Files
gbrain/test/voyage-multimodal.test.ts
T
bfab1ded08 v0.28.11 feat: embedding_multimodal_model — separate model routing for multimodal embeddings (#719)
* feat: embedding_multimodal_model — separate model routing for multimodal embeddings

v0.28.9 shipped multimodal image embeddings via Voyage, but
embedMultimodal() hardcodes to the primary embedding_model. Brains
using OpenAI text-embedding-3-large (1536-dim) for text cannot use
Voyage voyage-multimodal-3 (1024-dim) for images without switching
their entire embedding pipeline.

This adds embedding_multimodal_model as a distinct config key that
embedMultimodal() prefers over embedding_model when set. The dual-
column schema (embedding vs embedding_image) already supports
different dimensions — this patch completes the routing.

Config surface:
  - gbrain config set embedding_multimodal_model voyage:voyage-multimodal-3
  - env: GBRAIN_EMBEDDING_MULTIMODAL_MODEL=voyage:voyage-multimodal-3

Files changed:
  - core/ai/types.ts: AIGatewayConfig gains embedding_multimodal_model
  - core/ai/gateway.ts: configureGateway stores it; embedMultimodal reads it
  - core/config.ts: GBrainConfig type + env loader + DB merge path
  - cli.ts: threads config into gateway; reconfigures after DB merge

Tested on a 96K-page brain with OpenAI text + Voyage multimodal
running side by side. Voyage returns 1024-dim vectors into
embedding_image column; text embeddings unchanged.

* refactor(cli): extract buildGatewayConfig + always re-config after DB merge

Two related changes co-located so the un-gate doesn't leave the duplicated
configureGateway shapes drifting:

1. Extract file-local `buildGatewayConfig(c: GBrainConfig): AIGatewayConfig`
   helper. Both configureGateway sites in connectEngine() now pass through
   it; future fields touch one place.

2. Drop the field-name-gated re-config trigger. The previous gate fired
   only when `merged.embedding_multimodal_model` was truthy, coupling the
   trigger to one field name. Future DB-mutable gateway fields would
   silently miss it. Re-config now always fires when loadConfigWithEngine
   returns non-null. One extra cache+shrinkState clear per startup is
   microseconds, no hot path.

Schema-sizing fields stay stable because loadConfigWithEngine respects
file/env first; merged.embedding_dimensions equals config.embedding_dimensions
when no DB override exists.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(ai): model-level multimodal validation + getMultimodalModel accessor

Codex review of PR #719 (F1) caught a real footgun: the Voyage recipe
shares supports_multimodal: true across all 12 models in its embedding
touchpoint, of which only voyage-multimodal-3 is valid at
/multimodalembeddings. A user setting embedding_multimodal_model to a
text-only Voyage model (e.g. voyage-3-large) passes local validation and
fails at the endpoint with HTTP 400 — which gateway.ts:626 misclassifies
as transient (TODO: reclassify, tracked in TODOS.md).

Adds:
- EmbeddingTouchpoint.multimodal_models?: string[] (optional, model-level
  allow-list inside a recipe that mixes text-only + multimodal models).
- Voyage declares multimodal_models: ['voyage-multimodal-3'].
- embedMultimodal() validates parsed.modelId against the allow-list AFTER
  the existing recipe-level supports_multimodal check. Throws AIConfigError
  with the full multimodal_models list in the fix hint.
- getMultimodalModel() public accessor mirroring getEmbeddingModel /
  getChatModel — needed by the cli-multimodal-integration test and useful
  for future doctor checks.

Recipe-level fast-fail stays so non-multimodal providers (Anthropic /
OpenAI today) keep their AIConfigError path unchanged.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* test: cover embedding_multimodal_model precedence + gateway override + cli integration

PR #719 originally shipped zero tests for the new code paths. Closes
that gap with three layers:

1. test/loadConfig-merge.test.ts — extends the existing env > file > DB
   precedence pattern (which already covers embedding_image_ocr_model)
   with four cases for embedding_multimodal_model: DB-only fills in,
   file wins over DB, all-unset stays undefined, null/empty DB ignored.

2. test/voyage-multimodal.test.ts — four cases for embedMultimodal model
   resolution: prefers multimodal_model over embedding_model, falls back
   to embedding_model when unset (regression guard), AIConfigError on
   non-multimodal recipe, AIConfigError on Voyage text-only model
   (Codex F1 model-level validation).

