestor is EFFECTIVELY on, and
+				checked={graph?.enabled ?? true}
-				// (below the master switch) only when a model is configured.
+				label="graph enabled"
-				const parentOn = needs === null ? true : effective[needs];
+				onChange={(on) => onSectionChange({ enabled: on })}
-				const inert = !parentOn || (needsEmbeddingModel(key) && modelUnset);
+			/>
-				return (
+		</SettingsRow>
 					<SettingsRow
 						key={key}
 						label={label}
 						testid={`graph-row-${key}`}
 						dimmed={inert}
 						hint={parentOn && needsEmbeddingModel(key) && modelUnset ? "Needs an embedding model" : undefined}
 					>
 						<Toggle
 							checked={stored[key]}
 							disabled={inert}
 							label={`graph ${key}`}
 							onChange={(on) => onSectionChange({ ...graph, [key]: on })}
 						/>
 					</SettingsRow>
 				);
 			})}
 			{/* The embedding-model picker — the setting every rung above
 			    depends on. Empty catalog means no provider key is set: the
 			    trigger BECOMES the deep link to the Keys section. */}
 			<SettingsRow label="Embedding model" testid="graph-row-embedding-model">
 				{catalog === null ? (
 					<span className="text-xs text-muted-foreground">Loading…</span>
 				) : catalog.length === 0 ? (
 					<button
 						type="button"
 						data-testid="embedding-model-empty"
 						onClick={() => openSettingsSection("keys")}
 						className="rounded-lg border border-border/60 px-2 py-1 text-xs text-muted-foreground transition-colors hover:bg-muted"
 					>
 						Add a provider key first…
 					</button>
 				) : (
 					<button
 						ref={anchorRef}
 						type="button"
 						data-testid="embedding-model-trigger"
 						onClick={() => setMenuOpen((o) => !o)}
 						className="max-w-56 truncate rounded-lg border border-border/60 px-2 py-1 text-xs text-card-foreground transition-colors hover:bg-muted"
 						title={graph?.embeddingModel ?? undefined}
 					>
 						{currentName ?? "—"}
 					</button>
 				)}
 			</SettingsRow>
 			{/* The re-embed confirm — switching models rewrites every vector
 			    in the graph (the staleness machinery, next background pass).
 			    Honesty over comfort: the count is the user's decision input. */}
 			{pending && (
 				<div
 					data-testid="embedding-reembed-confirm"
 					className="rounded-xl border border-border/60 bg-background p-3 text-xs"
 				>
 					<p className="text-card-foreground">Switch to {pending.name}?</p>
 					<p className="mt-1 text-muted-foreground">
 						{pendingCount === null
 							? "Counting the graph’s propositions…"
 							: pendingCount > 0
 							? `All ${pendingCount.toLocaleString()} propositions in the graph will be re-embedded with the new model (runs with the next background pass).`
 							: "All propositions in the graph will be re-embedded with the new model (runs with the next background pass)."}
 					</p>
 					{pendingCount !== null && (
 						<div className="mt-2 flex gap-2">
 							<button
 								type="button"
 								data-testid="embedding-reembed-yes"
 								onClick={() => {
 									commitRef.current?.(pending.id);
 								setPending(null);
 								setPendingCount(null);
 							}}
 								className="rounded-lg border border-border/60 px-2.5 py-1 text-card-foreground transition-colors hover:bg-muted"
 							>
 								Switch model
 							</button>
 							<button
 								type="button"
 								data-testid="embedding-reembed-no"
 								onClick={() => {
 									setPending(null);
 								setPendingCount(null);
 							}}
 								className="rounded-lg border border-border/60 px-2.5 py-1 text-muted-foreground transition-colors hover:bg-muted"
 							>
 								Cancel
 							</button>
 						</div>
 					)}
 				</div>
 			)}
 			{menuOpen && catalog !== null && catalog.length > 0 && (
 				<MenuShell
 					anchorRef={anchorRef}
 					testid="embedding-model-menu"
 					preferredSide="down"
 					align="right"
 					menuClass="flex w-[20rem] flex-col gap-0.5 rounded-xl border bg-card p-2 text-xs shadow-xl"
 				>
 					<button
 						type="button"
 						data-testid="embedding-option-unset"
 						onClick={() => pick(null)}
 						className={`flex items-center justify-between gap-2 rounded-lg px-2 py-1.5 text-left outline-none transition-colors hover:bg-muted ${
 							modelUnset ? "bg-muted/60" : ""
 						}`}
 					>
 						<span className="text-card-foreground">Unset</span>
 						<span className="text-muted-foreground">stop embedding</span>
 					</button>
 					{catalog.map((m) => (
 						<button
 							key={m.id}
 							type="button"
 							data-testid={`embedding-option-${m.id.replace(/[^a-z0-9-]/gi, "_")}`}
 							onClick={() => pick(m)}
 							className={`flex flex-col gap-0.5 rounded-lg px-2 py-1.5 text-left outline-none transition-colors hover:bg-muted ${
 								m.id === graph?.embeddingModel ? "bg-muted/60" : ""
 							}`}
 					>
 						<span className="flex items-center justify-between gap-2">
 							<span className="truncate text-card-foreground">{m.name}</span>
 							<span
 								className="shrink-0 tabular-nums text-muted-foreground"
 								title="USD per 1M input tokens"
 							>
 								{embeddingPriceLabel(m.id)}
 							</span>
 						</span>
 						{/* The full slug — ids are colon-safe (openrouter:openai/…),
 					    never split, shown as stored. */}
 						<span className="truncate text-muted-foreground">{m.id}</span>
 					</button>
 					))}
 				</MenuShell>
 			)}
 		</>
 	);
 }
@@ -150,10 +150,11 @@ export function SettingsDialogBody({
 		};
 	}, []);
 	const [active, setActive] = useState<SectionId>(initialSection);
-	// A deep-link fired while the dialog is ALREADY open (wave 6: the
+	// A deep-link fired while the dialog is ALREADY open (first exercised by
-	// embedding picker's "Add a provider key first…" lands on Keys from
+	// the wave-6 embedding picker's "Add a provider key first…" link — the
-	// inside the dialog) changes initialSection under our feet — follow it,
+	// picker itself died with wave 7, the mechanism is general) changes
-	// or the link silently does nothing.
+	// initialSection under our feet — follow it, or the link silently does
 	// nothing.
 	useEffect(() => {
 		setActive(initialSection);
 	}, [initialSection]);
@@ -14,10 +14,6 @@ const nextConfig: NextConfig = {
 	outputFileTracingExcludes: {
 		"*": [
 			"data/**/*",
 			// Wave 6 (2026-09-23): the local transformers embeddings died — any
 			// @huggingface copy (or its ~300MB model cache, if one ever
 			// reappears) must never sweep into the standalone bundle again.
 			"node_modules/@huggingface/**/*",
 		],
 	},
 	// Empirically verified gaps (2026-09-17 standalone smoke): the tracer
@@ -8,14 +8,13 @@
 import { chatStore } from "../src/store/chats.ts";
 import { graphStore } from "../src/store/graph.ts";
 import { createExtractionPipeline } from "../src/agent/subagents/extraction.ts";
 import { embedNewNodes } from "../src/server/embeddings.ts";
 import { getGraph } from "../src/core/graph.ts";
 async function main() {
 	const only = process.argv[2]; // optional: extract just one chat id
 	const chats = only ? chatStore.listChats().filter((c) => c.id === only) : chatStore.listChats();
 	console.log(`[backfill] ${chats.length} chats to extract`);
-	const pipeline = createExtractionPipeline({ chatStore, graphStore, embedder: embedNewNodes });
+	const pipeline = createExtractionPipeline({ chatStore, graphStore, embedder: async () => ({ embedded: 0, total: 0, skipped: "embeddings removed (wave 7)" }) });
 	for (const chat of chats) {
 		const t0 = Date.now();
 		try {
@@ -1,47 +0,0 @@
 /**
 * Embed the whole graph (backfill / cold start / model change): one-time
 * pass that fills the embedding ledger for every node — the same
 * embedNewNodes the post-merge hook runs, just with an empty (or
 * stale-model) ledger so every node is "new".
 *
 * BYOK (wave 6, 2026-09-23): vectors come from the configured provider
 * (graph.embeddingModel, keys from the auth store) — this script resolves
 * the live state up front so an unconfigured machine says so before
 * anything bills. A model CHANGE re-embeds everything (ledger staleness).
 *
 *   npx tsx scripts/embed-graph.ts
 */
 import { embedNewNodes, embeddingPort } from "../src/server/embeddings.ts";
 import { graphStore } from "../src/store/graph.ts";
 async function main() {
 	const resolved = embeddingPort();
 	if (!resolved.ok) {
 		console.error(`[embed] cannot run — ${resolved.reason}`);
 		process.exit(1);
 	}
 	const t0 = Date.now();
 	console.log(`[embed] model: ${resolved.port.model} (provider embeddings, batch 32)`);
 	const { embedded, total, usage, failed } = await embedNewNodes(graphStore);
 	const secs = ((Date.now() - t0) / 1000).toFixed(1);
 	if (usage) {
 		const tokens = usage.steps.reduce((sum, s) => sum + (s.inputTokens ?? 0), 0);
 		console.log(`[embed] embedding spend: ${tokens} prompt tokens over ${usage.steps.length} call(s)`);
 	}
 	// Usage-on-error + pay-once (owner ruling 2026-09-23): a mid-run provider
 	// failure still reports the landed batches' spend above; the landed
 	// vectors are persisted, so the honest count is what LANDED — the
 	// in-memory `total` would read as a ledger claim it can't make.
 	if (failed) {
 		console.error(
 			`[embed] failed after ${embedded} node(s) landed (${secs}s) — the ledger keeps exactly those (a retry embeds only the rest): ${failed}`,
 		);
 		process.exit(1);
 	}
 	console.log(`[embed] embedded ${embedded} nodes → ledger holds ${total} vectors (${secs}s)`);
 }
 main().catch((err) => {
 	console.error("[embed] failed:", err);
 	process.exit(1);
 });
@@ -15,7 +15,6 @@
 import * as fs from "node:fs";
 import * as path from "node:path";
 import { createExtractionPipeline } from "../src/agent/subagents/extraction.ts";
 import { embedNewNodes } from "../src/server/embeddings.ts";
 import { clusterNamingSettled } from "../src/agent/subagents/cluster-namer.ts";
 import { getGraph } from "../src/core/graph.ts";
 import { stores } from "../src/server/stores.ts";
@@ -29,7 +28,7 @@ async function main() {
 		.sort();
 	console.log(`[import-notes] ${files.length} note files from ${dir} (concurrency ${limit})`);
-	const pipeline = createExtractionPipeline({ ...stores, embedder: embedNewNodes });
+	const pipeline = createExtractionPipeline({ ...stores, embedder: async () => ({ embedded: 0, total: 0, skipped: "embeddings removed (wave 7)" }) });
 	const queue = [...files];
 	const totals = { nodes: 0, edges: 0, events: 0, failed: 0 };
 	const workers = Array.from({ length: Math.min(limit, files.length) }, async () => {
@@ -18,6 +18,7 @@ const depthArg = process.argv.find((a) => a.startsWith("--depth="));
 const depthMode: "off" | "vertical" | "radial" =
 	depthArg === "--depth=vertical" ? "vertical" : depthArg === "--depth=radial" ? "radial" : "off";
 import { incrementalLayout } from "../src/core/layout.ts";
 import { birthSessionPartition } from "../src/core/session-clusters.ts";
 import type { GraphNode, NodeMapping, Position } from "../src/core/contracts.ts";
 function main() {
@@ -32,12 +33,9 @@ function main() {
 		nodes.push(line);
 	}
 	const edges = graphStore.readEdges();
-	// The semantic-topic source: the embedding ledger (run
+	// The cluster source (wave 7): the birth-session partition, derived on
-	// scripts/embed-graph.ts first if missing/stale).
+	// read — same as the real settle (core/layout-settle.ts).
-	const embeddings = graphStore.readEmbeddings();
+	const birthClusters = birthSessionPartition(nodes);
 	if (Object.keys(embeddings.vectors).length === 0) {
 		console.warn("[replay] embedding ledger is empty — clusters will be absent (run scripts/embed-graph.ts)");
 	}
 	// Batches: each imported file, in real import order (sources.json
 	// importedAt). A node belongs to the file whose mapping first claims it
@@ -85,9 +83,8 @@ function main() {
 		const batchNodes = nodes.filter((n) => delivered.has(n.id));
 		positions = incrementalLayout(batchNodes, activeEdges, positions, {
 			// The same partition the real settle would see for this batch:
-			// the persisted semantic topics, filtered to delivered nodes
+			// the birth-session clusters, filtered to delivered nodes.
-			// (never recomputed — the agglomerative pass is ~1.5s at scale).
