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* fix: adaptive embed batch sizing for Voyage token limits Voyage's tokenizer is 3-4x denser than OpenAI tiktoken, causing batches of 50+ texts to exceed the 120K token-per-batch limit even when DB token counts (from tiktoken) suggest they'd fit. Changes: - Add max_batch_tokens to EmbeddingTouchpoint type (provider-declared limit) - Set Voyage recipe to 120K token limit - Gateway embed() now auto-splits batches using conservative char-to-token estimate (1:1 ratio, 80% budget utilization) - On token-limit errors, embedSubBatch recursively halves and retries (down to single-text batches before giving up) - Reduce embedding.ts BATCH_SIZE from 100 to 50 as a secondary guard - Add tests for batch splitting logic and error pattern matching Fixes infinite retry loops where the same oversized batch would fail repeatedly because WHERE embedding IS NULL re-fetches identical rows. * feat(ai): per-recipe chars_per_token + safety_factor on EmbeddingTouchpoint Voyage's tokenizer runs ~3-4× denser than OpenAI tiktoken on mixed content (code/JSON/CJK), so a global "1 char ≈ 1 token at 80%" estimate either overshoots Voyage's batch cap on dense payloads or kills OpenAI throughput. Move the policy onto the recipe. - types.ts: extend EmbeddingTouchpoint with optional chars_per_token (default 4) and safety_factor (default 0.8). Both only consulted when max_batch_tokens is also set. - voyage.ts: declare chars_per_token=1 + safety_factor=0.5 (60K char budget). * feat(ai/gateway): transport DI + adaptive shrink-on-miss + startup warning Architectural changes to make the embed pipeline testable through the public embed() seam (no private-function DI) and self-healing under tokenizer miscalibration. Per /codex outside-voice review of the original PR #680 plan. - Export splitByTokenBudget + isTokenLimitError as @internal pure helpers; the test file now imports the real functions instead of re-implementing them. - splitByTokenBudget takes chars_per_token as a third parameter (defaults to 4 for OpenAI density when omitted); 0/negative ratios fall back to default. - New __setEmbedTransportForTests(fn) seam — tests inject an embedMany stub and drive recursion / fast-path scenarios through the real embed() call. Production code never reads the override; resetGateway() restores the SDK. - New module-scoped _shrinkState Map<recipeId, {factor, consecutiveSuccesses}>: on token-limit miss, shrink the recipe's effective safety_factor by 0.5 (floor 0.05) so the next embed() pre-splits tighter; after 10 consecutive batch successes, heal back ×1.5 toward the recipe-declared ceiling. - Startup warning (once per process per recipe): configureGateway walks every registered recipe; any embedding touchpoint without max_batch_tokens (except the canonical OpenAI fast-path recipe) emits one stderr line. Future Cohere/Mistral/Jina recipes that forget the field re-create the v0.27 Voyage backfill loop — the warning catches it before traffic hits the cliff. - Embed an ASCII flow diagram in the embed() JSDoc covering the shrinkState + per-recipe budget computation. Test rewrite (23 cases): - Pure helpers: splitByTokenBudget chars_per_token threading, default fallback, isTokenLimitError pattern coverage including non-Error throwables. - Recursion via embed() with stubbed transport: halving + concat-in-order, order preservation across boundaries (slot-0 sentinel asserts mapping), terminal MIN_SUB_BATCH=1 throws normalized error (no infinite loop). - OpenAI fast path: transport called exactly once, no partition, no cross-recipe leakage of voyage shrink state. - Shrink-on-miss: first miss halves factor, floors at 0.05 under repeated misses, heals after wins, healing capped at recipe ceiling. - Startup warning: first call fires once per recipe; subsequent configureGateway calls suppressed within the same process. * chore(embedding): revert BATCH_SIZE 50→100 The PR initially dropped BATCH_SIZE to 50 as a safety guard for Voyage's batch cap, but that halved OpenAI throughput on every embed page even though OpenAI has no such cap. With per-recipe pre-split + recursive halving + adaptive shrink-on-miss now living in the gateway, the outer paginator goes back to its original purpose: progress-callback granularity, not batch protection. * chore: bump version and changelog (v0.28.7) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * docs: annotate v0.28.7 changes in CLAUDE.md key files --------- Co-authored-by: garrytan-agents <garrytan-agents@users.noreply.github.com> Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
391 lines
15 KiB
TypeScript
391 lines
15 KiB
TypeScript
/**
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* Tests for the adaptive embed batch system (PR #680, ships v0.28.7).
