The model-control wave (user-decided design, 2026-09-12): ONE controller
for the conversation agents (prober/teacher are functionally one agent);
background sub-agents stay config-pinned. pi-ai owns the registry — the
shipped 366-model OpenRouter catalog (cost, context, modality,
thinkingLevelMap), getSupportedThinkingLevels/clampThinkingLevel for the
per-model thinking truth, and streamSimple's automatic clamp. No second
registry to drift out of sync.
Seams:
- config.ts: ReasoningEffort retired for pi's ThinkingLevel
(off|minimal|low|medium|high|xhigh|max). GLM roles go honest at "low" —
pi's catalog says GLM-5.3-flash accepts exactly low|high|max, and the old
"minimal" was silently clamped to low by streamSimple since the swap;
nothing changes behaviorally. visualMaker → qwen/qwen3.8-flash (fixes
the dead qwen/qwen3.8-max slug — the catalog only ships -0902; also the
user's cost pick, 0.15/0.47 per-1M).
- src/agent/models.ts: piEngineFor(role, settings?) — provider+id resolved
from the catalog, thinking level from settings ?? config, CLAMPED to the
model's supported list (a settings change between models can never send
an invalid level). PiEngine gains `level` (observability/tests).
- ChatRecord.settings {model?: {provider, id}, thinkingLevel?} — written
by PUT /api/chats/[id]/settings (replace-whole, no updatedAt churn),
read by chat-handler at turn composition: agents are stateless per turn,
so the change is next-turn-effective by construction. The turns log
still records the ACTUAL billed model (the honest witness).
- GET /api/models: the configured-provider catalog as client-safe DTOs
(core/contracts/models.ts) + config defaults; a second provider joins
the picker by registering it in piModels() — nothing else changes.
- PUT settings validation gate (src/server/model-catalog.ts): unknown
provider/model or unsupported level → 400 carrying the supported list,
never a silent clamp.
- scripts/sync-pricing.ts: regenerated PRICES/CONTEXT_LIMITS from pi-ai's
shipped openrouter.json (offline; same source as the engine + picker).
All 366 models priced; glm's stale 0.15/0.5 → pi's 0.075/0.25, limit
1310720 → 1048576 — cost is inferred at read time, so history re-prices.
- UI (components/model-picker.tsx): stats-bar right side becomes model +
thinking buttons; menus open ABOVE; fuzzy search over "provider id
name" (core/fuzzy.ts — pi's /model pattern); model rows show name/id/
price/context; the thinking menu lists exactly the chosen model's
supported levels (GLM: low|high|max — no dead options, no "off").
Model switch drops an incompatible pinned level (falls back to config,
which clamps honestly) rather than carrying a 400.
Tests (350/350, +15): catalog projection + defaults, the settings gate,
pi thinking truth pinned against upgrades (glm low|high|max; minimal→low;
off→low — GLM cannot disable thinking), the engine factory's
override/fallback/clamp/throw matrix, fuzzy ranking, store round-trip,
and the re-priced folds (usage, stats bar, activity dock).
Live smoke (disposable driver, deleted): catalog route 366 + defaults;
glm+minimal → 400 + supported; turn 1 billed qwen/qwen3.8-flash under
the override; mid-session switch flipped turn 2 to glm; clear-overrides
restored the config pin. ALL CHECKS PASS on real OpenRouter.
531 lines
24 KiB
TypeScript
531 lines
24 KiB
TypeScript
/**
|
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* Scripted integration test — the full teacher loop with zero tokens:
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*
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* learner opens chat → faux model calls `quiz` → server streams + persists
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* → client grades and sends the answer back as the tool output
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* → faux model reacts → transcript persisted.
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*
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* Drives handleChatRequest directly (plain Request/Response — no Next server
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* needed) with a scripted pi engine (pi-ai's faux provider, docs/
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* pi-engine-swap.md §7): the quiz bridge is gone, the tool part IS the
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* pending state, and the persisted transcript is the resume record.