+			clusters: birthClusters
 			clusters: embeddings.clusters
 				.map((c) => ({ members: c.members.filter((id) => delivered.has(id)) }))
 				.filter((c) => c.members.length > 0),
 			...(depthMode === "off"
@@ -55,12 +55,13 @@ export interface GraphCostSummary {
 	totalDollars: number;
 	/** Runs where EVERY stage was unpriced (totalDollars excludes them). */
 	unpricedRuns: number;
-	/** The billable pipeline stages (wave 6: the embedder became one —
+	/** The billable pipeline stages. (Wave 7, owner ruling 2026-09-23:
-	 *  provider embeddings cost money; merger is the only free stage). */
+	 *  back to three — the embedding stage is gone with the provider
 	 *  embedder; historical records' stages.embedding spend is simply not
 	 *  displayed.) */
 	stages: {
 		extraction: StageCost;
 		audit: StageCost;
 		embedding: StageCost;
 		naming: StageCost;
 	};
 	/** Newest first, capped at `recentCap` (default 10). */
@@ -115,7 +116,6 @@ function mergeTokens(into: CallUsage, steps: readonly CallUsage[]): void {
 export function graphCostSummary(input: CostSummaryInput): GraphCostSummary {
 	const extraction = emptyStage();
 	const audit = emptyStage();
 	const embedding = emptyStage();
 	const naming = emptyStage();
 	const runs: RecentRun[] = [];
@@ -134,25 +134,12 @@ export function graphCostSummary(input: CostSummaryInput): GraphCostSummary {
 		if (audDollars === null) audit.unpricedRuns++;
 		else audit.dollars += audDollars;
 		// Wave 6: the embedding tail — present only when an embedding model
 		// ran (absent stage = the run embedded nothing, never "free").
 		let embDollars: number | null = null;
 		let embSteps: CallUsage[] = [];
 		if (stages.embedding) {
 			embedding.runs++;
 			embSteps = stages.embedding.steps;
 			mergeTokens(embedding.tokens, embSteps);
 			embDollars = stageDollars(stages.embedding.model, embSteps);
 			if (embDollars === null) embedding.unpricedRuns++;
 			else embedding.dollars += embDollars;
 		}
 		// Run-level: null only when EVERY stage is unpriced.
-		const allStages: Array<number | null> = [extDollars, audDollars, embDollars];
+		const allStages: Array<number | null> = [extDollars, audDollars];
 		const runDollars = allStages.every((d) => d === null)
 			? null
 			: allStages.reduce<number>((a, d) => a + (d ?? 0), 0);
-		const allSteps = [...stages.extractor.steps, ...stages.auditor.steps, ...embSteps];
+		const allSteps = [...stages.extractor.steps, ...stages.auditor.steps];
 		runs.push({
 			startedAt: run.startedAt,
 			at: run.at,
@@ -195,9 +182,9 @@ export function graphCostSummary(input: CostSummaryInput): GraphCostSummary {
 	// A run is "unpriced" when it billed nothing countable at all.
 	const unpricedRuns = runs.filter((r) => r.dollars === null).length;
 	return {
-		totalDollars: extraction.dollars + audit.dollars + embedding.dollars + naming.dollars,
+		totalDollars: extraction.dollars + audit.dollars + naming.dollars,
 		unpricedRuns,
-		stages: { extraction, audit, embedding, naming },
+		stages: { extraction, audit, naming },
 		runs: capped,
 	};
 }
@@ -128,11 +128,15 @@ export interface GraphStore {
 	/** Replace the whole ledger (the settle is a full-graph computation). */
 	writePositions(positions: Record<string, Position>): void;
-	/** The embedding ledger (config.EMBEDDINGS_FILE). WRITTEN ONLY by
+	/** The embeddings ledger (config.EMBEDDINGS_FILE) — INERT DATA since
-	 *  the embedder (server/embeddings.ts) after a merge — read-only for
+	 *  wave 7 (owner ruling 2026-09-23: the provider embedder is dead;
-	 *  clustering consumers (snapshot, settle, namer). */
+	 *  clustering is birth-session, labels are session names). Kept ONLY
 	 *  because the graph-context injection (agent band, Teacher's pass
 	 *  removes it) still reads it for vector-space staleness — the seam
 	 *  dies with it in the follow-up interlude. graph-deletion keeps
 	 *  pruning dead vectors meanwhile (harmless housekeeping). */
 	readEmbeddings(): EmbeddingsLedger;
-	/** Replace the whole ledger (embedding is a full-batch computation). */
+	/** Replace the whole ledger (graph-deletion's vector pruning only). */
 	writeEmbeddings(ledger: EmbeddingsLedger): void;
 	/** THE DESTRUCTIVE SEAM (session-deletion wave 6, owner 2026-09-22 —
@@ -244,48 +248,3 @@ export interface AuthStore {
 	deleteAuthVar(name: string): void;
 }
 // ─── EmbeddingPort — BYOK provider embeddings (wave 6, 2026-09-23) ───────────
 /**
 * The provider-embedding seam (the local transformers embedder died with
 * this wave): vectors come from the user's keyed provider — OpenRouter,
 * OpenAI, or Google — named by graph.embeddingModel ("provider:id", null =
 * honestly off: embedding/clustering/injection skip with an info log).
 *
 * Owner decisions (2026-09-23) baked in:
 *  - The adapters are resolved FRESH per call from the setting + the auth
 *    store (key rotations land immediately — the search port's treatment).
 *  - Vectors are stored as the provider returns them (default dims, no
 *    truncation, no normalization); the ledger is self-describing on
 *    model + dim, and any model change voids it (full re-embed).
 *  - Usage (prompt tokens) flows back to the money channel: adapters
 *    report it, the caller records it as a CallUsage step.
 *
 * Pure types only (browser-safe): the adapters are server-side
 * (src/server/embeddings.ts) and arrive as injected dependencies.
 */
 /** Which side of retrieval the text is — providers bill identically but
 *  quality-optimize differently (OpenRouter's input_type, Gemini's
 *  taskType); every input in one call shares the task. */
 export type EmbeddingTask = "document" | "query";
 /** One embedding batch's result: vectors in input order, the model's
 *  identity + dim (the ledger stamps), and the provider-reported prompt
 *  token count (undefined = provider didn't report; cost stays honest
 *  "—" via the existing unpriced path). */
 export interface EmbeddingResult {
 	vectors: number[][];
 	/** The full "provider:id" slug — ledger/cost keys use it verbatim. */
 	model: string;
 	dim: number;
 	promptTokens?: number;
 }
 /** A live provider embedding adapter. `embed` throws on failure (adapters
 *  map HTTP errors to clear Error messages); callers treat failure as
 *  non-fatal per the extraction contract. */
 export interface EmbeddingPort {
 	model: string;
 	embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult>;
 }
@@ -2752,30 +2752,6 @@ export const CONTEXT_LIMITS: Record<string, number> = {
 // ─── Pure helpers (folds + cost inference) ───────────────────────────────────
 // ─── Embedding prices (wave 6, 2026-09-23) ─────────────────────────────────
 /** Hand-maintained BYOK embedding prices, USD per 1M INPUT tokens — the
 *  sync script never touches this block (pi-ai's catalogs carry no
 *  embedding models, so the generated PRICES table has none of these).
 *  VERIFIED against provider pricing pages 2026-09-23: OpenAI's model
 *  docs ($0.02 3-small / $0.13 3-large / $0.10 ada-002), Google's pricing
 *  page ($0.15 gemini-embedding-001 / $0.20 Gemini Embedding 2 text),
 *  OpenRouter passes provider pricing through. output: 0 — embeddings
 *  emit no output tokens. Unlisted models stay unpriced ("—", never
 *  zero); tokens still get captured. */
 export const EMBEDDING_PRICES: Record<string, ModelPrice> = {
 	"openai:text-embedding-3-small": { input: 0.02, output: 0 },
 	"openai:text-embedding-3-large": { input: 0.13, output: 0 },
 	"openai:text-embedding-ada-002": { input: 0.1, output: 0 },
 	"google:gemini-embedding-001": { input: 0.15, output: 0 },
 	"google:gemini-embedding-2": { input: 0.2, output: 0 },
 	"openrouter:openai/text-embedding-3-small": { input: 0.02, output: 0 },
 	"openrouter:openai/text-embedding-3-large": { input: 0.13, output: 0 },
 	"openrouter:openai/text-embedding-ada-002": { input: 0.1, output: 0 },
 	"openrouter:google/gemini-embedding-001": { input: 0.15, output: 0 },
 };
 /** Sum a list of per-step usages into one CallUsage. A field stays
 *  undefined when EVERY step left it unreported — absent is not zero. */
 export function sumSteps(steps: readonly CallUsage[]): CallUsage {
@@ -2797,8 +2773,7 @@ export function callCost(
 	usage: { inputTokens?: number; outputTokens?: number; cacheReadTokens?: number; cacheWriteTokens?: number },
 	model: string,
 ): number | null {
-	// Wave 6: the hand-maintained embedding overlay backs the lookup too.
+	const price = PRICES[model];
 	const price = PRICES[model] ?? EMBEDDING_PRICES[model];
 	if (!price) return null;
 	if (usage.inputTokens === undefined || usage.outputTokens === undefined) return null;
 	const cacheRead = usage.cacheReadTokens ?? 0;
@@ -201,13 +201,10 @@ export function resolveSettings(record: UserSettings | null): EffectiveSettings
 			},
 		},
 		graph: {
 			// Wave 7 collapse (owner ruling 2026-09-23): the master switch ONLY —
 			// the embedding/clustering/labeling rungs and the BYOK embeddingModel
 			// are gone (stale record keys are simply never read here).
 			enabled: graph.enabled ?? FACTORY_SETTINGS.graph.enabled,
 			embedding: graph.embedding ?? FACTORY_SETTINGS.graph.embedding,
 			clustering: graph.clustering ?? FACTORY_SETTINGS.graph.clustering,
 			labeling: graph.labeling ?? FACTORY_SETTINGS.graph.labeling,
 			// BYOK (wave 6): null is the factory floor and a VALID resolved
 			// value — ?? only fills the absent case, null passes through.
 			embeddingModel: graph.embeddingModel ?? FACTORY_SETTINGS.graph.embeddingModel,
 		},
 	};
 }
@@ -216,23 +213,13 @@ export function resolveSettings(record: UserSettings | null): EffectiveSettings
 /**
 * The WRITE channel gate (extraction + its auditor): dies with the master
- * switch. Used by the chat-handler's post-turn trigger; deeper pipeline
+ * switch. Used by the chat-handler's post-turn trigger. (Wave 7: the master
- * consumers (embedder, clustering, labeling — later waves) gate on the
+ * switch is the ONLY graph setting left — the section collapsed.)
 * deeper rungs the same way.
 */
 export function extractionEnabled(graph: Required<GraphSettings>): boolean {
 	return graph.enabled;
 }
 /**
 * The READ channel gate (graph-context injection): needs the master switch
 * AND the embedding rung — injection is a vector query, so a graph without
 * embeddings has nothing to inject.
 */
 export function graphContextEnabled(graph: Required<GraphSettings>): boolean {
 	return graph.enabled && graph.embedding;
 }
 /**
 * The PROBER GRAPH TOOLS gate (wave 7, owner ruling 2026-09-23): the
 * toggle is an agent-capability switch (it gates tool registration), but
@@ -246,3 +233,5 @@ export function graphToolsEnabled(effective: {
 }): boolean {
 	return effective.graph.enabled && (effective.agent.graphTools.enabled ?? true);
 }
@@ -249,23 +249,14 @@ export interface AppearanceSettings {
 *  meaningful when its parent is on (the UI grays dependents out; stored
 *  values persist but resolve inert). All default true. */
 export interface GraphSettings {
-	/** MASTER: off = no extraction, no auditor, no embedding, no
+	/** MASTER (and, since wave 7, ONLY): off = no extraction, no auditor.
