Split contracts.ts by domain: plan / graph / chat / jobs

Purely mechanical: src/core/contracts/{plan,graph,chat,jobs}.ts carry the
sections verbatim (graph = nodes + ledgers + extraction findings; jobs =
the async ack-and-steer protocol + tool results + demo bridge), and
contracts.ts becomes a re-exporting barrel so every existing import path
(@/src/core and ./contracts.ts) is unchanged.

Wave-2 lane ownership follows the split: the graph stream owns
contracts/graph.ts, the teacher stream owns contracts/jobs.ts — same-file
contract collisions between streams are structurally impossible now;
cross-domain schema changes go through master.
This commit is contained in:
amosblomqvist
2026-09-05 21:20:38 +03:00
parent d8324ca9bb
commit 8a9497bf59
5 changed files with 550 additions and 524 deletions
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/** /**
* Shared contracts — the single source of truth every component codes against. * Shared contracts — the single source of truth every component codes against.
* Pure types + protocol docs + tiny pure helpers; type-only imports of "ai"
* (safe everywhere: server, client, tests). See docs/vision.md (§ refs).
*/
import type { UIMessage } from "ai";
import type { Phase3 } from "./phases.ts";
// ─── Plan tree (vision §2 "formal plan tree") ────────────────────────────────
// A session-scoped dependency tree authored by the teacher via plan tools.
// One substrate with the global graph: tree nodes fold into graph nodes.
export interface PlanNode {
/** Opaque, agent-chosen slug, stable across edits: "chain-rule-intuition". */
id: string;
/** Short label for the tree panel. */
title: string;
/** 1-2 sentences: what this node covers. */
summary?: string;
}
export interface PlanEdge {
/** Prerequisite node id. */
from: string;
/** Node that depends on `from`. */
to: string;
}
export interface PlanTree {
nodes: PlanNode[];
edges: PlanEdge[];
currentNodeId: string | null;
doneNodeIds: string[];
/** Increments on every mutation — lets the UI animate/react to changes. */
version: number;
}
/** Derived, never stored: status of a node for the panel (vision §2). */
export type PlanNodeStatus = "done" | "current" | "upcoming";
export function planNodeStatus(tree: PlanTree, nodeId: string): PlanNodeStatus {
if (tree.currentNodeId === nodeId) return "current";
if (tree.doneNodeIds.includes(nodeId)) return "done";
return "upcoming";
}
/**
* Plan tools (W1 implements; server-side WITH execute, mutate the chat's
* stored tree, and return the full updated tree so the panel can render the
* latest state straight from the stream). Every tool returns PlanToolResult.
* Tool names: plan_init, plan_add_node, plan_update_node, plan_remove_node,
* plan_add_edge, plan_remove_edge, plan_set_current, plan_mark_done.
* The tree panel derives its display from the LATEST plan tool output in the
* transcript (live during streaming) — no separate state channel.
*/
export interface PlanToolResult {
ok: boolean;
error?: string;
tree: PlanTree;
}
// ─── Knowledge graph (vision §1 — global, cross-session) ────────────────────
export type EdgeType = "requires" | "supports";
export interface GraphNode {
/** Opaque stable id, store-assigned: "n_" + nanoid-like. */
id: string;
/** Short name / header for the fact (2-6 words) — the rendering handle.
* NODE THEORY: a node is an OBJECTIVE fact, never learner- or
* session-relative. No "the learner understands…", no "this session
* covered…" — just the fact, world-facing. The learner's relationship
* to the fact lives ENTIRELY in the event ledger: state labels are
* derived from quiz outcomes, never written into the node. */
name: string;
/** Canonical proposition statement — the full fact, one reasoning step,
* also objective (no learner / teacher / session references). */
statement: string;
/** Verbatim alternate statements from sessions (merge target audit trail). */
aliases: string[];
kind: "truth" | "misconception";
createdAt: string;
}
export interface GraphEdge {
/** Prerequisite GraphNode id. */
from: string;
to: string;
type: EdgeType;
/** Chat ids that asserted this edge (teacher may simplify — auditable). */
provenance: string[];
}
/** mappings.jsonl line — the session→graph identity map: the extraction
* pipeline's proposition (identified by its source id) → the graph node it
* merged into. The merge audit trail: one line per (chat, graph node). */
export interface NodeMapping {
chatId: string;
/** The writer's local id for the proposition — a plan-tree node id when
* tree-derived, else the extractor's proposition id ("p1", …). */
sourceId: string;
graphNodeId: string;
}
/** sessions.jsonl line — the emission checkpoint for one chat (which chats
* have been folded, and through which updatedAt — never fold twice). */
export interface SessionCheckpoint {
chatId: string;
/** The chat record's `updatedAt` at the time of the fold. */
foldedAt: string;
}
export type LedgerEventKind =
| "probe-correct"
| "probe-miss"
| "dont-know"
| "misconception-signaled"
| "taught"
| "check-correct"
| "check-incorrect";
export interface LedgerEvent {
/** null = not yet attributed (untagged probe events; consolidation assigns). */
nodeId: string | null;
kind: LedgerEventKind;
chatId: string;
at: string;
/** Idempotency key: the quiz part's toolCallId or `taught:<treeNodeId>` —
* re-emission of the same chat must never double-record an event. */
ref?: string;
/** The learner's quiz note — diagnostic gold (vision §1 calibration). */
note?: string;
/** The quiz question text, when nodeId is null (later attribution input). */
question?: string;
}
/**
* Store format (src/store/graph.ts): JSONL ledgers at config.LEDGER_FILES
* (nodes/edges/events/mappings/sessions). The derived graph is rebuilt by
* reading the ledgers — store events, derive state. sessions.jsonl is the
* extraction checkpoint: which chats the extraction pipeline has processed,
* and through which updatedAt (never extract the same turns twice).
