amosblomqvist
2678f04e7a
THE MESSAGE IS THE UNIT OF COVERAGE (vision-extraction-coverage.md): a per-chat append-only ledger (coverage.jsonl) records which messages the pipeline has extracted, by content fingerprint over exactly what the extraction view sees (one shared line builder — core/coverage.ts); the uncovered remainder is DERIVED at read time, never dual-tracked. A quiz answer merging into the asking message re-opens only that message; plan-block data-part attach never re-opens anything. - The trigger (turn-service) replaces every-3-quiz-answers with uncovered-tokens >= EXTRACTION.chunkTokens (4096) — quiz-less sessions extract too, content size is the honest signal. - runExtraction is now chunked: token-budgeted batches of the oldest uncovered consecutive messages (a tool call + result never split), each merged batch checkpointing its messages' coverage atomically — an abort or error loses at most the in-flight batch; merged batches stay covered. - Observation refs are batch-tagged (obs:<batchTag>:<sourceId>): re-runs of the same batch dedupe; a re-opened message's fresh understanding lands as a new event. Fixes the old obs:p1 position-collision. - New API: GET /api/graph/coverage (exact per-session derived coverage + running flags, most-uncovered first) and POST /api/graph/coverage/extract (manual run for one session, 409 while in flight). - Graph view chrome: the coverage button opens a panel with every session's covered/uncovered tokens and a per-session Extract button — the manual answer to aborted runs, errors, and dormant tails. Renders even when the graph is empty: an empty graph with uncovered sessions is exactly when it matters. Polls only while open. - Notes imports keep the whole-file checkpoint path (coverage undefined). - Tests: pure-core coverage suite (11), chunked-pipeline suite (3: multi-batch + no-op, abort-keeps-earlier-batches + remainder re-run, re-open alone + data-part invisibility), panel render suite (5); pendingQuizAnswers and its tests deleted as dead code. 486/486. Live-smoked on real GLM: manual extract of "teach me modal realism" ran one batch, wrote 4 coverage entries, panel flipped to "1.3k covered · complete" with the button disabled, graph refetched (nodes 11), usage persisted with real stats.
1038 lines
42 KiB
TypeScript
1038 lines
42 KiB
TypeScript
/**
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* The extraction pipeline tests (vision §1 construction, 2026-09-02 redesign):
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* extractor → auditor → merger, the sole graph writer — tree-blind,
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* free-form, with the auditor owning faithfulness and identity.
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*
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* transcriptView — the compact view both LLM stages see (pure):
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* message-id-tagged verbatim text + quiz lines, NO tree
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* pipeline — scripted faux engines drive both agents: findings out,
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* audit verdict (with linkTo identity) in, deterministic
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* merge applied; failure paths checkpoint nothing;
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* single-flight per chat
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*
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* The merger itself (identity verdicts, event rebuild, idempotency) is
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* tested in tests/graph.test.ts directly against mergeFindings — here it is
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* the integration: what one full pipeline run does to the ledgers.
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*
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* Ledger: in-memory (the GraphStore port) — this suite never touches
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* data/graph. Chats: real ChatRecords in the real chat store (created and
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* deleted by this suite).
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*/
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import assert from "node:assert/strict";
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import * as fs from "node:fs";
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import { after, describe, it } from "node:test";
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import { createFauxCore, fauxAssistantMessage, fauxToolCall, type FauxResponseStep } from "@earendil-works/pi-ai";
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import type { UIMessage } from "ai";
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import type { PiEngine } from "../src/agent/models.ts";
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import type {
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AuditResult,
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ClusterLabeling,
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CoverageEntry,
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ExtractionRunUsage,
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GraphEdge,
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GraphNode,
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LedgerEvent,
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NodeMapping,
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NotesSource,
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SessionCheckpoint,
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SessionFindings,
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} from "../src/core/contracts.ts";
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import type { GraphStore } from "../src/core/ports.ts";
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import { abortExtraction, createExtractionPipeline, extractionStatuses, transcriptView } from "../src/agent/subagents/extraction.ts";
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import { batchTag, coverageOf, coverageSlots } from "../src/core/coverage.ts";
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import { clusterNamingSettled, refreshClusterLabels } from "../src/agent/subagents/cluster-namer.ts";
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import { semanticClusters } from "../src/core/semantic-clusters.ts";
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import { chatStore } from "../src/store/chats.ts";
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import { PATHS } from "../config.ts";
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// ─── fixtures ────────────────────────────────────────────────────────────────
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const createdChats: string[] = [];
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/** A scripted pi engine (faux provider, docs/pi-engine-swap.md §7): the
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* response queue replaces the old doGenerate turn counter. Usage is
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* ESTIMATED by faux from the prompt/response text (deterministic, > 0) —
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* the tests assert structure and positivity, not flat mock numbers. */
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function fauxEngine(steps: FauxResponseStep[]): PiEngine {
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const faux = createFauxCore({});
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faux.setResponses(steps);
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return { model: faux.getModel(), streamFn: faux.streamSimple };
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}
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/** An in-memory GraphStore (core/ports.ts) — this suite never touches data/graph. */
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function memoryLedgers() {
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const nodes: GraphNode[] = [];
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const edges: GraphEdge[] = [];
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const events: LedgerEvent[] = [];
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const mappings: NodeMapping[] = [];
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const checkpoints: SessionCheckpoint[] = [];
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const coverage: CoverageEntry[] = [];
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const sources = new Map<string, NotesSource>();
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const clusterLabels: ClusterLabeling[] = [];
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const positions = new Map<string, { x: number; y: number }>();
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const embeddings = {
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model: "",
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dim: 0,
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updatedAt: "",
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vectors: new Map<string, number[]>(),
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clusters: new Array<Array<string>>(),
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};
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return {
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appendNode: (n: GraphNode) => void nodes.push(n),
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readNodes: () => nodes.map((n) => ({ ...n })),
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appendEdge: (e: GraphEdge) => void edges.push(e),
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readEdges: () => edges.map((e) => ({ ...e })),
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appendEvent: (e: LedgerEvent) => void events.push(e),
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readEvents: () => events.map((e) => ({ ...e })),
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appendMapping: (m: NodeMapping) => void mappings.push(m),
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readMappings: () => mappings.map((m) => ({ ...m })),
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appendCheckpoint: (c: SessionCheckpoint) => void checkpoints.push(c),
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readCheckpoints: () => checkpoints.map((c) => ({ ...c })),
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// Extraction coverage (vision-extraction-coverage.md) — append-only;
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// the chat pipeline checkpoints one entry per covered message.
