Extraction pipeline: extractor → auditor → merger — the sole graph writer (stage 5)
The knowledge graph's write channel is now the pivot's target
architecture (vision §1 construction):
- src/agent/subagents/extraction.ts — the pipeline factory
(createExtractionPipeline({ chatStore, graphStore, extractor?,
auditor? }); models env LH_EXTRACT_MODEL ?? LH_MODEL):
* transcriptView(): the compact view both LLM stages see — final plan
tree with node ids, conversation verbatim, QUIZ lines with outcomes,
mark_done lines; job tool parts are one-liners, plan ops fold into
the tree line.
* Extractor: transcript → SessionFindings (propositions incl.
misconceptions and prose-only supports edges, attribution for
UNTAGGED probes, all with verbatim evidence) via one zod-validated
submit_findings call.
* Auditor: transcript + findings → the verified subset + drops (the
faithfulness net) via submit_review.
- src/core/graph.ts — emitChat/foldStaleChats/replayFinalTree retire;
mergeFindings() replaces them as the pipeline's deterministic apply
stage: exact/alias identity resolution (one mapping line per (chat,
graph node)), edge dedupe, and event rebuild FROM THE TRANSCRIPT's tool
parts — kinds/timestamps/refs/notes deterministic, the LLM's only event
say being WHICH proposition an untagged probe tested (retroactive —
the vision's open question #3, resolved structurally: tagged events
resolve deterministically; attribution rescues untagged and
tagged-but-unmapped ones; unattributable land null + question text).
pendingQuizAnswers() is the trigger helper.
- Trigger (turn-service.ts triggerExtraction, replacing the old
foldTurnIntoGraph body): once a chat accumulates
EXTRACTION.everyNQuizAnswers (LH_EXTRACT_N, default 3) new answered
quizzes, fire the pipeline async — single-flight per chat, failure
checkpoints nothing and re-runs on the next trigger. Deliberately a v1
placeholder isolated in ONE seam, trigger-agnostic pipeline.
- GET /api/graph is now a pure read (foldStaleChats gone).
- NodeMapping.treeNodeId → sourceId (the mapping key is now the writer's
local proposition id — tree node when tree-derived); store strict-read
schema updated. Local ledger data regenerates through the pipeline.
- Tests: graph.test.ts rewritten around mergeFindings (identity,
attribution paths incl. ghost-rescue, idempotency, cross-session
provenance, trigger counting), graph-context.test.ts merge section
converted to findings-driven, NEW extraction.test.ts — transcriptView
units + the full pipeline with mock models (audit drops, failure
checkpoints nothing, single-flight). 195/195, tsc/eslint/build clean.
Probe attribution (vision open question): probes attach retroactively by
the extractor against the whole transcript; a pre-tree probe maps to a
proposition the tree only articulates later. Fuzzy/embedding merge
adjudication stays deferred (conservative exact/alias only).
This commit is contained in:
Amos
@@ -0,0 +1,373 @@
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/**
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* The extraction pipeline tests (vision §1 construction): extractor →
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* auditor → merger, the sole graph writer.
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*
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* transcriptView — the compact view both LLM stages see (pure)
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* pipeline — MockLanguageModelV4 drives both agents: findings out,
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* audit verdict in, deterministic merge applied; failure
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* paths checkpoint nothing; single-flight per chat
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*
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* The merger itself (identity resolution, 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 { MockLanguageModelV4 } from "ai/test";
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import type { UIMessage } from "ai";
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import type { GraphEdge, GraphNode, LedgerEvent, NodeMapping, SessionCheckpoint, SessionFindings } from "../src/core/contracts.ts";
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import type { GraphStore } from "../src/core/ports.ts";
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import { createTree, markDone } from "../src/core/plan-tree.ts";
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import { createExtractionPipeline, transcriptView } from "../src/agent/subagents/extraction.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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function usage() {
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return {
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inputTokens: { total: 10, noCache: 10, cacheRead: 0, cacheWrite: 0 },
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outputTokens: { total: 5, text: 5, reasoning: 0 },
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};
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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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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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} 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: ReturnType<typeof createTree>): 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 → plan → check → mark_done. */
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function fixtureChat() {
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const tree = markDone(
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createTree(
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[
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{ id: "cr", title: "Chain rule intuition" },
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{ id: "apply", title: "Applying the chain rule" },
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],
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[{ from: "cr", to: "apply" }],
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),
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"cr",
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);
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const chat = chatStore.createChat("Extraction fixture");
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createdChats.push(chat.id);
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chatStore.saveTree(chat.id, tree);
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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: [] }, tree),
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]),
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assistant("a3", [
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quizPart("check-1", { question: "Check", nodeId: "apply" }, {
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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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assistant("a4", [planPart("p2", "plan_mark_done", { id: "cr" }, tree)]),
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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. */
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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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statement: "Chain rule intuition",
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aliases: [],
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kind: "truth",
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treeNodeId: "cr",
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evidence: ["TEACHER: The derivative measures how fast a function changes."],
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},
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{
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id: "p2",
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statement: "Applying the chain rule",
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aliases: [],
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kind: "truth",
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treeNodeId: "apply",
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evidence: ["FINAL PLAN TREE (node ids): cr = Chain rule intuition; apply = Applying the chain rule"],
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},
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],
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edges: [{ from: "p1", to: "p2", type: "requires", evidence: ["TREE EDGES: cr requires apply"] }],
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attribution: { quiz: { "probe-1": "p1" } },
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};
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/** A model whose ONE tool call is `name` with the given input; then text. */
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function submitModel(name: string, input: unknown): MockLanguageModelV4 {
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let turn = 0;
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return new MockLanguageModelV4({
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doGenerate: async () => {
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turn++;
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if (turn === 1) {
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return {
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content: [
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{
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type: "tool-call" as const,
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toolCallId: `${name}-call`,
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toolName: name,
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input: JSON.stringify(input),
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},
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],
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finishReason: { unified: "tool-calls" as const, raw: "tool-calls" },
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usage: usage(),
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warnings: [],
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};
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}
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return {
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content: [{ type: "text" as const, text: "submitted" }],
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finishReason: { unified: "stop" as const, raw: "stop" },
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usage: usage(),
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warnings: [],
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};
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},
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});
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}
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/** A model that never calls a tool (the failure path). */
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function silentModel(): MockLanguageModelV4 {
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return new MockLanguageModelV4({
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doGenerate: async () => ({
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content: [{ type: "text" as const, text: "I refuse to use tools" }],
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finishReason: { unified: "stop" as const, raw: "stop" },
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usage: usage(),
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warnings: [],
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}),
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});
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}
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// ─── transcriptView (pure) ───────────────────────────────────────────────────
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describe("transcriptView (pure)", () => {
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it("shows the final tree, verbatim conversation, structured events — plan ops folded", () => {
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const { record } = fixtureChat();
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const view = transcriptView(record);
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// The final tree with node ids (the extractor's backbone + the
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// pruned-out stays pruned — only the FINAL tree is shown).
