Embeddings

embed() turns text into a vector. Search, RAG, and clustering then compare those vectors.

Multimodal models also embed images.

Quick setup

Install the core package and an adapter:

shell

npm i @tanstack/ai @tanstack/ai-openai

shell

pnpm add @tanstack/ai @tanstack/ai-openai

shell

yarn add @tanstack/ai @tanstack/ai-openai

shell

bun add @tanstack/ai @tanstack/ai-openai

Put your key on the server. The adapter reads it at runtime:

shell

OPENAI_API_KEY=your-openai-api-key

Call embed() on the server:

import { embed } from "@tanstack/ai";
import { openaiEmbedding } from "@tanstack/ai-openai";

const result = await embed({
  adapter: openaiEmbedding("text-embedding-3-small"),
  input: ["a red guitar", "a blue drum kit", "a vintage synthesizer"],
});

for (const embedding of result.embeddings) {
  console.log(embedding.index, embedding.vector.length);
}

You now have one number[] vector per input string. Store them in your vector database.

How it works

input is one item or an array of items.
Each item gives exactly one vector.
result.embeddings keeps the input order. Each entry has an index and a vector.
Every provider uses the same embed() call. To change provider, change the adapter.

Providers

Provider	Adapter	Models	Input
OpenAI	openaiEmbedding (@tanstack/ai-openai)	text-embedding-3-small, text-embedding-3-large	text
Cohere	cohereEmbedding (@tanstack/ai-cohere)	embed-v4.0	text + image
Google Gemini	geminiEmbedding (@tanstack/ai-gemini)	gemini-embedding-001	text
Mistral	mistralEmbedding (@tanstack/ai-mistral)	mistral-embed, codestral-embed	text
Amazon Bedrock	bedrockEmbedding (@tanstack/ai-bedrock)	Titan Text Embeddings V2 (text), Titan Multimodal Embeddings G1 (text + image), Cohere Embed v3 (text)	text, or text + image
Ollama	ollamaEmbedding (@tanstack/ai-ollama)	nomic-embed-text, mxbai-embed-large, and any local embedding model	text
Vercel AI Gateway	vercelGatewayEmbedding (@tanstack/ai-vercel-gateway)	OpenAI-compatible models such as openai/text-embedding-3-small	text
Neon AI Gateway	neonEmbedding (@tanstack/ai-neon)	qwen3-embedding-0-6b, gte-large-en, bge-large-en (1024 dimensions)	text

Vercel AI Gateway takes a creator/model id:

import { embed } from "@tanstack/ai";
import { vercelGatewayEmbedding } from "@tanstack/ai-vercel-gateway";

const result = await embed({
  adapter: vercelGatewayEmbedding("openai/text-embedding-3-small"),
  input: "a red guitar",
});

Neon AI Gateway uses the same call. The vectors fit a vector(1024) pgvector column in your Neon branch:

import { embed } from "@tanstack/ai";
import { neonEmbedding } from "@tanstack/ai-neon";

const result = await embed({
  adapter: neonEmbedding("qwen3-embedding-0-6b"),
  input: "a red guitar",
});

Pick the vector size

Some models (Matryoshka models) let you choose the number of dimensions. Pass dimensions:

import { embed } from "@tanstack/ai";
import { openaiEmbedding } from "@tanstack/ai-openai";

const result = await embed({
  adapter: openaiEmbedding("text-embedding-3-large"),
  input: "a red guitar",
  dimensions: 1024,
});

Fixed-size models (for example mistral-embed or Ollama models) throw a runtime error when you set dimensions.

Embed documents and queries differently

Retrieval models give better results when they know if the text is a document or a query.