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appendCoverage: (e) => void coverage.push({ ...e }),
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readCoverage: () => coverage.map((e) => ({ ...e })),
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upsertSource: (s: NotesSource) => {
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// Mirrors the real store: an upsert KEEPS the existing usage log.
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const prev = sources.get(s.id);
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sources.set(s.id, { ...s, extractions: [...(prev?.extractions ?? [])] });
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},
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readSources: () => [...sources.values()].map((s) => ({ ...s, extractions: [...(s.extractions ?? [])] })),
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appendSourceUsage: (id: string, entry: ExtractionRunUsage) => {
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const source = sources.get(id);
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if (!source) return;
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if (!source.extractions?.some((r) => r.startedAt === entry.startedAt)) {
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source.extractions = [...(source.extractions ?? []), entry];
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}
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},
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// Cluster labelings — mirrors the real store: append dedupes by id.
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appendClusterLabel: (l: ClusterLabeling) => {
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if (!clusterLabels.some((x) => x.id === l.id)) clusterLabels.push(l);
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},
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readClusterLabels: () => clusterLabels.map((l) => ({ ...l, core: [...l.core] })),
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// The layout ledger — settleGraphLayout writes it after each merge.
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readPositions: () => Object.fromEntries([...positions].map(([id, p]) => [id, { ...p }])),
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writePositions: (next) => {
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positions.clear();
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for (const [id, p] of Object.entries(next)) positions.set(id, { x: p.x, y: p.y });
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},
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// The embedding ledger — the embedder writes it after a merge.
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readEmbeddings: () => ({ model: embeddings.model, dim: embeddings.dim, vectors: Object.fromEntries([...embeddings.vectors].map(([id, v]) => [id, [...v]])), clusters: [...embeddings.clusters.map((c) => ({ members: [...c] }))], updatedAt: embeddings.updatedAt }),
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writeEmbeddings: (next) => {
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embeddings.model = next.model;
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embeddings.dim = next.dim;
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embeddings.updatedAt = next.updatedAt;
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embeddings.vectors.clear();
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for (const [id, v] of Object.entries(next.vectors)) embeddings.vectors.set(id, [...v]);
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embeddings.clusters = next.clusters.map((c) => [...c.members]);
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},
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} satisfies GraphStore;
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}
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function assistant(id: string, parts: unknown[]): UIMessage {
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return { id, role: "assistant", parts } as unknown as UIMessage;
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}
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function user(id: string, text: string): UIMessage {
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return { id, role: "user", parts: [{ type: "text", text }] } as unknown as UIMessage;
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}
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function quizPart(toolCallId: string, input: Record<string, unknown>, output: Record<string, unknown>): unknown {
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return { type: "tool-quiz", toolCallId, state: "output-available", input, output };
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}
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function planPart(toolCallId: string, name: string, input: Record<string, unknown>, tree: unknown): unknown {
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return {
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type: `tool-${name}`,
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toolCallId,
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state: "output-available",
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input,
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output: { ok: true, tree },
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};
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}
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/** A session-shaped fixture chat: probe → teaching text → check. Tree-blind
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* world: the plan tree is NOT part of extraction — it is noise. */
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function fixtureChat() {
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const chat = chatStore.createChat("Extraction fixture");
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createdChats.push(chat.id);
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chatStore.saveMessages(chat.id, [
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user("u1", "teach me the chain rule"),
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assistant("a1", [
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quizPart("probe-1", { question: "What is a derivative, roughly?" }, {
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questionId: "probe-1", kind: "quiz", questionText: "What is a derivative, roughly?",
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correct: false, dontKnow: false, answeredAt: "2026-08-22T01:00:00.000Z",
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}),
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]),
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assistant("a2", [
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{ type: "text", text: "The derivative measures how fast a function changes." },
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planPart("p1", "plan_init", { nodes: [], edges: [] }, { nodes: [], edges: [], currentNodeId: null, doneNodeIds: [], version: 1 }),
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]),
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assistant("a3", [
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quizPart("check-1", { question: "Check" }, {
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questionId: "check-1", kind: "quiz", questionText: "Check", correct: true, dontKnow: false,
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answeredAt: "2026-08-22T02:00:00.000Z",
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}),
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]),
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]);
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return { chatId: chat.id, record: chatStore.getChat(chat.id)! };
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}
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after(() => {
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for (const id of createdChats) {
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try {
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fs.unlinkSync(`${PATHS.chats}/${id}.json`);
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} catch {
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/* already gone */
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}
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}
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});
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/** What the extractor would produce for the fixture session — tree-blind,
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* free-form, with message-granular provenance + an understanding note. */
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const FINDINGS: SessionFindings = {
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propositions: [
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{
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id: "p1",
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name: "Derivative",
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statement: "The derivative measures how fast a function changes",
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aliases: [],
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messages: ["a2"],
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evidence: ["TEACHER [a2]: The derivative measures how fast a function changes."],
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understanding: "solid",
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},
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{
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id: "p2",
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name: "Chain rule",
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statement: "The chain rule differentiates composed functions by multiplying local rates.",
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aliases: [],
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messages: ["a2"],
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evidence: ["LEARNER [u1]: teach me the chain rule"],
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},
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],
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edges: [
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{
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from: "p1",
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to: "p2",
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type: "follows",
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messages: ["a2"],
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evidence: ["LEARNER [u1]: teach me the chain rule"],
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},
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],
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attribution: { quiz: { "probe-1": "p1", "check-1": "p2" } },
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};
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/** An engine whose ONE response is the `name` tool call with the given
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* input — the whole deliverable; the pipeline stops after a clean submit
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* (shouldStop), so no trailing text step exists anymore. `onCall` fires at
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* the moment of the model call — the observation point for mid-run
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* assertions. */
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function submitModel(name: string, input: unknown, onCall?: () => void): PiEngine {
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return fauxEngine([
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(context) => {
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onCall?.();
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void context;
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return fauxAssistantMessage(
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[fauxToolCall(name, input as Record<string, unknown>, { id: `${name}-call` })],
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{ stopReason: "toolUse" },
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);
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},
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]);
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}
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/** An engine whose model call never resolves until its abort signal fires
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* — mimicking a cancelled provider request (the abort path). Like a real
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* provider, an ALREADY-aborted signal resolves immediately (the abort can
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* win the race before the call attaches its listener); the faux stream
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* then sees the aborted signal and ends the message as aborted. */
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function hangingModel(): PiEngine {
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return fauxEngine([
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(_context, options) =>
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new Promise((resolve) => {
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const sig = options?.signal;
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if (sig?.aborted) {
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resolve(fauxAssistantMessage("late"));
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return;
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}
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sig?.addEventListener("abort", () => resolve(fauxAssistantMessage("late")), { once: true });
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}),
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]);
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}
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/** An engine that never calls a tool (the failure path). */
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function silentModel(): PiEngine {
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return fauxEngine([fauxAssistantMessage("I refuse to use tools"), fauxAssistantMessage("…")]);
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}
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// ─── transcriptView (pure) ───────────────────────────────────────────────────
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describe("transcriptView (pure)", () => {
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it("shows message-id-tagged verbatim conversation + quiz lines — tree-blind, plan ops folded", () => {
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const { record } = fixtureChat();
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const view = transcriptView(record);
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assert.match(view, /SESSION: Extraction fixture/);
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// Conversation verbatim, each line tagged with its message id —
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// the provenance anchors the extractor reports back.