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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 * as path from "node:path";
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import { after, describe, it } from "node:test";
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import {
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createFauxCore,
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fauxAssistantMessage,
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fauxText,
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fauxThinking,
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fauxToolCall,
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type FauxResponseStep,
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} from "@earendil-works/pi-ai";
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import { createTeacherAgent } from "../src/agent/teacher-agent.ts";
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import { handleChatRequest } from "../src/server/chat-handler.ts";
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import { isTurnRunning } from "../src/server/turn-liveness.ts";
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import { recordToPiMessages } from "../src/server/pi/record-adapter.ts";
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import { chatStore } from "../src/store/chats.ts";
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import { buildAnswer, coerceCorrectAnswer, normalizeOptions, resolveCorrect, type QuestionData } from "../src/core/question-core.ts";
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import { findPendingToolCall } from "../src/core/pending.ts";
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import type { PiEngine } from "../src/agent/models.ts";
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import type { TeachingAgent } from "../src/agent/teacher-agent.ts";
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import type { UIMessage } from "ai";
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import { PATHS } from "../config.ts";
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|
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const QUIZ_INPUT = {
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question: "Which planet is closest to the sun?",
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options: [
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{ label: "Venus" },
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{ label: "Mercury" },
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{ label: "Mars" },
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],
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correctAnswer: "Mercury",
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explanation: "Mercury is the innermost planet.",
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};
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|
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/** Turn 1: a bit of reasoning, then the quiz tool call. Turn 2: plain text.
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* Each response factory records the prompt it received (the transcript the
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* model saw) — the old doStream capture, engine-shaped. */
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function scriptedTeacher(steps: (calls: string[][]) => FauxResponseStep[]): {
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engine: PiEngine;
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calls: string[][];
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} {
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const calls: string[][] = [];
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const faux = createFauxCore({});
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faux.setResponses(steps(calls));
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return { engine: { model: faux.getModel(), streamFn: faux.streamSimple }, calls };
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}
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|
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/** The default two-turn lesson: probe quiz, then react to the answer. */
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function defaultLesson(calls: string[][]): FauxResponseStep[] {
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return [
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(context) => {
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calls.push(context.messages.map((m) => JSON.stringify((m as { content: unknown }).content)));
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return fauxAssistantMessage(
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[fauxThinking("Probe the basics first."), fauxToolCall("quiz", QUIZ_INPUT, { id: "call-1" })],
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{ stopReason: "toolUse" },
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);
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},
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(context) => {
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calls.push(context.messages.map((m) => JSON.stringify((m as { content: unknown }).content)));
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return fauxAssistantMessage([fauxText("Right — Mercury it is.")]);
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},
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];
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}
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|
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/** The handler seam (stateless per turn): each request builds a fresh agent
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* off the transcript it is about to run — the factory tests inject. */
|
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function agentFactory(engine: PiEngine) {
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|
return (outgoing: UIMessage[]): TeachingAgent =>
|
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createTeacherAgent(undefined, { engine, messages: recordToPiMessages(outgoing) });
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|
}
|
|
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|
async function drain(res: Response): Promise<string> {
|
|
if (res.status !== 200) {
|
|
assert.fail(`expected 200, got ${res.status}: ${await res.text()}`);
|
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}
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return res.text(); // consumes the SSE stream to completion (fires onFinish)
|
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}
|
|
|
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function post(chatId: string, messages: unknown[]): Request {
|
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return new Request("http://test/api/chat", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({ chatId, messages }),
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});
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}
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|
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const createdChats: string[] = [];
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after(() => {
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for (const id of createdChats) {
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try {
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fs.unlinkSync(path.join(PATHS.chats, `${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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describe("teacher chat flow (scripted model)", () => {
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it("quiz tool call persists as a pending part; the answer continues the lesson", async () => {
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const { engine, calls } = scriptedTeacher(defaultLesson);
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const chat = chatStore.createChat("Test: solar system");
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createdChats.push(chat.id);
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// ── Turn 1: learner opens with a topic; teacher probes with a quiz ──
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const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "teach me the solar system" }] };
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await drain(await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)));
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const saved1 = chatStore.getChat(chat.id)!;
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assert.equal(saved1.messages.length, 2, "user message + assistant response persisted");
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const assistantMsg = saved1.messages[1]!;
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assert.equal(assistantMsg.role, "assistant");
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|
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const reasoningPart = assistantMsg.parts.find((p) => p.type === "reasoning");
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assert.ok(reasoningPart && reasoningPart.type === "reasoning", "reasoning part streamed + persisted");
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assert.match((reasoningPart as { text?: string }).text!, /Probe the basics/);
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const quizPart = assistantMsg.parts.find((p) => p.type === "tool-quiz");
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assert.ok(quizPart && quizPart.type === "tool-quiz", "quiz tool part persisted");
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assert.equal((quizPart as { state?: string }).state, "input-available", "unanswered quiz = pending state (resume record)");
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assert.deepEqual((quizPart as { input?: unknown }).input, QUIZ_INPUT);
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// The pending-question gate sees it.