Cohere requires an inputType. The types make modelOptions required for this model:

import { embed } from "@tanstack/ai";
import { cohereEmbedding } from "@tanstack/ai-cohere";

// Index time: embed the documents
const docs = await embed({
  adapter: cohereEmbedding("embed-v4.0"),
  input: ["doc one", "doc two"],
  modelOptions: { inputType: "search_document" },
});

// Query time: embed the query
const query = await embed({
  adapter: cohereEmbedding("embed-v4.0"),
  input: "which doc mentions one?",
  modelOptions: { inputType: "search_query" },
});

Gemini has an optional taskType for the same purpose:

import { embed } from "@tanstack/ai";
import { geminiEmbedding } from "@tanstack/ai-gemini";

const result = await embed({
  adapter: geminiEmbedding("gemini-embedding-001"),
  input: "a red guitar",
  modelOptions: { taskType: "RETRIEVAL_DOCUMENT" },
});

Embed images

Multimodal models embed images, or an image and text together as one vector. Image items use the same content-part shape as chat messages.

The types narrow the accepted items per model. An image sent to a text-only model is a type error.

Each item in the top-level input array gives one vector:

A string or a text part embeds that text.
An image part embeds that image.
A nested array of parts ([textPart, imagePart]) fuses the parts into one vector.

So [textPart, imagePart] gives two vectors, and [[textPart, imagePart]] gives one fused vector.

import { embed } from "@tanstack/ai";
import { cohereEmbedding } from "@tanstack/ai-cohere";

const productPhoto = "iVBORw0KGgo..."; // base64 image data

const result = await embed({
  adapter: cohereEmbedding("embed-v4.0"),
  input: [
    "a red guitar",
    {
      type: "image",
      source: {
        type: "data",
        value: productPhoto,
        mimeType: "image/png",
      },
    },
    // A nested array fuses its parts into one vector.
    [
      { type: "text", content: "Fender Stratocaster, sunburst finish" },
      {
        type: "image",
        source: {
          type: "data",
          value: productPhoto,
          mimeType: "image/png",
        },
      },
    ],
  ],
  modelOptions: { inputType: "search_document" },
});

console.log(result.embeddings.length); // 3, one vector per input item

Amazon Titan Multimodal works the same way. Nest the parts to fuse them:

import { embed } from "@tanstack/ai";
import { bedrockEmbedding } from "@tanstack/ai-bedrock";

const productPhoto = "iVBORw0KGgo..."; // base64 image data

const result = await embed({
  adapter: bedrockEmbedding("amazon.titan-embed-image-v1"),
  input: [
    [
      { type: "text", content: "a red guitar" },
      {
        type: "image",
        source: {
          type: "data",
          value: productPhoto,
          mimeType: "image/png",
        },
      },
    ],
  ],
  dimensions: 1024,
});

Pass images as base64 data or as a data: URI. Adapters do not download remote image URLs by default.

Need to rank the results after a vector search? See Reranking.

Advanced

Remote image URLs

The Cohere adapter has an allowUrlFetch config option. When you turn it on, the adapter downloads http(s) image URLs for you.

Token usage and middleware

Adapters report token usage when the provider does. embed() accepts the same observe-only generation middleware as the media activities. See Generation Hooks.

import { embed } from "@tanstack/ai";
import { openaiEmbedding } from "@tanstack/ai-openai";

const result = await embed({
  adapter: openaiEmbedding("text-embedding-3-small"),
  input: ["a red guitar", "a blue drum kit"],
  middleware: [
    {
      name: "usage-logger",
      onUsage: (ctx, usage) => {
        console.log(`${ctx.model}: ${usage.promptTokens} tokens`);
      },
    },
  ],
});

console.log(result.usage?.promptTokens);

Errors

embed() rejects with the provider error. Middleware onError hooks run before the rejection reaches your code.

import { embed } from "@tanstack/ai";
import { openaiEmbedding } from "@tanstack/ai-openai";

try {
  await embed({
    adapter: openaiEmbedding("text-embedding-3-small"),
    input: "a red guitar",
  });
} catch (error) {
  console.error("embedding failed", error);
}