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openchamber/packages/web/server/lib/small-model/index.test.js
T
Bohdan Triapitsyn 1d17cb87b3 feat(walkthrough): write walkthroughs in the reader's language
A guided explanation is only useful in a language the reader reads, so the
panel header gets a language picker alongside the model one, defaulting to
the interface language. Like the model, it is request state rather than a
setting: the language travels with the read and the generation, and the one
a walkthrough was written in is stored with it, so reopening a review
describes what is there instead of what a fresh one would be.

Only prose is translated. Hunk aliases resolve back to hunk ids and
icon/importance are validated against fixed English values, so a translated
one would be dropped by the normalizer — silently losing an anchor or a
style. Identifiers and paths stay as they appear in the code.

The language is part of the cache key, and a read now asks the cache for the
exact request it was given before falling back to the pointer. Without that
the panel answered a request to switch languages with the text it already
had, leaving the other language unused in the cache.

Alongside it:

- The answer budget is derived from the resolved model instead of a flat 24k.
  That number was the same for a 64k-context model and for one that admits to
  384k output tokens, and on the latter it was the only reason generation
  failed: the model spent the whole allowance reasoning and returned nothing.
  It is now min(96k, max(24k, a quarter of the context)) capped by the
  catalog's output limit, decided once so the input reserve and the request
  cannot drift apart.
- A read no longer offers Cancel. It is a few hundred milliseconds of git with
  nothing to cancel, and the button flickered on every model or language
  change. When the panel is showing a fallback, a banner names what is on
  screen versus what was asked for — only once the read has settled.
- The header keeps one 32px control height and drops its labels below 680px
  instead of squeezing them to two letters and an ellipsis.

Docs and module documentation updated in every locale.
2026-08-03 01:27:27 +03:00

