@mastra/core/dist/docs/references/reference-rag-embeddings.md

Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.

# 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:

```typescript
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

**model** (`EmbeddingModel`): The embedding model to use (e.g. openai.embedding('text-embedding-3-small'))

**value** (`string | Record<string, any>`): The text content or object to embed

**maxRetries** (`number`): Maximum number of retries per embedding call. Set to 0 to disable retries. (Default: `2`)

**abortSignal** (`AbortSignal`): Optional abort signal to cancel the request

**headers** (`Record<string, string>`): Additional HTTP headers for the request (only for HTTP-based providers)

### Return value

**embedding** (`number[]`): The embedding vector for the input

## Multiple embeddings

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

```typescript
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

**model** (`EmbeddingModel`): The embedding model to use (e.g. openai.embedding('text-embedding-3-small'))

**values** (`string[] | Record<string, any>[]`): Array of text content or objects to embed

**maxRetries** (`number`): Maximum number of retries per embedding call. Set to 0 to disable retries. (Default: `2`)

**abortSignal** (`AbortSignal`): Optional abort signal to cancel the request

**headers** (`Record<string, string>`): Additional HTTP headers for the request (only for HTTP-based providers)

### Return value

**embeddings** (`number[][]`): Array of embedding vectors corresponding to the input values

## Example usage

```typescript
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](https://sdk.vercel.ai/docs/ai-sdk-core/embeddings)
- [embed()](https://sdk.vercel.ai/docs/reference/ai-sdk-core/embed)
- [embedMany()](https://sdk.vercel.ai/docs/reference/ai-sdk-core/embed-many)