3. test/cli-multimodal-integration.test.ts (NEW) — three PGLite-based
   integration tests for the cli.ts re-config glue itself (Codex F3:
   the actual bug site that "mechanical glue" claims hide). Drives the
   loadConfigWithEngine + buildGatewayConfig + configureGateway sequence
   connectEngine() runs and asserts the gateway observed the DB-set value.

11 new test cases total. All pass against the production code in this
PR.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* docs(todos): follow-ups from PR #719 codex review

Three items surfaced during /codex outside-voice review of PR #719's plan
that are out of scope for the current PR but worth tracking:

- gbrain doctor: warn on misconfigured multimodal model (P2). Two checks:
  multimodal_model set without recipe API key; embedding_multimodal flag
  on without a multimodal-capable embedding_model.

- Reclassify Voyage HTTP 4xx as AIConfigError (P2, Codex F2). Today
  gateway.ts:626 throws AITransientError for any non-401/403 4xx, so
  permanent config bugs (malformed body, model not in multimodal_models)
  trigger retry storms. Aligns with normalizeAIError's contract.

- gbrain config unset <key> (P3, Codex F6). Once a user sets a key in DB
  there's no normal CLI path to clear it. Pre-existing UX gap; PR #719's
  new key surfaces it again.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* chore: bump version and changelog (v0.28.11)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* docs: v0.28.11 annotations for ai/types, ai/gateway, voyage recipe

Updates the Key Files section so the per-file annotations reflect the
multimodal_model routing + model-level validation that landed in #719.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: garrytan-agents <garrytan-agents@users.noreply.github.com>
Co-authored-by: Garry Tan <garrytan@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-07 13:41:46 -07:00

