Purge legacy pre-pivot scaffold (decided: extractor/merger-only architecture)

Delete src/extract/ (pi-session normalize/extract/merge stubs), src/ledger.ts,
src/schema.ts, src/derive.ts, src/queries/, src/cli.ts (npm run ingest/graph/
what-next), data/legacy-graph/, data/agent/ (pi agentDir vestige), and their
config.ts/package.json references.

The retroactive-bootstrap goal is dropped: the live chat store (AI SDK
UIMessages) is the only session format now, and the old pi JSONLs in
data/sessions/ stay on disk, inert. contracts.ts is the single schema.
This commit is contained in:
Amos
2026-08-31 19:35:20 +03:00
parent 8dd14b5235
commit bfb31568fc
11 changed files with 0 additions and 654 deletions
-8
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@@ -21,10 +21,6 @@ npm test # all tests (node:test via tsx; deterministic, no LLM)
npm run typecheck # tsc --noEmit
npx tsx scripts/bench-graph.ts # force-layout benchmark (synthetic graphs)
# ── legacy v0 pipeline (dormant, see "Legacy scaffold" below) ──────────
npm run ingest -- <session.jsonl> # normalize a pi session JSONL
npm run graph # ledger stats
```
## Environment (`.env.local`)
@@ -148,10 +144,6 @@ data/ chats/ · graph/ · jobs/ — runtime state, git
**The live graph.** Every completed turn folds the chat into append-only JSONL ledgers (`emitChat`: tree nodes/edges, quiz outcome events, taught events — idempotent, checkpointed, so partial sessions still deposit value). The home view derives node states from the ledgers and lays them out with forceAtlas2 (run-then-freeze — no idle simulation). At session start the derived graph is rendered into a compact markdown block appended to the teacher's instructions (`buildGraphContext`) — whole graph while small, hotspots + 1-hop neighborhood past 150 nodes.
## Legacy scaffold (dormant)
The pre-pivot v0 pipeline — `src/ledger.ts`, `src/cli.ts` (`npm run ingest` / `graph`), `src/extract/` (normalize ✅, extract/merge stubs), `src/schema.ts`, `src/derive.ts`, `src/queries/` — is kept compiling as reference for a future **retroactive bootstrap** over the old pi session JSONLs (`data/sessions/`, bootstrap input only). It writes to `data/legacy-graph/` and shares nothing with the live store (`data/graph/`, owned by `src/store/graph.ts` per the `contracts.ts` shapes). See `docs/architecture.md` for its design.
## Testing
All deterministic, no LLM (models are injected as `MockLanguageModelV4` where an agent loop is under test):
-16
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@@ -11,8 +11,6 @@ export const PATHS = {
data: path.join(ROOT, "data"),
/** Chat sessions (AI SDK UIMessage transcripts, one JSON file per chat). */
chats: path.join(ROOT, "data", "chats"),
/** Old pi session JSONLs — retroactive bootstrap input only. */
sessions: path.join(ROOT, "data", "sessions"),
graph: path.join(ROOT, "data", "graph"),
} as const;
@@ -27,17 +25,3 @@ export const LEDGER_FILES = {
mappings: path.join(PATHS.graph, "mappings.jsonl"),
sessions: path.join(PATHS.graph, "sessions.jsonl"),
} as const;
/**
* The legacy V0 scaffold (src/ledger.ts, src/cli.ts, src/extract/) writes
* HERE instead — dormant pre-pivot reference code with its own line shapes
* (src/schema.ts). Kept compiling for the future retroactive bootstrap,
* which will target the contracts.ts shapes, not these.
