learnharness/tests/usage.test.ts

/**
 * Usage-accounting tests — the money channel's pure helpers (core/prices.ts)
 * and the ChatStore usage-log seam (appendTurnUsage).
 *
 * Pure helper tests are pure. The store test uses the real fs chatStore with
 * a fixture chat, cleaned up in after().
 */
import assert from "node:assert/strict";
import test from "node:test";
import * as fs from "node:fs";

import { chatStore } from "../src/store/chats.ts";
import { PATHS } from "../config.ts";
import { callCost, contextWindow, jobCost, sessionCost, sumSteps, turnCost } from "../src/core/prices.ts";
import type { CallUsage, JobRecord, TurnUsage } from "../src/core/contracts.ts";

const call = (over: Partial<CallUsage> = {}): CallUsage => ({
	inputTokens: 0,
	outputTokens: 0,
	cacheReadTokens: undefined,
	cacheWriteTokens: undefined,
	reasoningTokens: undefined,
	...over,
});

test("sumSteps folds steps; absent stays absent until a value appears", () => {
	const sum = sumSteps([
		call({ inputTokens: 100, outputTokens: 20 }),
		call({ inputTokens: 50, outputTokens: 10, cacheReadTokens: 30 }),
	]);
	assert.equal(sum.inputTokens, 150);
	assert.equal(sum.outputTokens, 30);
	assert.equal(sum.cacheReadTokens, 30);
	assert.equal(sum.cacheWriteTokens, undefined); // never reported → stays undefined
	// all-absent → all-undefined (helper defaults input/output to 0, so
	// explicit undefined here)
	const empty = sumSteps([call({ inputTokens: undefined, outputTokens: undefined }), call({ inputTokens: undefined, outputTokens: undefined })]);
	assert.equal(empty.inputTokens, undefined);
});

test("callCost: glm-5.3-flash math — fresh input, cached read, output", () => {
	const cost = callCost(call({ inputTokens: 100_000, outputTokens: 20_000, cacheReadTokens: 10_000 }), "z-ai/glm-5.3-flash");
	assert.ok(cost !== null);
	// fresh 90k @ $0.15/M + cached 10k @ $0.03/M + output 20k @ $0.50/M
	assert.ok(Math.abs(cost! - (0.0135 + 0.0003 + 0.01)) < 1e-9);
});

test("callCost: cache writes bill at the cacheWrite rate", () => {
	const cost = callCost(
		call({ inputTokens: 1_000_000, outputTokens: 0, cacheWriteTokens: 500_000 }),
		"qwen/qwen3.8-max-0902",
	);
	assert.ok(cost !== null);
	// fresh 1M @ $2/M + cache write 500k @ $2.5/M
	assert.ok(Math.abs(cost! - (2 + 1.25)) < 1e-9);
});

test("callCost: unknown model and absent fields are null, never zero", () => {
	assert.equal(callCost(call({ inputTokens: 100, outputTokens: 5 }), "not/a-model"), null);
	assert.equal(callCost(call({ inputTokens: 100, outputTokens: undefined }), "z-ai/glm-5.3-flash"), null);
});

test("turnCost sums steps; sessionCost reports unpriced turns", () => {
	const turn: TurnUsage = {
		messageId: "m1",
		at: "2026-09-09T00:00:00Z",
		model: "z-ai/glm-5.3-flash",
		steps: [call({ inputTokens: 10_000, outputTokens: 2_000 }), call({ inputTokens: 12_000, outputTokens: 1_000 })],
	};
	assert.ok(turnCost(turn) !== null);
	const totals = sessionCost({
		turns: [turn, { messageId: "m2", at: "x", model: "not/a-model", steps: [call({ inputTokens: 1, outputTokens: 1 })] }],
	});
	assert.equal(totals.unpriced, 1);
	assert.ok(totals.cost > 0);
});

test("contextWindow: last call's footprint against the model's limit", () => {
	const cw = contextWindow({
		turns: [
			{ messageId: "m1", at: "x", model: "z-ai/glm-5.3-flash", steps: [call({ inputTokens: 9_000, outputTokens: 1_000 })] },
			{ messageId: "m2", at: "x", model: "z-ai/glm-5.3-flash", steps: [call({ inputTokens: 90_000, outputTokens: 10_000 })] },
		],
	});
	assert.ok(cw);
	assert.equal(cw.model, "z-ai/glm-5.3-flash");
	assert.equal(cw.usedTokens, 100_000);
	assert.equal(cw.limit, 1_310_720);
	assert.equal(contextWindow({ turns: [] }), null);
});

test("jobCost: null without usage, computed with it", () => {
	assert.equal(jobCost({}), null);
	const job: Pick<JobRecord, "usage"> = {
		usage: {
			model: "z-ai/glm-5.3-flash",
			inputTokens: 1_000_000,
			outputTokens: 100_000,
			cacheReadTokens: 0,
			cacheWriteTokens: 0,
			reasoningTokens: 0,
			steps: 4,
		},
	};
	assert.ok(jobCost(job) !== null);
	assert.ok(jobCost(job)! > 0);
});

// ─── the store seam ──────────────────────────────────────────────────────────

test("appendTurnUsage: appends, is idempotent per messageId, survives saveMessages", () => {
	const record = chatStore.createChat("usage test chat");
	const entry: TurnUsage = {
		messageId: "msg_a",
		at: "2026-09-09T00:00:00Z",
		model: "z-ai/glm-5.3-flash",
		steps: [call({ inputTokens: 100, outputTokens: 10 })],
	};
	chatStore.appendTurnUsage(record.id, entry);
	chatStore.appendTurnUsage(record.id, entry); // replay (abort/retry) → no-op
	chatStore.appendTurnUsage(record.id, { ...entry, messageId: "msg_b" });
	let reread = chatStore.getChat(record.id);
	assert.equal(reread?.turns?.length, 2);

	// saveMessages must not clobber the log (read-mutate-write record)
	chatStore.saveMessages(record.id, [{ id: "msg_a", role: "user", parts: [] }]);
	reread = chatStore.getChat(record.id);
	assert.equal(reread?.turns?.length, 2);

	// and the list payload must NOT leak turns
	const listed = chatStore.listChats().find((c) => c.id === record.id);
	assert.ok(listed);
	assert.equal("turns" in listed, false);

	fs.unlinkSync(`${PATHS.chats}/${record.id}.json`);
});