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assert.match(view, /LEARNER \[u1\] teach me the chain rule/);
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assert.match(view, /TEACHER \[a2\] The derivative measures how fast a function changes\./);
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// Structured events, attribution-ready.
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assert.match(view, /QUIZ \[probe-1\] What is a derivative, roughly\? — the learner answered incorrectly/);
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assert.match(view, /QUIZ \[check-1\] Check — the learner answered correctly/);
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// TREE-BLIND: no plan tree, no tree edges, no mark_done lines — plan
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// ops fold to the generic one-liner like every other non-quiz tool.
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assert.doesNotMatch(view, /FINAL PLAN TREE/);
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assert.doesNotMatch(view, /TREE EDGES/);
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assert.match(view, /\(plan_init tool result delivered\)/);
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assert.doesNotMatch(view, /output-available/);
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});
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it("summarizes job tool results as one line and drops empty text", () => {
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const chat = chatStore.createChat("View noise fixture");
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createdChats.push(chat.id);
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chatStore.saveMessages(chat.id, [
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user("u1", "go"),
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assistant("a1", [
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{
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type: "tool-research",
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toolCallId: "r1",
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state: "output-available",
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input: { question: "q" },
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output: { brief: "long brief", sources: [] },
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},
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{ type: "text", text: " " },
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]),
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]);
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const view = transcriptView(chatStore.getChat(chat.id)!);
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assert.match(view, /\(research tool result delivered\)/);
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assert.doesNotMatch(view, /long brief/);
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assert.doesNotMatch(view, /TEACHER: {2}/);
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});
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});
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// ─── the pipeline ───────────────────────────────────────────────────────────
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describe("the extraction pipeline (extractor → auditor → merger)", () => {
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it("runs end to end: findings → audit verdict (identity) → deterministic merge + checkpoint", async () => {
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const { chatId } = fixtureChat();
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const ledgers = memoryLedgers();
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// The auditor passes p1 through and LINKS it to a pre-existing node
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// (identity verdict), drops p2 (unbacked) + the edge with it.
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const existingId = "n_existing";
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ledgers.appendNode({
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id: existingId,
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name: "Derivative",
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statement: "Derivatives measure rates of change",
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aliases: [],
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createdAt: "2026-08-01T00:00:00.000Z",
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provenance: [],
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});
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const verdict: AuditResult = {
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propositions: [
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{ ...FINDINGS.propositions[0]!, linkTo: existingId },
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],
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edges: [],
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attribution: { quiz: { "probe-1": "p1" } },
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dropped: [{ ref: "p2", reason: "evidence not found" }, { ref: "p1→p2", reason: "endpoint dropped" }],
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};
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const pipeline = createExtractionPipeline({
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chatStore,
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graphStore: ledgers,
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embedder: fakeEmbedder,
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extractor: submitModel("submit_findings", FINDINGS),
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auditor: submitModel("submit_review", verdict),
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labeler: submitModel("submit_labels", { labels: [] }),
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});
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const stats = await pipeline.runExtraction(chatId);
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// The linked proposition created NO new node; the understanding note
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// became an observation; both quizzes became events.
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assert.deepEqual(stats, { nodes: 0, edges: 0, events: 3 });
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// Link-append: the phrasing joined the existing node's aliases and
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// the provenance unioned; no second node line defines a new node.
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assert.equal(ledgers.readNodes().length, 2); // the pre-seeded line + the link line
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assert.deepEqual(ledgers.readMappings(), [{ chatId, sourceId: "p1", graphNodeId: existingId }]);
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// Events rebuilt deterministically: quizzes in transcript order, then
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// the observation. check-1's attribution pointed at the DROPPED p2 →
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// unresolvable → null (the question text is kept for later attribution).
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// The observation ref is BATCH-TAGGED (obs:<tag>:p1) so a re-run of
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// the same batch dedupes while a re-opened message's update lands as
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// a new event (vision-extraction-coverage.md).
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const events = ledgers.readEvents();
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const record = chatStore.getChat(chatId)!;
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const expectedTag = batchTag(coverageSlots(record.messages as UIMessage[]));
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assert.deepEqual(
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events.map((e) => [e.kind, e.ref, e.nodeId]),
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[
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["miss", "probe-1", existingId],
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["correct", "check-1", null],
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["observation", `obs:${expectedTag}:p1`, existingId],
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],
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);
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assert.equal(events[0]!.nodeId, existingId, "probe attributed via the verdict's mapping");
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assert.equal(events[2]!.note, "solid", "the understanding note joins the ledger");
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// COVERAGE checkpointed per message (not a whole-chat checkpoint):
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// one entry per transcript message, fingerprinted content each —
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// the same turns are never extracted twice.