*/
export const LEGACY_LEDGER_FILES = {
nodes: path.join(PATHS.data, "legacy-graph", "nodes.jsonl"),
edges: path.join(PATHS.data, "legacy-graph", "edges.jsonl"),
events: path.join(PATHS.data, "legacy-graph", "events.jsonl"),
mappings: path.join(PATHS.data, "legacy-graph", "mappings.jsonl"),
sessions: path.join(PATHS.data, "legacy-graph", "sessions.jsonl"),
} as const;
-3
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@@ -8,9 +8,6 @@
"dev": "next dev",
"build": "next build",
"start": "next start",
"ingest": "tsx src/cli.ts ingest",
"graph": "tsx src/cli.ts graph",
"what-next": "tsx src/cli.ts what-next",
"test": "NODE_ENV=test tsx --test tests/*.test.ts tests/*.test.tsx",
"typecheck": "tsc --noEmit"
},
-69
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@@ -1,69 +0,0 @@
#!/usr/bin/env node
/**
* learning-harness CLI
*
* ingest <file> normalize a session JSONL and print the canonical document
* (Stage 1 only — extractor/merger not yet implemented)
* graph print graph/ledger stats
* what-next (not yet implemented)
*
* The web app (Next.js + AI SDK): npm run dev
*/
import * as fs from "node:fs";
import { normalizeSession } from "./extract/normalize.ts";
import { ledgers } from "./ledger.ts";
import { deriveAll } from "./derive.ts";
import { PATHS } from "../config.ts";
async function main() {
const [command, ...rest] = process.argv.slice(2);
switch (command) {
case "ingest": {
const file = rest[0];
if (!file) {
console.error("Usage: ingest <session.jsonl>");
process.exit(1);
}
const doc = normalizeSession(file);
ledgers.appendSession(doc.session);
console.log(`Session: ${doc.session.id}`);
console.log(`Topic: ${doc.session.topic}`);
console.log(`Entries: ${doc.entries.length}`);
const quizzes = doc.entries.filter((e) => e.kind === "quiz");
console.log(`Quizzes: ${quizzes.length}`);
// Full canonical document for inspection / later pipeline stages.
fs.mkdirSync(PATHS.graph, { recursive: true });
const out = `${PATHS.graph}/canonical-${doc.session.id.slice(0, 8)}.json`;
fs.writeFileSync(out, JSON.stringify(doc, null, "\t"), "utf-8");
console.log(`Canonical document written: ${out}`);
break;
}
case "graph": {
const nodes = ledgers.nodes();
const edges = ledgers.edges();
const events = ledgers.events();
const sessions = ledgers.sessions();
const derived = deriveAll();
console.log(`Sessions: ${sessions.length}`);
console.log(`Nodes: ${nodes.length} (known: ${derived.filter((d) => d.known).length})`);
console.log(`Edges: ${edges.length}`);
console.log(`Events: ${events.length}`);
break;
}
case "what-next": {
console.error("Not implemented yet — needs extractor + merger first.");
process.exit(1);
}
default: {
console.log("Commands: ingest <file> | graph | what-next");
process.exit(command ? 1 : 0);
}
}
}
main().catch((err) => {
console.error(err);
process.exit(1);
});
-37
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@@ -1,37 +0,0 @@
/**
* Derived state — pure code, no LLM.
* known (evidence) / understood (structure, aggregation semantics PARKED).
* NOT YET IMPLEMENTED beyond the known-set.
*/
import { ledgers } from "./ledger.ts";
import { KNOWING_ORDER, type DerivedNodeState, type KnowingLevel } from "./schema.ts";
/** Rubric → scalar. Deterministic code, never an LLM. */
export function knowingScore(level: KnowingLevel): number {
return KNOWING_ORDER.indexOf(level) / (KNOWING_ORDER.length - 1);
}
/** Known set: nodes with direct evidence (taught, probe-correct, check-correct...). */
export function knownNodeIds(): Set<string> {
const evidenced = new Set<string>();
for (const event of ledgers.events()) {
if (event.kind === "taught" || event.kind === "check-correct" || event.kind === "probe-correct" || event.kind === "recall-at-delay") {
evidenced.add(event.node);
}
}
return evidenced;
}
/** Full derived state. Understanding aggregation (weakest-link vs mean vs two-level)
* is PARKED pending real extracted data — see docs/architecture.md §7. */
export function deriveAll(): DerivedNodeState[] {
const known = knownNodeIds();
return ledgers.nodes().map((node) => ({
node: node.id,
known: known.has(node.id),
knowing: 0,
understood: 0,
floating: false,
}));
}
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@@ -1,26 +0,0 @@
/**
* Stage 2 — Extract (one LLM pass over the canonical session document).
