> Discover all available pages from the documentation index: https://mastra.zisheng.pro/en/llms.txt

# Embedding models

Mastra's model router supports embedding models using the same `provider/model` string format as language models. This provides a unified interface for both chat and embedding models with TypeScript autocomplete support.

## Quickstart

```typescript
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'
import { embedMany } from 'ai'

// Generate embeddings
const { embeddings } = await embedMany({
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  values: ['Hello world', 'Semantic search is powerful'],
})
```

## Supported models

### OpenAI

- `text-embedding-3-small` - 1536 dimensions, 8191 max tokens
- `text-embedding-3-large` - 3072 dimensions, 8191 max tokens
- `text-embedding-ada-002` - 1536 dimensions, 8191 max tokens

```typescript
const embedder = new ModelRouterEmbeddingModel('openai/text-embedding-3-small')
```

### Google

- `gemini-embedding-001` - 768 dimensions, 2048 max tokens

```typescript
const embedder = new ModelRouterEmbeddingModel('google/gemini-embedding-001')
```

### VoyageAI

VoyageAI provides specialized embedding models optimized for retrieval tasks. These models are available as standalone packages:

**npm**:

```bash
npm install @mastra/voyageai
```

**pnpm**:

```bash
pnpm add @mastra/voyageai
```

**Yarn**:

```bash
yarn add @mastra/voyageai
```

**Bun**:

```bash
bun add @mastra/voyageai
```

**Available models:**

- `voyage-4-large` - 1024 dimensions (default), supports 256-2048 dimensions, best general-purpose and multilingual retrieval quality (120k max tokens per batch)
- `voyage-4` - 1024 dimensions (default), supports 256-2048 dimensions, optimized for general-purpose and multilingual retrieval (320k max tokens per batch)
- `voyage-4-lite` - 1024 dimensions (default), supports 256-2048 dimensions, optimized for latency and cost (1M max tokens per batch)
- `voyage-code-3` - 1024 dimensions (default), supports 256-2048 dimensions, optimized for code retrieval
- `voyage-finance-2` - 1024 dimensions, optimized for finance retrieval and RAG
- `voyage-law-2` - 1024 dimensions, optimized for legal retrieval and RAG (16k context)
- `voyage-3-large` - 1024 dimensions (default), supports 256-2048 dimensions (previous generation)
- `voyage-3.5` - 1024 dimensions (default), supports 256-2048 dimensions (previous generation)
- `voyage-3.5-lite` - 1024 dimensions (default), supports 256-2048 dimensions, optimized for latency and cost (previous generation)
- `voyage-multimodal-3.5` - 1024 dimensions, supports text + images

```typescript
import { voyage, voyageEmbedding } from '@mastra/voyageai'

// Use default model (voyage-3.5)
const { embeddings } = await voyage.doEmbed({
  values: ['Hello world'],
})

// Use specific model (voyage-3-large)
const largeEmbeddings = await voyage.large.doEmbed({
  values: ['More complex content'],
})

// Custom configuration
const customModel = voyageEmbedding({
  model: 'voyage-3.5',
  inputType: 'query', // or 'document'
  outputDimension: 512, // 256, 512, 1024, or 2048
  baseUrl: 'https://ai.mongodb.com/v1', // Optional: custom endpoint (e.g. MongoDB-hosted Voyage)
})

const { embeddings: customEmbeddings } = await customModel.doEmbed({
  values: ['Custom configuration example'],
})
```

**VoyageAI with MongoDB:**

VoyageAI works seamlessly with MongoDB Atlas Vector Search:

```typescript
import { voyage } from '@mastra/voyageai'
import { MongoDBVector } from '@mastra/mongodb'

const mongoVector = new MongoDBVector({
  id: 'mongodb-vector',
  uri: process.env.MONGODB_URI,
  dbName: process.env.MONGODB_DB_NAME,
})

// Create index matching VoyageAI dimensions
await mongoVector.createIndex({
  indexName: 'documents',
  dimension: 1024, // voyage-3.5 default
})

// Generate and store embeddings
const { embeddings } = await voyage.doEmbed({
  values: chunks.map(chunk => chunk.text),
})

await mongoVector.upsert({
  indexName: 'documents',
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
})
```