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const covered = ledgers.readCoverage();
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assert.equal(covered.length, record.messages.length);
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assert.ok(covered.every((c) => c.chatId === chatId && c.fp.length === 64 && c.tokens > 0));
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assert.equal(ledgers.readCheckpoints().length, 0, "the chat path writes coverage, not the legacy checkpoint");
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assert.equal(
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coverageOf(record.messages as UIMessage[], covered, chatId).uncoveredCount,
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0,
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"every message is now covered",
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);
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});
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it("checkpoints nothing when a stage fails", async () => {
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const { chatId } = fixtureChat();
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const ledgers = memoryLedgers();
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const pipeline = createExtractionPipeline({
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chatStore,
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graphStore: ledgers,
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embedder: fakeEmbedder,
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extractor: submitModel("submit_findings", FINDINGS),
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auditor: silentModel(), // never submits the review
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labeler: submitModel("submit_labels", { labels: [] }),
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});
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await assert.rejects(pipeline.runExtraction(chatId), /the auditor never submitted a review/);
|
||
assert.equal(ledgers.readNodes().length, 0);
|
||
assert.equal(ledgers.readCheckpoints().length, 0, "no checkpoint — the next trigger re-runs");
|
||
|
||
const failingExtractor = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: silentModel(),
|
||
auditor: submitModel("submit_review", { ...FINDINGS, dropped: [] }),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await assert.rejects(failingExtractor.runExtraction(chatId), /the extractor never submitted findings/);
|
||
assert.equal(ledgers.readCheckpoints().length, 0);
|
||
});
|
||
|
||
it("reports its phase through the status registry while running, and clears it when done", async () => {
|
||
const { chatId } = fixtureChat();
|
||
const ledgers = memoryLedgers();
|
||
const existingId = "n_existing";
|
||
ledgers.appendNode({
|
||
id: existingId,
|
||
name: "Derivative",
|
||
statement: "Derivatives measure rates of change",
|
||
aliases: [],
|
||
createdAt: "2026-08-01T00:00:00.000Z",
|
||
provenance: [],
|
||
});
|
||
const seen: (string | undefined)[] = [];
|
||
const phaseOf = () => extractionStatuses().find((r) => r.chatId === chatId)?.phase;
|
||
const verdict: AuditResult = {
|
||
propositions: [{ ...FINDINGS.propositions[0]!, linkTo: existingId }],
|
||
edges: [],
|
||
attribution: { quiz: {} },
|
||
dropped: [{ ref: "p2", reason: "evidence not found" }],
|
||
};
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
// Each stage's first generate is the observation point: the
|
||
// registry must already carry this chat at the right phase.
|
||
extractor: submitModel("submit_findings", FINDINGS, () => seen.push(phaseOf())),
|
||
auditor: submitModel("submit_review", verdict, () => seen.push(phaseOf())),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await pipeline.runExtraction(chatId);
|
||
assert.deepEqual(seen, ["extracting", "auditing"]);
|
||
assert.equal(extractionStatuses().length, 0, "the registry clears when the run ends");
|
||
});
|
||
|
||
it("captures per-stage usage live, persists the run's usage into the chat record", async () => {
|
||
const { chatId } = fixtureChat();
|
||
const ledgers = memoryLedgers();
|
||
const existingId = "n_existing";
|
||
ledgers.appendNode({
|
||
id: existingId,
|
||
name: "Derivative",
|
||
statement: "Derivatives measure rates of change",
|
||
aliases: [],
|
||
createdAt: "2026-08-01T00:00:00.000Z",
|
||
provenance: [],
|
||
});
|
||
const usageSeen: (number | undefined)[] = [];
|
||
const verdict: AuditResult = {
|
||
propositions: [{ ...FINDINGS.propositions[0]!, linkTo: existingId }],
|
||
edges: [],
|
||
attribution: { quiz: {} },
|
||
dropped: [{ ref: "p2", reason: "evidence not found" }],
|
||
};
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
// During the auditor's generate, the registry must already carry
|
||
// the extractor's fully accumulated live usage (2 steps × 10/5).
|
||
extractor: submitModel("submit_findings", FINDINGS),
|
||
auditor: submitModel("submit_review", verdict, () =>
|
||
usageSeen.push(extractionStatuses().find((r) => r.chatId === chatId)?.usage.extractor?.steps.at(-1)?.inputTokens),
|
||
),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await pipeline.runExtraction(chatId);
|
||
|
||
// Live: mid-audit the extractor's usage was fully accumulated (faux
|
||
// estimates tokens from the prompt/response text — deterministic,
|
||
// positive, no cache).
|
||
assert.equal(usageSeen.length, 1);
|
||
assert.ok(typeof usageSeen[0] === "number" && usageSeen[0]! > 0, "the extractor's step billed input tokens");
|
||
|
||
// Persisted: the run appended ONE usage entry to the chat's
|
||
// extractions log — per-stage model + steps, with merge stats.
|
||
const record = chatStore.getChat(chatId)!;
|
||
assert.equal(record.extractions?.length, 1);
|
||
const run = record.extractions![0]!;
|
||
assert.equal(run.stats?.nodes, 0); // linked, not created
|
||
assert.equal(run.stats?.events, 3);
|
||
assert.equal(run.stages.extractor.model, "faux-1"); // the scripted engine's model id
|
||
assert.equal(run.stages.extractor.steps.length, 1); // the submit call — the run STOPS after a clean submit
|
||
const step = run.stages.extractor.steps[0]!;
|
||
assert.ok(step.inputTokens! > 0 && step.outputTokens! > 0, "estimated usage captured");
|
||
assert.equal(step.cacheReadTokens, 0);
|
||
assert.equal(step.cacheWriteTokens, 0);
|
||
assert.equal(step.reasoningTokens, 0);
|
||
assert.equal(run.stages.auditor.steps.length, 1);
|
||
assert.ok(!Number.isNaN(Date.parse(run.at)));
|
||
});
|
||
|
||
it("aborts mid-run: cancels the model call, persists partial usage, checkpoints nothing", async () => {
|
||
const { chatId } = fixtureChat();
|
||
const ledgers = memoryLedgers();
|
||
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: hangingModel(), // hangs until aborted — no step finishes
|
||
auditor: submitModel("submit_review", { propositions: [], edges: [], attribution: { quiz: {} }, dropped: [] }),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
const runPromise = pipeline.runExtraction(chatId);
|
||
|
||
// The registry entry exists synchronously after the call (setPhase
|
||
// runs before the first await); abort is TRUE cancellation.