* NOT YET IMPLEMENTED. Interface specified so the shape is fixed:
*
* Input: CanonicalSessionDocument
* Output: ExtractedSession (see schema.ts) — nodes, edges, attributed events,
* per-node knowing on the anchored ordinal rubric.
*
* Constraints:
* - One pass for nodes+edges+events+knowing (they are entangled).
* - Zero inference about knowledge beyond the evidence: it extracts facts,
* edges, and evidence — nothing the session doesn't show.
* - Knowing is mapped onto the anchored rubric (KnowingLevel), never a scalar.
* - Edges come primarily from the plan's mermaid DAG (already asserted by the
* teacher); only prose-articulated Connect-step edges need judgment.
*
* Followed by an audit pass (extract/audit.ts): a second LLM call verifying the
* extraction JSON against the session for hallucination/misattribution.
*/
import type { CanonicalSessionDocument } from "./normalize.ts";
import type { ExtractedSession } from "../schema.ts";
export async function extractSession(doc: CanonicalSessionDocument): Promise<ExtractedSession> {
throw new Error("Not implemented — Stage 2 (extractor LLM pass) is the next build step.");
}
-22
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@@ -1,22 +0,0 @@
/**
* Stage 3 — Merge (deterministic code + LLM adjudication for node identity).
* NOT YET IMPLEMENTED. Interface specified:
*
* Input: ExtractedSession (Stage 2 output) + canonical graph
* 1. Embed canonical node statements (cached) → kNN candidates per incoming node.
* 2. ONE LLM adjudication call per session: each pair →
* same → alias + attach events to canonical node
* related → new node + proposed cross-session edge (the quiet superpower)
* unrelated → new node, no edge
* 3. Apply deterministically; log every decision as a SessionMapping
* (reversible by design — wrong merges must be one-line fixes).
*
* Policy: fully automatic, conservative — prefer duplicates over wrong merges.
* No domain filter on candidates (cross-domain merging is the point).
*/
import type { ExtractedSession } from "../schema.ts";
export async function mergeSession(extraction: ExtractedSession): Promise<void> {
throw new Error("Not implemented — Stage 3 (merger) comes after the extractor.");
}
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@@ -1,217 +0,0 @@
/**
* Stage 1 of the extraction pipeline — DETERMINISTIC, no LLM.
*
* pi session JSONL → canonical session document.
*
* The quiz tool writes full structured details into the session JSONL
* (question, options, correct/dontKnow, note, explanation), so quiz events are
* read as gold-standard structured data, not parsed from prose.
*/
import * as fs from "node:fs";
import type {
EventKind,
Provenance,
SessionRecord,
} from "../schema.ts";
// ─── pi session JSONL entry types (subset we consume) ───────────────────────
interface PiEntryBase {
type: string;
id: string;
parentId: string | null;
timestamp: string;
}
interface PiSessionHeader {
type: "session";
version: number;
id: string;
timestamp: string;
cwd: string;
}
interface PiMessageEntry extends PiEntryBase {
type: "message";
message: {
role: "user" | "assistant" | "toolResult" | string;
content?: unknown;
toolCallId?: string;
toolName?: string;
details?: unknown;
timestamp?: number;
};
}
// ─── Canonical session document ──────────────────────────────────────────────
export interface CanonicalQuizEvent {
kind: "quiz";
entryId: string;
toolCallEntryId: string; // the assistant entry containing the toolCall
question: string;
context?: string;
options: Array<{ index: number; label: string }>;
answers: Array<{ index: number; label: string }>;
correctIndices: number[];
correct: boolean;
dontKnow: boolean;
note?: string;
explanation?: string;
}
export interface CanonicalAskEvent {
kind: "ask";
entryId: string;
toolCallEntryId: string;
question: string;
answers: Array<{ label: string; value: string }>;
}
export interface CanonicalTextEvent {
kind: "user" | "assistant";
entryId: string;
text: string;
}
export type CanonicalEntry = CanonicalQuizEvent | CanonicalAskEvent | CanonicalTextEvent;
export interface CanonicalSessionDocument {
session: SessionRecord;
entries: CanonicalEntry[];
}
// ─── Parsing ─────────────────────────────────────────────────────────────────
function textFromContent(content: unknown): string {
if (typeof content === "string") return content;