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const pending = findPendingToolCall(saved1.messages);
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assert.equal(pending?.toolCallId, "call-1");
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assert.equal(pending?.tool, "quiz");
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assert.deepEqual(pending?.input, QUIZ_INPUT);
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// ── The learner answers: client grades, output goes back on the part ──
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const options = normalizeOptions(QUIZ_INPUT.options);
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const qd: QuestionData = {
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id: "call-1",
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kind: "quiz",
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question: QUIZ_INPUT.question,
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mode: "single-select",
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options,
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correctIndices: resolveCorrect(coerceCorrectAnswer(QUIZ_INPUT.correctAnswer), options).indices,
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explanation: QUIZ_INPUT.explanation,
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createdAt: new Date().toISOString(),
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};
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const answer = buildAnswer(qd, { values: ["Mercury"] });
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assert.equal(answer.correct, true);
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const messages2 = saved1.messages.map((m) =>
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m.id === assistantMsg.id
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? {
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...m,
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parts: m.parts.map((p) =>
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p.type === "tool-quiz" ? { ...p, state: "output-available" as const, output: answer } : p,
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),
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}
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: m,
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);
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await drain(await handleChatRequest(post(chat.id, messages2), agentFactory(engine)));
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// ── The teacher reacts — as a CONTINUATION of the same assistant message
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// (the last client message is the assistant message carrying the tool
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// output, so the new text appends to that message's parts). ──
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const saved2 = chatStore.getChat(chat.id)!;
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assert.equal(saved2.messages.length, 2, "the reaction continues the same assistant message");
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const finalParts = saved2.messages[1]!.parts;
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const textPart = finalParts.find((p) => p.type === "text");
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assert.ok(textPart && textPart.type === "text", "reaction text appended after the tool part");
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assert.match((textPart as { text?: string }).text!, /Mercury it is/);
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// The answered quiz part is still there, now with its output.
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const answeredQuiz = finalParts.find((p) => p.type === "tool-quiz");
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assert.ok(answeredQuiz && answeredQuiz.type === "tool-quiz" && (answeredQuiz as { state?: string }).state === "output-available");
|
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// The model actually received the graded answer as a tool result.
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assert.equal(calls.length, 2, "model saw exactly two prompts");
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const toolResultSent = calls[1]!.some((c) => c.includes("toolResult") || c.includes("Mercury"));
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assert.ok(toolResultSent, "continuation prompt carries the tool result to the model");
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// No pending question remains.
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assert.equal(findPendingToolCall(saved2.messages), null);
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});
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it("rejects unknown chats", async () => {
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const res = await handleChatRequest(post("chat_nonexistent", []));
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assert.equal(res.status, 404);
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});
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it("streams tool-input-start before tool-input-available (protocol pin for the 'Writing a question…' indicator)", async () => {
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// The client's writing indicator keys off tool parts in state
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// 'input-streaming', which the stream processor sets from the
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// tool-input-start chunk. If a transport change ever stopped emitting
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// it, the indicator would silently die — pin the protocol.
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const { engine } = scriptedTeacher(defaultLesson);
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const chat = chatStore.createChat("Test: protocol pin");
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createdChats.push(chat.id);
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const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "quiz me" }] };
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const body = await drain(await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)));
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const startIdx = body.indexOf('"type":"tool-input-start"');
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const availIdx = body.indexOf('"type":"tool-input-available"');
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assert.ok(startIdx !== -1, "tool-input-start chunk present in the stream");
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assert.ok(availIdx !== -1, "tool-input-available chunk present in the stream");
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assert.ok(startIdx < availIdx, "start precedes available");
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});
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it("persists even when the stream is aborted (resume keeps partial state)", async () => {
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const { engine } = scriptedTeacher(defaultLesson);
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const chat = chatStore.createChat("Test: abort");
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createdChats.push(chat.id);
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|
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const req = post(chat.id, [{ id: "u1", role: "user", parts: [{ type: "text", text: "quiz me" }] }]);
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const res = await handleChatRequest(req, agentFactory(engine));
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assert.equal(res.status, 200);
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// Read one chunk then cancel — simulates the browser closing mid-stream.