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*
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* Coverage matrix (per the eng-review plan):
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*
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* 1. Pure helpers exported from gateway.ts:
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* - splitByTokenBudget pure-function semantics + chars_per_token threading
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* - isTokenLimitError regex coverage
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*
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* 2. Recursion through public embed() with the AI-SDK transport stubbed.
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* We do NOT call private functions; the test seam is the
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* __setEmbedTransportForTests hook on the gateway.
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*
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* 3. Order preservation across recursive halving (left/right concat).
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*
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* 4. Terminal MIN_SUB_BATCH=1 — single text whose transport always fails
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* must throw normalizeAIError, not loop forever.
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*
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* 5. OpenAI fast path (D3) — recipe with no max_batch_tokens calls the
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* transport exactly once with no pre-split.
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*
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* 6. Shrink-on-miss adaptive cache (D8-A) — first miss halves the factor;
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* after SHRINK_HEAL_AFTER successes the factor heals back toward the
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* recipe-declared safety_factor.
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*
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* 7. Startup warning (D9-B) — gateway construction warns once per recipe
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* with an embedding touchpoint missing max_batch_tokens (excluding the
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* OpenAI canonical fast-path recipe).
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*/
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import { afterEach, beforeEach, describe, expect, mock, test } from 'bun:test';
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import {
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configureGateway,
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resetGateway,
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embed,
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splitByTokenBudget,
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isTokenLimitError,
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__setEmbedTransportForTests,
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__getShrinkStateForTests,
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} from '../../src/core/ai/gateway.ts';
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import { AIConfigError, AITransientError } from '../../src/core/ai/errors.ts';
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// --------- Test helpers ---------
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/**
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* Build an embedding-shape return for an arbitrary number of values. Each
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* embedding is `dims` floats, all set to a sentinel index so tests can
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* assert order preservation.
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*/
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function fakeEmbeddings(values: string[], dims: number): { embeddings: number[][] } {
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return {
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embeddings: values.map((_, i) =>
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// First slot encodes the input index so we can verify ordering.
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Array.from({ length: dims }, (_, j) => (j === 0 ? i : 0.1)),
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),
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};
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}
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const VOYAGE_TOKEN_LIMIT_ERROR = new Error(
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"Request to model 'voyage-3-large' failed. The max allowed tokens per submitted batch is 120000.",
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);
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function configureVoyage(): void {
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configureGateway({
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embedding_model: 'voyage:voyage-3-large',
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embedding_dimensions: 1024,
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env: { VOYAGE_API_KEY: 'sk-fake' },
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});
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}
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function configureOpenAI(): void {
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configureGateway({
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embedding_model: 'openai:text-embedding-3-large',
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embedding_dimensions: 1536,
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env: { OPENAI_API_KEY: 'sk-fake' },
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});
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}
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// --------- 1. Pure helpers ---------
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describe('splitByTokenBudget (pure helper)', () => {
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test('single small text stays in one batch', () => {
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const result = splitByTokenBudget(['hello'], 120_000, 1);
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expect(result).toEqual([['hello']]);
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});
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test('texts fitting within budget stay in one batch', () => {
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const texts = Array.from({ length: 10 }, () => 'a'.repeat(1000));
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const result = splitByTokenBudget(texts, 96_000, 1);
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expect(result).toHaveLength(1);
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expect(result[0]).toHaveLength(10);
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});
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test('texts exceeding budget are split into multiple batches', () => {
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// chars_per_token=1, so each 50K-char text counts as 50K tokens.
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// Budget 96K → first text fits, second pushes over → new batch.