if (!Array.isArray(content)) return "";
return content
.filter((b): b is { type: "text"; text: string } => typeof b === "object" && b !== null && (b as { type: string }).type === "text")
.map((b) => b.text)
.join("\n");
}
interface QuizDetails {
status: string;
question: string;
mode: string;
answers: Array<{ index: number; label: string }>;
correctIndices: number[];
options?: Array<{ index: number; label: string }>;
correct?: boolean;
dontKnow?: boolean;
note?: string;
explanation?: string;
}
interface AskDetails {
status: string;
question: string;
mode: string;
answers?: Array<{ label: string; value: string }>;
}
export function normalizeSession(file: string): CanonicalSessionDocument {
const lines = fs.readFileSync(file, "utf-8").split("\n").filter((l) => l.trim().length > 0);
const parsed = lines.map((l) => JSON.parse(l));
const header = parsed[0] as PiSessionHeader;
if (header?.type !== "session") throw new Error(`${file}: not a pi session file (missing header)`);
const entries: CanonicalEntry[] = [];
const quizArgsByCallId = new Map<string, { entryId: string; question: string; context?: string }>();
for (const raw of parsed.slice(1)) {
const entry = raw as PiMessageEntry;
if (entry.type !== "message") continue;
const msg = entry.message;
if (msg.role === "user") {
const text = textFromContent(msg.content).trim();
if (text) entries.push({ kind: "user", entryId: entry.id, text });
}
if (msg.role === "assistant" && Array.isArray(msg.content)) {
// Collect assistant text (lesson prose — node articulations live here).
const text = textFromContent(msg.content).trim();
if (text) entries.push({ kind: "assistant", entryId: entry.id, text });
// Remember quiz/ask toolCall args (the question) keyed by call id.
for (const block of msg.content) {
if (typeof block === "object" && block !== null && (block as { type: string }).type === "toolCall") {
const call = block as { type: "toolCall"; id: string; name: string; arguments: Record<string, unknown> };
if (call.name === "quiz" || call.name === "ask_user_question") {
quizArgsByCallId.set(call.id, {
entryId: entry.id,
question: String(call.arguments.question ?? ""),
context: call.arguments.details ? String(call.arguments.details) : undefined,
});
}
}
}
}
if (msg.role === "toolResult" && (msg.toolName === "quiz" || msg.toolName === "ask_user_question")) {
const args = quizArgsByCallId.get(msg.toolCallId ?? "");
if (msg.toolName === "quiz") {
const d = msg.details as QuizDetails | undefined;
if (d && d.status === "answered") {
entries.push({
kind: "quiz",
entryId: entry.id,
toolCallEntryId: args?.entryId ?? entry.id,
question: d.question ?? args?.question ?? "",
context: args?.context,
options: d.options ?? [],
answers: d.answers ?? [],
correctIndices: d.correctIndices ?? [],
correct: d.correct ?? false,
dontKnow: d.dontKnow ?? false,
note: d.note,
explanation: d.explanation,
});
}
} else {
const d = msg.details as AskDetails | undefined;
if (d && d.status === "answered") {
entries.push({
kind: "ask",
entryId: entry.id,
toolCallEntryId: args?.entryId ?? entry.id,
question: d.question ?? args?.question ?? "",
answers: d.answers ?? [],
});
}
}
}
}
const firstUser = entries.find((e): e is CanonicalTextEvent => e.kind === "user");
return {
session: {
id: header.id,
startedAt: header.timestamp,
topic: firstUser ? firstUser.text.slice(0, 120) : "(no user message)",
file,
},
entries,
};
}
/** Coarse pre-plan / post-plan classification for quiz events (refined by the extractor LLM). */
export function quizPhase(index: number, planIndex: number): "probe" | "check" {
return index < planIndex ? "probe" : "check";
}
/** Provisional event kind from a quiz outcome + phase (the extractor refines/attribution). */
export function provisionalEventKind(quiz: CanonicalQuizEvent, phase: "probe" | "check"): EventKind {
if (quiz.dontKnow) return "dontKnow";
if (phase === "probe") return quiz.correct ? "probe-correct" : "probe-miss";
return quiz.correct ? "check-correct" : "check-incorrect";
}
export function provenanceOf(sessionId: string, entryId: string): Provenance {
return { session: sessionId, entryId };
}
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@@ -1,68 +0,0 @@
/**
* Append-only JSONL ledgers. Same philosophy as the design: the ledgers are
* the source of truth; everything else is derived and recomputable.