* *
* The EXTRACTION PIPELINE is the sole graph writer (extractor → auditor → * Split by domain (src/core/contracts/): plan.ts (the session plan tree),
* merger): the extractor proposes propositions/edges/attribution with * graph.ts (the global knowledge graph + its extraction pipeline), chat.ts
* verbatim evidence, the auditor drops anything unbacked, and the merger * (session records + UI extension points), jobs.ts (the async sub-agent
* (core/graph.ts, deterministic) resolves identity against the existing * protocol). Each domain file is owned outright by its stream; schema
* ledgers and appends. See SessionFindings below. * changes to a domain you don't own go through master. This barrel keeps
* every existing import path ("@/src/core" and "./contracts.ts") unchanged.
*/ */
// ─── Extraction findings (extractor output → audit → merger input) ─────────── export * from "./contracts/plan.ts";
export * from "./contracts/graph.ts";
/** One proposed proposition — a node candidate. */ export * from "./contracts/chat.ts";
export interface ExtractedProposition { export * from "./contracts/jobs.ts";
/** Extractor-local id ("p1", …) referenced by edges + attribution. */
id: string;
/** Short name / header for the fact (2-6 words) — the rendering handle. */
name: string;
/** Canonical one-reasoning-step statement — an objective, world-facing
* fact (no learner / teacher / session references; the learner's
* relationship to it is carried only by the event attribution). */
statement: string;
/** Verbatim alternate phrasings from the session (merge aliases). */
aliases: string[];
kind: "truth" | "misconception";
/** The plan-tree node this came from, when it did (mapping provenance). */
treeNodeId?: string;
/** Verbatim transcript quotes backing the proposition. */
evidence: string[];
}
/** One proposed relation between two propositions. */
export interface ExtractedEdge {
/** Proposition id that is the prerequisite / support. */
from: string;
/** Proposition id that depends on / is supported by `from`. */
to: string;
type: EdgeType;
/** Verbatim transcript quotes backing the relation. */
evidence: string[];
}
/** The extractor's event attribution — its ONLY say about events. The kinds,
* timestamps, idempotency refs, notes and question texts are all rebuilt
* deterministically by the merger from the transcript's quiz / mark_done
* tool parts; the LLM supplies exactly the judgment call: WHICH proposition
* each one is about (vision open question: probes attribute retroactively,
* against the whole transcript — a pre-tree probe can map to a proposition
* the tree only articulates later). */
/** The extractor's event attribution — its ONLY say about events. Event
* kinds, timestamps, idempotency refs, notes and question texts are all
* rebuilt deterministically by the merger from the transcript's tool
* parts, and TAGGED events (quiz nodeId, mark_done tree node) resolve
* deterministically through the proposition mapping. The LLM supplies
* exactly the one judgment call that needs it — the vision's open
* probe-attribution question: WHICH proposition did an UNTAGGED probe
* test? Answered retroactively against the whole transcript — a pre-tree
* probe can map to a proposition the tree only articulates later. */
export interface ExtractionAttribution {
/** UNTAGGED quiz toolCallId → proposition id; missing / null =
* unattributed (the event lands with nodeId null + the question text,
* exactly like unattributable probes always did). */
quiz: Record<string, string | null>;
}
/** The extractor's — and, filtered, the auditor's — output shape. */
export interface SessionFindings {
propositions: ExtractedProposition[];
edges: ExtractedEdge[];
attribution: ExtractionAttribution;
}
/** The auditor's verdict: the verified subset, plus the drops (for the log —
* the audit trail of what was rejected and why). */
export interface AuditResult extends SessionFindings {
/** Dropped candidates: proposition id, edge "from→to", or attribution
* toolCallId — with the reason. */
dropped: { ref: string; reason: string }[];
}
/** Per-node state class for the home view (vision §1 "Home page") — derived,
* never stored: taught if ANY taught event; else the LATEST quiz event decides
* (known / missed / dont-know); a node with no events is a candidate. */
export type NodeStateClass = "taught" | "known" | "missed" | "dont-know" | "candidate";
/** The GET /api/graph payload — everything the home view's PURE renderer needs. */
export interface GraphSnapshot {
nodes: GraphNode[];
edges: GraphEdge[];
nodeStates: Record<string, NodeStateClass>;
/** chatId → title, for every chat that touched the graph. */
sessions: Record<string, string>;
/** nodeId → chatIds that touched the node (click-through provenance). */
nodeSessions: Record<string, string[]>;
}
/** Derive a short chat title from the learner's first message (pure).