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import fs from 'fs';
import os from 'os';
import path from 'path';
import { afterAll, afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
// The settings override is read straight from disk at module load, so without
// this the suite would resolve whatever small model the developer running it
// happens to have configured.
const TEMP_DATA_DIR = fs.mkdtempSync(path.join(os.tmpdir(), 'small-model-settings-'));
process.env.OPENCHAMBER_DATA_DIR = TEMP_DATA_DIR;
vi.mock('../opencode/auth.js', () => ({ readAuthFile: vi.fn() }));
vi.mock('../opencode/shared.js', () => ({
readConfig: vi.fn(),
readConfigLayers: vi.fn(),
}));
vi.mock('./catalog.js', () => ({
getModelCatalog: vi.fn(),
getCatalogProvider: vi.fn(),
}));
vi.mock('./call.js', () => ({ callSmallModel: vi.fn() }));
const { generateSmallModelText, describeSmallModel } = await import('./index.js');
const { readAuthFile } = await import('../opencode/auth.js');
const { readConfigLayers } = await import('../opencode/shared.js');
const { getModelCatalog } = await import('./catalog.js');
const { callSmallModel } = await import('./call.js');
// 8k context leaves 4k input tokens after the output reserve → 16k chars.
const CATALOG = {
anthropic: {
id: 'anthropic',
models: {
'claude-haiku-4-5': { id: 'claude-haiku-4-5', limit: { context: 8_000 }, structured_output: true },
'legacy-tiny': { id: 'legacy-tiny', limit: { context: 8_000 }, structured_output: false },
'unlisted-capability': { id: 'unlisted-capability', limit: { context: 8_000 } },
},
},
};
const request = (overrides = {}) => ({
prompt: 'x'.repeat(20_000),
model: 'anthropic/claude-haiku-4-5',
directory: '/proj',
...overrides,
});
describe('generateSmallModelText — oversized input', () => {
beforeEach(() => {
readAuthFile.mockReturnValue({ anthropic: { type: 'api', key: 'sk-ant' } });
readConfigLayers.mockReturnValue({ mergedConfig: {} });
getModelCatalog.mockResolvedValue(CATALOG);
callSmallModel.mockReset();
callSmallModel.mockResolvedValue('generated');
});
afterEach(() => {
vi.clearAllMocks();
});
it('truncates and flags the response by default', async () => {
const result = await generateSmallModelText(request());
expect(result.inputTruncated).toBe(true);
const sent = callSmallModel.mock.calls.at(-1)[0].prompt;
expect(sent.length).toBeLessThan(20_000);
expect(sent.endsWith('…')).toBe(true);
});
it('refuses without calling the provider when the caller cannot survive truncation', async () => {
await expect(generateSmallModelText(request({ onOverflow: 'error' })))
.rejects.toMatchObject({
statusCode: 413,
code: 'context-too-small',
requiredChars: 20_000,
availableChars: 16_000,
});
expect(callSmallModel).not.toHaveBeenCalled();
});
it('leaves an input that fits untouched under either policy', async () => {
const result = await generateSmallModelText(request({ prompt: 'short prompt', onOverflow: 'error' }));
expect(result.inputTruncated).toBeUndefined();
expect(callSmallModel.mock.calls.at(-1)[0].prompt).toBe('short prompt');
});
it('forwards schema, timeout, and abort signal to the transport', async () => {
const controller = new AbortController();
const schema = { type: 'object' };
await generateSmallModelText(request({
prompt: 'short',
responseSchema: schema,
timeoutMs: 240_000,
signal: controller.signal,
}));
expect(callSmallModel.mock.calls.at(-1)[0]).toMatchObject({
responseSchema: schema,
timeoutMs: 240_000,
signal: controller.signal,
});
});
});
describe('describeSmallModel — capability reporting', () => {
beforeEach(() => {
readAuthFile.mockReturnValue({ anthropic: { type: 'api', key: 'sk-ant' } });
readConfigLayers.mockReturnValue({ mergedConfig: { small_model: 'anthropic/claude-haiku-4-5' } });
getModelCatalog.mockResolvedValue(CATALOG);
});
afterEach(() => {
vi.clearAllMocks();
});
it('reports the input budget and a known structured-output capability', async () => {
const described = await describeSmallModel({ directory: '/proj' });
expect(described).toMatchObject({
providerID: 'anthropic',
modelID: 'claude-haiku-4-5',
inputCharBudget: 16_000,
contextTokens: 8_000,
contextKnown: true,
structuredOutput: true,
});
});
it('reports an explicit false so callers can block the model', async () => {
readConfigLayers.mockReturnValue({ mergedConfig: { small_model: 'anthropic/legacy-tiny' } });
const described = await describeSmallModel({ directory: '/proj' });
expect(described.structuredOutput).toBe(false);
});
it('reports null — not false — when the catalog omits the capability', async () => {
readConfigLayers.mockReturnValue({ mergedConfig: { small_model: 'anthropic/unlisted-capability' } });
const described = await describeSmallModel({ directory: '/proj' });
expect(described.structuredOutput).toBeNull();
});
});
// The input reserve and the requested output budget are the same number seen
// from two sides; if they drift, a caller that asks for a large answer overruns
// the model's context and the failure looks like a truncation bug.
describe('output budget and input reserve', () => {
beforeEach(() => {
readAuthFile.mockReturnValue({ anthropic: { type: 'api', key: 'sk-ant' } });
readConfigLayers.mockReturnValue({ mergedConfig: {} });
getModelCatalog.mockResolvedValue({
anthropic: {
id: 'anthropic',
models: {
roomy: { id: 'roomy', limit: { context: 100_000, output: 8_000 } },
unlisted: { id: 'unlisted', limit: { context: 100_000 } },
},
},
});
callSmallModel.mockReset();
callSmallModel.mockResolvedValue('generated');
});
afterEach(() => {
vi.clearAllMocks();
});
it('caps the request at the model\'s advertised output limit', async () => {
await generateSmallModelText({
prompt: 'short',
model: 'anthropic/roomy',
maxOutputTokens: 24_000,
});
expect(callSmallModel.mock.calls.at(-1)[0].maxOutputTokens).toBe(8_000);
});
it('honours the requested budget when the catalog states no output limit', async () => {
await generateSmallModelText({
prompt: 'short',
model: 'anthropic/unlisted',
maxOutputTokens: 24_000,
});
expect(callSmallModel.mock.calls.at(-1)[0].maxOutputTokens).toBe(24_000);
});
it('reserves exactly the requested output budget from the input allowance', async () => {
// 100k context 24k reserved for the answer = 76k tokens ≈ 304k chars.
await expect(generateSmallModelText({
prompt: 'x'.repeat(304_001),
model: 'anthropic/unlisted',
maxOutputTokens: 24_000,
onOverflow: 'error',
})).rejects.toMatchObject({ code: 'context-too-small', availableChars: 304_000 });
await expect(generateSmallModelText({
prompt: 'x'.repeat(303_999),
model: 'anthropic/unlisted',
maxOutputTokens: 24_000,
onOverflow: 'error',
})).resolves.toBeTruthy();
});
it('reports the same budget through describeSmallModel', async () => {
readConfigLayers.mockReturnValue({ mergedConfig: { small_model: 'anthropic/unlisted' } });
const described = await describeSmallModel({ directory: '/proj', outputReserveTokens: 24_000 });
expect(described.inputCharBudget).toBe(304_000);
});
// A caller that wants "as much room as this model allows" cannot name a
// number before knowing which model it got, so it hands over the decision.
it('lets the reserve be decided from the resolved model\'s limits', async () => {
readConfigLayers.mockReturnValue({ mergedConfig: { small_model: 'anthropic/roomy' } });
const described = await describeSmallModel({
directory: '/proj',
outputReserveTokens: ({ contextTokens, outputTokenLimit }) => Math.min(contextTokens / 10, outputTokenLimit),
});
// 100k context, 8k output limit -> 8k reserved, leaving 92k tokens.
expect(described.outputTokens).toBe(8_000);
expect(described.inputCharBudget).toBe(92_000 * 4);
});
it('reports the reserve it used so the caller can request the same number', async () => {
readConfigLayers.mockReturnValue({ mergedConfig: { small_model: 'anthropic/unlisted' } });
const described = await describeSmallModel({ directory: '/proj', outputReserveTokens: 24_000 });
expect(described.outputTokens).toBe(24_000);
});
});
afterAll(() => {
fs.rmSync(TEMP_DATA_DIR, { recursive: true, force: true });
});