8f09ed1ef6
Add pgvector embeddings on cards, server-side cohere/embed-v4.0 via AI Gateway, kNN identify route, and L0→L1→L2 client orchestration with empty-index fast escalate and id-cursor backfill job. Co-authored-by: Cursor <cursoragent@cursor.com>
35 lines
1.1 KiB
JavaScript
35 lines
1.1 KiB
JavaScript
import { describe, expect, it, vi, afterEach } from 'vitest';
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import { embedCardImage, formatEmbeddingForPg } from '../../lib/card-embed.js';
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describe('formatEmbeddingForPg', () => {
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it('formats vectors for pgvector literals', () => {
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expect(formatEmbeddingForPg([0.1, 0.2, 0.3])).toBe('[0.10000000,0.20000000,0.30000000]');
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});
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});
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describe('embedCardImage', () => {
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afterEach(() => {
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vi.unstubAllGlobals();
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delete process.env.AI_GATEWAY_API_KEY;
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});
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it('throws when AI_GATEWAY_API_KEY is missing', async () => {
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await expect(embedCardImage('data:image/jpeg;base64,abc')).rejects.toThrow(
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'AI_GATEWAY_API_KEY is not configured'
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);
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});
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it('returns embedding values from the gateway response', async () => {
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process.env.AI_GATEWAY_API_KEY = 'test-key';
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vi.stubGlobal(
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'fetch',
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vi.fn(async () => ({
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ok: true,
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json: async () => ({ data: [{ embedding: [0.5, 0.25] }] }),
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}))
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);
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const embedding = await embedCardImage('data:image/jpeg;base64,abc');
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expect(embedding).toEqual([0.5, 0.25]);
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});
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});
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