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const texts = ['a'.repeat(50_000), 'b'.repeat(50_000), 'c'.repeat(50_000)];
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const result = splitByTokenBudget(texts, 96_000, 1);
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expect(result).toHaveLength(3);
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expect(result.map(b => b.length)).toEqual([1, 1, 1]);
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});
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test('chars_per_token=4 (OpenAI density) packs 4× more chars per batch', () => {
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// Each 50K-char text = 12.5K tokens at chars_per_token=4. Budget 96K
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// tokens → 7 fit; the 8th would overflow into a new batch.
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const texts = Array.from({ length: 10 }, (_, i) => `${i}`.repeat(50_000));
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const result = splitByTokenBudget(texts, 96_000, 4);
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expect(result[0].length).toBe(7);
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expect(result[1].length).toBe(3);
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});
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test('default chars_per_token (4) when ratio omitted', () => {
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// Same payload as above without the explicit ratio.
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const texts = Array.from({ length: 10 }, (_, i) => `${i}`.repeat(50_000));
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const explicit = splitByTokenBudget(texts, 96_000, 4);
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const implicit = splitByTokenBudget(texts, 96_000);
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expect(implicit).toEqual(explicit);
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});
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test('empty input returns empty array', () => {
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expect(splitByTokenBudget([], 120_000, 1)).toEqual([]);
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});
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test('single text larger than budget still goes in a batch (split helper does not subdivide)', () => {
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const result = splitByTokenBudget(['a'.repeat(200_000)], 120_000, 1);
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expect(result).toHaveLength(1);
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expect(result[0]).toHaveLength(1);
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});
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test('zero or negative chars_per_token falls back to default', () => {
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const texts = ['a'.repeat(40_000)];
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expect(splitByTokenBudget(texts, 96_000, 0)).toEqual(splitByTokenBudget(texts, 96_000, 4));
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expect(splitByTokenBudget(texts, 96_000, -1)).toEqual(splitByTokenBudget(texts, 96_000, 4));
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});
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});
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describe('isTokenLimitError (pure helper)', () => {
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test('matches Voyage error format', () => {
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expect(isTokenLimitError(VOYAGE_TOKEN_LIMIT_ERROR)).toBe(true);
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});
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test('matches "token limit exceeded" variant', () => {
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expect(isTokenLimitError(new Error('Token limit exceeded for batch request'))).toBe(true);
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});
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test('matches "batch too many tokens" variant', () => {
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expect(isTokenLimitError(new Error('Batch contains too many tokens'))).toBe(true);
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});
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test('does not match unrelated errors', () => {
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expect(isTokenLimitError(new Error('Connection refused'))).toBe(false);
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expect(isTokenLimitError(new Error('Invalid API key'))).toBe(false);
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expect(isTokenLimitError(new Error('429 rate limited'))).toBe(false);
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});
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test('handles non-Error throwables', () => {
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expect(isTokenLimitError('Token limit exceeded')).toBe(true);
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expect(isTokenLimitError({ message: 'some other thing' })).toBe(false);
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expect(isTokenLimitError(null)).toBe(false);
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expect(isTokenLimitError(undefined)).toBe(false);
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});
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});
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// --------- 2-4. Recursion via embed() with stubbed transport ---------
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describe('embed() recursion via stubbed transport', () => {
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beforeEach(() => resetGateway());
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afterEach(() => __setEmbedTransportForTests(null));
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test('halves on token-limit error and concatenates left+right in order', async () => {
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configureVoyage();
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const stub = mock(async ({ values }: { values: string[] }) => {
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// First call: full batch fails. Halved calls: succeed.
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if (values.length === 50) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(stub as any);
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// Build 50 texts that each fit comfortably under any pre-split budget
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// (1 char ≈ 1 token in voyage's recipe; 0.5 × 120K = 60K char budget).
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const texts = Array.from({ length: 50 }, (_, i) => `t${i}`);
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const result = await embed(texts);
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// Stub fired 3 times: 1 fail (length 50) + 2 success (length 25 each).
