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.
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@@ -1,7 +1,8 @@
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import type { UIMessage } from "ai";
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import type { AgentMessage } from "@earendil-works/pi-agent-core";
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import type { AssistantMessage, Message, ToolResultMessage } from "@earendil-works/pi-ai";
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import { HANDOFF_TEXT, verdictText } from "../../core/contracts/chat.ts";
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import { graphContextOfMessage } from "../../core/graph-context.ts";
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import { graphContextText, HANDOFF_TEXT, verdictText } from "../../core/contracts/chat.ts";
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/**
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* The record → pi-ai transcript adapter (docs/pi-engine-swap.md §4). The
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@@ -55,12 +56,19 @@ function zeroUsage() {
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/** The model-facing text of a marker-only user message, or null when the
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* message has no model face at all (UI-only data parts). */
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function systemTextOf(parts: ReadonlyArray<{ type: string; data?: unknown }>): string | null {
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for (const part of parts) {
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function systemTextOf(message: { parts: ReadonlyArray<{ type: string; data?: unknown }> }): string | null {
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for (const part of message.parts) {
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if (part.type === "data-verdict") {
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return verdictText((part.data as { accepted?: unknown } | undefined)?.accepted === true);
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}
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if (part.type === "data-probe-handoff") return HANDOFF_TEXT;
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// Graph context injection: the block text derives from the part's
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// validated data (parse-don't-cast — a malformed part has no model
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// face, and the message then never reaches the provider).
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if (part.type === "data-graph-context") {
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const data = graphContextOfMessage({ parts: message.parts });
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return data ? graphContextText(data) : null;
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}
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}
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return null;
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}
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@@ -79,7 +87,7 @@ export function recordToPiMessages(messages: UIMessage[]): Message[] {
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// wording lives in the adapter's reach (core contracts), never
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// authored as fake human speech in the transcript.
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if (texts.length === 0) {
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const flat = systemTextOf(message.parts);
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const flat = systemTextOf(message);
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if (flat === null) continue; // pure UI data part, no model face
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texts.push(flat);
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}
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