*/
import * as fs from "node:fs";
import * as path from "node:path";
import { randomBytes } from "node:crypto";
import { LEGACY_LEDGER_FILES as LEDGER_FILES } from "../config.ts";
export function newId(prefix: string): string {
return `${prefix}_${randomBytes(5).toString("hex")}`;
}
export function append<T>(file: string, record: T): void {
fs.mkdirSync(path.dirname(file), { recursive: true });
fs.appendFileSync(file, JSON.stringify(record) + "\n", "utf-8");
}
export function readAll<T>(file: string): T[] {
if (!fs.existsSync(file)) return [];
const out: T[] = [];
for (const line of fs.readFileSync(file, "utf-8").split("\n")) {
const trimmed = line.trim();
if (trimmed.length > 0) out.push(JSON.parse(trimmed) as T);
}
return out;
}
// Typed helpers ───────────────────────────────────────────────────────────────
import type {
GraphEdge,
GraphNode,
KnowledgeEvent,
SessionMapping,
SessionRecord,
} from "./schema.ts";
export const ledgers = {
appendSession(record: SessionRecord) {
// Session records are unique per id — skip if already ingested.
if (readAll<SessionRecord>(LEDGER_FILES.sessions).some((s) => s.id === record.id)) return false;
append(LEDGER_FILES.sessions, record);
return true;
},
sessions: () => readAll<SessionRecord>(LEDGER_FILES.sessions),
appendNode(node: GraphNode) {
append(LEDGER_FILES.nodes, node);
},
nodes: () => readAll<GraphNode>(LEDGER_FILES.nodes),
appendEdge(edge: GraphEdge) {
append(LEDGER_FILES.edges, edge);
},
edges: () => readAll<GraphEdge>(LEDGER_FILES.edges),
appendEvent(event: KnowledgeEvent) {
append(LEDGER_FILES.events, event);
},
events: () => readAll<KnowledgeEvent>(LEDGER_FILES.events),
appendMapping(mapping: SessionMapping) {
append(LEDGER_FILES.mappings, mapping);
},
mappings: () => readAll<SessionMapping>(LEDGER_FILES.mappings),
};
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@@ -1,18 +0,0 @@
/**
* What-next queries: territory / repairs / conversions (+ unlock-value over
* the expanded frontier). NOT YET IMPLEMENTED — needs the graph populated first.
*
* These run as pure traversals over the derived graph:
* territory — frontier candidates ranked by work-to-ready (missing
* requires-subtree mass), NOT fewest edges.
* repairs — weak/shaky/misconceived nodes INSIDE the held region,
* ranked by downstream load.
* conversions — missing requires-roots that would flip known-but-floating
* nodes into understood.
* unlock-value — over the lazily expanded frontier, discounted by depth in
* generated territory; presented as ranked heuristic, never a number.
*/
export async function whatNext(): Promise<void> {
throw new Error("Not implemented — needs a populated graph (extractor + merger first).");
}
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@@ -1,170 +0,0 @@
/**
* Knowledge graph data model.
*
* Design invariants (see docs/architecture.md and the design session):
* 1. Store the LEDGERS, derive the GRAPH. Events/edges/mappings are append-only
* evidence; node states and strengths are always recomputable from them.
* 2. Nodes are propositions at ~one-reasoning-step grain.
* 3. Edges carry NO scalars — only type (requires/supports) and provenance.
* All mass lives in nodes. "Load-bearing" is derived, never stored.
* 4. known ≠ understood. Known = evidence property (from the event ledger).
* Understood = derived structural property (known + requires-subtree held).
* 5. Candidates (frontier-generated nodes) are a separate state class;
* promotion only happens when a session touches them or the learner
* picks them as a goal — never automatically.
* 6. Every event carries session + entryId → jump-to-point provenance.
