feat: graph context injection — the session's prior knowledge, steered in at session start (vision-context-injection.md)

One shot per chat, fired at the first turn's onFinish: the session's
topic (its only typed message) is embedded as an EmbeddingGemma retrieval
query; the semantically closest graph propositions are selected under the
session's token budget (pure budget, no similarity threshold — the
decided doctrine) and delivered as a marker-only steer through the wake
pipeline, queuing behind pending questions like every sub-agent result.

- contracts: ChatSettings.graphContextTokens (default 1024, max 4096,
  step 128, 0 = off); GraphContextData + graphContextText; the
  graph-context JobKind + buildGraphContextSteer (id steer_ctx_<chatId>)
- core/graph-context.ts: brute-force cosine top-K, greedy chars/4 fill
  under budget, most-recent UnderstandingLevel fold, count cap 64,
  parse-don't-cast at the marker seam
- server: embedQuery (role-asymmetric query format, same warm singleton
  — vector-space parity by construction); the runner (startGraphContext-
  Injection one-shot gates; runGraphContextJob validates ledger parity);
  wake branches (chatIdOf/jobIdOf, finalizeJob done, recoverStaleJobs
  re-deliver or contextRespawn); abortJob kills context jobs SILENTLY
  (nothing was promised in the lesson); turn-service trigger seam
- adapter: data-graph-context flattens to the canonical block (malformed
  parts have no model face)
- UI: the budget slider (home dock + /chat/new draft, the session-data
  strip); the quiet SystemLine "~537 · 10 propositions"; dock kind
  "Knowledge context"; model-picker PUT now spreads settings (a model
  pick can no longer clear the sibling override)

Live-smoked end to end on the real embedder + real GLM: seeded ledger
(10 propositions), session on "modal logic: possible worlds and the
necessity box" — job in the dock, queued behind the prober's question,
delivered on the answer, SystemLine in the transcript, wake turn reads
the block and continues the probe. Gates: tsc, lint, 459/459, build.

src/core/contracts/chat.ts

@@ -10,7 +10,7 @@ import type { UIMessage } from "ai";
 import type { ThinkingLevel } from "@earendil-works/pi-agent-core";
 import type { Phase3 } from "../phases.ts";
 import type { PlanNodeStatus, PlanTree } from "./plan.ts";
-import type { EmissionStats } from "./graph.ts";
+import type { EmissionStats, UnderstandingLevel } from "./graph.ts";
 
 // ─── Chat records (the session store's on-disk format) ───────────────────────
 
@@ -58,8 +58,23 @@ export interface ChatSettings {
 	/** pi thinking level — must be in the chosen model's supported list
 	 *  (getSupportedThinkingLevels; enforced by the settings route). */
 	thinkingLevel?: ThinkingLevel;
+	/** Graph context injection (vision-context-injection.md): the session's
+	 *  token budget for the prior-knowledge block — the top semantically
+	 *  closest propositions from the knowledge graph, injected once, at
+	 *  session start, as a steer. Absent = the default (on); 0 = off. The
+	 *  settings route enforces the range; the trigger (turn-service →
+	 *  subagents/graph-context.ts) is one-shot, so a mid-session change
+	 *  affects only sessions that haven't injected yet. */
+	graphContextTokens?: number;
 }
 
+/** The injection budget's decided knobs (2026-09-14): default 1024 (a
+ *  meaningful, modest slice of prior knowledge), hard max 4096, slider
+ *  step 128, 0 = off. */
+export const GRAPH_CONTEXT_TOKENS_DEFAULT = 1024;
+export const GRAPH_CONTEXT_TOKENS_MAX = 4096;
+export const GRAPH_CONTEXT_TOKENS_STEP = 128;
+
 // ─── Background-extraction usage (the money channel, extraction side) ────────
 
 /** One pipeline stage's model usage — the stage's model + every step it
@@ -176,6 +191,54 @@ export const HANDOFF_TEXT =
 	"The probe is concluded — the learner assessment is recorded. " +
 	"Author the session's plan map now (call plan_init) and present it, then begin teaching.";
 
+// ─── Graph context injection (vision-context-injection.md, 2026-09-14) ────────
+
+/** One selected prior-knowledge proposition: similarity-ranked, annotated
+ *  with the learner's CURRENT understanding level — the fold is MOST
+ *  RECENT extraction instance (levels are per-instance, tied to moments in
+ *  time; the latest evidence is the best estimate of current understanding).
+ *  null = the node has no observation event at all. */
+export interface GraphContextProposition {
+	id: string;
+	statement: string;
+	level: UnderstandingLevel | null;
+}
+
+/** The data-graph-context part payload: what the UI renders (the counts)
+ *  AND the audit trail (the full block content — the transcript shows
+ *  exactly what was injected, and when). The model-facing text is derived
+ *  from this by graphContextText below — the adapter flattens the marker
+ *  part to it (the custom-message scheme: no authored human speech). */
+export interface GraphContextData {
+	chatId: string;
+	/** The session's budget at injection time (for the card's readout). */
+	budget: number;
+	/** Estimated tokens of the injected block — chars/4 heuristic. */
+	tokens: number;
+	/** The selected propositions, similarity-descending. */
+	propositions: GraphContextProposition[];
+}
+
+/** The model-facing block text — single-sourced (the adapter, the tests,
+ *  and reality can't drift). The level word travels with each line: the
+ *  closing gloss tells the agent what to do with each. */
+export function graphContextText(data: GraphContextData): string {
+	const n = data.propositions.length;
+	if (n === 0) return "";
+	const lines = data.propositions.map(
+		(p) => `- ${p.level ? `[${p.level}] ` : ""}${p.statement}`,
+	);
+	return [
+		`Prior-knowledge context — what this learner already knows (from their knowledge graph, ` +
+			`${n} proposition${n === 1 ? "" : "s"}, ~${data.tokens} tokens):`,
+		"",
+		...lines,
+		"",
+		"Treat this as reliable prior knowledge: build on what is solid, firm up what is shaky, " +
+			"and teach around anything resisted. Do not re-teach solid ground.",
+	].join("\n");
+}
+
 export interface PlanBlockData {
 	/** Only "full" survives (the 2026-09-11 pivot): the plan renders IN
 	 *  FLOW once, at authoring. The neighborhood fragments are retired —