Embeddings

Embeddings allow you to convert text into vector representations, which are useful for semantic search, clustering, and similarity comparisons.

Setup

You need an embedding model. Ollama supports models like mxbai-embed-large or nomic-embed-text.

import { EmbeddingsGateway } from 'mojentic';

// Initialize gateway
const gateway = new EmbeddingsGateway('mxbai-embed-large');

Generating Embeddings

const text = "The quick brown fox jumps over the lazy dog.";
const result = await gateway.embed(text);

if (result.isOk()) {
  console.log(result.value.slice(0, 5));
  // => [0.123, -0.456, ...]
}

Batch Processing

You can embed multiple texts at once:

const texts = ["Hello", "World"];
const result = await gateway.embedBatch(texts);

Cosine Similarity

Mojentic provides utilities to calculate similarity between vectors:

import { cosineSimilarity } from 'mojentic';

const similarity = cosineSimilarity(vector1, vector2);