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const reader = res.body!.getReader();
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await reader.read();
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await reader.cancel();
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// Give the aborted run a tick to settle (agent_end → onFinish).
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await new Promise((r) => setTimeout(r, 200));
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const saved = chatStore.getChat(chat.id)!;
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assert.ok(saved.messages.length >= 1, "partial transcript persisted");
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});
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it("an ungradeable quiz is rejected at the call boundary, the error reaches the model, and the model retries", async () => {
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// The authoring contract (question-core superRefine): a quiz whose
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// correctAnswer matches no option value must NEVER reach the dock —
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// toPiTool re-runs the zod schema at the call boundary, marks the
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// call invalid, and returns the validation error to the teacher as
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// a tool result. The loop re-prompts the model, which retries in the
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// same turn. This pins that whole mechanism (the live Nietzsche bug).
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const BAD_QUIZ_INPUT = {
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question: "What is Nietzsche's genealogical claim about religion?",
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options: [{ label: "A cultural diagnosis" }, { label: "Atheism as a conclusion" }],
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correctAnswer: "cultural-diagnosis", // the exact live failure shape: a slug, no match
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explanation: "Genealogy diagnoses, it does not convert.",
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};
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|
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const steps = (calls: string[][]): FauxResponseStep[] => [
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(context) => {
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calls.push(context.messages.map((m) => JSON.stringify((m as { content: unknown }).content)));
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return fauxAssistantMessage(
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[fauxToolCall("quiz", BAD_QUIZ_INPUT, { id: "call-bad" })],
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{ stopReason: "toolUse" },
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);
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|
},
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(context) => {
|
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calls.push(context.messages.map((m) => JSON.stringify((m as { content: unknown }).content)));
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return fauxAssistantMessage(
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[fauxToolCall("quiz", QUIZ_INPUT, { id: "call-good" })],
|
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{ stopReason: "toolUse" },
|
|
);
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|
},
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|
];
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const { engine, calls } = scriptedTeacher(steps);
|
|
|
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const chat = chatStore.createChat("Test: bad quiz retry");
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createdChats.push(chat.id);
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const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "quiz me" }] };
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const body = await drain(await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)));
|
|
|
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// WIRE pin (the live modal-realism crash): the invalid call must NEVER
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// become input-available — the dock mounts on input-available parts and
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// crashed on a malformed quiz before tool-input-error could replace it.
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// The SDK gated this with InvalidToolInputError; the bridge gates it on
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// clean tool_execution_end.
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assert.ok(
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!body.includes('"type":"tool-input-available","toolCallId":"call-bad"'),
|
|
"the invalid call never reached input-available on the wire",
|
|
);
|
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assert.ok(
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|
body.includes('"type":"tool-input-error","toolCallId":"call-bad"'),
|
|
"the invalid call surfaced as tool-input-error",
|
|
);
|
|
assert.ok(
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|
body.includes('"type":"tool-input-available","toolCallId":"call-good"'),
|
|
"the valid retry DID become input-available",
|
|
);
|
|
|
|
// The model was re-prompted (the error did not end the turn)…
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|
assert.equal(calls.length, 2, "the invalid call triggered a second model prompt");
|
|
// …and the second prompt carried the validation error back as a tool result.
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|
assert.match(
|
|
calls[1]!.join(" "),
|
|
/does not match any option value/,
|
|
"the validation error reached the model",
|
|
);
|
|
|
|
// The transcript: the failed call persisted as an error part (renders
|
|
// nothing client-side), the retry as the pending quiz.