* Used on chat creation; deterministic so tests can pin it. */
export function titleFromPrompt(prompt: string): string {
const oneLine = prompt.replace(/\s+/g, " ").trim();
return oneLine.length > 72 ? `${oneLine.slice(0, 72)}…` : oneLine;
}
// ─── Chat records (the session store's on-disk format) ───────────────────────
/** One persisted session: data/chats/<id>.json. The stored messages are full
* AI SDK UIMessages (parts included) — the canonical session record: a
* pending quiz is a tool part in `input-available` state, which is exactly
* what makes resume-mid-question free. */
export interface ChatRecord {
id: string;
title: string;
createdAt: string;
updatedAt: string;
messages: UIMessage[];
/** The session's plan tree (vision §2) — written by plan tools via saveTree. */
tree?: PlanTree;
}
/** The list view's shape (messageCount instead of full messages) + the
* per-session status the picker renders. `phase` and `pendingQuestion`
* derive from the stored record (tree + messages); the store computes
* them in listChats. Liveness (`busy`) is NOT record-derived — the API
* route merges it in (see ChatListItem).
*
* Phase semantics (three phases, data-only): probe = no plan yet, plan =
* tree exists but no node occupied, teach = a node is occupied
* (core/phases.ts phaseOfTree).
*/
export interface ChatSummary {
id: string;
title: string;
createdAt: string;
updatedAt: string;
messageCount: number;
/** The session's current phase, derived from the stored plan tree. */
phase: Phase3;
/** An unanswered quiz/ask question is waiting for the learner. */
pendingQuestion: boolean;
}
/** The sidebar's session row: a summary + live liveness. The chats list
* route attaches `busy` from the turn-liveness registry and the job store
* (a running turn OR a background job = the session is working). The store
* itself can't see liveness (import law: store never imports server),
* hence the split at exactly the record/liveness seam. */
export interface ChatListItem extends ChatSummary {
busy: boolean;
}
// ─── Async sub-agent jobs (vision §2.5; plan.md Wave 0 protocol) ────────────
export type JobKind = "research" | "demo" | "visual";
export type JobStatus = "running" | "done" | "error";
export interface JobRecord {
id: string;
kind: JobKind;
chatId: string;
/** The tool part this job fulfills. */
toolCallId: string;
status: JobStatus;
/** Latest human-readable activity line, e.g. `web_search: "sigma.js v3"`. */
activity: string;
createdAt: string;
updatedAt: string;
/** The sub-agent's result (steer-artifact payload). */
result?: unknown;
error?: string;
}
/**
* THE ASYNC PROTOCOL — "ack now, steer later" (the pi sub-agents model,
* vision §2.5):
*
* 1. Job tools (research / demo / visual) are SERVER-EXECUTED. The model
* calls one MID-TURN; execute creates the job record, fires the
* background runner, and resolves IMMEDIATELY with an acknowledgement
* ("dispatched — continue your turn"). The teacher's turn does NOT end
* at the call: it keeps teaching, may dispatch other independent jobs
* (full parallelism), and ends whenever it ends. The tool part's output
* is the ack, forever.
*
* 2. The background runner (src/agent/subagents/runner.ts) runs the kind's
* sub-agent loop — isolated context, modelFor(role), the
* verify-never-one-shot doctrine (headless-tested demos, looked-at SVGs,
* sourced briefs) — tracking a live activity line in the job record.
* ONE central client poll of GET /api/jobs?chatId=… (in Chat) drives every
* job surface: the transcript dispatch cards (live activity by
* toolCallId, quiet one-liner after delivery), the sub-agents sidebar
* panel, and the single re-sync when a job settles.
*
* 3. On completion the runner builds a STEER MESSAGE (buildSubagentSteer)
* and hands it to the wake pipeline (src/server/jobs.ts): a user-role
* UIMessage carrying the result — a text part with what the MODEL needs
* (the research brief, the builder's notes), plus a data-subagent-artifact
* part with what the UI renders (the full result: brief + sources /
* SVG / demo HTML). Provider conversion drops data parts, so artifact
* payloads never enter the teacher's context. metadata.subagentResult
* identifies the job; the UI renders these as sub-agent result cards,
* NEVER as learner interjections.
*
* 4. The wake pipeline delivers the steer by firing a SERVER-SIDE teacher
* turn through the same turn composition as a client request
* (handleChatRequest) — write-ahead persist, turn liveness, extraction
* trigger: one code path. A per-chat serialized queue: a steer waits
* while a turn is in flight OR a question tool part is pending (an
* unanswered client-side tool call breaks provider conversion — the
* steer fires right after the learner's continuation), then fires; a 409
* (a client turn raced in) retries. Delivery is idempotent per jobId, so
* a crashed wake can be re-entered by the stale-job recovery sweep. The
* job's status flips to done/error only AFTER the wake turn completes —
* a polling client that sees the flip re-syncs once and gets the
* finished record.
*
* 5. No pending job parts ever exist, so the composer never gates on jobs
* (the learner can interject mid-job; the steer queues behind their
* turn) and job tool outputs never trigger the client-side
* sendAutomatically continuation — that mechanism stays quiz/ask-only.
