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/server/turn-service.ts

@@ -21,6 +21,7 @@ import { pendingQuizAnswers } from "../core/graph.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";
 
 /**
  * The canonical state write: the client owns the message list between turns
@@ -98,3 +99,22 @@ export function triggerExtraction(
 		console.error(`[chat ${chatId}] extraction trigger failed:`, err);
 	}
 }
+
+/**
+ * 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);
+	}
+}