|
||
assert.equal(abortExtraction(chatId), true);
|
||
const stats = await runPromise;
|
||
|
||
// A deliberate stop, not a failure: zero emissions, no throw.
|
||
assert.deepEqual(stats, { nodes: 0, edges: 0, events: 0 });
|
||
assert.equal(extractionStatuses().length, 0, "the registry clears");
|
||
assert.equal(ledgers.readNodes().length, 0, "nothing merged");
|
||
assert.equal(ledgers.readCheckpoints().length, 0, "nothing checkpointed — the next trigger re-runs");
|
||
|
||
// Partial usage still persisted (money was spent): stats absent,
|
||
// no completed steps in either stage.
|
||
const record = chatStore.getChat(chatId)!;
|
||
assert.equal(record.extractions?.length, 1);
|
||
const run = record.extractions![0]!;
|
||
assert.equal(run.stats, undefined);
|
||
assert.equal(run.stages.extractor.steps.length, 0);
|
||
assert.equal(run.stages.auditor.steps.length, 0);
|
||
|
||
// Aborting a non-running chat is an idempotent no-op.
|
||
assert.equal(abortExtraction(chatId), false);
|
||
});
|
||
|
||
it("is single-flight per chat — a concurrent second run is a no-op", async () => {
|
||
const { chatId } = fixtureChat();
|
||
const ledgers = memoryLedgers();
|
||
|
||
// The extractor's first call hangs until we release it.
|
||
let release!: () => void;
|
||
const gate = new Promise<void>((resolve) => {
|
||
release = resolve;
|
||
});
|
||
let calls = 0;
|
||
const gatedExtractor = fauxEngine([
|
||
async () => {
|
||
calls++;
|
||
if (calls === 1) await gate;
|
||
return fauxAssistantMessage("…");
|
||
},
|
||
]);
|
||
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: gatedExtractor,
|
||
auditor: silentModel(),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
|
||
const first = pipeline.runExtraction(chatId);
|
||
const second = await pipeline.runExtraction(chatId); // in-flight → zeros
|
||
assert.deepEqual(second, { nodes: 0, edges: 0, events: 0 });
|
||
|
||
release();
|
||
await assert.rejects(first, /the extractor never submitted findings/);
|
||
});
|
||
|
||
it("throws for unknown chats", async () => {
|
||
const ledgers = memoryLedgers();
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: silentModel(),
|
||
auditor: silentModel(),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await assert.rejects(pipeline.runExtraction("chat_does_not_exist"), /Chat not found/);
|
||
});
|
||
});
|
||
|
||
// ── the notes front door (format-agnostic import) ────────────────────────────
|
||
|
||
/** Raw personal notes — arbitrary format, no session, no message ids. */
|
||
const NOTES_TEXT = `> [!quote] YOU
|
||
> teach me vim
|
||
|
||
> [!abstract] PI
|
||
> Vim always starts in Normal mode — keys are commands, not text.`;
|
||
|
||
/** What the notes-mode extractor submits: same shapes minus attribution,
|
||
* and messages always empty (the file is the provenance). */
|
||
const NOTES_FINDINGS = {
|
||
propositions: [
|
||
{
|
||
id: "p1",
|
||
name: "Vim normal mode",
|
||
statement: "Vim starts in Normal mode, where keys are commands.",
|
||
aliases: [],
|
||
messages: [],
|
||
evidence: ["Vim always starts in Normal mode — keys are commands, not text."],
|
||
understanding: "solid",
|
||
},
|
||
],
|
||
edges: [],
|
||
} as const;
|
||
|
||
const NOTES_VERDICT = {
|
||
propositions: [NOTES_FINDINGS.propositions[0]],
|
||
edges: [],
|
||
dropped: [],
|
||
} as const;
|
||
|
||
const CLUSTER_FINDINGS = {
|
||
propositions: [
|
||
{ id: "p1", name: "Modal editing", statement: "Vim edits text through modal command modes.", aliases: [], messages: [], evidence: ["Normal mode keys are commands."], understanding: "solid" },
|
||
{ id: "p2", name: "Verb-object grammar", statement: "Vim commands compose as verb and object.", aliases: [], messages: [], evidence: ["d then motion deletes."], understanding: "solid" },
|
||
{ id: "p3", name: "Operator-pending", statement: "Vim enters operator-pending mode between verb and object.", aliases: [], messages: [], evidence: ["After d, a motion is expected."], understanding: "solid" },
|
||
],
|
||
edges: [
|
||
{ from: "p1", to: "p2", type: "follows", messages: [], evidence: ["e"] },
|
||
{ from: "p2", to: "p3", type: "follows", messages: [], evidence: ["e"] },
|
||
],
|
||
} as const;
|
||
|
||
const CLUSTER_VERDICT = {
|
||
propositions: CLUSTER_FINDINGS.propositions,
|
||
edges: CLUSTER_FINDINGS.edges,
|
||
dropped: [],
|
||
} as const;
|
||
|
||
/** Test seam for the embedder: fakes the post-merge embedding batch —
|
||
* one identical unit vector per node → semanticClusters merges them all
|
||
* into ONE topic — then persists the partition like the real embedder. */
|
||
const fakeEmbedder = async (store: GraphStore) => {
|
||
const seen = new Set<string>();
|
||
const ids: string[] = [];
|
||
for (const n of store.readNodes()) {
|
||
if (seen.has(n.id)) continue;
|
||
seen.add(n.id);
|
||
ids.push(n.id);
|
||
}
|
||
const vectors = Object.fromEntries(ids.map((id) => [id, [1, 0, 0]]));
|
||
store.writeEmbeddings({
|
||
model: "test/embedder",
|
||
dim: 3,
|
||
vectors,
|
||
clusters: semanticClusters(vectors),
|
||
updatedAt: "2026-09-13T00:00:00.000Z",
|
||
});
|
||
};
|
||
|
||
describe("cluster naming (the post-extraction garnish)", () => {
|
||
it("names a new community after a notes merge — one record, claimed on the next refresh", async () => {
|
||
const ledgers = memoryLedgers();
|
||
const namerCalls: number[] = [];
|
||
const labeler = submitModel("submit_labels", { labels: [{ key: "c0", label: "Vim modal grammar" }] }, () => namerCalls.push(1));
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: submitModel("submit_findings", CLUSTER_FINDINGS),
|
||
auditor: submitModel("submit_review", CLUSTER_VERDICT),
|
||
labeler,
|
||
});
|
||
const stats = await pipeline.importNotes({ filePath: "/vault/modal.md", text: "raw notes, any format" });
|
||
assert.deepEqual(stats, { nodes: 3, edges: 2, events: 3 });
|
||
// The refresh is fire-and-forget inside the pipeline — settle it.