*/
export interface ResearchToolInput {
question: string;
/** What the teacher needs it for — focuses the research brief. */
context?: string;
}
export interface ResearchResult {
brief: string;
sources: { title: string; url: string }[];
}
export interface DemoToolInput {
title: string;
/** Precise brief: concept, parameters/interactions, visual style. */
brief: string;
}
export interface DemoResult {
title: string;
/** Self-contained single-file HTML/JS document (sandboxed-iframe ready). */
html: string;
/** The builder sub-agent headlessly executed the demo before delivery. */
tested: true;
notes?: string;
}
export interface VisualToolInput {
/** Precise brief for ONE visual (what it must show, exactly). */
brief: string;
}
export interface VisualResult {
/** Complete SVG markup (verified by the maker looking at the render). */
svg: string;
notes?: string;
}
// ─── Sub-agent steer messages (THE ASYNC PROTOCOL §3–4) ─────────────────────
/** Message metadata marking a sub-agent steer (contracts.ts §3). */
export interface SubagentResultMeta {
jobId: string;
kind: JobKind;
toolCallId: string;
}
/** The data-subagent-artifact part payload: what the UI renders. */
export interface SubagentArtifactData {
kind: JobKind;
result: ResearchResult | DemoResult | VisualResult;
}
/**
* The steer message: user-role on the wire (UIMessage roles are fixed), but
* NEVER a learner interjection — metadata.subagentResult is the marker the
* UI branches on, and the artifact rides the data part (stripped by provider
* conversion, so the model sees only the text part).
*/
export interface SubagentSteerMessage extends UIMessage {
role: "user";
metadata: { subagentResult: SubagentResultMeta };
parts: [
{ type: "text"; text: string },
{ type: "data-subagent-artifact"; data: SubagentArtifactData },
];
}
/** Whether a result is the failure shape (html/svg empty, failure brief). */
export function isSubagentFailure(kind: JobKind, result: ResearchResult | DemoResult | VisualResult): boolean {
if (kind === "research") {
const r = result as ResearchResult;
return r.sources.length === 0 && /^Research failed:/i.test(r.brief);
}
if (kind === "demo") {
const r = result as DemoResult;
return typeof r.html !== "string" || r.html.length === 0;
}
const r = result as VisualResult;
return typeof r.svg !== "string" || r.svg.length === 0;
}
/** The model-facing text of a steer: what the result IS, what to do with it. */
function steerText(kind: JobKind, result: ResearchResult | DemoResult | VisualResult): string {
if (isSubagentFailure(kind, result)) {
if (kind === "research")
return `${(result as ResearchResult).brief}\n\nContinue with what you can establish and flag the uncertainty to the learner.`;
if (kind === "demo")
return `The demo "${(result as DemoResult).title}" could not be built: ${
(result as DemoResult).notes ?? "unknown reason"
}. Fall back to explaining in prose; if the brief was the problem, re-call demo with a tighter one.`;
return `The figure could not be made: ${(result as VisualResult).notes ?? "unknown reason"}. Fall back to prose or mermaid, or re-call visual with a tighter brief.`;
}
if (kind === "research")
return `Research complete — the researcher's full brief follows. Weave it into the lesson and cite its sources when you use its findings:\n\n${(result as ResearchResult).brief}`;
if (kind === "demo")
return `The demo "${(result as DemoResult).title}" is built, headlessly tested, and rendered in the lesson. Builder notes: ${
(result as DemoResult).notes ?? "(none)"
}.\nTeach with it — reference its controls by name and point the learner at what to try.`;
return `The figure is drawn, verified by inspection, and rendered in the lesson. Maker notes: ${
(result as VisualResult).notes ?? "(none)"
}.\nReference its elements as you teach.`;
}
/**
* Build the steer message for a finished job (pure — shared by the runner,
* the wake pipeline, and tests). Deterministic id per job makes re-delivery
* idempotent.
*/
export function buildSubagentSteer(
job: Pick<JobRecord, "id" | "kind" | "toolCallId">,
result: ResearchResult | DemoResult | VisualResult,
): SubagentSteerMessage {
return {
id: `steer_${job.id}`,
role: "user",
metadata: { subagentResult: { jobId: job.id, kind: job.kind, toolCallId: job.toolCallId } },
parts: [
{ type: "text", text: steerText(job.kind, result) },
{ type: "data-subagent-artifact", data: { kind: job.kind, result } },
],
};
}
/** True when the transcript already carries this job's steer (idempotent delivery). */
export function steerDelivered(messages: UIMessage[], jobId: string): boolean {
return messages.some(
(m) => (m.metadata as { subagentResult?: { jobId?: string } } | undefined)?.subagentResult?.jobId === jobId,
);
}
// ─── Demo iframe bridge (W6) ────────────────────────────────────────────────
// postMessage protocol between the sandboxed demo iframe and its parent card.
// The bridge script is auto-injected into every demo HTML by the demo-card
// renderer (never the model's job): ResizeObserver → DEMO_RESIZE with the
// document height; window.onerror → DEMO_ERROR with the message.
export interface DemoResizeMessage {
type: "demo-resize";
height: number;
}
export interface DemoErrorMessage {
type: "demo-error";
message: string;
}
export type DemoBridgeMessage = DemoResizeMessage | DemoErrorMessage;
// ─── Artifact renderer registry (client extension point) ────────────────────
/**
* components/chat.tsx renders any tool part it doesn't specially handle via
* components/artifacts/index.ts's registry, keyed by tool name ("research",
* "demo", "visual", …). Workers add renderers there — chat.tsx is off-limits
* to artifact work. Renderer receives the raw tool part; it typically renders
* an activity card while state is input-available (poll /api/jobs) and the
* artifact (iframe / svg) when output-available.