-	 *  graph-context injection, no clustering. Data stays stored. */
+	 *  The wave-7 collapse (owner ruling 2026-09-23) deleted the three
 	 *  subordinate rungs — embedding/clustering/labeling — and the BYOK
 	 *  embeddingModel: clustering is the free birth-session partition
 	 *  derived on read (core/session-clusters.ts), labels ARE session
 	 *  names, and the prober graph tools read the ledgers directly — there
 	 *  is nothing left to configure. Data stays stored either way. */
 	enabled?: boolean;
 	/** Vector embedding + graph-context injection (consumes vectors even
 	 *  without clustering — hence its own rung). */
 	embedding?: boolean;
 	/** Cluster computation + settle (needs embedding). */
 	clustering?: boolean;
 	/** The LLM cluster-namer (needs clustering). */
 	labeling?: boolean;
 	/** BYOK embeddings (wave 6, 2026-09-23): the provider embedding model
 	 *  as a "provider:id" slug (openrouter:…, openai:…, google:…). null =
 	 *  no model — embedding/clustering/injection skip honestly (info log,
 	 *  graph stays textual); no factory floor exists. Keys live in the auth
 	 *  store, resolved fresh per call. A model CHANGE re-embeds everything
 	 *  (the ledger's staleness semantics). */
 	embeddingModel?: string | null;
 }
 /** THE record. Every section optional (delta); unknown top-level keys are
@@ -318,7 +309,7 @@ export const FACTORY_SETTINGS: FactorySettings = {
 		sessionNameSerif: true,
 		mdRendering: { headingWeight: 900, boldColor: null, italicColor: null },
 	},
-	graph: { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null },
+	graph: { enabled: true },
 };
 // ─── Sanitizer ─────────────────────────────────────────────────────────────
@@ -467,23 +458,15 @@ function sanitizeAppearance(raw: Record<string, unknown>): AppearanceSettings {
 }
 function sanitizeGraph(raw: Record<string, unknown>): GraphSettings {
 	// Wave 7 collapse (owner ruling 2026-09-23): the section is the master
 	// switch ONLY. The dead rungs' keys (embedding/clustering/labeling/
 	// embeddingModel) in existing records are simply UNREAD and DROPPED by
 	// this reconstruction — the migration story: the next graph-section
 	// write (PUT is replace-whole) persists the collapsed shape, and no
 	// active code ever looks at the stale fields.
 	const out: GraphSettings = {};
 	const enabled = boolOrDrop(raw.enabled);
 	if (enabled !== undefined) out.enabled = enabled;
 	const embedding = boolOrDrop(raw.embedding);
 	if (embedding !== undefined) out.embedding = embedding;
 	const clustering = boolOrDrop(raw.clustering);
 	if (clustering !== undefined) out.clustering = clustering;
 	const labeling = boolOrDrop(raw.labeling);
 	if (labeling !== undefined) out.labeling = labeling;
 	// BYOK embeddings (wave 6): null or a provider-qualified slug; anything
 	// else (bad type, bad shape) DROPS to absent — an invalid write never
 	// becomes a live model (the PUT parser 400s it first; this is the
 	// read-side belt-and-suspenders for hand-edited files).
 	if (raw.embeddingModel === null) out.embeddingModel = null;
 	else if (typeof raw.embeddingModel === "string" && /^\S+:\S+$/.test(raw.embeddingModel)) {
 		out.embeddingModel = raw.embeddingModel;
 	}
 	return out;
 }
@@ -4,23 +4,20 @@ import { createProberAgent, createTeacherAgent, type AgentBuildOptions, type Tea
 import { piEngineForSpec, type PiEngine } from "../agent/models.ts";
 import { createJobRunner } from "../agent/subagents/runner.ts";
 import { createExtractionPipeline } from "../agent/subagents/extraction.ts";
 import { runGraphContextJob, type GraphContextDeps } from "../agent/subagents/graph-context.ts";
 import { embedNewNodes, embeddingPort } from "./embeddings.ts";
 import type { ModelRole } from "../../config.ts";
-import { claimSteers, deliverJobResult, setContextRespawn, setJobRespawn, setTurnRunner } from "./jobs.ts";
+import { claimSteers, deliverJobResult, setJobRespawn, setTurnRunner } from "./jobs.ts";
 import { recordToPiMessages } from "./pi/record-adapter.ts";
 import { createPiTurnResponse } from "./pi/ui-stream.ts";
 import { effectiveSettings, stores } from "./stores.ts";
 import { searchAvailable, searchProvider } from "./search.ts";
 import {
 	extractionEnabled,
 	graphContextEnabled,
 	resolveConversationSpec,
 	resolveRoleSpec,
 } from "../core/settings-resolution.ts";
 import { modelsNotConfigured } from "../core/settings.ts";
 import { beginTurn, endTurn } from "./turn-liveness.ts";
-import { persistTurn, triggerExtraction, triggerGraphContext, withMessageIds } from "./turn-service.ts";
+import { persistTurn, triggerExtraction, withMessageIds } from "./turn-service.ts";
 import type { CallUsage } from "../core/contracts.ts";
 /**
@@ -63,9 +60,11 @@ const jobRunner = createJobRunner({
 *  (/api/graph/import) hit the same single-flight registry. */
 export const extraction = createExtractionPipeline({
 	...stores,
-	// The provider-embedder batch (BYOK wave 6): post-merge embedding —
+	// Wave 7 (owner ruling 2026-09-23): embeddings are DEAD — the pipeline's
-	// resolves the model fresh per call; null model = info-skip inside.
+	// embed seam becomes a no-op until Teacher's pass removes the phase
-	embedder: embedNewNodes,
+	// outright. Nothing embedded, nothing spent, the ledger untouched
 	// (its data is inert anyway: nothing reads it but the dying injection).
 	embedder: async () => ({ embedded: 0, total: 0, skipped: "embeddings removed (wave 7)" }),
 	// BYOK (owner, 2026-09-22): the extraction stages resolve the chain —
 	// role override → slot → NOTHING — read FRESH per stage call; an empty
 	// chain flows through as undefined and the pipeline refuses the run
@@ -74,30 +73,6 @@ export const extraction = createExtractionPipeline({
 		resolveEngine(role),
 });
 /** Graph context injection (vision-context-injection.md): the runner's
 *  deps — same storage ports, the provider-embedder RESOLVER as the query
 *  seam (BYOK wave 6: fresh setting + key reads per run, so vector-space
 *  parity with the ledger is checked against the CURRENT model; a key or
 *  settings PUT between dispatch and spawn lands), and the wake pipeline
 *  as the delivery seam. */
 export const graphContext: GraphContextDeps = {
 	chatStore: stores.chatStore,
 	jobStore: stores.jobStore,
 	graphStore: stores.graphStore,
 	embedder: () => {
 		const resolved = embeddingPort();
 		if (!resolved.ok) return null;
 		const port = resolved.port;
 		return {
 			model: port.model,
 			embedQuery: async (text: string) => {
 				const result = await port.embed([text], "query");
 				return { vector: result.vectors[0]!, promptTokens: result.promptTokens };
 			},
 		};
 	},
 	deliver: deliverJobResult,
 };
 // Wake-pipeline wiring (contracts.ts "THE ASYNC PROTOCOL" §4): the pipeline
 // fires its server-side teacher turns through THIS composition root, and
@@ -125,7 +100,6 @@ function resolveEngine(role: ModelRole): PiEngine | undefined {
 }
 setJobRespawn((jobId) => void jobRunner.runJob(jobId));
 setContextRespawn((jobId) => void runGraphContextJob(graphContext, jobId));
 /** The message-shape gate the SDK's UIMessageValidator owned: a malformed
 *  body must fail BEFORE the stream exists — the path where no onFinish will
@@ -219,27 +193,7 @@ export async function handleChatRequest(
 		console.error(`[chat ${chatId}] write-ahead persist failed:`, err);
 	}
-	// THE GRAPH READ CHANNEL (vision-context-injection.md): one shot per
+
 	// chat, at session start — fired HERE, at turn start, not in onFinish:
 	// the injection's only input is the first typed message (the topic),
 	// which the write-ahead persist above just made durable, so it runs
 	// CONCURRENT with the prober's turn (embedding + cosine scan, no model
 	// call) and the dock shows the job the moment the session starts. Its
 	// steer then waits in the wake queue through the turn (waitForIdle) —
 	// exactly the same hold as a sub-agent result finishing mid-turn. All
 	// gates (exactly-one-text-message topic, budget, one-shot) live in the
 	// runner module; for every session past its start this is a no-op.
 	// Extraction stays in onFinish by contrast: it depends on quiz
 	// answers accumulated DURING the turn.
 	// GRAPH GATES (settings-storage wave, 2026-09-19): the read channel
 	// needs BOTH the master switch and the embedding rung (injection is a
 	// vector query); with graph off there is no context job at all.
 	// BYOK (wave 6): an unconfigured embedding model means no vectors to
 	// query — the dispatch skips honestly (the runner's own resolution is
 	// the belt-and-suspenders for respawn races).
 	if (graphContextEnabled(effective.graph) && effective.graph.embeddingModel !== null) {
 		triggerGraphContext(graphContext, chatId);
 	}
 	// MONEY CHANNEL (wave 4): one collector per request, fed by the agent's
 	// message_end events. Every model call's usage lands in the chat record's
@@ -1,493 +0,0 @@
 /**
 * BYOK PROVIDER EMBEDDINGS (wave 6, 2026-09-23): the local transformers
 * embedder is dead — vectors now come from the user's keyed provider
 * (OpenRouter / OpenAI / Google), named by graph.embeddingModel
 * ("provider:id", null = honestly off; skip with an info log, the graph
 * stays textual — owner ruling 2026-09-23).
 *
 * Owner decisions baked in:
 *  - NO factory model (BYOK, like the model slots): null is the floor.
 *  - Adapters resolve FRESH per call from the setting + the auth store
 *    (the search port's treatment — key rotations land immediately); a
 *    key missing for a SET model is an honest error, never a fallback.
 *  - Vectors stored as the provider returns them (default dims, no
 *    truncation, no normalization). The ledger is self-describing on
 *    model + dim; a model change voids it (full re-embed, minutes at
 *    current scale — the local embedder's same staleness rule).
 *  - Usage (prompt tokens) flows to the money channel: every API call
 *    reports one CallUsage step (outputTokens: 0 — embeddings emit no
 *    output; priced via the hand-maintained EMBEDDING_PRICES overlay).
 *
 * The adapters are injected-fetch dependencies so tests never touch the
 * network; wire protocols VERIFIED against provider docs 2026-09-23:
 *  - OpenRouter: POST /api/v1/embeddings (Bearer), input_type
 *    search_document|search_query; catalog GET /api/v1/embeddings/models
 *    (VERIFIED live 2026-09-23 — the inverse path /api/v1/models/
 *    embeddings 404s).
 *  - OpenAI: POST /v1/embeddings (Bearer); catalog GET /v1/models.
 *  - Google: POST /v1beta/models/{m}:batchEmbedContents (x-goog-api-key),
 *    taskType RETRIEVAL_DOCUMENT|RETRIEVAL_QUERY; catalog GET /v1beta/models
 *    filtered on supportedGenerationMethods ∋ embedContent.