**Multimodal embeddings (text + images):**

```typescript
import { voyage } from '@mastra/voyageai'

const { embeddings } = await voyage.multimodal.doEmbed({
  values: [
    {
      content: [
        { type: 'text', text: 'Product description' },
        { type: 'image_url', image_url: 'https://example.com/image.jpg' },
      ],
    },
  ],
})
```

For more details, see the [MongoDB + VoyageAI integration guide](https://mastra.zisheng.pro/en/reference/vectors/mongodb).

## Authentication

The model router automatically detects API keys from environment variables:

- **OpenAI**: `OPENAI_API_KEY`
- **Google**: `GOOGLE_API_KEY` (falls back to `GOOGLE_GENERATIVE_AI_API_KEY`)
- **VoyageAI**: `VOYAGE_API_KEY`

```bash
# .env
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=...
VOYAGE_API_KEY=pa-...
```

## Custom Providers

You can use any OpenAI-compatible embedding endpoint with a custom URL:

```typescript
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'

const embedder = new ModelRouterEmbeddingModel({
  providerId: 'ollama',
  modelId: 'nomic-embed-text',
  url: 'http://localhost:11434/v1',
  apiKey: 'not-needed', // Some providers don't require API keys
})
```

## Usage with Memory

The embedding model router integrates seamlessly with Mastra's memory system:

```typescript
import { Memory } from '@mastra/memory'
import { Agent } from '@mastra/core/agent'
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'

const agent = new Agent({
  id: 'my-agent',
  name: 'my-agent',
  instructions: 'You are a helpful assistant',
  model: 'openai/gpt-5.1',
  memory: new Memory({
    embedder: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  }),
})
```

> **Info:** The `embedder` field accepts:
>
> - `EmbeddingModelId` (string with autocomplete)
> - `EmbeddingModel<string>` (AI SDK v1)
> - `EmbeddingModelV2<string>` (AI SDK v2)

## Usage with RAG

Use embedding models for document chunking and retrieval:

```typescript
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'
import { embedMany } from 'ai'

// Embed document chunks
const { embeddings } = await embedMany({
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  values: chunks.map(chunk => chunk.text),
})

// Store embeddings in your vector database
await vectorStore.upsert(
  chunks.map((chunk, i) => ({
    id: chunk.id,
    vector: embeddings[i],
    metadata: chunk.metadata,
  })),
)
```

## TypeScript Support

The model router provides full TypeScript autocomplete for embedding model IDs:

```typescript
import type { EmbeddingModelId } from '@mastra/core'

// Type-safe embedding model selection
const modelId: EmbeddingModelId = 'openai/text-embedding-3-small'
//                                  ^ Autocomplete shows all supported models

const embedder = new ModelRouterEmbeddingModel(modelId)
```

## Error handling

The model router validates provider and model IDs at construction time:

```typescript
try {
  const embedder = new ModelRouterEmbeddingModel('invalid/model')
} catch (error) {
  console.error(error.message)
  // "Unknown provider: invalid. Available providers: openai, google"
}
```

Missing API keys are also caught early:

```typescript
try {
  const embedder = new ModelRouterEmbeddingModel('openai/text-embedding-3-small')
  // Throws if OPENAI_API_KEY is not set
} catch (error) {
  console.error(error.message)
  // "API key not found for provider openai. Set OPENAI_API_KEY environment variable."
}
```

## Next Steps

- [Memory & Semantic Recall](https://mastra.zisheng.pro/en/docs/memory/semantic-recall): Use embeddings for agent memory
- [RAG & Chunking](https://mastra.zisheng.pro/en/guides/rag/chunking-and-embedding): Build retrieval-augmented generation systems
- [Vector Databases](https://mastra.zisheng.pro/en/guides/rag/vector-databases): Store and query embeddings