*/
export interface ArtifactPartProps {
part: {
type: string;
state: string;
toolCallId: string;
input?: unknown;
output?: unknown;
};
}
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/**
* Chat-session contracts — records, summaries, UI extension points
*
* Part of the domain-split contracts (src/core/contracts/): each domain file
* is owned outright by its stream — cross-domain schema changes go through
* master. Pure types + protocol docs + tiny pure helpers; type-only imports
* of "ai" (safe everywhere: server, client, tests). See docs/vision.md.
*/
import type { UIMessage } from "ai";
import type { Phase3 } from "../phases.ts";
import type { PlanTree } from "./plan.ts";
// ─── Chat records (the session store's on-disk format) ───────────────────────
/** One persisted session: data/chats/<id>.json. The stored messages are full
* AI SDK UIMessages (parts included) — the canonical session record: a
* pending quiz is a tool part in `input-available` state, which is exactly
* what makes resume-mid-question free. */
export interface ChatRecord {
id: string;
title: string;
createdAt: string;
updatedAt: string;
messages: UIMessage[];
/** The session's plan tree (vision §2) — written by plan tools via saveTree. */
tree?: PlanTree;
}
/** The list view's shape (messageCount instead of full messages) + the
* per-session status the picker renders. `phase` and `pendingQuestion`
* derive from the stored record (tree + messages); the store computes
* them in listChats. Liveness (`busy`) is NOT record-derived — the API
* route merges it in (see ChatListItem).
*
* Phase semantics (three phases, data-only): probe = no plan yet, plan =
* tree exists but no node occupied, teach = a node is occupied
* (core/phases.ts phaseOfTree).
*/
export interface ChatSummary {
id: string;
title: string;
createdAt: string;
updatedAt: string;
messageCount: number;
/** The session's current phase, derived from the stored plan tree. */
phase: Phase3;
/** An unanswered quiz/ask question is waiting for the learner. */
pendingQuestion: boolean;
}
/** The sidebar's session row: a summary + live liveness. The chats list
* route attaches `busy` from the turn-liveness registry and the job store
* (a running turn OR a background job = the session is working). The store
* itself can't see liveness (import law: store never imports server),
* hence the split at exactly the record/liveness seam. */
export interface ChatListItem extends ChatSummary {
busy: boolean;
}
// ─── Artifact renderer registry (client extension point) ────────────────────
/**
* components/chat.tsx renders any tool part it doesn't specially handle via
* components/artifacts/index.ts's registry, keyed by tool name ("research",
* "demo", "visual", …). Workers add renderers there — chat.tsx is off-limits
* to artifact work. Renderer receives the raw tool part; it typically renders
* an activity card while state is input-available (poll /api/jobs) and the
* artifact (iframe / svg) when output-available.
*/
export interface ArtifactPartProps {
part: {
type: string;
state: string;
toolCallId: string;
input?: unknown;
output?: unknown;
};
}
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/**
* Knowledge-graph contracts — nodes, ledgers, extraction findings
*
* Part of the domain-split contracts (src/core/contracts/): each domain file
* is owned outright by its stream — cross-domain schema changes go through
* master. Pure types + protocol docs + tiny pure helpers; type-only imports
* of "ai" (safe everywhere: server, client, tests). See docs/vision.md.
*/
// ─── Knowledge graph (vision §1 — global, cross-session) ────────────────────
export type EdgeType = "requires" | "supports";
export interface GraphNode {
/** Opaque stable id, store-assigned: "n_" + nanoid-like. */
id: string;
/** Short name / header for the fact (2-6 words) — the rendering handle.
* NODE THEORY: a node is an OBJECTIVE fact, never learner- or
* session-relative. No "the learner understands…", no "this session
* covered…" — just the fact, world-facing. The learner's relationship
* to the fact lives ENTIRELY in the event ledger: state labels are
* derived from quiz outcomes, never written into the node. */
name: string;
/** Canonical proposition statement — the full fact, one reasoning step,
* also objective (no learner / teacher / session references). */
statement: string;
/** Verbatim alternate statements from sessions (merge target audit trail). */
aliases: string[];
kind: "truth" | "misconception";
createdAt: string;
}
export interface GraphEdge {
/** Prerequisite GraphNode id. */
from: string;
to: string;
type: EdgeType;
/** Chat ids that asserted this edge (teacher may simplify — auditable). */
provenance: string[];
}
/** mappings.jsonl line — the session→graph identity map: the extraction
* pipeline's proposition (identified by its source id) → the graph node it
* merged into. The merge audit trail: one line per (chat, graph node). */
export interface NodeMapping {
chatId: string;
/** The writer's local id for the proposition — a plan-tree node id when
* tree-derived, else the extractor's proposition id ("p1", …). */
sourceId: string;
graphNodeId: string;
}
/** sessions.jsonl line — the emission checkpoint for one chat (which chats
* have been folded, and through which updatedAt — never fold twice). */
export interface SessionCheckpoint {
chatId: string;
/** The chat record's `updatedAt` at the time of the fold. */
foldedAt: string;
}
export type LedgerEventKind =
| "probe-correct"
| "probe-miss"
| "dont-know"
| "misconception-signaled"
| "taught"
| "check-correct"
| "check-incorrect";
export interface LedgerEvent {
/** null = not yet attributed (untagged probe events; consolidation assigns). */
nodeId: string | null;
kind: LedgerEventKind;
chatId: string;
at: string;
/** Idempotency key: the quiz part's toolCallId or `taught:<treeNodeId>` —
* re-emission of the same chat must never double-record an event. */
ref?: string;
/** The learner's quiz note — diagnostic gold (vision §1 calibration). */
note?: string;
/** The quiz question text, when nodeId is null (later attribution input). */
question?: string;
}
/**
* Store format (src/store/graph.ts): JSONL ledgers at config.LEDGER_FILES
* (nodes/edges/events/mappings/sessions). The derived graph is rebuilt by
* reading the ledgers — store events, derive state. sessions.jsonl is the
* extraction checkpoint: which chats the extraction pipeline has processed,
* and through which updatedAt (never extract the same turns twice).