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expect(stub).toHaveBeenCalledTimes(3);
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const callLengths = stub.mock.calls.map(([arg]) => (arg as { values: string[] }).values.length);
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expect(callLengths.sort((a, b) => a - b)).toEqual([25, 25, 50]);
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expect(result).toHaveLength(50);
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});
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test('preserves input order across halving boundaries', async () => {
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configureVoyage();
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const stub = mock(async ({ values }: { values: string[] }) => {
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if (values.length === 10) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(stub as any);
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const texts = Array.from({ length: 10 }, (_, i) => String.fromCharCode(97 + i)); // a..j
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const result = await embed(texts);
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expect(result).toHaveLength(10);
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// The fakeEmbeddings helper encodes the within-call index in slot 0;
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// halved calls each receive sub-arrays of length 5, so slot 0 reads
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// [0,1,2,3,4,0,1,2,3,4] — that's the contract that proves order
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// preservation despite the embeddings being concatenated from two calls.
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const slotZero = result.map(v => v[0]);
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expect(slotZero).toEqual([0, 1, 2, 3, 4, 0, 1, 2, 3, 4]);
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});
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test('terminal case: single text always fails → normalizes and throws (no infinite loop)', async () => {
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configureVoyage();
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const stub = mock(async () => { throw VOYAGE_TOKEN_LIMIT_ERROR; });
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__setEmbedTransportForTests(stub as any);
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let caught: unknown = null;
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try {
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await embed(['just one text']);
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} catch (e) {
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caught = e;
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}
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expect(caught).not.toBeNull();
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expect(caught instanceof AIConfigError || caught instanceof AITransientError).toBe(true);
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// Stub fires once for the single-element batch; cannot halve further so
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// the recursion gives up at MIN_SUB_BATCH=1 and rethrows.
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expect(stub).toHaveBeenCalledTimes(1);
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});
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});
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// --------- 5. OpenAI fast path (D3) ---------
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describe('embed() OpenAI fast path (no max_batch_tokens)', () => {
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beforeEach(() => resetGateway());
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afterEach(() => __setEmbedTransportForTests(null));
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test('recipe without max_batch_tokens calls transport exactly once with no partition', async () => {
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configureOpenAI();
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const stub = mock(async ({ values }: { values: string[] }) => fakeEmbeddings(values, 1536));
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__setEmbedTransportForTests(stub as any);
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const texts = Array.from({ length: 100 }, (_, i) => `text-${i}`);
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const result = await embed(texts);
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expect(stub).toHaveBeenCalledTimes(1);
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const callValues = (stub.mock.calls[0][0] as { values: string[] }).values;
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expect(callValues).toEqual(texts);
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expect(result).toHaveLength(100);
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});
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test('OpenAI fast path is unaffected by Voyage shrink state', async () => {
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// Configure Voyage first and trigger a shrink…
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configureVoyage();
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const voyageStub = mock(async ({ values }: { values: string[] }) => {
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if (values.length === 4) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(voyageStub as any);
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await embed(['a', 'b', 'c', 'd']);
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expect(__getShrinkStateForTests('voyage')?.factor).toBe(0.25);
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// …then reconfigure to OpenAI. The shrink state belongs to the prior
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// gateway's lifecycle and must not leak.
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configureOpenAI();
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const openaiStub = mock(async ({ values }: { values: string[] }) => fakeEmbeddings(values, 1536));
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__setEmbedTransportForTests(openaiStub as any);
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await embed(['x', 'y']);
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expect(openaiStub).toHaveBeenCalledTimes(1);
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expect(__getShrinkStateForTests('voyage')).toBeUndefined();
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});
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});
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// --------- 6. Shrink-on-miss adaptive cache (D8-A) ---------
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describe('shrink-on-miss adaptive cache', () => {
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beforeEach(() => resetGateway());
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afterEach(() => __setEmbedTransportForTests(null));
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test('first token-limit miss halves the recipe safety factor', async () => {
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configureVoyage();
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expect(__getShrinkStateForTests('voyage')).toBeUndefined();
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const stub = mock(async ({ values }: { values: string[] }) => {
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if (values.length === 4) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(stub as any);
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await embed(['a', 'b', 'c', 'd']);
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// Voyage declares safety_factor=0.5; after one miss → 0.5 × 0.5 = 0.25.
