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

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

const embedder = new ModelRouterEmbeddingModel("openai/text-embedding-3-small");

Google

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

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

npm install @mastra/voyageai

pnpm

pnpm add @mastra/voyageai

Yarn

yarn add @mastra/voyageai

Bun

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

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
});

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

VoyageAI with MongoDB:

VoyageAI works seamlessly with MongoDB Atlas Vector Search:

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):

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.

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

# .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:

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:

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:

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:

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:

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:

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: Use embeddings for agent memory
• RAG & Chunking: Build retrieval-augmented generation systems
• Vector Databases: Store and query embeddings