*
* The EXTRACTION PIPELINE is the sole graph writer (extractor → auditor →
* merger): the extractor proposes propositions/edges/attribution with
* verbatim evidence, the auditor drops anything unbacked, and the merger
* (core/graph.ts, deterministic) resolves identity against the existing
* ledgers and appends. See SessionFindings below.
*/
// ─── Extraction findings (extractor output → audit → merger input) ───────────
/** One proposed proposition — a node candidate. */
export interface ExtractedProposition {
/** Extractor-local id ("p1", …) referenced by edges + attribution. */
id: string;
/** Short name / header for the fact (2-6 words) — the rendering handle. */
name: string;
/** Canonical one-reasoning-step statement — an objective, world-facing
* fact (no learner / teacher / session references; the learner's
* relationship to it is carried only by the event attribution). */
statement: string;
/** Verbatim alternate phrasings from the session (merge aliases). */
aliases: string[];
kind: "truth" | "misconception";
/** The plan-tree node this came from, when it did (mapping provenance). */
treeNodeId?: string;
/** Verbatim transcript quotes backing the proposition. */
evidence: string[];
}
/** One proposed relation between two propositions. */
export interface ExtractedEdge {
/** Proposition id that is the prerequisite / support. */
from: string;
/** Proposition id that depends on / is supported by `from`. */
to: string;
type: EdgeType;
/** Verbatim transcript quotes backing the relation. */
evidence: string[];
}
/** The extractor's event attribution — its ONLY say about events. The kinds,
* timestamps, idempotency refs, notes and question texts are all rebuilt
* deterministically by the merger from the transcript's quiz / mark_done
* tool parts; the LLM supplies exactly the judgment call: WHICH proposition
* each one is about (vision open question: probes attribute retroactively,
* against the whole transcript — a pre-tree probe can map to a proposition
* the tree only articulates later). */
/** The extractor's event attribution — its ONLY say about events. Event
* kinds, timestamps, idempotency refs, notes and question texts are all
* rebuilt deterministically by the merger from the transcript's tool
* parts, and TAGGED events (quiz nodeId, mark_done tree node) resolve
* deterministically through the proposition mapping. The LLM supplies
* exactly the one judgment call that needs it — the vision's open
* probe-attribution question: WHICH proposition did an UNTAGGED probe
* test? Answered retroactively against the whole transcript — a pre-tree
* probe can map to a proposition the tree only articulates later. */
export interface ExtractionAttribution {
/** UNTAGGED quiz toolCallId → proposition id; missing / null =
* unattributed (the event lands with nodeId null + the question text,
* exactly like unattributable probes always did). */
quiz: Record<string, string | null>;
}
/** The extractor's — and, filtered, the auditor's — output shape. */
export interface SessionFindings {
propositions: ExtractedProposition[];
edges: ExtractedEdge[];
attribution: ExtractionAttribution;
}
/** The auditor's verdict: the verified subset, plus the drops (for the log —
* the audit trail of what was rejected and why). */
export interface AuditResult extends SessionFindings {
/** Dropped candidates: proposition id, edge "from→to", or attribution
* toolCallId — with the reason. */
dropped: { ref: string; reason: string }[];
}
/** Per-node state class for the home view (vision §1 "Home page") — derived,
* never stored: taught if ANY taught event; else the LATEST quiz event decides
* (known / missed / dont-know); a node with no events is a candidate. */
export type NodeStateClass = "taught" | "known" | "missed" | "dont-know" | "candidate";
/** The GET /api/graph payload — everything the home view's PURE renderer needs. */
export interface GraphSnapshot {
nodes: GraphNode[];
edges: GraphEdge[];
nodeStates: Record<string, NodeStateClass>;
/** chatId → title, for every chat that touched the graph. */
sessions: Record<string, string>;
/** nodeId → chatIds that touched the node (click-through provenance). */
nodeSessions: Record<string, string[]>;
}
/** Derive a short chat title from the learner's first message (pure).