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|
const saved = chatStore.getChat(chat.id)!;
|
|
const assistantMsg = saved.messages[1]!;
|
|
const quizParts = assistantMsg.parts.filter((p) => p.type === "tool-quiz");
|
|
const failed = quizParts.find(
|
|
(p) => p.type === "tool-quiz" && (p as { state?: string }).state === "output-error",
|
|
);
|
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assert.ok(failed, "the invalid call persisted as an error-state part");
|
|
assert.match(
|
|
String((failed as { errorText?: string }).errorText),
|
|
/does not match any option value/,
|
|
);
|
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const pending = findPendingToolCall(saved.messages);
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assert.equal(pending?.toolCallId, "call-good", "the RETRIED quiz is the pending one");
|
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assert.deepEqual(pending?.input, QUIZ_INPUT);
|
|
});
|
|
|
|
it("a structurally invalid quiz (the live double-encoded options string) never reaches the dock and retries clean", async () => {
|
|
// The exact live modal-realism crash shape: GLM occasionally serializes
|
|
// a nested array as a JSON STRING inside the arguments. The structural
|
|
// layer (JSON Schema: options must be array) rejects the call at the
|
|
// boundary — it must surface as tool-input-error, never as a pending
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|
// question, and the model's retry must become the pending quiz.
|
|
const STRINGY_QUIZ_INPUT = {
|
|
question: "Lewis's indexical analysis of 'actual' — which is it?",
|
|
options: JSON.stringify([
|
|
{ label: "Like 'I'", value: "indexical" },
|
|
{ label: "Like a title", value: "unique" },
|
|
]), // ← the bug: a string, not an array
|
|
correctAnswer: "indexical",
|
|
explanation: "Indexical: every world is actual to its own inhabitants.",
|
|
};
|
|
|
|
const steps: FauxResponseStep[] = [
|
|
() => fauxAssistantMessage([fauxToolCall("quiz", STRINGY_QUIZ_INPUT, { id: "call-stringy" })], { stopReason: "toolUse" }),
|
|
() => fauxAssistantMessage([fauxToolCall("quiz", QUIZ_INPUT, { id: "call-clean" })], { stopReason: "toolUse" }),
|
|
];
|
|
const { engine } = scriptedTeacher(() => steps);
|
|
|
|
const chat = chatStore.createChat("Test: string options");
|
|
createdChats.push(chat.id);
|
|
const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "quiz me" }] };
|
|
const body = await drain(await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)));
|
|
|
|
assert.ok(
|
|
!body.includes('"type":"tool-input-available","toolCallId":"call-stringy"'),
|
|
"the structurally invalid quiz never became input-available on the wire",
|
|
);
|
|
assert.ok(
|
|
body.includes('"type":"tool-input-error","toolCallId":"call-stringy"'),
|
|
"the invalid quiz surfaced as tool-input-error",
|
|
);
|
|
|
|
const saved = chatStore.getChat(chat.id)!;
|
|
const failed = saved.messages[1]!.parts.find(
|
|
(p) => (p as { toolCallId?: string }).toolCallId === "call-stringy",
|
|
);
|
|
assert.equal((failed as { state?: string }).state, "output-error", "persisted as an error part");
|
|
assert.match(String((failed as { errorText?: string }).errorText), /options/);
|
|
const pending = findPendingToolCall(saved.messages);
|
|
assert.equal(pending?.toolCallId, "call-clean", "the retried quiz is the pending one");
|
|
});
|
|
|
|
it("parallel quiz calls are asked, saved, and answered in the model's call order", async () => {
|
|
// The live modal-realism observation: the teacher batched two quizzes
|
|
// in one turn, and the dock showed the SECOND one first (the backward
|
|
// part scan), so the interaction order was reversed vs the record.
|
|
// Pins: (A) findPendingToolCall picks the FIRST unanswered question in
|
|
// part order; (B) question tools execute sequentially, so their
|
|
// tool-input-available chunks arrive in call order on the wire.
|
|
const QUIZ_A = { ...QUIZ_INPUT, question: "Quiz A: which planet is closest to the sun?" };
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|
const QUIZ_B = { ...QUIZ_INPUT, question: "Quiz B: which planet is largest?" };
|
|
|
|
const { engine } = scriptedTeacher(() => [
|
|
() =>
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|
fauxAssistantMessage(
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|
[fauxToolCall("quiz", QUIZ_A, { id: "call-a" }), fauxToolCall("quiz", QUIZ_B, { id: "call-b" })],
|
|
{ stopReason: "toolUse" },
|
|
),
|
|
]);
|
|
|
|
const chat = chatStore.createChat("Test: parallel quizzes");
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|
createdChats.push(chat.id);
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|
const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "quiz me twice" }] };
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|
const body = await drain(await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)));
|
|
|
|
// (B) wire determinism: the batches' input-available chunks in call order.