|
||
await clusterNamingSettled();
|
||
assert.equal(namerCalls.length, 1, "ONE singular naming call");
|
||
const records = ledgers.readClusterLabels();
|
||
assert.equal(records.length, 1);
|
||
assert.equal(records[0]!.label, "Vim modal grammar");
|
||
assert.equal(records[0]!.core.length, 3);
|
||
assert.match(records[0]!.id, /^clu_/);
|
||
// Second refresh: core-survival claims it — nothing new to name.
|
||
const again = await refreshClusterLabels({ graphStore: ledgers, engine: labeler });
|
||
assert.equal(again.named, 0);
|
||
assert.equal(again.appended, 0);
|
||
assert.ok(again.claimed >= 1);
|
||
assert.equal(ledgers.readClusterLabels().length, 1, "no duplicate records");
|
||
});
|
||
|
||
it("naming failure never fails the run", async () => {
|
||
const ledgers = memoryLedgers();
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: submitModel("submit_findings", CLUSTER_FINDINGS),
|
||
auditor: submitModel("submit_review", CLUSTER_VERDICT),
|
||
labeler: silentModel(), // never submits — the namer throws
|
||
});
|
||
const stats = await pipeline.importNotes({ filePath: "/vault/modal.md", text: "raw notes" });
|
||
assert.deepEqual(stats, { nodes: 3, edges: 2, events: 3 }, "the merge itself succeeded");
|
||
await clusterNamingSettled();
|
||
assert.equal(ledgers.readClusterLabels().length, 0, "nothing named, nothing appended");
|
||
});
|
||
});
|
||
|
||
describe("the notes front door (importNotes — format-agnostic)", () => {
|
||
it("imports raw notes end to end: source record, file-scoped provenance, no quiz events", async () => {
|
||
const ledgers = memoryLedgers();
|
||
const seen: { kind?: string; label?: string }[] = [];
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: submitModel("submit_findings", NOTES_FINDINGS),
|
||
// During the auditor's call the registry must show the notes run.
|
||
auditor: submitModel("submit_review", NOTES_VERDICT, () => {
|
||
const run = extractionStatuses().find((r) => r.chatId.startsWith("src_"));
|
||
seen.push({ kind: run?.kind, label: run?.label });
|
||
}),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
const stats = await pipeline.importNotes({ filePath: "/vault/learn vim.md", text: NOTES_TEXT });
|
||
|
||
assert.deepEqual(stats, { nodes: 1, edges: 0, events: 1 });
|
||
// The run surfaced as a NOTES run labeled with the file path.
|
||
assert.deepEqual(seen, [{ kind: "notes", label: "/vault/learn vim.md" }]);
|
||
assert.equal(extractionStatuses().length, 0, "the registry clears when the run ends");
|
||
|
||
// The source record: deterministic src id, the file path, the usage log.
|
||
const source = ledgers.readSources()[0]!;
|
||
assert.match(source.id, /^src_/);
|
||
assert.equal(source.filePath, "/vault/learn vim.md");
|
||
assert.ok(Date.parse(source.importedAt));
|
||
assert.equal(source.extractions?.length, 1, "the run's usage persisted into the source record");
|
||
assert.equal(source.extractions![0]!.stats?.nodes, 1);
|
||
assert.ok(source.extractions![0]!.stages.extractor.steps.length > 0, "faux usage captured");
|
||
|
||
// File-scoped provenance: the mapping line carries the source id;
|
||
// the node carries NO message anchors.
|
||
const node = ledgers.readNodes()[0]!;
|
||
assert.equal(node.statement, "Vim starts in Normal mode, where keys are commands.");
|
||
assert.deepEqual(node.provenance, []);
|
||
assert.deepEqual(ledgers.readMappings(), [{ chatId: source.id, sourceId: "p1", graphNodeId: node.id }]);
|
||
|
||
// One observation event, zero quiz events — outcomes inside notes
|
||
// surface only through understanding observations (user-decided).
|
||
const events = ledgers.readEvents();
|
||
assert.deepEqual(
|
||
events.map((e) => [e.kind, e.chatId, e.ref, e.note]),
|
||
[["observation", source.id, "obs:p1", "solid"]],
|
||
);
|
||
// The checkpoint stamps the import.