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assert.match(view, /SESSION: Extraction fixture/);
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assert.match(view, /FINAL PLAN TREE \(node ids\): cr = Chain rule intuition; apply = Applying the chain rule/);
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assert.match(view, /TREE EDGES: cr requires apply/);
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// Conversation verbatim — evidence quotes can come from it.
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assert.match(view, /LEARNER: teach me the chain rule/);
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assert.match(view, /TEACHER: 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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assert.match(view, /TEACHER marked tree node done \[p2\]: cr/);
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// plan_init / plan_mark_done tool parts themselves are folded into
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// the tree line — no raw JSON noise.
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assert.doesNotMatch(view, /plan_init/);
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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 → 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, drops p2 (unbacked), re-points the
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// probe attribution to p1, and records the drop.
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const verified: SessionFindings = {
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propositions: [FINDINGS.propositions[0]!],
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edges: [],
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attribution: { quiz: { "probe-1": "p1" } },
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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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extractor: submitModel("submit_findings", FINDINGS),
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auditor: submitModel("submit_review", { ...verified, dropped: [{ ref: "p2", reason: "evidence not found" }] }),
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});
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const stats = await pipeline.runExtraction(chatId);
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assert.deepEqual(stats, { nodes: 1, edges: 0, events: 3 });
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// Only the audited proposition became a node; mapping provenance kept.
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assert.equal(ledgers.readNodes().length, 1);
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assert.equal(ledgers.readNodes()[0]!.statement, "Chain rule intuition");
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assert.deepEqual(ledgers.readMappings(), [{ chatId, sourceId: "cr", graphNodeId: ledgers.readNodes()[0]!.id }]);
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// Events rebuilt deterministically from the transcript — the probe
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// retroactively attributed, the check tagged, mark_done mapped.
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const events = ledgers.readEvents();
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assert.deepEqual(
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events.map((e) => [e.kind, e.ref]),
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[
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["probe-miss", "probe-1"],
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["check-correct", "check-1"],
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["taught", "taught:cr"],
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],
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);
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const nodeId = ledgers.readNodes()[0]!.id;
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assert.equal(events[0]!.nodeId, nodeId, "probe attributed via audit");
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assert.equal(events[1]!.nodeId, null, "tagged to a dropped proposition → null");
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assert.equal(events[2]!.nodeId, nodeId, "taught via tree-node mapping");
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// Checkpointed through the record's updatedAt — never the same turns twice.
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assert.equal(ledgers.readCheckpoints().length, 1);
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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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extractor: submitModel("submit_findings", FINDINGS),
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auditor: silentModel(), // never submits the review
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});
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await assert.rejects(pipeline.runExtraction(chatId), /the auditor never submitted a review/);
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assert.equal(ledgers.readNodes().length, 0);
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assert.equal(ledgers.readCheckpoints().length, 0, "no checkpoint — the next trigger re-runs");
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const failingExtractor = createExtractionPipeline({
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chatStore,
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graphStore: ledgers,
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extractor: silentModel(),
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auditor: submitModel("submit_review", FINDINGS),
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});
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await assert.rejects(failingExtractor.runExtraction(chatId), /the extractor never submitted findings/);
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assert.equal(ledgers.readCheckpoints().length, 0);
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});
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it("is single-flight per chat — a concurrent second run is a no-op", async () => {
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const { chatId } = fixtureChat();
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const ledgers = memoryLedgers();
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// The extractor's first call hangs until we release it.
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let release!: () => void;
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const gate = new Promise<void>((resolve) => {
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release = resolve;
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});
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let calls = 0;
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const gatedExtractor = new MockLanguageModelV4({
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doGenerate: async () => {
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calls++;
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if (calls === 1) await gate;
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return {
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content: [{ type: "text" as const, text: "…" }],
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finishReason: { unified: "stop" as const, raw: "stop" },
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usage: usage(),
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warnings: [],
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};
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},
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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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extractor: gatedExtractor,
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auditor: silentModel(),
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});
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const first = pipeline.runExtraction(chatId);
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const second = await pipeline.runExtraction(chatId); // in-flight → zeros
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assert.deepEqual(second, { nodes: 0, edges: 0, events: 0 });
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release();
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await assert.rejects(first, /the extractor never submitted findings/);
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});
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it("throws for unknown chats", async () => {
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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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extractor: silentModel(),
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auditor: silentModel(),
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});
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await assert.rejects(pipeline.runExtraction("chat_does_not_exist"), /Chat not found/);
|
||||
});
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});
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Reference in New Issue
Block a user