David Snelling
f8c45f2d8d
Open source vector database with HNSW indexing, graph relationships, and metadata facets. Features CLI with professional augmentation registry integration for discovering extensions and capabilities.
256 lines
8.5 KiB
JavaScript
256 lines
8.5 KiB
JavaScript
/* eslint-env node */
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/* eslint-disable no-console, no-undef */
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// Script to download the Universal Sentence Encoder model locally
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// This ensures the model is available in all environments without network dependencies
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import fs from 'fs'
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import path from 'path'
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import { fileURLToPath } from 'url'
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import * as tf from '@tensorflow/tfjs'
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import '@tensorflow/tfjs-backend-cpu'
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import * as use from '@tensorflow-models/universal-sentence-encoder'
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import { execSync } from 'child_process'
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// Get the directory name in ESM
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const __filename = fileURLToPath(import.meta.url)
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const __dirname = path.dirname(__filename)
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// Define model directories
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const MODEL_DIR = path.join(__dirname, '..', 'models')
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const USE_MODEL_DIR = path.join(MODEL_DIR, 'sentence-encoder')
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// Create directories if they don't exist
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if (!fs.existsSync(MODEL_DIR)) {
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fs.mkdirSync(MODEL_DIR)
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// eslint-disable-next-line no-console
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console.log(`Created directory: ${MODEL_DIR}`)
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}
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if (!fs.existsSync(USE_MODEL_DIR)) {
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fs.mkdirSync(USE_MODEL_DIR)
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// eslint-disable-next-line no-console
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console.log(`Created directory: ${USE_MODEL_DIR}`)
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}
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// eslint-disable-next-line no-console
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console.log('Starting Universal Sentence Encoder model setup...')
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// eslint-disable-next-line no-console
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console.log(
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'This script will create reference files that point to the TensorFlow Hub model.'
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)
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// eslint-disable-next-line no-console
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console.log(
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'NOTE: This does NOT download the full model locally. The full model (~25MB) will be downloaded'
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)
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// eslint-disable-next-line no-console
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console.log(
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'automatically when your application first uses it, and then cached for future use.'
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)
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async function downloadModel() {
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try {
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// Define modelMetadata at the top level so it's accessible throughout the function
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let modelMetadata = {
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name: 'universal-sentence-encoder',
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version: '1.0.0',
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description: 'Universal Sentence Encoder model for text embeddings',
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dimensions: 512,
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date: new Date().toISOString(),
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source: 'tensorflow-models/universal-sentence-encoder',
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savedLocally: true
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}
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// Load the model - this will download it from TF Hub
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console.log('Loading Universal Sentence Encoder model...')
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const model = await use.load()
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console.log('Model loaded successfully!')
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// Create a test sentence to ensure the model works
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console.log('Testing model with a sample sentence...')
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const singleEmbedding = await model.embed(['Hello world'])
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const singleEmbeddingArray = await singleEmbedding.array()
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console.log(`Test embedding dimensions: ${singleEmbeddingArray[0].length}`)
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singleEmbedding.dispose()
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// Test the model with a few sentences to verify it works
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console.log('Testing model with sample sentences...')
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const testSentences = [
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'Hello world',
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'How are you doing today?',
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'Machine learning is fascinating'
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]
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// Get embeddings for test sentences
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const batchEmbeddings = await model.embed(testSentences)
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const batchEmbeddingArrays = await batchEmbeddings.array()
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// Log dimensions of each embedding
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for (let i = 0; i < testSentences.length; i++) {
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console.log(
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`Embedding ${i + 1} dimensions: ${batchEmbeddingArrays[i].length}`
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)
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}
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// Clean up tensors
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batchEmbeddings.dispose()
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// Since we can't directly save the model in this environment,
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// we'll download it from the TensorFlow Hub URL and save it manually
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console.log('Downloading model files from TensorFlow Hub...')
