# 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.

### 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.

### 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)