David Snelling
e476d45fac
- Introduced `@soulcraft/brainy-models` package with pre-bundled TensorFlow models for enhanced offline reliability. - Added `index.d.ts` and `index.js` allowing offline embedding workflows with the Universal Sentence Encoder model. - Included utility scripts for model compression, size retrieval, and availability checks. - Added `metadata.json` and `model.json` defining the Universal Sentence Encoder configuration with offline bundling. - Ensured comprehensive model documentation, error handling, and robust logging for seamless integration. - Supported optional model quantization placeholders for future TensorFlow.js enhancements. **Purpose**: Enable fully offline-ready embedding workflows via pre-bundled Universal Sentence Encoder models, ensuring maximum reliability and air-gapped environment compatibility.
28 lines
783 B
JavaScript
28 lines
783 B
JavaScript
#!/usr/bin/env node
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/**
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* Reproduction script for the TensorFlow.js isNullOrUndefined error
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*/
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import * as tf from '@tensorflow/tfjs-node'
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import * as use from '@tensorflow-models/universal-sentence-encoder'
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console.log('🔍 Loading Universal Sentence Encoder model...')
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try {
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const model = await use.load()
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console.log('✅ Model loaded successfully')
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console.log('🧪 Testing model functionality...')
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const testEmbedding = await model.embed(['Hello world'])
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const testArray = await testEmbedding.array()
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console.log(
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`✅ Model test passed - embedding dimensions: ${testArray[0].length}`
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)
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testEmbedding.dispose()
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model.dispose()
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} catch (error) {
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console.error('❌ Error:', error)
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console.error('Stack trace:', error.stack)
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process.exit(1)
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}
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