* Used on chat creation; deterministic so tests can pin it. */
export function titleFromPrompt(prompt: string): string {
const oneLine = prompt.replace(/\s+/g, " ").trim();
return oneLine.length > 72 ? `${oneLine.slice(0, 72)}…` : oneLine;
}
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/**
* Sub-agent job contracts — the async ack-and-steer protocol
*
* Part of the domain-split contracts (src/core/contracts/): each domain file
* is owned outright by its stream — cross-domain schema changes go through
* master. Pure types + protocol docs + tiny pure helpers; type-only imports
* of "ai" (safe everywhere: server, client, tests). See docs/vision.md.
*/
import type { UIMessage } from "ai";
// ─── Async sub-agent jobs (vision §2.5; plan.md Wave 0 protocol) ────────────
export type JobKind = "research" | "demo" | "visual";
export type JobStatus = "running" | "done" | "error";
export interface JobRecord {
id: string;
kind: JobKind;
chatId: string;
/** The tool part this job fulfills. */
toolCallId: string;
status: JobStatus;
/** Latest human-readable activity line, e.g. `web_search: "sigma.js v3"`. */
activity: string;
createdAt: string;
updatedAt: string;
/** The sub-agent's result (steer-artifact payload). */
result?: unknown;
error?: string;
}
/**
* THE ASYNC PROTOCOL — "ack now, steer later" (the pi sub-agents model,
* vision §2.5):
*
* 1. Job tools (research / demo / visual) are SERVER-EXECUTED. The model
* calls one MID-TURN; execute creates the job record, fires the
* background runner, and resolves IMMEDIATELY with an acknowledgement
* ("dispatched — continue your turn"). The teacher's turn does NOT end
* at the call: it keeps teaching, may dispatch other independent jobs
* (full parallelism), and ends whenever it ends. The tool part's output
* is the ack, forever.
*
* 2. The background runner (src/agent/subagents/runner.ts) runs the kind's
* sub-agent loop — isolated context, modelFor(role), the
* verify-never-one-shot doctrine (headless-tested demos, looked-at SVGs,
* sourced briefs) — tracking a live activity line in the job record.
* ONE central client poll of GET /api/jobs?chatId=… (in Chat) drives every
* job surface: the transcript dispatch cards (live activity by
* toolCallId, quiet one-liner after delivery), the sub-agents sidebar
* panel, and the single re-sync when a job settles.
*
* 3. On completion the runner builds a STEER MESSAGE (buildSubagentSteer)
* and hands it to the wake pipeline (src/server/jobs.ts): a user-role
* UIMessage carrying the result — a text part with what the MODEL needs
* (the research brief, the builder's notes), plus a data-subagent-artifact
* part with what the UI renders (the full result: brief + sources /
* SVG / demo HTML). Provider conversion drops data parts, so artifact
* payloads never enter the teacher's context. metadata.subagentResult
* identifies the job; the UI renders these as sub-agent result cards,
* NEVER as learner interjections.
*
* 4. The wake pipeline delivers the steer by firing a SERVER-SIDE teacher
* turn through the same turn composition as a client request
* (handleChatRequest) — write-ahead persist, turn liveness, extraction
* trigger: one code path. A per-chat serialized queue: a steer waits
* while a turn is in flight OR a question tool part is pending (an
* unanswered client-side tool call breaks provider conversion — the
* steer fires right after the learner's continuation), then fires; a 409
* (a client turn raced in) retries. Delivery is idempotent per jobId, so
* a crashed wake can be re-entered by the stale-job recovery sweep. The
* job's status flips to done/error only AFTER the wake turn completes —
* a polling client that sees the flip re-syncs once and gets the
* finished record.
*
* 5. No pending job parts ever exist, so the composer never gates on jobs
* (the learner can interject mid-job; the steer queues behind their
* turn) and job tool outputs never trigger the client-side
* sendAutomatically continuation — that mechanism stays quiz/ask-only.
*/
export interface ResearchToolInput {
question: string;
/** What the teacher needs it for — focuses the research brief. */
context?: string;
}
export interface ResearchResult {
brief: string;
sources: { title: string; url: string }[];
}
export interface DemoToolInput {
title: string;
/** Precise brief: concept, parameters/interactions, visual style. */
brief: string;
}
export interface DemoResult {
title: string;
/** Self-contained single-file HTML/JS document (sandboxed-iframe ready). */
html: string;
/** The builder sub-agent headlessly executed the demo before delivery. */
tested: true;
notes?: string;
}
export interface VisualToolInput {
/** Precise brief for ONE visual (what it must show, exactly). */
brief: string;
}
export interface VisualResult {
/** Complete SVG markup (verified by the maker looking at the render). */
svg: string;
notes?: string;
}
// ─── Sub-agent steer messages (THE ASYNC PROTOCOL §3–4) ─────────────────────
/** Message metadata marking a sub-agent steer (contracts.ts §3). */
export interface SubagentResultMeta {
jobId: string;
kind: JobKind;
toolCallId: string;
}
/** The data-subagent-artifact part payload: what the UI renders. */
export interface SubagentArtifactData {
kind: JobKind;
result: ResearchResult | DemoResult | VisualResult;
}
/**
* The steer message: user-role on the wire (UIMessage roles are fixed), but
* NEVER a learner interjection — metadata.subagentResult is the marker the
* UI branches on, and the artifact rides the data part (stripped by provider
* conversion, so the model sees only the text part).