*/
// ─── Sessions ────────────────────────────────────────────────────────────────
export interface SessionRecord {
/** pi session UUID (from the session JSONL header). */
id: string;
/** ISO timestamp from the header. */
startedAt: string;
/** Human topic label (first user message, or /name). */
topic: string;
/** Absolute path of the session JSONL. */
file: string;
}
// ─── Nodes ───────────────────────────────────────────────────────────────────
export type NodeKind = "truth" | "misconception";
export type NodeState = "known" | "not-held" | "candidate";
export interface GraphNode {
/** Opaque stable id, e.g. "n_a1b2c3". */
id: string;
/** The proposition, one reasoning step of grain. */
statement: string;
kind: NodeKind;
/** Domain tag — metadata for visualization, NEVER a merge barrier. */
domain: string;
/** candidate = generated by frontier expansion, not yet evidenced. */
state: NodeState;
createdAt: number;
}
// ─── Edges ───────────────────────────────────────────────────────────────────
export type EdgeType = "requires" | "supports";
export interface GraphEdge {
id: string;
/** The dependency (e.g. "gradient" for gradient descent). */
from: string;
/** The dependent (e.g. "gradient descent"). */
to: string;
/**
* requires — hard: understanding the target requires holding this.
* supports — soft: the target is more anchored with it, holdable without it.
*/
type: EdgeType;
provenance: Provenance;
}
export interface Provenance {
session: string;
/** Entry id in the session JSONL — jump-to-point. */
entryId: string;
}
// ─── Events (append-only ledger — the source of truth) ──────────────────────
export type EventKind =
| "probe-correct" // pre-plan quiz, answered correctly (floor evidence)
| "probe-miss" // pre-plan quiz, wrong answer (ceiling evidence)
| "dontKnow" // "I don't know" — genuine gap, not a guess
| "misconception-signaled" // specific wrong belief identified via distractor/note
| "taught" // node established in Phase 3
| "check-correct" // post-teach quiz-check, first try
| "check-incorrect" // post-teach quiz-check, missed
| "recall-at-delay"; // future: spaced repetition
export interface EventEvidence {
quizQuestion: string;
/** What the learner selected (labels). */
answer: string[];
/** Free-text note typed with the answer — diagnostic gold. */
note?: string;
}
export interface KnowledgeEvent {
id: string;
node: string;
kind: EventKind;
session: string;
/** Entry id in the session JSONL → jump to the exact point where this happened. */
entryId: string;
timestamp: number;
evidence?: EventEvidence;
}
// ─── Mappings (merge decisions — reversible by design) ──────────────────────
export interface SessionMapping {
/** Session-local node reference from the extraction JSON. */
sessionNodeRef: { session: string; localId: string };
canonicalNode: string;
decidedBy: "llm-adjudication";
confidence: number;
/** Mirror of the session-local statement, for auditability. */
sessionStatement: string;
}
// ─── Knowing assessment (anchored ordinal rubric — NEVER freehand scalars) ──
export type KnowingLevel =
| "misconception" // actively holds a wrong version
| "absent" // dontKnow or wrong with no reasoning
| "shakily-known" // correct after a miss, or correct with a hedging note
| "known" // first-try correct
| "fluent"; // correct AND the note/reasoning shows the right model
/** Ordinal index — deterministic code maps rubric → number, never an LLM. */
export const KNOWING_ORDER: readonly KnowingLevel[] = [
"misconception",
"absent",
"shakily-known",
"known",
"fluent",
];
// ─── Extraction pipeline (per-session JSON, Stage 2 output) ─────────────────
export interface ExtractedSession {
session: string;
nodes: Array<{
localId: string;
statement: string;
kind: NodeKind;
domain: string;
}>;
edges: Array<{
from: string; // localId
to: string; // localId
type: EdgeType;
}>;
events: Array<{
node: string; // localId
kind: EventKind;
entryId: string;
evidence?: EventEvidence;
}>;
knowing: Array<{
node: string; // localId
level: KnowingLevel;
justification: string;
}>;
}
// ─── Derived state (computed by code, never stored as primary data) ─────────
export interface DerivedNodeState {
node: string;
known: boolean;
knowing: number; // 0..1, from rubric + evidence events
understood: number; // 0..1, recursive over requires-subtree (aggregation semantics PARKED)
floating: boolean; // known but not understood
}