|
||
assert.equal(ledgers.readCheckpoints()[0]!.chatId, source.id);
|
||
});
|
||
|
||
it("re-importing the same file is idempotent: same source identity, refs dedupe, usage history grows", async () => {
|
||
const ledgers = memoryLedgers();
|
||
const mk = () =>
|
||
createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: submitModel("submit_findings", NOTES_FINDINGS),
|
||
auditor: submitModel("submit_review", NOTES_VERDICT),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
const first = await mk().importNotes({ filePath: "/vault/learn vim.md", text: NOTES_TEXT });
|
||
assert.deepEqual(first, { nodes: 1, edges: 0, events: 1 });
|
||
|
||
const second = await mk().importNotes({ filePath: "/vault/learn vim.md", text: NOTES_TEXT });
|
||
assert.deepEqual(second, { nodes: 0, edges: 0, events: 0 }, "the same source id dedupes everything");
|
||
|
||
assert.equal(ledgers.readSources().length, 1, "one source identity — the path hashes to the same id");
|
||
assert.equal(ledgers.readSources()[0]!.extractions?.length, 2, "both runs billed their usage");
|
||
assert.equal(ledgers.readNodes().length, 1, "no duplicate node");
|
||
});
|
||
|
||
it("aborts like a chat run: true cancellation, nothing merged, partial usage in the source record", async () => {
|
||
const ledgers = memoryLedgers();
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: hangingModel(),
|
||
auditor: submitModel("submit_review", NOTES_VERDICT),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
const runPromise = pipeline.importNotes({ filePath: "/vault/learn vim.md", text: NOTES_TEXT });
|
||
|
||
const srcId = extractionStatuses().find((r) => r.chatId.startsWith("src_"))!.chatId;
|
||
assert.equal(abortExtraction(srcId), true);
|
||
assert.deepEqual(await runPromise, { nodes: 0, edges: 0, events: 0 });
|
||
assert.equal(ledgers.readNodes().length, 0, "nothing merged");
|
||
assert.equal(ledgers.readCheckpoints().length, 0, "nothing checkpointed");
|
||
assert.equal(ledgers.readSources()[0]!.extractions?.length, 1, "partial usage still persisted");
|
||
assert.equal(ledgers.readSources()[0]!.extractions![0]!.stats, undefined, "never merged → no stats");
|
||
});
|
||
});
|
||
|
||
// ── the chunked pipeline (extraction coverage — vision-extraction-coverage.md) ──
|
||
|
||
/** Pull the pipeline's prompt view out of a model call: the pipeline sends
|
||
* `Transcript:\n\n<view>` as the user turn, so the LAST user message's
|
||
* text IS what the extractor saw. */
|
||
function capturedView(context: unknown): string {
|
||
const messages = (context as { messages?: { role: string; content: string }[] }).messages ?? [];
|
||
const last = [...messages].reverse().find((m) => m.role === "user");
|
||
return typeof last?.content === "string" ? last.content : "";
|
||
}
|
||
|
||
/** An extractor step that submits FINDINGS, optionally capturing its view. */
|
||
function findingsStep(capture?: (view: string) => void): FauxResponseStep {
|
||
return (context) => {
|
||
capture?.(capturedView(context));
|
||
return fauxAssistantMessage(
|
||
[fauxToolCall("submit_findings", FINDINGS as unknown as Record<string, unknown>, { id: "findings-call" })],
|
||
{ stopReason: "toolUse" },
|
||
);
|
||
};
|
||
}
|
||
|
||
/** An auditor step that passes p1 through (no linkTo — a fresh node), drops p2. */
|
||
function passThroughAuditStep(): FauxResponseStep {
|
||
return () =>
|
||
fauxAssistantMessage(
|
||
[
|
||
fauxToolCall(
|
||
"submit_review",
|
||
{
|
||
propositions: [{ ...FINDINGS.propositions[0]! }],
|
||
edges: [],
|
||
attribution: { quiz: {} },
|
||
dropped: [{ ref: "p2", reason: "evidence not found" }],
|
||
} as unknown as Record<string, unknown>,
|
||
{ id: "review-call" },
|
||
),
|
||
],
|
||
{ stopReason: "toolUse" },
|
||
);
|
||
}
|
||
|
||
/** A session with UNCOVERED-multiple-batch mass: five long distinct messages
|
||
* (~1200 tok each — 3600+1200 > EXTRACTION.chunkTokens 4096 → batches of
|
||
* [m1,m2,m3] and [m4,m5]). */
|
||
function chunkyChat() {
|
||
const chat = chatStore.createChat("Chunky coverage fixture");
|
||
createdChats.push(chat.id);
|
||
// ~1230 tok per message (chars/4): three fill the 4096 budget, the
|
||
// fourth would overflow — deterministic batches [m1,m2,m3] + [m4,m5].
|
||
chatStore.saveMessages(
|
||
chat.id,
|
||
[1, 2, 3, 4, 5].map((n) => user(`u${n}`, `topic ${n}: ${"detail ".repeat(700)}${n}`)),
|
||
);
|
||
return { chatId: chat.id, record: chatStore.getChat(chat.id)! };
|
||
}
|
||
|
||
describe("the chunked pipeline (extraction coverage)", () => {
|
||
it("extracts uncovered messages in token-budgeted batches, checkpoints coverage per batch, and no-ops once covered", async () => {
|
||
const { chatId, record } = chunkyChat();
|
||
const ledgers = memoryLedgers();
|
||
const views: string[] = [];
|
||
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: fauxEngine([findingsStep((v) => views.push(v)), findingsStep((v) => views.push(v))]),
|
||
auditor: fauxEngine([passThroughAuditStep(), passThroughAuditStep()]),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
const stats = await pipeline.runExtraction(chatId);
|
||
|
||
// Two batches: the first held messages 1-3, the second 4-5 — a
|
||
// message is never split, batches are consecutive oldest-first.
|
||
assert.equal(views.length, 2, "one extractor call per batch");
|
||
assert.ok(views[0]!.includes("LEARNER [u1]") && views[0]!.includes("LEARNER [u3]"));
|
||
assert.ok(!views[0]!.includes("LEARNER [u4]"), "the batch stops at the token budget");
|
||
assert.ok(views[1]!.includes("LEARNER [u4]") && views[1]!.includes("LEARNER [u5]"));
|
||
assert.ok(!views[1]!.includes("LEARNER [u3]"));
|
||
|
||
// p1 was a fresh node in batch 1; batch 2's p1 mapped to the same
|
||
// node (chat-scoped mapping) — but its understanding note is a NEW
|
||
// batch-tagged observation.
|
||
assert.deepEqual(stats, { nodes: 1, edges: 0, events: 2 });
|
||
|
||
// Per-batch coverage: every message checkpointed, nothing legacy.
|
||
const coverage = ledgers.readCoverage();
|
||
assert.equal(coverage.length, record.messages.length);
|
||
assert.equal(ledgers.readCheckpoints().length, 0);
|
||
assert.equal(coverageOf(record.messages as UIMessage[], coverage, chatId).uncoveredCount, 0);
|
||
|
||
// A second run is a NO-OP: nothing uncovered → no model calls, no
|
||
// new usage entry (no money spent on covered ground).
