# Embed
[EN] Source: https://mastra.ai/reference/rag/embeddings

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";

const result = await embed({
  model: openai.embedding("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:

```typescript
import { embedMany } from "ai";

const result = await embedMany({
  model: openai.embedding("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

```typescript
import { embed, embedMany } from "ai";
import { openai } from "@ai-sdk/openai";

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

// Multiple embeddings
const multipleResult = await embedMany({
  model: openai.embedding("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)


---
title: "Reference: ExtractParams | RAG"
description: Documentation for metadata extraction configuration in Mastra.
---