wehub-resource-sync
070959e133
landing-page-staging / Deploy landing page to staging (push) Has been skipped
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62 lines
2.0 KiB
TypeScript
62 lines
2.0 KiB
TypeScript
import { promises as fsp } from 'node:fs';
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import path from 'node:path';
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import { afterEach, beforeEach, describe, expect, it } from 'vitest';
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import { extractWithLLM } from '../src/memory-llm.js';
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import { memoryDir } from '../src/memory.js';
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import { __resetExtractionsForTests } from '../src/memory-extractions.js';
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const dataDir = path.join(process.env.OD_DATA_DIR as string, 'memory-google-default-test');
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const originalFetch = globalThis.fetch;
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beforeEach(async () => {
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await fsp.rm(memoryDir(dataDir), { recursive: true, force: true });
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__resetExtractionsForTests();
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});
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afterEach(() => {
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globalThis.fetch = originalFetch;
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});
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describe('memory-llm Google fast-model default', () => {
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it('uses gemini-3.5-flash (not a shut-down 2.0 model) when chatProvider omits a model', async () => {
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let capturedUrl: string | null = null;
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globalThis.fetch = async (input: Parameters<typeof fetch>[0]) => {
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const url =
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typeof input === 'string'
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? input
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: input instanceof URL
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? input.toString()
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: (input as Request).url;
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capturedUrl = url;
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// Return a valid Google Gemini response shape so extractWithLLM can parse it.
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return new Response(
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JSON.stringify({
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candidates: [{ content: { parts: [{ text: '{"entries":[]}' }] } }],
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}),
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{ status: 200, headers: { 'content-type': 'application/json' } },
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);
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};
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await extractWithLLM(
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dataDir,
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{ userMessage: 'I prefer dark mode.', assistantMessage: 'Noted.' },
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{
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projectRoot: null,
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chatAgentId: null,
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chatProvider: {
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provider: 'google',
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apiKey: 'AQ.TestKeyForUnitTests01234567890123456789012',
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baseUrl: 'https://generativelanguage.googleapis.com',
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apiVersion: '',
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model: '',
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},
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},
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);
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expect(capturedUrl).not.toBeNull();
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expect(capturedUrl).toContain('gemini-3.5-flash');
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expect(capturedUrl).not.toContain('gemini-2.0-flash');
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});
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});
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