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expect(__getShrinkStateForTests('voyage')?.factor).toBe(0.25);
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});
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test('factor floors at SHRINK_FLOOR (0.05) under repeated misses', async () => {
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configureVoyage();
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const stub = mock(async ({ values }: { values: string[] }) => {
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// Always throw on >1 to keep recursion going until MIN_SUB_BATCH=1
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// succeeds. That gives many shrink events per embed() call.
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if (values.length > 1) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(stub as any);
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// 16 texts will recurse 4 levels deep, generating multiple shrink events.
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await embed(Array.from({ length: 16 }, (_, i) => `t${i}`));
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const factor = __getShrinkStateForTests('voyage')?.factor ?? -1;
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expect(factor).toBeGreaterThanOrEqual(0.05);
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});
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test('factor heals back toward declared safety_factor after enough wins', async () => {
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configureVoyage();
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const stub = mock(async ({ values }: { values: string[] }) => {
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// Once: fail at length 2, succeed everywhere else. Subsequent calls
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// all succeed.
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if (stub.mock.calls.length === 1 && values.length === 2) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(stub as any);
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await embed(['a', 'b']); // 1 fail + 2 successes (length 1 each) → factor 0.25, wins 2
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const afterMiss = __getShrinkStateForTests('voyage')?.factor;
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expect(afterMiss).toBe(0.25);
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// Drive 10 more successful calls. SHRINK_HEAL_AFTER=10; on the 10th win
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// the factor multiplies by 1.5 (capped at the declared 0.5 ceiling).
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for (let i = 0; i < 8; i++) {
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await embed(['solo']);
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}
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const healed = __getShrinkStateForTests('voyage')?.factor ?? 0;
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// 0.25 × 1.5 = 0.375. Still below the recipe ceiling of 0.5; the next
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// round of 10 wins would bump it to min(0.5, 0.375 × 1.5) = 0.5.
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expect(healed).toBeCloseTo(0.375, 5);
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});
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test('healing path cannot exceed the recipe-declared safety_factor', async () => {
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configureVoyage();
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const stub = mock(async ({ values }: { values: string[] }) => {
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if (stub.mock.calls.length === 1) throw VOYAGE_TOKEN_LIMIT_ERROR;
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return fakeEmbeddings(values, 1024);
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});
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__setEmbedTransportForTests(stub as any);
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// Trigger one shrink, then drive enough wins to fully heal.
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await embed(['one', 'two']);
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for (let i = 0; i < 30; i++) {
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await embed(['solo']);
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}
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const factor = __getShrinkStateForTests('voyage')?.factor ?? 0;
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// Declared safety_factor is 0.5; healing must clamp at that ceiling.
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expect(factor).toBeLessThanOrEqual(0.5);
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expect(factor).toBeGreaterThan(0);
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});
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});
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// --------- 7. Startup warning (D9-B) ---------
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describe('startup warning for recipes missing max_batch_tokens', () => {
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beforeEach(() => resetGateway());
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test('first configureGateway call warns about each missing-cap recipe; subsequent calls suppressed', () => {
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const warnings: string[] = [];
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const original = console.warn;
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console.warn = (msg: string) => warnings.push(String(msg));
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try {
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configureOpenAI();
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const firstCallCount = warnings.length;
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// Reconfigure: the warning should NOT re-fire for the same recipes
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// within one process (we already told the operator).
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configureOpenAI();
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expect(warnings.length).toBe(firstCallCount);
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} finally {
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console.warn = original;
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}
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// The warning text should match the documented contract.
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const contractMatch = warnings.filter(w =>
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w.includes('[ai.gateway]') && w.includes('declares an embedding touchpoint'),
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);
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expect(contractMatch.length).toBeGreaterThan(0);
|
||
|
||
// Voyage declares max_batch_tokens → suppressed. OpenAI is the
|
||
// canonical fast-path recipe → also suppressed by id. Both must be
|
||
// absent from the warnings.
|
||
expect(warnings.find(w => w.includes('"voyage"'))).toBeUndefined();
|
||
expect(warnings.find(w => w.includes('"openai"'))).toBeUndefined();
|
||
});
|
||
});
|