Embed

Mastra uses the AI SDK's embed and embedMany functions to generate vector embeddings for text inputs, enabling similarity search and RAG workflows.

Single embedding

The embed function generates a vector embedding for a single text input:

import { embed } from 'ai'
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'

const result = await embed({
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  value: 'Your text to embed',
  maxRetries: 2, // optional, defaults to 2
})

Parameters

<PropertiesTable content={[ { name: 'model', type: 'EmbeddingModel', description: "The embedding model to use (e.g. openai.embedding('text-embedding-3-small'))", }, { name: 'value', type: 'string | Record<string, any>', description: 'The text content or object to embed', }, { name: 'maxRetries', type: 'number', description: 'Maximum number of retries per embedding call. Set to 0 to disable retries.', isOptional: true, defaultValue: '2', }, { name: 'abortSignal', type: 'AbortSignal', description: 'Optional abort signal to cancel the request', isOptional: true, }, { name: 'headers', type: 'Record<string, string>', description: 'Additional HTTP headers for the request (only for HTTP-based providers)', isOptional: true, }, ]} />

Return value

<PropertiesTable content={[ { name: 'embedding', type: 'number[]', description: 'The embedding vector for the input', }, ]} />

Multiple embeddings

For embedding multiple texts at once, use the embedMany function:

import { embedMany } from 'ai'

const result = await embedMany({
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  values: ['First text', 'Second text', 'Third text'],
  maxRetries: 2, // optional, defaults to 2
})

Parameters

<PropertiesTable content={[ { name: 'model', type: 'EmbeddingModel', description: "The embedding model to use (e.g. openai.embedding('text-embedding-3-small'))", }, { name: 'values', type: 'string[] | Record<string, any>[]', description: 'Array of text content or objects to embed', }, { name: 'maxRetries', type: 'number', description: 'Maximum number of retries per embedding call. Set to 0 to disable retries.', isOptional: true, defaultValue: '2', }, { name: 'abortSignal', type: 'AbortSignal', description: 'Optional abort signal to cancel the request', isOptional: true, }, { name: 'headers', type: 'Record<string, string>', description: 'Additional HTTP headers for the request (only for HTTP-based providers)', isOptional: true, }, ]} />

Return value

<PropertiesTable content={[ { name: 'embeddings', type: 'number[][]', description: 'Array of embedding vectors corresponding to the input values', }, ]} />

Example usage

import { embed, embedMany } from 'ai'

// Single embedding
const singleResult = await embed({
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  value: 'What is the meaning of life?',
})

// Multiple embeddings
const multipleResult = await embedMany({
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  values: [
    'First question about life',
    'Second question about universe',
    'Third question about everything',
  ],
})

For more detailed information about embeddings in the Vercel AI SDK, see:

• AI SDK Embeddings Overview
• embed()
• embedMany()