Embeddings

Generate dense vector representations of text for semantic search, clustering, and similarity matching.

For full API reference (embed(), embedMany(), streamEmbedMany(), options, result types, middleware, and custom providers), see the Core Embeddings guide.

See it in action

Try Semantic Search and PDF Search for working demos.

Model Configuration

import { transformers } from '@localmode/transformers';

const model = transformers.embedding('Xenova/bge-small-en-v1.5', {
  quantized: true,     // Use quantized model (default: true)
  device: 'webgpu',    // 'webgpu' or 'wasm' (auto-detected if omitted)
  onProgress: (p) => {
    console.log(`Loading model: ${(p.progress * 100).toFixed(1)}%`);
  },
});

Device Selection

Choose between WebGPU (faster, requires hardware support) and WASM (universal fallback):

import { transformers, isWebGPUAvailable } from '@localmode/transformers';

const device = (await isWebGPUAvailable()) ? 'webgpu' : 'wasm';

const model = transformers.embedding('Xenova/bge-small-en-v1.5', {
  device,
  quantized: true,
});

Device	Performance	Compatibility	When to Use
`'webgpu'`	Fastest (GPU-accelerated)	Chrome 113+, Edge 113+, Safari 18+	Default when available
`'wasm'`	Good (CPU)	All modern browsers	Universal fallback
(omitted)	Auto-detected	-	Recommended for most apps

Recommended Models

Model	Dimensions	Size	Speed	Use Case
`Snowflake/snowflake-arctic-embed-xs`	384	23MB	⚡⚡⚡	Tiny, best retrieval for size
`Xenova/bge-small-en-v1.5`	384	33MB	⚡⚡⚡	General purpose, recommended
`Xenova/bge-base-en-v1.5`	768	110MB	⚡⚡	Higher quality
`Xenova/paraphrase-multilingual-MiniLM-L12-v2`	384	117MB	⚡⚡	50+ languages

Multilingual Embeddings

For multilingual applications:

const model = transformers.embedding('Xenova/paraphrase-multilingual-MiniLM-L12-v2');

const { embeddings } = await embedMany({
  model,
  values: [
    'Hello world',           // English
    'Bonjour le monde',      // French
    'Hola mundo',            // Spanish
    'こんにちは世界',          // Japanese
    'مرحبا بالعالم',         // Arabic
  ],
});

// All embeddings are in the same vector space
// Cross-lingual similarity works!

Comparison: Model Quality vs Speed

import { cosineSimilarity } from '@localmode/core';

// Test sentences
const s1 = 'The cat sits on the mat';
const s2 = 'A feline rests on a rug';
const s3 = 'The stock market crashed yesterday';

// Fast model
const fastModel = transformers.embedding('Xenova/bge-small-en-v1.5');
const { embeddings: fastEmbeddings } = await embedMany({
  model: fastModel,
  values: [s1, s2, s3],
});

// Quality model
const qualityModel = transformers.embedding('Xenova/all-mpnet-base-v2');
const { embeddings: qualityEmbeddings } = await embedMany({
  model: qualityModel,
  values: [s1, s2, s3],
});

// Compare similarities
console.log('Fast model:');
console.log('  s1-s2:', cosineSimilarity(fastEmbeddings[0], fastEmbeddings[1]).toFixed(3));
console.log('  s1-s3:', cosineSimilarity(fastEmbeddings[0], fastEmbeddings[2]).toFixed(3));

console.log('Quality model:');
console.log('  s1-s2:', cosineSimilarity(qualityEmbeddings[0], qualityEmbeddings[1]).toFixed(3));
console.log('  s1-s3:', cosineSimilarity(qualityEmbeddings[0], qualityEmbeddings[2]).toFixed(3));

Caching Embeddings

Use caching middleware to avoid recomputation:

import { wrapEmbeddingModel, cachingMiddleware } from '@localmode/core';

const baseModel = transformers.embedding('Xenova/bge-small-en-v1.5');

const model = wrapEmbeddingModel(baseModel, [
  cachingMiddleware({
    maxSize: 10000,
    storage: 'indexeddb',
    dbName: 'embedding-cache',
  }),
]);

// First call computes embedding
const { embedding: e1 } = await embed({ model, value: 'Hello' });

// Second call returns from cache (instant)
const { embedding: e2 } = await embed({ model, value: 'Hello' });

Best Practices

Embedding Tips

Match dimensions — Ensure your vector DB dimensions match the model
Batch when possible — embedMany() is more efficient than multiple embed() calls
Cache embeddings — Use caching middleware for repeated queries
Normalize if needed — Some models benefit from L2 normalization
Choose model wisely — Balance quality vs speed for your use case

Showcase Apps

App	Description	Links
Semantic Search	Generate embeddings for document search	Demo · Source
Product Search	Embed product descriptions for catalog search	Demo · Source
PDF Search	Embed PDF chunks for document retrieval	Demo · Source

Next Steps

Core Embeddings API

Full API reference, options, result types, and custom providers.

Vector Database

Store and search embeddings.

RAG

Build retrieval-augmented generation pipelines.

Embeddings

Core Embeddings API

Vector Database

RAG

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