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|
const availA = body.indexOf('"type":"tool-input-available","toolCallId":"call-a"');
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|
const availB = body.indexOf('"type":"tool-input-available","toolCallId":"call-b"');
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|
assert.ok(availA !== -1 && availB !== -1, "both quizzes became available");
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|
assert.ok(availA < availB, "call A's input became available before call B's (sequential execution)");
|
|
|
|
// Saved in call order.
|
|
const saved = chatStore.getChat(chat.id)!;
|
|
const quizIds = saved.messages[1]!.parts
|
|
.filter((p) => p.type === "tool-quiz")
|
|
.map((p) => (p as { toolCallId: string }).toolCallId);
|
|
assert.deepEqual(quizIds, ["call-a", "call-b"], "parts persisted in call order");
|
|
|
|
// (A) the dock serializes answering in the same order: A first, then B.
|
|
const pending = findPendingToolCall(saved.messages);
|
|
assert.equal(pending?.toolCallId, "call-a", "the FIRST asked question is the dock subject");
|
|
// …the learner answers A (client-side grading seam — state flip only here)…
|
|
const answered = saved.messages.map((m) =>
|
|
(m as { id?: string }).id === saved.messages[1]!.id
|
|
? {
|
|
...m,
|
|
parts: (m as { parts: unknown[] }).parts.map((p) =>
|
|
(p as { toolCallId?: string }).toolCallId === "call-a"
|
|
? { ...(p as object), state: "output-available", output: { correct: true } }
|
|
: p,
|
|
),
|
|
}
|
|
: m,
|
|
);
|
|
const next = findPendingToolCall(answered as UIMessage[]);
|
|
assert.equal(next?.toolCallId, "call-b", "the SECOND question becomes the dock subject only after the first is answered");
|
|
});
|
|
});
|
|
|
|
describe("chat store", () => {
|
|
it("round-trips and lists chats", async () => {
|
|
const chat = chatStore.createChat("Store test");
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|
createdChats.push(chat.id);
|
|
chatStore.saveMessages(chat.id, [{ id: "m1", role: "user", parts: [{ type: "text", text: "hi" }] }]);
|
|
const loaded = chatStore.getChat(chat.id)!;
|
|
assert.equal(loaded.messages.length, 1);
|
|
assert.equal(loaded.title, "Store test");
|
|
});
|
|
|
|
it("persist the per-chat model settings (replace-whole, no updatedAt churn)", async () => {
|
|
const chat = chatStore.createChat("Settings test");
|
|
createdChats.push(chat.id);
|
|
const before = chatStore.getChat(chat.id)!.updatedAt;
|
|
chatStore.saveSettings(chat.id, { model: { provider: "openrouter", id: "z-ai/glm-5.3-flash" }, thinkingLevel: "high" });
|
|
const saved = chatStore.getChat(chat.id)!;
|
|
assert.deepEqual(saved.settings, { model: { provider: "openrouter", id: "z-ai/glm-5.3-flash" }, thinkingLevel: "high" });
|
|
assert.equal(saved.updatedAt, before, "a picker write is not conversation activity");
|
|
// replace-whole: a settings-less write clears the override (an empty
|
|
// object persists — every field absent = config defaults)
|
|
chatStore.saveSettings(chat.id, {});
|
|
assert.deepEqual(chatStore.getChat(chat.id)!.settings, {});
|
|
});
|
|
});
|
|
|
|
describe("mid-turn navigation (turn liveness + write-ahead)", () => {
|
|
it("one turn per chat: a concurrent request is 409'd; the next one after finish is accepted", async () => {
|
|
const { engine } = scriptedTeacher(defaultLesson);
|
|
const chat = chatStore.createChat("Test: turn guard");
|
|
createdChats.push(chat.id);
|
|
|
|
const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "teach me the solar system" }] };
|
|
const res1 = await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)); // NOT drained → in flight
|
|
assert.equal(isTurnRunning(chat.id), true, "turn registered as in flight");
|
|
|
|
// A stale client (remount racing a live turn) tries to start a second turn.