|
||
const before = views.length;
|
||
const again = await pipeline.runExtraction(chatId);
|
||
assert.deepEqual(again, { nodes: 0, edges: 0, events: 0 });
|
||
assert.equal(views.length, before);
|
||
assert.equal(chatStore.getChat(chatId)!.extractions?.length, 1, "the no-op run persists no usage");
|
||
});
|
||
|
||
it("aborting after a merged batch keeps it covered; the re-run extracts only the remainder", async () => {
|
||
const { chatId, record } = chunkyChat();
|
||
const ledgers = memoryLedgers();
|
||
const views: string[] = [];
|
||
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
// Batch 1 submits and merges; batch 2's extractor call HANGS
|
||
// until the abort fires — the dock's abort button, mid-run.
|
||
extractor: fauxEngine([
|
||
findingsStep((v) => views.push(v)),
|
||
(_context, options) =>
|
||
new Promise((resolve) => {
|
||
const sig = options?.signal;
|
||
if (sig?.aborted) {
|
||
resolve(fauxAssistantMessage("late"));
|
||
return;
|
||
}
|
||
sig?.addEventListener("abort", () => resolve(fauxAssistantMessage("late")), { once: true });
|
||
}),
|
||
]),
|
||
auditor: fauxEngine([passThroughAuditStep()]),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
const runPromise = pipeline.runExtraction(chatId);
|
||
// The abort must land while batch 2's extractor hangs. The faux
|
||
// stream only resolves on abort, so wait for the batch-1 merge to
|
||
// show in coverage, then abort.
|
||
await new Promise<void>((resolve) => {
|
||
const check = setInterval(() => {
|
||
if (ledgers.readCoverage().length > 0) {
|
||
clearInterval(check);
|
||
resolve();
|
||
}
|
||
}, 5);
|
||
});
|
||
assert.equal(abortExtraction(chatId), true);
|
||
const stats = await runPromise;
|
||
|
||
// Batch 1's emissions are kept and its messages stay covered.
|
||
assert.deepEqual(stats, { nodes: 1, edges: 0, events: 1 });
|
||
assert.equal(ledgers.readCoverage().length, 3, "batch 1 checkpointed its three messages");
|
||
const cov = coverageOf(record.messages as UIMessage[], ledgers.readCoverage(), chatId);
|
||
assert.equal(cov.uncoveredCount, 2, "the in-flight batch's messages stay uncovered");
|
||
|
||
// The re-run — the manual extract button — picks up ONLY the
|
||
// remainder: one batch, messages 4-5 alone in the prompt.
|
||
const reRunViews: string[] = [];
|
||
const finisher = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: fauxEngine([findingsStep((v) => reRunViews.push(v))]),
|
||
auditor: fauxEngine([passThroughAuditStep()]),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await finisher.runExtraction(chatId);
|
||
assert.equal(reRunViews.length, 1);
|
||
assert.ok(reRunViews[0]!.includes("LEARNER [u4]") && reRunViews[0]!.includes("LEARNER [u5]"));
|
||
assert.ok(!reRunViews[0]!.includes("LEARNER [u1]"), "the covered remainder never re-enters the prompt");
|
||
assert.equal(ledgers.readCoverage().length, 5, "the re-run closed the gap");
|
||
});
|
||
|
||
it("a re-opened message re-extracts alone; a data-part attach never re-opens it", async () => {
|
||
// A small session: one batch covers everything, so re-openings are
|
||
// the ONLY thing later runs can see.
|
||
const chat = chatStore.createChat("Re-open fixture");
|
||
createdChats.push(chat.id);
|
||
chatStore.saveMessages(chat.id, [user("s1", "short topic one"), user("s2", "short topic two")]);
|
||
const chatId = chat.id;
|
||
const ledgers = memoryLedgers();
|
||
const pipeline = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: fauxEngine([findingsStep()]),
|
||
auditor: fauxEngine([passThroughAuditStep()]),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await pipeline.runExtraction(chatId);
|
||
const messages = () => chatStore.getChat(chatId)!.messages as UIMessage[];
|
||
assert.equal(coverageOf(messages(), ledgers.readCoverage(), chatId).uncoveredCount, 0);
|
||
|
||
// Data parts are invisible to the extraction view — plan-block
|
||
// attach does NOT re-open a covered message.
|
||
const withDataPart = messages().map((m) =>
|
||
m.id === "u2"
|
||
? { ...m, parts: [...m.parts, { type: "data-plan-blocks", planBlocks: [] } as unknown as UIMessage["parts"][number]] }
|
||
: m,
|
||
);
|
||
chatStore.saveMessages(chatId, withDataPart);
|
||
const afterAttach = coverageOf(messages(), ledgers.readCoverage(), chatId);
|
||
assert.equal(afterAttach.uncoveredCount, 0, "data parts don't change the fingerprint");
|
||
|
||
// A quiz answer merging into an ASKING message changes its view →
|
||
// exactly that message re-opens, and the next run extracts it alone.
|
||
const mutated = messages().map((m) =>
|
||
m.id === "s1" ? { ...m, parts: [...m.parts, { type: "text", text: "the answer merged in late" }] } : m,
|
||
) as unknown as UIMessage[];
|
||
chatStore.saveMessages(chatId, mutated);
|
||
const afterMerge = coverageOf(messages(), ledgers.readCoverage(), chatId);
|
||
assert.equal(afterMerge.uncoveredCount, 1, "only the mutated message re-opens");
|
||
|
||
const reRunViews: string[] = [];
|
||
const reOpener = createExtractionPipeline({
|
||
chatStore,
|
||
graphStore: ledgers,
|
||
embedder: fakeEmbedder,
|
||
extractor: fauxEngine([findingsStep((v) => reRunViews.push(v))]),
|
||
auditor: fauxEngine([passThroughAuditStep()]),
|
||
labeler: submitModel("submit_labels", { labels: [] }),
|
||
});
|
||
await reOpener.runExtraction(chatId);
|
||
assert.equal(reRunViews.length, 1, "one batch: the single re-opened message");
|
||
assert.ok(reRunViews[0]!.includes("LEARNER [s1]") && !reRunViews[0]!.includes("LEARNER [s2]"));
|
||
assert.equal(coverageOf(messages(), ledgers.readCoverage(), chatId).uncoveredCount, 0, "re-covered");
|
||
});
|
||
});
|