358 lines
12 KiB
TypeScript

// Phase 6 (D1-D3) + Eng-3A: voyage-multimodal-3 recipe + gateway.embedMultimodal.
//
// Verifies recipe registration, gateway happy-path with mocked fetch,
// 401 / 429 / dim-mismatch error paths, and the off-by-one batch math
// (n=0, n=1, n=32, n=33, n=64) flagged by Eng-3A.
import { afterEach, beforeEach, describe, expect, test } from 'bun:test';
import { configureGateway, embedMultimodal, resetGateway } from '../src/core/ai/gateway.ts';
import { getRecipe } from '../src/core/ai/recipes/index.ts';
import { AIConfigError, AITransientError } from '../src/core/ai/errors.ts';
// Capture all fetch calls. Each test installs a fresh handler and asserts
// the request shape AND returns a plausible Voyage payload.
type FetchHandler = (url: string, init: RequestInit) => Promise<Response>;
let fetchHandler: FetchHandler | null = null;
const origFetch = globalThis.fetch;
beforeEach(() => {
fetchHandler = null;
globalThis.fetch = (async (url: string | URL | Request, init?: RequestInit) => {
if (!fetchHandler) {
throw new Error('fetch called but no handler installed');
}
return fetchHandler(typeof url === 'string' ? url : url.toString(), init ?? {});
}) as typeof fetch;
});
afterEach(() => {
globalThis.fetch = origFetch;
resetGateway();
});
function configureVoyageMultimodal(env: Record<string, string | undefined> = {}) {
configureGateway({
embedding_model: 'voyage:voyage-multimodal-3',
embedding_dimensions: 1024,
env: { VOYAGE_API_KEY: 'test-key', ...env },
});
}
function makeImage(mimeOverride?: string) {
return {
kind: 'image_base64' as const,
data: Buffer.from('fake-image-bytes').toString('base64'),
mime: mimeOverride ?? 'image/jpeg',
};
}
function fakeVoyageResponse(count: number, dims = 1024): Response {
const data = Array.from({ length: count }, (_, i) => ({
embedding: Array.from({ length: dims }, () => 0.1 * (i + 1)),
index: i,
}));
return new Response(JSON.stringify({ data, model: 'voyage-multimodal-3' }), {
status: 200,
headers: { 'Content-Type': 'application/json' },
});
}
describe('voyage recipe — multimodal registration', () => {
test('voyage-multimodal-3 is in the recipe model list', () => {
const voyage = getRecipe('voyage');
expect(voyage).toBeDefined();
expect(voyage!.touchpoints.embedding!.models).toContain('voyage-multimodal-3');
});
test('voyage embedding touchpoint declares supports_multimodal: true', () => {
const voyage = getRecipe('voyage');
expect(voyage!.touchpoints.embedding!.supports_multimodal).toBe(true);
});
test('voyage default_dims is 1024 (parity with multimodal output dim)', () => {
const voyage = getRecipe('voyage');
expect(voyage!.touchpoints.embedding!.default_dims).toBe(1024);
});
});
describe('gateway.embedMultimodal — happy path', () => {
test('single image produces a 1024-dim Float32Array', async () => {
configureVoyageMultimodal();
fetchHandler = async (url, init) => {
expect(url).toContain('/multimodalembeddings');
const body = JSON.parse(init.body as string);
expect(body.model).toBe('voyage-multimodal-3');
expect(body.inputs.length).toBe(1);
expect(body.inputs[0].content[0].type).toBe('image_base64');
expect(body.inputs[0].content[0].image_base64).toContain('data:image/jpeg;base64,');
return fakeVoyageResponse(1);
};
const out = await embedMultimodal([makeImage()]);
expect(out.length).toBe(1);
expect(out[0]).toBeInstanceOf(Float32Array);
expect(out[0].length).toBe(1024);
});
test('Authorization header is set with bearer token', async () => {
configureVoyageMultimodal();
let captured: Record<string, string> = {};
fetchHandler = async (_url, init) => {
captured = init.headers as Record<string, string>;
return fakeVoyageResponse(1);
};
await embedMultimodal([makeImage()]);
expect(captured.Authorization).toBe('Bearer test-key');
expect(captured['Content-Type']).toBe('application/json');
});
});
describe('gateway.embedMultimodal — Eng-3A batch boundary tests', () => {
test('n=0 short-circuits: returns [] without calling fetch', async () => {
configureVoyageMultimodal();
let called = false;
fetchHandler = async () => {
called = true;
return fakeVoyageResponse(0);
};
const out = await embedMultimodal([]);
expect(out).toEqual([]);
expect(called).toBe(false);
});
test('n=1: single batch, single embedding back', async () => {
configureVoyageMultimodal();
let calls = 0;
fetchHandler = async () => {
calls++;
return fakeVoyageResponse(1);
};
const out = await embedMultimodal([makeImage()]);
expect(out.length).toBe(1);
expect(calls).toBe(1);
});
test('n=32 (exact batch): one HTTP call', async () => {
configureVoyageMultimodal();
let calls = 0;
fetchHandler = async (_url, init) => {
calls++;
const body = JSON.parse(init.body as string);
expect(body.inputs.length).toBe(32);
return fakeVoyageResponse(body.inputs.length);
};
const out = await embedMultimodal(Array.from({ length: 32 }, () => makeImage()));
expect(out.length).toBe(32);
expect(calls).toBe(1);
});
test('n=33 (off-by-one): two HTTP calls, sizes 32 + 1', async () => {
configureVoyageMultimodal();
const seenSizes: number[] = [];
fetchHandler = async (_url, init) => {
const body = JSON.parse(init.body as string);
seenSizes.push(body.inputs.length);
return fakeVoyageResponse(body.inputs.length);
};
const out = await embedMultimodal(Array.from({ length: 33 }, () => makeImage()));
expect(out.length).toBe(33);
expect(seenSizes).toEqual([32, 1]);
});
test('n=64 (clean two batches): two calls of 32', async () => {
configureVoyageMultimodal();
const seenSizes: number[] = [];
fetchHandler = async (_url, init) => {
const body = JSON.parse(init.body as string);