|
|
const res2 = await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine));
|
|
assert.equal(res2.status, 409, "concurrent turn rejected — last-writer-wins persist would clobber it");
|
|
|
|
await drain(res1); // finish → onFinish → endTurn
|
|
assert.equal(isTurnRunning(chat.id), false, "registry cleared at finish");
|
|
|
|
const res3 = await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine));
|
|
assert.equal(res3.status, 200, "the next turn is accepted after the previous finished");
|
|
await drain(res3);
|
|
});
|
|
|
|
it("write-ahead: the learner's answer is durable BEFORE the turn finishes", async () => {
|
|
const { engine } = scriptedTeacher(defaultLesson);
|
|
const chat = chatStore.createChat("Test: write-ahead");
|
|
createdChats.push(chat.id);
|
|
|
|
// Turn 1: the teacher asks the quiz (pending part in the record).
|
|
const firstUser = { id: "u1", role: "user", parts: [{ type: "text", text: "teach me the solar system" }] };
|
|
await drain(await handleChatRequest(post(chat.id, [firstUser]), agentFactory(engine)));
|
|
const saved1 = chatStore.getChat(chat.id)!;
|
|
const assistantMsg = saved1.messages[1]!;
|
|
|
|
// The learner answers; the client posts the graded transcript back.
|
|
const options = normalizeOptions(QUIZ_INPUT.options);
|
|
const qd: QuestionData = {
|
|
id: "call-1",
|
|
kind: "quiz",
|
|
question: QUIZ_INPUT.question,
|
|
mode: "single-select",
|
|
options,
|
|
correctIndices: resolveCorrect(coerceCorrectAnswer(QUIZ_INPUT.correctAnswer), options).indices,
|
|
explanation: QUIZ_INPUT.explanation,
|
|
createdAt: new Date().toISOString(),
|
|
};
|
|
const answer = buildAnswer(qd, { values: ["Mercury"] });
|
|
const messages2 = saved1.messages.map((m) =>
|
|
m.id === assistantMsg.id
|
|
? {
|
|
...m,
|
|
parts: m.parts.map((p) =>
|
|
p.type === "tool-quiz" ? { ...p, state: "output-available" as const, output: answer } : p,
|
|
),
|
|
}
|
|
: m,
|
|
);
|
|
|
|
// NOT drained: the teacher turn is streaming, but the answer is already
|
|
// on disk — a remount (or crash) mid-turn sees the answered quiz, never
|
|
// the stale pre-answer question.
|
|
const res = await handleChatRequest(post(chat.id, messages2), agentFactory(engine));
|
|
const midTurn = chatStore.getChat(chat.id)!;
|
|
const midQuiz = midTurn.messages[1]!.parts.find((p) => p.type === "tool-quiz");
|
|
assert.ok(midQuiz && midQuiz.type === "tool-quiz");
|
|
assert.equal((midQuiz as { state?: string }).state, "output-available", "write-ahead persisted the answer before finish");
|
|
|
|
await drain(res); // finish → persist the full turn → endTurn
|
|
const saved2 = chatStore.getChat(chat.id)!;
|
|
assert.equal(saved2.messages.length, 2, "full turn persisted as usual at finish");
|
|
assert.equal(isTurnRunning(chat.id), false, "registry cleared at finish");
|
|
});
|
|
|
|
it("a turn that fails to start (malformed messages) clears the registry — no lockout", async () => {
|
|
const chat = chatStore.createChat("Test: turn failed to start");
|
|
createdChats.push(chat.id);
|
|
// A text part with no text: fails message validation before the stream
|
|
// exists — the path where no onFinish will ever run.
|
|
const bad = [{ id: "m1", role: "user", parts: [{ type: "text" }] }];
|
|
const res = await handleChatRequest(post(chat.id, bad), agentFactory(scriptedTeacher(defaultLesson).engine));
|
|
assert.equal(res.status, 500, "turn-start failure is a 500, not a hang");
|
|
assert.equal(isTurnRunning(chat.id), false, "registry cleared when the stream never started");
|
|
|
|
// And the chat is NOT locked: the next (valid) turn is accepted.
|
|
const ok = await handleChatRequest(
|
|
post(chat.id, [{ id: "m1", role: "user", parts: [{ type: "text", text: "teach me" }] }]),
|
|
agentFactory(scriptedTeacher(defaultLesson).engine),
|
|
);
|
|
assert.equal(ok.status, 200);
|
|
await drain(ok);
|
|
});
|
|
}); |