 */
 import type { CallUsage, EmbeddingsLedger, GraphNode } from "../core/contracts.ts";
 import type { ExtractionStageUsage } from "../core/contracts/chat.ts";
 import type { EmbeddingPort, EmbeddingResult, EmbeddingTask } from "../core/ports.ts";
 import { semanticClusters, SEMANTIC_SIM_THRESHOLD } from "../core/semantic-clusters.ts";
 import { keyValue } from "./keys.ts";
 import { effectiveSettings } from "./stores.ts";
 /** The wire-callable shape the adapters close over (tests inject a stub). */
 type FetchLike = (input: string, init: RequestInit) => Promise<Response>;
 /** One provider embedding API call — kept small enough to never trip
 * per-request input limits (propositions are tiny; 32 was the local
 * embedder's batch and it stays). */
 const EMBED_BATCH = 32;
 /** Per-request timeout: a hung provider call must not hang an extraction
 * run (the wave-1 hung-job lesson). */
 const REQUEST_TIMEOUT_MS = 60_000;
 /** The providers this wave ships + their auth vars (the curated key
 * catalog, same ids the auth store and /api/keys use). */
 const PROVIDER_KEYS = {
 	openrouter: "OPENROUTER_API_KEY",
 	openai: "OPENAI_API_KEY",
 	google: "GEMINI_API_KEY",
 } as const;
 export type EmbeddingProviderId = keyof typeof PROVIDER_KEYS;
 /** "provider:id" → {provider, id}; null when malformed (no guessing). */
 export function parseEmbeddingModelSlug(slug: string): { provider: EmbeddingProviderId; id: string } | null {
 	const colon = slug.indexOf(":");
 	if (colon <= 0 || colon === slug.length - 1) return null;
 	const provider = slug.slice(0, colon) as EmbeddingProviderId;
 	if (!(provider in PROVIDER_KEYS)) return null;
 	return { provider, id: slug.slice(colon + 1) };
 }
 // ─── Adapters (one per provider; pure injected-fetch factories) ──────────────
 /** Shared: HTTP-error → clear Error (the search adapters' mapping). */
 async function demandOk(resp: Response, label: string): Promise<void> {
 	if (resp.ok) return;
 	const body = await resp.text();
 	throw new Error(`${label} API ${resp.status}: ${body.slice(0, 200)}`);
 }
 /** Shared: normalize a provider's {embedding, index} rows to input order. */
 function rowsInOrder(
 	data: Array<{ embedding?: unknown; index?: unknown }>,
 	count: number,
 	label: string,
 ): number[][] {
 	const byIndex = new Map<number, number[]>();
 	for (const row of data) {
 		if (
 			Array.isArray(row.embedding) &&
 			row.embedding.every((x) => typeof x === "number") &&
 			typeof row.index === "number"
 		) {
 			byIndex.set(row.index, row.embedding as number[]);
 		}
 	}
 	const out: number[][] = [];
 	for (let i = 0; i < count; i++) {
 		const vec = byIndex.get(i);
 		if (!vec) throw new Error(`${label} API returned ${byIndex.size}/${count} vectors`);
 		out.push(vec);
 	}
 	return out;
 }
 /** One API call's money-channel step: embeddings emit no output, cache
 *  nothing, reason never — the fields exist (CallUsage requires them) as
 *  absent-is-not-zero undefineds. */
 function embeddingStep(promptTokens: number): CallUsage {
 	return {
 		inputTokens: promptTokens,
 		outputTokens: 0,
 		cacheReadTokens: undefined,
 		cacheWriteTokens: undefined,
 		reasoningTokens: undefined,
 	};
 }
 /** OpenRouter: POST https://openrouter.ai/api/v1/embeddings — Bearer auth,
 * input_type hints the retrieval side (verified against the docs 2026-09-23). */
 export function openrouterEmbeddingProvider(model: string, apiKey: string, fetchFn: FetchLike = fetch): EmbeddingPort {
 	const parsed = parseEmbeddingModelSlug(model);
 	if (!parsed) throw new Error(`invalid embedding model slug: ${model}`);
 	return {
 		model,
 		async embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult> {
 			const resp = await fetchFn("https://openrouter.ai/api/v1/embeddings", {
 				method: "POST",
 				headers: { "content-type": "application/json", authorization: `Bearer ${apiKey}` },
 				body: JSON.stringify({
 					model: parsed.id,
 					input: texts,
 					input_type: task === "query" ? "search_query" : "search_document",
 				}),
 				signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
 			});
 			await demandOk(resp, "OpenRouter embeddings");
 			const data = (await resp.json()) as {
 				data?: Array<{ embedding?: unknown; index?: unknown }>;
 				usage?: { prompt_tokens?: number };
 			};
 			return {
 				vectors: rowsInOrder(data.data ?? [], texts.length, "OpenRouter embeddings"),
 				model,
 				dim: 0, // filled by the caller from the first vector (below)
 				promptTokens: data.usage?.prompt_tokens,
 			};
 		},
 	};
 }
 /** OpenAI: POST https://api.openai.com/v1/embeddings — Bearer auth; the
 * response's usage carries prompt_tokens (verified 2026-09-23). */
 export function openaiEmbeddingProvider(model: string, apiKey: string, fetchFn: FetchLike = fetch): EmbeddingPort {
 	const parsed = parseEmbeddingModelSlug(model);
 	if (!parsed) throw new Error(`invalid embedding model slug: ${model}`);
 	return {
 		model,
 		async embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult> {
 			// OpenAI's embeddings API takes no task hint (one format per
 			// model); the argument exists for seam parity and is unused here.
 			void task;
 			const resp = await fetchFn("https://api.openai.com/v1/embeddings", {
 				method: "POST",
 				headers: { "content-type": "application/json", authorization: `Bearer ${apiKey}` },
 				body: JSON.stringify({ model: parsed.id, input: texts }),
 				signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
 			});
 			await demandOk(resp, "OpenAI embeddings");
 			const data = (await resp.json()) as {
 				data?: Array<{ embedding?: unknown; index?: unknown }>;
 				usage?: { prompt_tokens?: number };
 			};
 			return {
 				vectors: rowsInOrder(data.data ?? [], texts.length, "OpenAI embeddings"),
 				model,
 				dim: 0,
 				promptTokens: data.usage?.prompt_tokens,
 			};
 		},
 	};
 }
 /** Google: POST …/models/{m}:batchEmbedContents — x-goog-api-key, one
 * request per text with taskType RETRIEVAL_QUERY|RETRIEVAL_DOCUMENT
 * (verified against the Gemini API docs 2026-09-23). */
 export function googleEmbeddingProvider(model: string, apiKey: string, fetchFn: FetchLike = fetch): EmbeddingPort {
 	const parsed = parseEmbeddingModelSlug(model);
 	if (!parsed) throw new Error(`invalid embedding model slug: ${model}`);
 	return {
 		model,
 		async embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult> {
 			const resp = await fetchFn(
 				`https://generativelanguage.googleapis.com/v1beta/models/${encodeURIComponent(parsed.id)}:batchEmbedContents`,
 				{
 					method: "POST",
 					headers: { "content-type": "application/json", "x-goog-api-key": apiKey },
 					body: JSON.stringify({
 						requests: texts.map((text) => ({
 							model: `models/${parsed.id}`,
 							content: { parts: [{ text }] },
 							taskType: task === "query" ? "RETRIEVAL_QUERY" : "RETRIEVAL_DOCUMENT",
 						})),
 					}),
 					signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
 				},
 			);
 			await demandOk(resp, "Google embeddings");
 			const data = (await resp.json()) as {
 				embeddings?: Array<{ values?: unknown }>;
 				/** embedContent responses carry usageMetadata; the batch
 				 * shape may too — parsed opportunistically, never guessed. */
 				usageMetadata?: { promptTokenCount?: number };
 			};
 			const vectors = (data.embeddings ?? []).map((row, i) => {
 				if (!Array.isArray(row.values) || !row.values.every((x) => typeof x === "number")) {
 					throw new Error(`Google embeddings API returned a bad vector at index ${i}`);
 				}
 				return row.values as number[];
 			});
 			if (vectors.length !== texts.length) {
 				throw new Error(`Google embeddings API returned ${vectors.length}/${texts.length} vectors`);
 			}
 			return { vectors, model, dim: 0, promptTokens: data.usageMetadata?.promptTokenCount };
 		},
 	};
 }
 // ─── Resolution (fresh per call — the search port's treatment) ───────────────
 /** The resolved state: a live port, or the honest reason it isn't. */
 export type EmbeddingResolution =
 	| { ok: true; port: EmbeddingPort }
 	| { ok: false; reason: string };
 /** Pure resolution from a setting slug + a key lookup (testable): null
 *  slug = off; a set slug with no key = honest unavailability. */
 export function resolveEmbeddingPortFor(
 	slug: string | null | undefined,
 	keyOf: (envVar: string) => string | undefined,
 	fetchFn?: FetchLike,
 ): EmbeddingResolution {
 	if (slug === null || slug === undefined || slug === "") {
 		return { ok: false, reason: "no embedding model configured (graph.embeddingModel is null)" };
 	}
 	const parsed = parseEmbeddingModelSlug(slug);
 	if (!parsed) {
 		return { ok: false, reason: `invalid embedding model slug: ${slug}` };
 	}
 	const apiKey = keyOf(PROVIDER_KEYS[parsed.provider]);
 	if (!apiKey) {
 		return {
 			ok: false,
 			reason: `embedding model is set to "${slug}" but its ${PROVIDER_KEYS[parsed.provider]} key is missing`,
 		};
 	}
 	const factory =
 		parsed.provider === "openrouter"
 			? openrouterEmbeddingProvider
 			: parsed.provider === "openai"
 				? openaiEmbeddingProvider
 				: googleEmbeddingProvider;
 	return { ok: true, port: factory(slug, apiKey, fetchFn) };
 }
 /** The LIVE resolution — reads the settings record + auth store fresh on
 *  every call, so a settings PUT or a key save lands on the next call. */
 export function embeddingPort(): EmbeddingResolution {
 	return resolveEmbeddingPortFor(effectiveSettings().graph.embeddingModel, keyValue);
 }
 // ─── The post-merge batch (the extraction pipeline's embedding tail) ────────
 /** What the embedding tail reports back: counts for logs, the skip reason
 *  when nothing ran (owner ruling: null model = info skip, not error), the
 *  failure message when it died partway (owner ruling 2026-09-23:
 *  usage-on-error — a multi-batch embed that fails mid-run still reports
 *  the spend of the batches that landed, so the money channel never
 *  loses a billed call), and the money-channel stage when it billed (one
 *  CallUsage step per API call, outputTokens: 0 — priced via
 *  EMBEDDING_PRICES). */
 export interface EmbedBatchResult {
 	embedded: number;
 	total: number;
 	skipped?: string;
 	/** Set when an embed batch failed after others may have landed. The
 	 *  landed vectors are already persisted (pay-once, owner ruling A
 	 *  2026-09-23) — the next run embeds only what remains. */
 	failed?: string;
 	usage?: ExtractionStageUsage;
 }
 /**
 * Embed every node the ledger is missing — the post-merge hook (new
 * propositions only, typically a handful) AND the cold-start backfill
 * (empty ledger = every node) in one function. A model change voids the
 * ledger first (full re-embed — vectors from different models live in
 * incompatible spaces). Also prunes vectors for nodes that no longer
 * exist, then re-clusters ONCE per batch (consumers read the persisted
 * partition). Non-fatal by contract: the caller treats failure as
 * "clustering runs without the new nodes until the next merge retries".
 * Usage-on-error + pay-once (owner ruling 2026-09-23): an embed batch
 * that throws never propagates — the batches that already landed are
 * persisted (each text is paid for exactly once; a retry continues with
 * the remaining nodes), their spend still reaches the money channel via
 * usage, and `failed` carries the error for logs and callers.
 */
 export async function embedNewNodes(
 	graphStore: {
 		readNodes(): GraphNode[];
 		readEmbeddings(): EmbeddingsLedger;
 		writeEmbeddings(ledger: EmbeddingsLedger): void;
 	},
 	/** Test seam: the live resolution by default; tests inject a scripted
 	 *  port (they never read the machine's settings/auth files). */
 	resolvePort: () => EmbeddingResolution = embeddingPort,
 ): Promise<EmbedBatchResult> {
 	const resolved = resolvePort();
 	const ledger = graphStore.readEmbeddings();
 	if (!resolved.ok) {
 		console.info(`[embeddings] skipped — ${resolved.reason}`);
 		return { embedded: 0, total: Object.keys(ledger.vectors).length, skipped: resolved.reason };
 	}
 	const port = resolved.port;
 	const stale = ledger.model !== port.model;
 	const vectors: Record<string, number[]> = stale ? {} : { ...ledger.vectors };
 	const steps: NonNullable<EmbedBatchResult["usage"]>["steps"] = [];
 	let dim = ledger.dim;
 	// Node LINES may repeat an id — dedupe, first defines (getGraph rule).
 	const seen = new Set<string>();
 	const todo: GraphNode[] = [];
 	for (const node of graphStore.readNodes()) {
 		if (seen.has(node.id)) continue;
 		seen.add(node.id);
 		if (!vectors[node.id]) todo.push(node);
 	}
 	let landed = 0;
 	let failed: string | undefined;
 	for (let i = 0; i < todo.length; i += EMBED_BATCH) {
 		const batch = todo.slice(i, i + EMBED_BATCH);
 		let result: EmbeddingResult;
 		try {
 			result = await port.embed(
 				batch.map((n) => n.statement),
 				"document",
 			);
 		} catch (err) {
 			// Usage-on-error + pay-once (owner ruling 2026-09-23): stop
 			// here, keep the batches that landed, report the failure — the
 			// throw never escapes this function.
 			failed = err instanceof Error ? err.message : String(err);
 			break;
 		}
 		for (let j = 0; j < batch.length; j++) {
 			vectors[batch[j]!.id] = result.vectors[j]!;
 			landed++;
 		}
 		if (result.vectors[0]) dim = result.vectors[0].length;
 		if (result.promptTokens !== undefined) {
 			// outputTokens: 0 — embeddings emit no output; callCost needs it
 			// defined to price the input at all.
 			steps.push(embeddingStep(result.promptTokens));
 		}
 	}
 	if (failed !== undefined) {
 		console.error(
 				`[embeddings] embedding failed after ${landed}/${todo.length} nodes (non-fatal; landed vectors persist, the next run embeds only what remains): ${failed}`,
 		);
 	}
 	// Prune ghosts: vectors for node ids that no longer exist.
 	let pruned = 0;
 	for (const id of Object.keys(vectors)) {
 		if (!seen.has(id)) {
 			delete vectors[id];
 			pruned++;
 		}
 	}
 	// RECLUSTER AT THE END OF THE BATCH (user-decided 2026-09-13): the
 	// agglomerative pass runs HERE — once per embedding batch — and
 	// consumers (snapshot, settle, namer) read the persisted partition.