*/
export interface SubagentSteerMessage extends UIMessage {
role: "user";
metadata: { subagentResult: SubagentResultMeta };
parts: [
{ type: "text"; text: string },
{ type: "data-subagent-artifact"; data: SubagentArtifactData },
];
}
/** Whether a result is the failure shape (html/svg empty, failure brief). */
export function isSubagentFailure(kind: JobKind, result: ResearchResult | DemoResult | VisualResult): boolean {
if (kind === "research") {
const r = result as ResearchResult;
return r.sources.length === 0 && /^Research failed:/i.test(r.brief);
}
if (kind === "demo") {
const r = result as DemoResult;
return typeof r.html !== "string" || r.html.length === 0;
}
const r = result as VisualResult;
return typeof r.svg !== "string" || r.svg.length === 0;
}
/** The model-facing text of a steer: what the result IS, what to do with it. */
function steerText(kind: JobKind, result: ResearchResult | DemoResult | VisualResult): string {
if (isSubagentFailure(kind, result)) {
if (kind === "research")
return `${(result as ResearchResult).brief}\n\nContinue with what you can establish and flag the uncertainty to the learner.`;
if (kind === "demo")
return `The demo "${(result as DemoResult).title}" could not be built: ${
(result as DemoResult).notes ?? "unknown reason"
}. Fall back to explaining in prose; if the brief was the problem, re-call demo with a tighter one.`;
return `The figure could not be made: ${(result as VisualResult).notes ?? "unknown reason"}. Fall back to prose or mermaid, or re-call visual with a tighter brief.`;
}
if (kind === "research")
return `Research complete — the researcher's full brief follows. Weave it into the lesson and cite its sources when you use its findings:\n\n${(result as ResearchResult).brief}`;
if (kind === "demo")
return `The demo "${(result as DemoResult).title}" is built, headlessly tested, and rendered in the lesson. Builder notes: ${
(result as DemoResult).notes ?? "(none)"
}.\nTeach with it — reference its controls by name and point the learner at what to try.`;
return `The figure is drawn, verified by inspection, and rendered in the lesson. Maker notes: ${
(result as VisualResult).notes ?? "(none)"
}.\nReference its elements as you teach.`;
}
/**
* Build the steer message for a finished job (pure — shared by the runner,
* the wake pipeline, and tests). Deterministic id per job makes re-delivery
* idempotent.
*/
export function buildSubagentSteer(
job: Pick<JobRecord, "id" | "kind" | "toolCallId">,
result: ResearchResult | DemoResult | VisualResult,
): SubagentSteerMessage {
return {
id: `steer_${job.id}`,
role: "user",
metadata: { subagentResult: { jobId: job.id, kind: job.kind, toolCallId: job.toolCallId } },
parts: [
{ type: "text", text: steerText(job.kind, result) },
{ type: "data-subagent-artifact", data: { kind: job.kind, result } },
],
};
}
/** True when the transcript already carries this job's steer (idempotent delivery). */
export function steerDelivered(messages: UIMessage[], jobId: string): boolean {
return messages.some(
(m) => (m.metadata as { subagentResult?: { jobId?: string } } | undefined)?.subagentResult?.jobId === jobId,
);
}
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/**
* Plan-tree contracts — the session-scoped dependency tree
*
* Part of the domain-split contracts (src/core/contracts/): each domain file
* is owned outright by its stream — cross-domain schema changes go through
* master. Pure types + protocol docs + tiny pure helpers; type-only imports
* of "ai" (safe everywhere: server, client, tests). See docs/vision.md.
*/
// ─── Plan tree (vision §2 "formal plan tree") ────────────────────────────────
// A session-scoped dependency tree authored by the teacher via plan tools.
// One substrate with the global graph: tree nodes fold into graph nodes.
export interface PlanNode {
/** Opaque, agent-chosen slug, stable across edits: "chain-rule-intuition". */
id: string;
/** Short label for the tree panel. */
title: string;
/** 1-2 sentences: what this node covers. */
summary?: string;
}
export interface PlanEdge {
/** Prerequisite node id. */
from: string;
/** Node that depends on `from`. */
to: string;
}
export interface PlanTree {
nodes: PlanNode[];
edges: PlanEdge[];
currentNodeId: string | null;
doneNodeIds: string[];
/** Increments on every mutation — lets the UI animate/react to changes. */
version: number;
}
/** Derived, never stored: status of a node for the panel (vision §2). */
export type PlanNodeStatus = "done" | "current" | "upcoming";
export function planNodeStatus(tree: PlanTree, nodeId: string): PlanNodeStatus {
if (tree.currentNodeId === nodeId) return "current";
if (tree.doneNodeIds.includes(nodeId)) return "done";
return "upcoming";
}
/**
* Plan tools (W1 implements; server-side WITH execute, mutate the chat's
* stored tree, and return the full updated tree so the panel can render the
* latest state straight from the stream). Every tool returns PlanToolResult.
* Tool names: plan_init, plan_add_node, plan_update_node, plan_remove_node,
* plan_add_edge, plan_remove_edge, plan_set_current, plan_mark_done.
* The tree panel derives its display from the LATEST plan tool output in the
* transcript (live during streaming) — no separate state channel.
*/
export interface PlanToolResult {
ok: boolean;
error?: string;
tree: PlanTree;
}