seenSizes.push(body.inputs.length);
return fakeVoyageResponse(body.inputs.length);
};
const out = await embedMultimodal(Array.from({ length: 64 }, () => makeImage()));
expect(out.length).toBe(64);
expect(seenSizes).toEqual([32, 32]);
});
});
describe('gateway.embedMultimodal — error paths', () => {
test('401 → AIConfigError with auth fix hint', async () => {
configureVoyageMultimodal();
fetchHandler = async () => new Response('{"error":"unauthorized"}', { status: 401 });
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AIConfigError);
expect((err as AIConfigError).message).toContain('401');
});
test('429 → AITransientError', async () => {
configureVoyageMultimodal();
fetchHandler = async () => new Response('rate limited', { status: 429 });
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AITransientError);
});
test('5xx → AITransientError', async () => {
configureVoyageMultimodal();
fetchHandler = async () => new Response('server error', { status: 503 });
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AITransientError);
});
test('dim mismatch → AIConfigError', async () => {
configureVoyageMultimodal();
fetchHandler = async () => fakeVoyageResponse(1, 768); // wrong dim
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AIConfigError);
expect((err as AIConfigError).message).toContain('1024');
});
test('malformed JSON → AITransientError', async () => {
configureVoyageMultimodal();
fetchHandler = async () =>
new Response('not json', { status: 200, headers: { 'Content-Type': 'application/json' } });
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AITransientError);
});
test('embedding count mismatch → AITransientError', async () => {
configureVoyageMultimodal();
fetchHandler = async () => fakeVoyageResponse(1); // returns 1, sent 2
let err: unknown;
try {
await embedMultimodal([makeImage(), makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AITransientError);
});
test('missing API key → AIConfigError', async () => {
configureGateway({
embedding_model: 'voyage:voyage-multimodal-3',
embedding_dimensions: 1024,
env: {}, // no VOYAGE_API_KEY
});
fetchHandler = async () => fakeVoyageResponse(1); // never called
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AIConfigError);
expect((err as AIConfigError).message).toContain('VOYAGE_API_KEY');
});
test('non-multimodal recipe → AIConfigError', async () => {
configureGateway({
embedding_model: 'openai:text-embedding-3-large',
embedding_dimensions: 1536,
env: { OPENAI_API_KEY: 'sk-test' },
});
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AIConfigError);
expect((err as AIConfigError).message).toMatch(/does not support multimodal|not implemented/i);
});
});
// v0.28.11 (PR #719): embedding_multimodal_model override + model-level
// validation. Confirms the gateway's two-layer multimodal gate:
// 1. recipe.touchpoints.embedding.supports_multimodal (recipe scope)
// 2. recipe.touchpoints.embedding.multimodal_models[] (model scope)
describe('gateway.embedMultimodal — multimodal_model override + model-level validation', () => {
test('prefers embedding_multimodal_model over embedding_model when both set', async () => {
configureGateway({
embedding_model: 'openai:text-embedding-3-large',
embedding_dimensions: 1536,
embedding_multimodal_model: 'voyage:voyage-multimodal-3',
env: { VOYAGE_API_KEY: 'voyage-key', OPENAI_API_KEY: 'sk-test' },
});
let capturedUrl = '';
let capturedBody: { model?: string } = {};
fetchHandler = async (url, init) => {
capturedUrl = url;
capturedBody = JSON.parse(init.body as string);
return fakeVoyageResponse(1);
};
const out = await embedMultimodal([makeImage()]);
expect(out.length).toBe(1);
expect(capturedUrl).toContain('/multimodalembeddings');
expect(capturedBody.model).toBe('voyage-multimodal-3');
});
test('falls back to embedding_model when embedding_multimodal_model is unset', async () => {
// Regression guard for the existing single-model setup.
configureVoyageMultimodal();
fetchHandler = async () => fakeVoyageResponse(1);
const out = await embedMultimodal([makeImage()]);
expect(out.length).toBe(1);
});
test('embedding_multimodal_model pointing at non-multimodal recipe → AIConfigError', async () => {
configureGateway({
embedding_model: 'voyage:voyage-multimodal-3', // would normally work
embedding_multimodal_model: 'openai:text-embedding-3-large', // override breaks it
embedding_dimensions: 1536,
env: { VOYAGE_API_KEY: 'voyage-key', OPENAI_API_KEY: 'sk-test' },
});
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AIConfigError);
expect((err as AIConfigError).message).toMatch(/does not support multimodal/i);
});
test('embedding_multimodal_model pointing at Voyage text-only model → AIConfigError (D4 / Codex F1)', async () => {
// Voyage shares supports_multimodal: true across all 12 models in the
// recipe. Without the model-level multimodal_models gate, voyage-3-large
// would pass validation locally and fail at /multimodalembeddings with
// HTTP 400 — which gateway.ts:626 misclassifies as transient. Change 3
// closes this gap.
configureGateway({
embedding_model: 'openai:text-embedding-3-large',
embedding_dimensions: 1536,
embedding_multimodal_model: 'voyage:voyage-3-large', // text-only Voyage
env: { VOYAGE_API_KEY: 'voyage-key', OPENAI_API_KEY: 'sk-test' },
});
let err: unknown;
try {
await embedMultimodal([makeImage()]);
} catch (e) {
err = e;
}
expect(err).toBeInstanceOf(AIConfigError);
expect((err as AIConfigError).message).toMatch(/voyage-3-large.*not.*multimodal/i);
expect((err as AIConfigError).fix ?? '').toMatch(/voyage:voyage-multimodal-3/);
});
});