 	const clusters = semanticClusters(vectors, { similarityThreshold: SEMANTIC_SIM_THRESHOLD });
 	const wrote =
 		failed !== undefined
 			? landed > 0
 			: todo.length > 0 || pruned > 0 || stale || ledger.clusters.length !== clusters.length;
 	if (wrote) {
 		graphStore.writeEmbeddings({
 			model: port.model,
 			dim,
 			vectors,
 			clusters,
 			updatedAt: new Date().toISOString(),
 		});
 	}
 	const result: EmbedBatchResult = {
 		embedded: landed,
 		total: Object.keys(vectors).length,
 		usage: steps.length > 0 ? { model: port.model, steps } : undefined,
 	};
 	if (failed !== undefined) result.failed = failed;
 	return result;
 }
 // ─── The federated embedding catalog (wave 6, owner ruling 2026-09-23) ──────
 /** One catalog row: provider-qualified id (the setting's exact slug
 *  format) + display name. */
 export interface EmbeddingCatalogModel {
 	/** The full "provider:id" slug — what graph.embeddingModel stores. */
 	id: string;
 	/** Human label for the picker (the provider's name, when given). */
 	name: string;
 }
 /** The embedding models of every KEYED provider, fetched live (the
 *  chat-models picker is a static pi-ai catalog, but pi-ai carries no
 *  embedding models — so this endpoint federates the providers' own
 *  lists). A provider without a key contributes nothing; a provider whose
 *  catalog call fails is skipped with an error log (never poisons the
 *  rest, never fails the endpoint). Empty array = nothing keyed. */
 export async function embeddingModelCatalog(
 	fetchFn: FetchLike = fetch,
 	/** Test seam: the live key lookup by default; tests inject scripted
 	 *  keys (they never read the machine's auth file). */
 	keyOf: (id: string) => string | undefined = keyValue,
 ): Promise<EmbeddingCatalogModel[]> {
 	const out: EmbeddingCatalogModel[] = [];
 	const fetchJson = async (url: string, headers: Record<string, string>): Promise<unknown> => {
 		const resp = await fetchFn(url, { headers, signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS) });
 		await demandOk(resp, "Embeddings catalog");
 		return resp.json();
 	};
 	// OpenRouter: GET /api/v1/embeddings/models — the dedicated embedding
 	// list (VERIFIED live 2026-09-23: the inverse path /api/v1/models/
 	// embeddings 404s; this one returns {data: [{id, name, …}]}). Paginated
 	// — limit=500 covers the whole list at current size (33).
 	if (keyOf(PROVIDER_KEYS.openrouter)) {
 		try {
 			const data = (await fetchJson("https://openrouter.ai/api/v1/embeddings/models?limit=500", {
 				authorization: `Bearer ${keyOf(PROVIDER_KEYS.openrouter)}`,
 			})) as { data?: Array<{ id?: unknown; name?: unknown }> };
 			for (const m of data.data ?? []) {
 				if (typeof m.id !== "string" || !m.id) continue;
 				out.push({ id: `openrouter:${m.id}`, name: typeof m.name === "string" && m.name ? m.name : m.id });
 			}
 		} catch (err) {
 			console.error("[embeddings] OpenRouter catalog fetch failed:", err);
 		}
 	}
 	// OpenAI: GET /v1/models — no embedding-only list, so filter the
 	// generic one on the embedding naming convention.
 	if (keyOf(PROVIDER_KEYS.openai)) {
 		try {
 			const data = (await fetchJson("https://api.openai.com/v1/models", {
 				authorization: `Bearer ${keyOf(PROVIDER_KEYS.openai)}`,
 			})) as { data?: Array<{ id?: unknown }> };
 			for (const m of data.data ?? []) {
 				if (typeof m.id !== "string" || !/^text-embedding-/.test(m.id)) continue;
 				out.push({ id: `openai:${m.id}`, name: m.id });
 			}
 		} catch (err) {
 			console.error("[embeddings] OpenAI catalog fetch failed:", err);
 		}
 	}
 	// Google: GET /v1beta/models — filter on the methods the model
 	// actually serves (embedContent present).
 	if (keyOf(PROVIDER_KEYS.google)) {
 		try {
 			const data = (await fetchJson("https://generativelanguage.googleapis.com/v1beta/models?pageSize=1000", {
 				"x-goog-api-key": keyOf(PROVIDER_KEYS.google)!,
 			})) as {
 				models?: Array<{ name?: unknown; displayName?: unknown; supportedGenerationMethods?: unknown }>;
 			};
 			for (const m of data.models ?? []) {
 				if (typeof m.name !== "string" || !m.name.startsWith("models/")) continue;
 				if (!Array.isArray(m.supportedGenerationMethods) || !m.supportedGenerationMethods.includes("embedContent")) continue;
 				const id = m.name.slice("models/".length);
 				out.push({
 					id: `google:${id}`,
 					name: typeof m.displayName === "string" && m.displayName ? m.displayName : id,
 				});
 			}
 		} catch (err) {
 			console.error("[embeddings] Google catalog fetch failed:", err);
 		}
 	}
 	return out;
 }
@@ -6,7 +6,6 @@
 export * from "./chat-handler.ts";
 export * from "./chats.ts";
 export * from "./embeddings.ts";
 export * from "./graph.ts";
 export * from "./commit-positions.ts";
 export * from "./jobs.ts";
@@ -289,41 +289,22 @@ export function parseAppearanceSettingsBody(body: unknown): SettingsBodyResult<A
 	return { ok: true, settings };
 }
-/** graph: the four-rung ladder { enabled?, embedding?, clustering?, labeling? }
+/** graph: the collapsed section { enabled? } (wave 7, owner ruling
- *  + embeddingModel? — the BYOK provider embedding model (wave 6,
+ *  2026-09-23): the master switch ONLY — the embedding/clustering/labeling
- *  2026-09-23): null or a "provider:id" slug of a shipped provider.
+ *  rungs and the BYOK embeddingModel died with the provider embedder
- *  Catalog validity is network-dependent — the parser checks SHAPE only
+ *  (clustering is the free birth-session partition, labels are session
- *  (the federated /api/models/embeddings gives the picker real options). */
+ *  names). Old-shape keys in a body are ignored, not rejected. */
 export function parseGraphSettingsBody(body: unknown): SettingsBodyResult<GraphSettings> {
 	const raw = asRecord(body);
 	if (!raw) return { ok: false, error: "Invalid JSON body" };
 	// Wave 7 collapse (owner ruling 2026-09-23): the section is the master
 	// switch ONLY. PUT is replace-whole — a body like {enabled: true} IS the
 	// whole section now; the dead rungs' keys in old records are unread
 	// (the sanitizer drops them; see sanitizeGraph).
 	const settings: GraphSettings = {};
-	for (const key of ["enabled", "embedding", "clustering", "labeling"] as const) {
+	if (raw.enabled !== undefined) {
-		if (raw[key] !== undefined) {
+		if (typeof raw.enabled !== "boolean") return { ok: false, error: "enabled must be a boolean" };
-			if (typeof raw[key] !== "boolean") {
+		settings.enabled = raw.enabled;
 				return { ok: false, error: `${key} must be a boolean` };
 			}
 			settings[key] = raw[key];
 		}
 	}
 	if (raw.embeddingModel !== undefined) {
 		if (raw.embeddingModel !== null) {
 			// NOTE: the id may itself contain colons (OpenRouter suffixes like
 			// "liquid/lfm-2.5-embedding-350m:free") — the provider is the part
 			// before the FIRST colon, so the id half just forbids whitespace.
 			if (
 				typeof raw.embeddingModel !== "string" ||
 				!/^(openrouter|openai|google):\S+$/.test(raw.embeddingModel)
 			) {
 				return {
 					ok: false,
 					error: 'embeddingModel must be null or a "openrouter|openai|google:model-id" slug',
 				};
 			}
 			settings.embeddingModel = raw.embeddingModel;
 		} else {
 			settings.embeddingModel = null;
 		}
 	}
 	return { ok: true, settings };
 }
@@ -21,7 +21,6 @@ import { coverageOf } from "../core/coverage.ts";
 import type { ChatStore, GraphStore } from "../core/ports.ts";
 import { EXTRACTION } from "../../config.ts";
 import type { ExtractionPipeline } from "../agent/subagents/extraction.ts";
 import { startGraphContextInjection, type GraphContextDeps } from "../agent/subagents/graph-context.ts";
 import { ModelsNotConfiguredError } from "../core/settings.ts";
 /**
@@ -113,21 +112,3 @@ export function triggerExtraction(
 	}
 }
 /**
 * The graph READ channel (vision-context-injection.md): one shot per chat,
 * at session start — the first typed message is the session's topic; the
 * semantically closest propositions are selected under the session's token
 * budget and steered in through the wake pipeline. Fire-and-forget like
 * triggerExtraction; the one-shot gates (topic, budget, prior injection)
 * live in the runner module, not here — this seam only decides WHEN to ask.
 * Skipped under tests, same doctrine: the runner is tested directly with a
 * scripted embedder.
 */
 export function triggerGraphContext(deps: GraphContextDeps, chatId: string): void {
 	if (process.env.NODE_ENV === "test") return;
 	try {
 		startGraphContextInjection(deps, chatId);
 	} catch (err) {
 		console.error(`[chat ${chatId}] graph-context trigger failed:`, err);
 	}
 }
@@ -96,39 +96,6 @@ describe("graphCostSummary (the cost pill's fold)", () => {
 		assert.equal(s.unpricedRuns, 1); // run-level: only the both-stages run
 	});
 	test("the embedding stage (wave 6): folds when present, absent when the run embedded nothing", () => {
 		const EMBED_MODEL = "openai:text-embedding-3-small";
 		const embStep = callCost(step(5000, 0), EMBED_MODEL)!;
 		assert.ok(embStep > 0, "the overlay prices the known embedding models");
 		const withEmbed = extractionRun(100, PRICED, PRICED);
 		withEmbed.stages.embedding = { model: EMBED_MODEL, steps: [step(5000, 0)] };
 		const s = graphCostSummary({
 			chats: [{ title: "a", extractions: [withEmbed] }, { title: "b", extractions: [extractionRun(200, PRICED, PRICED)] }],
 			sources: [],
 			naming: [],
 		});
 		assert.equal(s.stages.embedding.runs, 1, "only the run that embedded");
 		assert.equal(s.stages.embedding.dollars, embStep);
 		assert.equal(s.stages.embedding.tokens.inputTokens, 5000);
 		assert.equal(s.stages.extraction.runs, 2, "the two-stage era still folds");
 		assert.equal(s.stages.extraction.runs, 2);
 		// Run-level: the embedding dollars ride the run's total.
 		const extStep = callCost(step(1000, 200), PRICED)!;
 		const audStep = callCost(step(2000, 100), PRICED)!;
 		assert.equal(s.runs.find((r) => r.startedAt === 100)!.dollars, extStep + audStep + embStep);
 		assert.equal(s.runs.find((r) => r.startedAt === 200)!.dollars, extStep + audStep);
 		assert.equal(s.totalDollars, (extStep + audStep) * 2 + embStep);
 	});
 	test("an UNPRICED embedding model stays honest: counted, never a silent zero; a priced sibling saves the run total", () => {
 		const unpricedEmbed = extractionRun(100, PRICED, "unknown-model");
 		unpricedEmbed.stages.embedding = { model: "some/new-embed", steps: [step(900, 0)] };
 		const s = graphCostSummary({ chats: [{ title: "x", extractions: [unpricedEmbed] }], sources: [], naming: [] });
 		assert.equal(s.stages.embedding.unpricedRuns, 1);
 		assert.equal(s.stages.embedding.dollars, 0);
 		assert.notEqual(s.runs[0]!.dollars, null, "the priced extractor saves the run total");
 	});
 	test("recent runs cap: newest survive", () => {
 		const extractions = [1, 2, 3, 4].map((n) => extractionRun(n * 100, PRICED, PRICED));
 		const s = graphCostSummary({
@@ -1,91 +0,0 @@
 import React from "react";
 (globalThis as Record<string, unknown>).React = React;
 import { renderToStaticMarkup } from "react-dom/server";
 import { describe, it } from "node:test";
 import assert from "node:assert/strict";
 import {
 	embeddingPriceLabel,
 	GraphSection,
 	needsEmbeddingModel,
 } from "../components/settings/appearance-section.tsx";
 import { EMBEDDING_PRICES, type GraphSettings } from "../src/core/index.ts";
 /**
 * Wave 6 chunk B — the embeddings settings seams: the price line for
 * catalog rows (EMBEDDING_PRICES lookup, honestly "—" when unlisted),
 * the model-dependency predicate that drives the ladder gray-out, and
 * GraphSection's SSR shape with and without a configured model.
 *
 * The fetches (catalog, node count) live in effects — dead under
 * renderToStaticMarkup, so the render pins the STORED-state markup only;
 * the interactive round-trips are live-verified on the dev server.
