StillHammer
dbf1a3de8c
Added plan.md with complete architecture for format-agnostic content generation: - Support for Markdown, HTML, Plain Text, JSON formats - New FormatExporter module with neutral data structure - Integration strategy with existing ContentAssembly and ArticleStorage - Bonus features: SEO metadata generation, readability scoring, WordPress Gutenberg format - Implementation roadmap with 4 phases (6h total estimated) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
82 lines
3.3 KiB
JavaScript
82 lines
3.3 KiB
JavaScript
import test from 'node:test';
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import assert from 'node:assert';
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import { safeImport } from '../_helpers/path.js';
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import { MemoryArticleStorage } from '../_helpers/memoryStorage.js';
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import { MockLLMManager } from '../_helpers/mockLLMManager.js';
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function skip(msg) { console.warn('[SKIP]', msg); }
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test('Pipeline dry-run with mock LLM returns structured article', async (t) => {
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const extr = safeImport('ElementExtraction');
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const gen = safeImport('ContentGeneration');
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const asm = safeImport('ContentAssembly');
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const enh = safeImport('SelectiveEnhancement');
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const miss = safeImport('MissingKeywords');
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const utils= safeImport('Utils');
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const stor = new MemoryArticleStorage();
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const llm = new MockLLMManager();
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if (![extr,gen,asm,enh,miss].every(r=>r.ok)) {
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skip('One or more pipeline modules missing; dry-run skipped.');
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return;
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}
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const ElementExtraction = extr.mod;
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const ContentGeneration = gen.mod;
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const ContentAssembly = asm.mod;
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const SelectiveEnh = enh.mod;
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const MissingKeywords = miss.mod;
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// Inputs minimaux (adapte selon tes contrats)
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const inputs = {
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instructions: 'Write about hunting jackets: materials, weatherproofing, sizes.',
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persona: { name: 'Persona A', tone: 'pragmatic' },
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template: '<article><h1>{{title}}</h1>{{body}}</article>',
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seed: 42
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};
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// Étape 1: extraction
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const elements = await (ElementExtraction.extractElements
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? ElementExtraction.extractElements(inputs)
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: { topics: ['materials', 'weatherproofing', 'sizes'] });
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assert.ok(elements, 'elements should be produced');
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// Étape 2: génération (mock via injection simple si API supporte un client)
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// Si ContentGeneration accepte un LLM param, on l’utilise, sinon on simule simple:
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const parts = await (ContentGeneration.generateArticleParts
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? ContentGeneration.generateArticleParts({ inputs, elements, llm })
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: (Array.isArray(elements.topics) ? elements.topics : Object.keys(elements))
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.map(t => ({ key:String(t), text:`MOCK SECTION ${t}` })));
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assert.ok(parts && Array.isArray(parts), 'parts array expected');
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assert.ok(parts.length > 0, 'non-empty parts');
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// Étape 3: enhancement sélectif (facultatif)
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const enhanced = await (SelectiveEnh.enhanceParts
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? SelectiveEnh.enhanceParts({ parts, llm })
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: parts);
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assert.ok(enhanced && Array.isArray(enhanced), 'enhanced parts array');
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// Étape 4: missing keywords
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const completed = await (MissingKeywords.fillMissingKeywords
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? MissingKeywords.fillMissingKeywords({ parts: enhanced, targetKeywords:['chasse','imperméable'] , llm })
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: enhanced);
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// Étape 5: assemblage
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const html = await (ContentAssembly.assembleArticle
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? ContentAssembly.assembleArticle({ template: inputs.template, parts: completed, meta:{title:'Veste de chasse: guide complet'} })
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: `<article>${completed.map(p=>`<section>${p.text}</section>`).join('')}</article>`);
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assert.equal(typeof html, 'string');
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assert.ok(html.includes('<article'), 'html should be article-like');
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// “Stockage final” simulé
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const key = JSON.stringify(inputs); // simplifié
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await stor.writeFinal(key, { html });
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assert.ok(await stor.hasFinal(key), 'final should be stored');
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});
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