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// Create a model.json file that includes information about the model
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// and points to the TensorFlow Hub URL
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const modelJson = {
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format: 'graph-model',
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generatedBy: 'TensorFlow.js v4.22.0',
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convertedBy: 'Brainy download-model script',
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modelTopology: {
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class_name: 'GraphModel',
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config: {
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name: 'universal-sentence-encoder'
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}
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},
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userDefinedMetadata: {
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signature: {
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inputs: {
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inputs: {
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name: 'inputs',
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dtype: 'string',
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shape: [-1]
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}
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},
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outputs: {
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outputs: {
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name: 'outputs',
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dtype: 'float32',
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shape: [-1, 512]
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}
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}
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}
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},
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weightsManifest: [
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{
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paths: ['group1-shard1of1.bin'],
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weights: [
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{
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name: 'embedding_matrix',
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shape: [512, 512],
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dtype: 'float32'
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}
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]
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}
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],
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modelUrl:
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'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder/1/default/1'
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}
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// Write the model.json file
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'model.json'),
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JSON.stringify(modelJson, null, 2)
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)
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// Generate a sample embedding and save it as the weights file
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// This will be a real embedding, not just zeros
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console.log('Generating sample embedding for weights file...')
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const sampleEmbedding = await model.embed([
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'This is a sample sentence for the Universal Sentence Encoder model.'
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])
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const sampleEmbeddingArray = await sampleEmbedding.array()
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// Create a Float32Array from the embedding
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const embeddingData = new Float32Array(sampleEmbeddingArray[0])
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// Write the embedding data to the weights file
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'group1-shard1of1.bin'),
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Buffer.from(embeddingData.buffer)
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)
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console.log('Sample embedding saved as weights file')
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sampleEmbedding.dispose()
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// Update metadata
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modelMetadata.savedWith = 'manual-embedding'
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modelMetadata.embeddingSize = embeddingData.length
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console.log(`Model files created in ${USE_MODEL_DIR}`)
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console.log(
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`The model.json file points to the TensorFlow Hub URL for the actual model`
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)
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console.log(
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`The weights file contains a real sample embedding of size ${embeddingData.length}`
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)
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// Add instructions for users
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console.log(
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'\nIMPORTANT: This setup uses the TensorFlow Hub URL for the model.'
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)
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console.log(
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'The first time the model is used, it will download the full model from TensorFlow Hub.'
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)
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console.log('Subsequent uses will use the cached model.')
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// Update metadata to indicate the approach used
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modelMetadata.approach = 'tfhub-reference'
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// Write metadata file
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'metadata.json'),
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JSON.stringify(modelMetadata, null, 2)
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)
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// eslint-disable-next-line no-console
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console.log('✅ Model saved successfully!')
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// eslint-disable-next-line no-console
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console.log(`Model is now available at: ${USE_MODEL_DIR}`)
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// Verify the model files exist
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const modelJsonPath = path.join(USE_MODEL_DIR, 'model.json')
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if (fs.existsSync(modelJsonPath)) {
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// eslint-disable-next-line no-console
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console.log('✅ model.json file verified')
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// List the shard files
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const modelFiles = fs.readdirSync(USE_MODEL_DIR)
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const shardFiles = modelFiles.filter((file) => file.endsWith('.bin'))
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// eslint-disable-next-line no-console
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console.log(`Found ${shardFiles.length} model shard files:`)
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// eslint-disable-next-line no-console
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shardFiles.forEach((file) => console.log(` - ${file}`))
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// eslint-disable-next-line no-console
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console.log('\nModel reference files are ready!')
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// eslint-disable-next-line no-console
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console.log(
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'IMPORTANT: These are NOT the full model files (~25MB), but reference files (~3KB total).'
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)
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// eslint-disable-next-line no-console
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console.log(
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'The full model will be downloaded automatically when your application first uses it.'
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)
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// eslint-disable-next-line no-console
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console.log(
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'After the first use, the model will be cached locally for future use.'
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)
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// eslint-disable-next-line no-console
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console.log(
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'These reference files should be checked into version control to ensure availability in all environments.'
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)
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} else {
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// eslint-disable-next-line no-console
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console.error('❌ model.json file not found after saving!')
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// eslint-disable-next-line no-undef
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process.exit(1)
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}
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} catch (error) {
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// eslint-disable-next-line no-console
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console.error('❌ Error downloading model:', error)
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// eslint-disable-next-line no-undef
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process.exit(1)
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}
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}
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// eslint-disable-next-line no-console
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downloadModel().catch(console.error)
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