 */
 describe("embeddingPriceLabel", () => {
 	it("prices a listed model: USD per 1M input tokens", () => {
 		// Straight from the hand-maintained table (openai small = $0.02).
 		assert.equal(embeddingPriceLabel("openai:text-embedding-3-small"), "$0.02/1M in");
 	});
 	it("prices every entry in EMBEDDING_PRICES without zero or 'NaN' leaking", () => {
 		for (const id of Object.keys(EMBEDDING_PRICES)) {
 			const label = embeddingPriceLabel(id);
 			assert.match(label, /^\$\d+(\.\d+)?\/1M in$/);
 			assert.notEqual(label, "$0/1M in");
 		}
 	});
 	it("trims a trailing zero the model-menu way ($0.2, not $0.20)", () => {
 		assert.equal(embeddingPriceLabel("google:gemini-embedding-2"), "$0.2/1M in");
 	});
 	it("stays honestly '—' for unlisted models (never zero)", () => {
 		assert.equal(embeddingPriceLabel("openrouter:some/model-not-in-table"), "—");
 	});
 });
 describe("needsEmbeddingModel", () => {
 	it("spares only the master switch", () => {
 		assert.equal(needsEmbeddingModel("enabled"), false);
 		assert.equal(needsEmbeddingModel("embedding"), true);
 		assert.equal(needsEmbeddingModel("clustering"), true);
 		assert.equal(needsEmbeddingModel("labeling"), true);
 	});
 });
 describe("GraphSection (SSR)", () => {
 	const render = (graph: GraphSettings | undefined) =>
 		renderToStaticMarkup(<GraphSection graph={graph} onSectionChange={() => {}} />);
 	it("grays out + hints the model-dependent rungs when no model is set", () => {
 		const html = render({ enabled: true } as GraphSettings);
 		// Master switch: never model-dimmed.
 		const enabledRow = html.split('data-testid="graph-row-enabled"')[1]?.split("</div>")[0] ?? "";
 		assert.ok(!enabledRow.includes("opacity-40"), "master switch must not dim on unset model");
 		// The three dependent rungs dim and each carries the hint.
 		for (const key of ["embedding", "clustering", "labeling"] as const) {
 			const row = html.split(`data-testid="graph-row-${key}"`)[1]?.split('data-testid="graph-row-')[0] ?? "";
 			assert.ok(row.includes("opacity-40"), `${key} row dims without a model`);
 			assert.ok(row.includes("Needs an embedding model"), `${key} row carries the hint`);
 		}
 		assert.ok(html.includes('data-testid="graph-row-embedding-model"'));
 	});
 	it("keeps the ladder touchable once a model is set", () => {
 		const html = render({
 			enabled: true,
 			embeddingModel: "openai:text-embedding-3-small",
 		} as GraphSettings);
 		for (const key of ["embedding", "clustering", "labeling"] as const) {
 			const row = html.split(`data-testid="graph-row-${key}"`)[1]?.split('data-testid="graph-row-')[0] ?? "";
 			assert.ok(!row.includes("opacity-40"), `${key} row stays lit with a model`);
 			assert.ok(!row.includes("Needs an embedding model"), "no hint with a model");
 		}
 	});
 	it("renders the picker row (catalog fetch is an effect — inert in SSR)", () => {
 		const html = render(undefined);
 		assert.ok(html.includes("Loading…"), "pre-fetch trigger state renders");
 	});
 });
@@ -1,474 +0,0 @@
 /**
 * BYOK provider embeddings (wave 6, 2026-09-23): the adapters' wire
 * protocols (injected fetch — never the network), the slug/keys
 * resolution rules (null = honestly off; a set model with a missing key
 * is an honest error), the post-merge batch's staleness/re-embed/skip
 * semantics (scripted port — never the machine's settings/auth files),
 * and the federated catalog's federation rules.
 */
 import assert from "node:assert/strict";
 import { describe, it } from "node:test";
 import { callCost, EMBEDDING_PRICES } from "../src/core/prices.ts";
 import {
 	embedNewNodes,
 	embeddingModelCatalog,
 	googleEmbeddingProvider,
 	openaiEmbeddingProvider,
 	openrouterEmbeddingProvider,
 	parseEmbeddingModelSlug,
 	resolveEmbeddingPortFor,
 	type EmbeddingResolution,
 } from "../src/server/embeddings.ts";
 import type { EmbeddingPort, EmbeddingTask } from "../src/core/ports.ts";
 import type { EmbeddingsLedger, GraphNode } from "../src/core/contracts/graph.ts";
 // ─── scripted fetch + in-memory graph (no fs, no network) ────────────────────
 type RecordedCall = { url: string; init: RequestInit };
 function jsonResponse(body: unknown, status = 200): Response {
 	return new Response(JSON.stringify(body), {
 		status,
 		headers: { "content-type": "application/json" },
 	});
 }
 function recordingFetch(
 	route: (url: string, body: unknown) => Response | Promise<Response>,
 ): { calls: RecordedCall[]; fetch: (input: string, init: RequestInit) => Promise<Response> } {
 	const calls: RecordedCall[] = [];
 	const fetchFn = async (input: string, init: RequestInit): Promise<Response> => {
 		calls.push({ url: input, init });
 		const body = init.body ? (JSON.parse(String(init.body)) as unknown) : null;
 		return route(input, body);
 	};
 	return { calls, fetch: fetchFn };
 }
 function node(id: string, statement = `statement of ${id}`): GraphNode {
 	return { id, name: id, statement, aliases: [], provenance: [], createdAt: "2026-09-23T00:00:00.000Z" };
 }
 function fakeGraphStore(start?: Partial<EmbeddingsLedger>): {
 	ledger: EmbeddingsLedger;
 	writes: EmbeddingsLedger[];
 	store: {
 		readNodes(): GraphNode[];
 		readEmbeddings(): EmbeddingsLedger;
 		writeEmbeddings(ledger: EmbeddingsLedger): void;
 	};
 	nodes: GraphNode[];
 } {
 	const nodes = [node("n1", "alpha fact"), node("n2", "beta fact")];
 	const ledger: EmbeddingsLedger = {
 		model: start?.model ?? "test-embed",
 		dim: start?.dim ?? 2,
 		vectors: { ...start?.vectors },
 		clusters: [...start?.clusters ?? []],
 		updatedAt: start?.updatedAt ?? "2026-09-23T00:00:00.000Z",
 	};
 	const writes: EmbeddingsLedger[] = [];
 	return {
 		ledger,
 		writes,
 		nodes,
 		store: {
 			readNodes: () => nodes,
 			readEmbeddings: () => ledger,
 			writeEmbeddings: (next) => {
 				writes.push(next);
 				Object.assign(ledger, next);
 			},
 		},
 	};
 }
 /** A scripted port: unit vectors by text hash, records (texts, task) calls. */
 function scriptedPort(model = "openai:test-embed", dim = 2, tokensPerCall = 120): {
 	calls: Array<{ texts: string[]; task: EmbeddingTask }>;
 	port: EmbeddingPort;
 } {
 	const calls: Array<{ texts: string[]; task: EmbeddingTask }> = [];
 	return {
 		calls,
 		port: {
 			model,
 			async embed(texts, task) {
 				calls.push({ texts: [...texts], task });
 				return {
 					vectors: texts.map((_, i) => Array.from({ length: dim }, (_, j) => (j === i ? 1 : 0))),
 					model,
 					dim,
 					promptTokens: tokensPerCall,
 				};
 			},
 		},
 	};
 }
 const okPort = (port: EmbeddingPort): EmbeddingResolution => ({ ok: true, port });
 // ─── usage-on-error + pay-once (owner ruling 2026-09-23, interlude) ──────────
 describe("embedNewNodes partial-failure accounting (Teacher review follow-up)", () => {
 	it("a mid-run batch failure: landed vectors persist, usage survives, failed carries the error, next run resumes", async () => {
 		// 40 nodes → batch 1 (32) lands, batch 2 (8) throws.
 		const many: GraphNode[] = Array.from({ length: 40 }, (_, i) => node(`n${i}`, `statement ${i}`));
 		const ledger: EmbeddingsLedger = { model: "m", dim: 2, vectors: {}, clusters: [], updatedAt: "" };
 		let writes = 0;
 		let call = 0;
 		const failing: EmbeddingPort = {
 			model: "m",
 			async embed(texts) {
 				call++;
 				if (call === 2) throw new Error("provider exploded mid-run");
 				return {
 					vectors: texts.map((_, i) => (i === 0 ? [1, 0] : [0, 1])),
 					model: "m",
 					dim: 2,
 					promptTokens: 100,
 				};
 			},
 		};
 		const store = {
 			readNodes: () => many,
 			readEmbeddings: () => ledger,
 			writeEmbeddings: (next: EmbeddingsLedger) => {
 				writes++;
 				Object.assign(ledger, next);
 			},
 		};
 		const result = await embedNewNodes(store, () => okPort(failing));
 		// Usage-on-error: batch 1 billed — the money channel keeps it.
 		assert.equal(result.failed, "provider exploded mid-run");
 		assert.equal(result.embedded, 32, "only the landed batch counts as embedded");
 		assert.equal(result.usage?.steps.length, 1);
 		assert.equal(result.usage?.steps[0]?.inputTokens, 100);
 		// Pay-once: the landed vectors are persisted — a retry embeds only the rest.
 		assert.equal(writes, 1);
 		assert.equal(Object.keys(ledger.vectors).length, 32);
 		assert.equal(ledger.model, "m");
 		const resume = await embedNewNodes(store, () => okPort(scriptedPort("m", 2).port));
 		assert.equal(resume.embedded, 8, "the retry resumes — it re-embeds only the 8 remaining");
 		assert.equal(resume.failed, undefined);
 		assert.equal(Object.keys(ledger.vectors).length, 40);
 		assert.equal(resume.usage?.steps[0]?.inputTokens, 120, "scripted port tokens — the retry billed once");
 	});
 	it("a FIRST-batch failure: nothing written, nothing billed, the old ledger stays intact", async () => {
 		const g = fakeGraphStore({ vectors: { n1: [1, 0] } });
 		const alwaysFails: EmbeddingPort = {
 			model: "m",
 			embed: async () => {
 				throw new Error("total outage");
 			},
 		};
 		const result = await embedNewNodes(g.store, () => okPort(alwaysFails));
 		assert.equal(result.failed, "total outage");
 		assert.equal(result.embedded, 0);
 		assert.equal(result.usage, undefined, "no call landed — nothing to bill");
 		assert.equal(g.writes.length, 0, "the ledger is untouched (its own model/dim still true)");
 		assert.ok(g.ledger.vectors.n1, "the old vector survives a zero-landed failure");
 	});
 	it("success still carries no `failed` key and counts every todo node", async () => {
 		const g = fakeGraphStore();
 		const scripted = scriptedPort(g.ledger.model, 2);
 		const result = await embedNewNodes(g.store, () => okPort(scripted.port));
 		assert.equal(result.failed, undefined);
 		assert.equal(result.embedded, 2);
 		assert.equal(result.skipped, undefined);
 	});
 });
 // ─── the money channel's embedding overlay ─────────────────────────────────
 describe("the embedding price overlay (hand-maintained, outside the sync block)", () => {
 	it("prices the known embedding models at their verified input rates", () => {
 		// USD per 1M input tokens, verified against provider pages 2026-09-23.
 		assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "openai:text-embedding-3-small"), 0.02);
 		assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "openai:text-embedding-3-large"), 0.13);
 		assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "google:gemini-embedding-001"), 0.15);
 		assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "google:gemini-embedding-2"), 0.2);
 		assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "openrouter:openai/text-embedding-3-small"), 0.02);
 	});
 	it("unknown embedding models stay unpriced (—, never zero), tokens still captured", () => {
 		assert.equal(callCost({ inputTokens: 100, outputTokens: 0 }, "openrouter:mystery/embed"), null);
 		assert.equal(!("openrouter:mystery/embed" in EMBEDDING_PRICES), true);
 	});
 });
 // ─── the slug grammar ─────────────────────────────────────────────────────────
 describe("parseEmbeddingModelSlug", () => {
 	it("parses the three shipped providers", () => {
 		assert.deepEqual(parseEmbeddingModelSlug("openai:text-embedding-3-small"), { provider: "openai", id: "text-embedding-3-small" });
 		assert.deepEqual(parseEmbeddingModelSlug("openrouter:openai/text-embedding-3-large"), { provider: "openrouter", id: "openai/text-embedding-3-large" });
 		assert.deepEqual(parseEmbeddingModelSlug("google:gemini-embedding-001"), { provider: "google", id: "gemini-embedding-001" });
 		// OpenRouter ids may carry a :free suffix (live catalog fact) — the
 		// provider is the part before the FIRST colon.
 		assert.deepEqual(parseEmbeddingModelSlug("openrouter:liquid/lfm-2.5-embedding-350m:free"), { provider: "openrouter", id: "liquid/lfm-2.5-embedding-350m:free" });
 	});
 	it("rejects malformed slugs (no guessing)", () => {
 		assert.equal(parseEmbeddingModelSlug("text-embedding-3-small"), null, "no provider prefix");
 		assert.equal(parseEmbeddingModelSlug("anthropic:embed"), null, "unshipped provider");
 		assert.equal(parseEmbeddingModelSlug("openai:"), null, "empty id");
 		assert.equal(parseEmbeddingModelSlug(":id"), null, "empty provider");
 	});
 });
 // ─── resolution: BYOK honesty rules ───────────────────────────────────────────
 describe("resolveEmbeddingPortFor", () => {
 	it("null/absent setting = honestly off", () => {
 		for (const slug of [null, undefined, ""]) {
 			const res = resolveEmbeddingPortFor(slug, () => "key");
 			assert.equal(res.ok, false);
 			assert.match((res as { reason: string }).reason, /no embedding model configured/);
 		}
 	});
 	it("a set model with no key is an honest error naming the key", () => {
 		const res = resolveEmbeddingPortFor("openai:text-embedding-3-small", () => undefined);
 		assert.equal(res.ok, false);
 		assert.match((res as { reason: string }).reason, /OPENAI_API_KEY/);
 	});
 	it("an invalid slug never resolves", () => {
 		const res = resolveEmbeddingPortFor("bedrock:whatever", () => "key");
 		assert.equal(res.ok, false);
 		assert.match((res as { reason: string }).reason, /invalid embedding model slug/);
 	});
 	it("a keyed model resolves to its provider adapter", () => {
 		const res = resolveEmbeddingPortFor("openai:text-embedding-3-small", () => "sk-test");
 		assert.equal(res.ok, true);
 		assert.equal((res as { port: EmbeddingPort }).port.model, "openai:text-embedding-3-small");
 	});
 });
 // ─── the adapters' wire protocols (injected fetch) ────────────────────────────
 describe("openrouterEmbeddingProvider", () => {
 	it("POSTs /api/v1/embeddings with Bearer auth and the task input_type", async () => {
 		const { calls, fetch } = recordingFetch(() =>
 			jsonResponse({ data: [{ embedding: [1, 0], index: 0 }], usage: { prompt_tokens: 42 } }),
 		);
 		const port = openrouterEmbeddingProvider("openrouter:openai/text-embedding-3-small", "k", fetch);
 		const result = await port.embed(["hello"], "document");
 		assert.equal(calls.length, 1);
 		assert.equal(calls[0]!.url, "https://openrouter.ai/api/v1/embeddings");
 		assert.equal((calls[0]!.init.headers as Record<string, string>).authorization, "Bearer k");
 		assert.deepEqual(JSON.parse(String(calls[0]!.init.body)), {
 			model: "openai/text-embedding-3-small",
 			input: ["hello"],
 			input_type: "search_document",
 		});
 		assert.deepEqual(result.vectors, [[1, 0]]);
 		assert.equal(result.promptTokens, 42);
 		await port.embed(["q"], "query");
 		assert.equal((JSON.parse(String(calls[1]!.init.body)) as { input_type: string }).input_type, "search_query");
 	});
 });
 describe("openaiEmbeddingProvider", () => {
 	it("POSTs /v1/embeddings, Bearer auth, no task hint; index ordering honored", async () => {
 		const { calls, fetch } = recordingFetch(() =>
 			jsonResponse({
 				data: [{ embedding: [0, 1], index: 1 }, { embedding: [1, 0], index: 0 }],
 				usage: { prompt_tokens: 7, total_tokens: 7 },
 			}),
 		);
 		const port = openaiEmbeddingProvider("openai:text-embedding-3-small", "sk", fetch);
 		const result = await port.embed(["a", "b"], "document");
 		assert.equal(calls[0]!.url, "https://api.openai.com/v1/embeddings");
 		assert.equal((calls[0]!.init.headers as Record<string, string>).authorization, "Bearer sk");
 		const body = JSON.parse(String(calls[0]!.init.body)) as Record<string, unknown>;
 		assert.deepEqual(body.input, ["a", "b"]);
 		assert.equal("input_type" in body, false, "OpenAI takes no task hint");
 		// Rows arrive out of order — the adapter must restore input order.
 		assert.deepEqual(result.vectors, [[1, 0], [0, 1]]);
 		assert.equal(result.promptTokens, 7);
 	});
 });
 describe("googleEmbeddingProvider", () => {
 	it("POSTs :batchEmbedContents with x-goog-api-key and per-text taskType", async () => {
 		const { calls, fetch } = recordingFetch((_url, body) => {
 		const n = ((body as { requests?: unknown[] } | null)?.requests ?? []).length;
 		return jsonResponse({ embeddings: Array.from({ length: n }, (_, i) => ({ values: i === 0 ? [1, 0] : [0, 1] })) });
 	});
 		const port = googleEmbeddingProvider("google:gemini-embedding-001", "gk", fetch);
 		const result = await port.embed(["a", "b"], "document");
 		assert.equal(calls[0]!.url, "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents");
 		assert.equal((calls[0]!.init.headers as Record<string, string>)["x-goog-api-key"], "gk");
 		assert.deepEqual(JSON.parse(String(calls[0]!.init.body)), {
 			requests: [
 				{ model: "models/gemini-embedding-001", content: { parts: [{ text: "a" }] }, taskType: "RETRIEVAL_DOCUMENT" },
 				{ model: "models/gemini-embedding-001", content: { parts: [{ text: "b" }] }, taskType: "RETRIEVAL_DOCUMENT" },
 			],
 		});
 		assert.deepEqual(result.vectors, [[1, 0], [0, 1]]);
 		assert.equal(result.promptTokens, undefined, "batch responses carry no token count we can trust");
 		await port.embed(["q"], "query");
 		const second = JSON.parse(String(calls[1]!.init.body)) as { requests: Array<{ taskType: string }> };
 		assert.equal(second.requests[0]!.taskType, "RETRIEVAL_QUERY");
 	});
 	it("a short/missing vector is an honest error, never a silent misalignment", async () => {
 		const { fetch } = recordingFetch(() => jsonResponse({ embeddings: [{ values: [1, 0] }] }));
 		const port = googleEmbeddingProvider("google:gemini-embedding-001", "gk", fetch);
 		await assert.rejects(port.embed(["a", "b"], "document"), /1\/2 vectors/);
 	});
 });
 describe("adapter error mapping (all providers)", () => {
 	it("HTTP failures become clear Errors carrying the status", async () => {
 		for (const factory of [openrouterEmbeddingProvider, openaiEmbeddingProvider, googleEmbeddingProvider]) {
 			const { fetch } = recordingFetch(() => jsonResponse({ error: { message: "bad key" } }, 401));
 			const port = factory("openai:text-embedding-3-small", "k", fetch);
 			await assert.rejects(port.embed(["x"], "document"), /API 401/);
 		}
 	});
 });
 // ─── the post-merge batch (scripted port — the machine's files stay unread) ───
 describe("embedNewNodes (the extraction tail)", () => {
 	it("embeds missing nodes, stamps model/dim, records one usage step per API call", async () => {
 		const g = fakeGraphStore();
 		const scripted = scriptedPort("openai:text-embedding-3-small", 2, 120);
 		const result = await embedNewNodes(g.store, () => okPort(scripted.port));
 		assert.deepEqual(result, {
 			embedded: 2,
 			total: 2,
 			usage: {
 				model: "openai:text-embedding-3-small",
 				steps: [
 					{ inputTokens: 120, outputTokens: 0, cacheReadTokens: undefined, cacheWriteTokens: undefined, reasoningTokens: undefined },
 				],
 			},
 		});
 		assert.equal(g.writes.length, 1, "the ledger is written once per batch");
 		assert.equal(g.ledger.model, "openai:text-embedding-3-small");
 		assert.equal(g.ledger.dim, 2);
 		assert.ok(g.ledger.vectors.n1 && g.ledger.vectors.n2);
 		assert.deepEqual(scripted.calls[0]!.texts, ["alpha fact", "beta fact"]);
 		assert.equal(scripted.calls[0]!.task, "document");
 		assert.equal(result.usage?.steps.length, 1);
 	});
 	it("a MODEL CHANGE voids the ledger and re-embeds everything", async () => {
 		// The ledger holds a stale-model vector for a node that still exists.
 		const g = fakeGraphStore({ model: "openai:old-embed", vectors: { n1: [9, 9] } });
 		const scripted = scriptedPort("openai:new-embed", 2);
 		const result = await embedNewNodes(g.store, () => okPort(scripted.port));
 		assert.equal(result.embedded, 2, "the stale vector dies — BOTH nodes re-embed");
 		assert.deepEqual(scripted.calls[0]!.texts, ["alpha fact", "beta fact"]);
 		assert.equal(g.ledger.model, "openai:new-embed");
 		assert.ok(!g.ledger.vectors.n1!.every((x, i) => x === [9, 9][i]), "the old vector was replaced");
 	});
 	it("null model = info skip: nothing written, nothing embedded, the reason rides the result", async () => {
 		const g = fakeGraphStore({ vectors: { n1: [1, 0] } });
 		const result = await embedNewNodes(g.store, () => ({
 			ok: false,
 			reason: "no embedding model configured (graph.embeddingModel is null)",
 		}));
 		assert.deepEqual(result, { embedded: 0, total: 1, skipped: "no embedding model configured (graph.embeddingModel is null)" });
 		assert.equal(g.writes.length, 0, "a skipped batch never touches the ledger");
 	});
 	it("a set model with a missing key is the same skip path (its reason says so)", async () => {
 		const g = fakeGraphStore();
 		const result = await embedNewNodes(g.store, () => ({
 			ok: false,
 			reason: 'embedding model is set to "openai:x" but its OPENAI_API_KEY key is missing',
 		}));
 		assert.match(result.skipped ?? "", /OPENAI_API_KEY/);
 	});
 	it("prunes ghost vectors for nodes that no longer exist", async () => {
 		const g = fakeGraphStore({ vectors: { n1: [1, 0], ghost: [0, 1] } });
 		const scripted = scriptedPort(g.ledger.model, 2);
 		const result = await embedNewNodes(g.store, () => okPort(scripted.port));
 		assert.equal(result.embedded, 1, "n1 already has a vector; only n2 embeds");
 		assert.ok(!("ghost" in g.ledger.vectors), "the ghost vector is pruned");
 	});
 	it("batches at 32 (a 40-node backfill = two API calls)", async () => {
 		const many: GraphNode[] = Array.from({ length: 40 }, (_, i) => node(`n${i}`, `statement ${i}`));
 		const ledger: EmbeddingsLedger = { model: "m", dim: 2, vectors: {}, clusters: [], updatedAt: "" };
 		const writes: EmbeddingsLedger[] = [];
 		const scripted = scriptedPort("m", 2);
 		const result = await embedNewNodes(
 			{
 				readNodes: () => many,
 				readEmbeddings: () => ledger,
 				writeEmbeddings: (next) => writes.push(next),
 			},
 			() => okPort(scripted.port),
 		);
 		assert.equal(result.embedded, 40);
 		assert.equal(scripted.calls.length, 2);
 		assert.equal(scripted.calls[0]!.texts.length, 32);
 		assert.equal(scripted.calls[1]!.texts.length, 8);
 		assert.equal(writes.length, 1);
 	});
 });
 // ─── the federated catalog (scripted keys + fetch) ────────────────────────────
 describe("embeddingModelCatalog", () => {
 	it("no keys = empty catalog", async () => {
 		const models = await embeddingModelCatalog(
 			recordingFetch(() => jsonResponse({})).fetch,
 			() => undefined,
 		);
 		assert.deepEqual(models, []);
 	});
 	it("federates every keyed provider with its filter rules", async () => {
 		const { fetch, calls } = recordingFetch((url) => {
 			if (url.includes("openrouter.ai/api/v1/embeddings/models")) {
 				return jsonResponse({ data: [{ id: "openai/text-embedding-3-small", name: "Text Embedding 3 Small" }, { id: "other/embed" }] });
 			}
 			if (url.includes("api.openai.com")) {
 				return jsonResponse({ data: [{ id: "text-embedding-3-large" }, { id: "text-embedding-3-small" }, { id: "gpt-6-sol" }] });
 			}
 			if (url.includes("generativelanguage")) {
 				return jsonResponse({
 					models: [
 						{ name: "models/gemini-embedding-001", displayName: "Gemini Embedding", supportedGenerationMethods: ["embedContent", "generateContent"] },
 						{ name: "models/gemini-6-pro", supportedGenerationMethods: ["generateContent"] },
 					],
 				});
 			}
 			return jsonResponse({});
 		});
 		const models = await embeddingModelCatalog(fetch, (id) => (id === "OPENROUTER_API_KEY" || id === "OPENAI_API_KEY" || id === "GEMINI_API_KEY" ? "k" : undefined));
 		assert.equal(calls[0]!.url, "https://openrouter.ai/api/v1/embeddings/models?limit=500", "the real catalog path (the inverse 404s — verified live)");
 		assert.deepEqual(models, [
 			{ id: "openrouter:openai/text-embedding-3-small", name: "Text Embedding 3 Small" },
 			{ id: "openrouter:other/embed", name: "other/embed" },
 			{ id: "openai:text-embedding-3-large", name: "text-embedding-3-large" },
 			{ id: "openai:text-embedding-3-small", name: "text-embedding-3-small" },
 			{ id: "google:gemini-embedding-001", name: "Gemini Embedding" },
 		]);
 	});
 	it("a failing provider never poisons the rest", async () => {
 		const { fetch } = recordingFetch((url) => {
 			if (url.includes("openrouter.ai")) return jsonResponse({}, 500); // their side broke
 			if (url.includes("api.openai.com")) return jsonResponse({ data: [{ id: "text-embedding-3-small" }] });
 			return jsonResponse({});
 		});
 		const models = await embeddingModelCatalog(fetch, (id) => (id === "OPENROUTER_API_KEY" || id === "OPENAI_API_KEY" ? "k" : undefined));
 		assert.deepEqual(models, [{ id: "openai:text-embedding-3-small", name: "text-embedding-3-small" }]);
 	});
 });
@@ -134,37 +134,24 @@ describe("AgentSection (thin SSR)", () => {
 	});
 });
-describe("GraphSection — the dependency ladder", () => {
+describe("GraphSection — the collapsed master switch (wave 7)", () => {
-	// Wave 6: without an embedding model the three dependent rungs are
+	// Wave 7 (owner ruling 2026-09-23): the section is the master switch
-	// inert (dimmed, disabled, hinted) — pinned in embedding-settings.
+	// ONLY — the embedding/clustering/labeling rungs and the BYOK model
-	// These ladder pins isolate the CHAIN logic with a model configured.
+	// picker are gone (clustering is the free birth-session partition,
-	const MODEL: GraphSettings = { enabled: true, embeddingModel: "openai:text-embedding-3-small" };
+	// labels ARE session names). One row, one toggle.
-
+	it("renders the single master-switch row", () => {
-	it("defaults with a model set: all four rungs on, none disabled", () => {
+		const html = renderToStaticMarkup(<GraphSection graph={undefined} onSectionChange={() => {}} />);
-		const html = renderToStaticMarkup(<GraphSection graph={MODEL} onSectionChange={() => {}} />);
+		assert.ok(html.includes('data-testid="graph-row-enabled"'));
-		for (const key of ["enabled", "embedding", "clustering", "labeling"]) {
+		assert.ok(!html.includes("graph-row-embedding"), "the dead rungs render nothing");
-			assert.ok(html.includes(`data-testid="graph-row-${key}"`), key);
+		assert.ok(!html.includes("Embedding model"), "the picker is gone");
-			assert.ok(!tagOf(html, `toggle-graph-${key}`).includes("disabled"), `${key} touchable`);
+		assert.ok(!html.includes("graph-row-clustering"));
-		}
+		assert.ok(!html.includes("graph-row-labeling"));
 		assert.ok(tagOf(html, "toggle-graph-enabled").includes('aria-checked="true"'), "absent record = factory ON");
 	});
-	it("master off grays EVERY dependent (stored values persist, resolve inert)", () => {
+	it("a stored off record shows the switch off", () => {
-		const html = renderToStaticMarkup(
+		const html = renderToStaticMarkup(<GraphSection graph={{ enabled: false }} onSectionChange={() => {}} />);
-			<GraphSection graph={{ ...MODEL, enabled: false, embedding: true, clustering: true, labeling: true }} onSectionChange={() => {}} />,
+		assert.ok(tagOf(html, "toggle-graph-enabled").includes('aria-checked="false"'));
 		);
 		for (const key of ["embedding", "clustering", "labeling"]) {
 			assert.ok(tagOf(html, `toggle-graph-${key}`).includes("disabled"), `${key} disabled`);
 		}
 		assert.ok(!tagOf(html, "toggle-graph-enabled").includes("disabled"), "master itself stays touchable");
 	});
 	it("a mid-ladder break grays only what's below it", () => {
 		const html = renderToStaticMarkup(
 			<GraphSection graph={{ ...MODEL, embedding: true, clustering: false }} onSectionChange={() => {}} />,
 		);
 		assert.ok(!tagOf(html, "toggle-graph-embedding").includes("disabled"), "embedding touchable");
 		assert.ok(!tagOf(html, "toggle-graph-clustering").includes("disabled"), "clustering touchable");
 		assert.ok(tagOf(html, "toggle-graph-labeling").includes("disabled"), "labeling grayed");
 	});
 });
@@ -150,47 +150,25 @@ describe("parseAppearanceSettingsBody", () => {
 });
 describe("parseGraphSettingsBody", () => {
-	it("parses the four-rung ladder replace-whole", () => {
+	it("parses the collapsed section replace-whole (wave 7: the master switch ONLY)", () => {
-		const r = parseGraphSettingsBody({ enabled: false, embedding: false });
+		const r = parseGraphSettingsBody({ enabled: false });
 		assert.ok(r.ok);
-		assert.deepEqual(r.settings, { enabled: false, embedding: false });
+		assert.deepEqual(r.settings, { enabled: false });
 		assert.equal(parseGraphSettingsBody({}).ok, true);
 	});
-	it("rejects non-boolean rungs and unknown bodies", () => {
+	it("rejects non-boolean switches and unknown bodies", () => {
 		assert.equal(parseGraphSettingsBody({ enabled: "off" }).ok, false);
 		assert.equal(parseGraphSettingsBody("nope").ok, false);
 	});
-	it("the ladder DEPENDENCY (graph → embedding → clustering → labeling) is a UI concern, not a parse concern", () => {
+	it("the dead wave-6 rungs in a body are ignored — the section collapses on write", () => {
-		// Nothing stops a stored {enabled: false, labeling: true} — the
+		// Old clients may still send {embedding: true, embeddingModel: …};
-		// values persist but resolve inert (grays out in the panel).
+		// the parser reads ONLY the master switch — the dead keys are not
 		// an error, they are just not part of the section anymore.
 		const r = parseGraphSettingsBody({ enabled: false, clustering: true, labeling: true });
 		assert.ok(r.ok);
-	});
+		assert.deepEqual(r.settings, { enabled: false });
 	it("embeddingModel (wave 6): null or a shipped-provider slug; anything else is a 400", () => {
 		const nulled = parseGraphSettingsBody({ embeddingModel: null });
 		assert.ok(nulled.ok);
 		assert.deepEqual(nulled.settings, { embeddingModel: null });
 		for (const slug of [
 			"openai:text-embedding-3-small",
 			"openrouter:openai/text-embedding-3-large",
 			"google:gemini-embedding-001",
 			// OpenRouter ids may carry a :free-style suffix — the provider is
 			// the part before the FIRST colon (live catalog fact, 2026-09-23).
 			"openrouter:liquid/lfm-2.5-embedding-350m:free",
 		]) {
 			const r = parseGraphSettingsBody({ embeddingModel: slug });
 			assert.ok(r.ok, slug);
 			assert.deepEqual(r.settings, { embeddingModel: slug });
 		}
 		// Bad shapes never become a live model.
 		assert.equal(parseGraphSettingsBody({ embeddingModel: "text-embedding-3-small" }).ok, false, "no provider prefix");
 		assert.equal(parseGraphSettingsBody({ embeddingModel: "bedrock:whatever" }).ok, false, "unshipped provider");
 		assert.equal(parseGraphSettingsBody({ embeddingModel: "openai:" }).ok, false, "empty id");
 		assert.equal(parseGraphSettingsBody({ embeddingModel: 42 }).ok, false, "non-string");
 		assert.equal(parseGraphSettingsBody({ embeddingModel: "openai:has spaces" }).ok, false, "whitespace id");
 	});
 });
@@ -11,7 +11,6 @@ import assert from "node:assert/strict";
 import { describe, it } from "node:test";
 import {
 	extractionEnabled,
 	graphContextEnabled,
 	graphToolsEnabled,
 	resolveConversationSpec,
 	resolveModelSpec,
@@ -43,7 +42,7 @@ describe("resolveSettings", () => {
 		assert.equal(eff.appearance.proseFontSize, 16);
 		assert.equal(eff.appearance.sessionNameSerif, true);
 		assert.equal(eff.appearance.mdRendering.headingWeight, 900);
-		assert.deepEqual(eff.graph, { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null }); // BYOK wave 6: no factory embedding model
+		assert.deepEqual(eff.graph, { enabled: true }); // wave 7 collapse: the master switch ONLY
 	});
 	it("explicit record values win over every factory default", () => {
@@ -76,9 +75,8 @@ describe("resolveSettings", () => {
 		assert.equal(eff.appearance.mode, "light");
 		assert.equal(eff.appearance.proseFontSize, 20);
 		assert.equal(eff.appearance.mdRendering.headingWeight, 600);
-		// graph: enabled=false explicit; absent rungs stay factory-on (stored
+		// wave 7 collapse: the section IS the master switch.
-		// but inert — the ladder grays them out in the UI).
+		assert.deepEqual(eff.graph, { enabled: false });
 		assert.deepEqual(eff.graph, { enabled: false, embedding: true, clustering: true, labeling: true, embeddingModel: null }); // wave 6: unset embeddingModel resolves to the null floor
 	});
 	it("partial role override: thinkingLevel alone borrows the SLOT's model (or nothing, without one)", () => {
@@ -224,29 +222,20 @@ describe("resolveConversationSpec (the turn gate's chain)", () => {
 });
 describe("the graph channel gates (the wave's seam honoring)", () => {
-	it("WRITE channel (extraction): dies with the master switch only", () => {
+	it("WRITE channel (extraction): dies with the master switch — the ONLY graph setting since wave 7", () => {
-		assert.equal(extractionEnabled({ enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null }), true);
+		assert.equal(extractionEnabled({ enabled: true }), true);
-		assert.equal(extractionEnabled({ enabled: true, embedding: false, clustering: false, labeling: false, embeddingModel: null }), true);
+		assert.equal(extractionEnabled({ enabled: false }), false);
 		assert.equal(extractionEnabled({ enabled: false, embedding: true, clustering: true, labeling: true, embeddingModel: null }), false);
 	});
-	it("READ channel (graph-context): needs master AND the embedding rung", () => {
+	it("the write gate rides resolveSettings' factory defaults (all on → open)", () => {
 		assert.equal(graphContextEnabled({ enabled: true, embedding: true, clustering: false, labeling: false, embeddingModel: null }), true);
 		assert.equal(graphContextEnabled({ enabled: true, embedding: false, clustering: true, labeling: true, embeddingModel: null }), false);
 		assert.equal(graphContextEnabled({ enabled: false, embedding: true, clustering: true, labeling: true, embeddingModel: null }), false);
 	});
 	it("both gates ride resolveSettings' factory defaults (all on → both open)", () => {
 		const eff = resolveSettings(null);
 		assert.equal(extractionEnabled(eff.graph), true);
 		assert.equal(graphContextEnabled(eff.graph), true);
 		const off = resolveSettings({ version: 1, graph: { enabled: false } });
 		assert.equal(extractionEnabled(off.graph), false);
 		assert.equal(graphContextEnabled(off.graph), false);
 	});
 	it("PROBER GRAPH TOOLS gate (wave 7, owner ruling 2026-09-23): the agent toggle AND the graph master switch compose", () => {
-		const graph = { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null };
+		const graph = { enabled: true };
 		assert.equal(graphToolsEnabled({ graph, agent: { graphTools: {} } }), true, "absent subsection = ON");
 		assert.equal(graphToolsEnabled({ graph, agent: { graphTools: { enabled: true } } }), true);
 		assert.equal(graphToolsEnabled({ graph, agent: { graphTools: { enabled: false } } }), false);
@@ -21,7 +21,7 @@ describe("FACTORY_SETTINGS (the compiled factory layer)", () => {
 	it("pins the owner-decided defaults", () => {
 		assert.equal(CURRENT_SETTINGS_VERSION, 1);
 		assert.deepEqual(FACTORY_SETTINGS.general, { autoUpdate: true });
-		assert.deepEqual(FACTORY_SETTINGS.graph, { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null }); // BYOK wave 6: no factory embedding model
+		assert.deepEqual(FACTORY_SETTINGS.graph, { enabled: true }); // wave 7 collapse: the master switch ONLY
 		const a = FACTORY_SETTINGS.appearance;
 		assert.equal(a.mode, "dark");
 		assert.equal(a.background, null); // the mode's own default, not a factory hex
@@ -75,7 +75,7 @@ describe("sanitizeUserSettings", () => {
 				sessionNameSerif: false,
 				mdRendering: { headingWeight: 700, boldColor: "#ff8800", italicColor: null },
 			},
-			graph: { enabled: false, embedding: true, clustering: false, labeling: true },
+			graph: { enabled: false },
 		};
 		assert.deepEqual(sanitizeUserSettings(record), record);
 	});