Jina AI Provider

patelvivekdev/jina-ai-provider is a community provider that uses Jina AI to provide text and multimodal embedding support for the AI SDK.

Setup

The Jina provider is available in the jina-ai-provider module. You can install it with

<Tabs items={['pnpm', 'npm', 'yarn', 'bun']}> <Tab> <Snippet text="pnpm add jina-ai-provider" dark /> </Tab> <Tab> <Snippet text="npm install jina-ai-provider" dark /> </Tab> <Tab> <Snippet text="yarn add jina-ai-provider" dark /> </Tab> <Tab> <Snippet text="bun add jina-ai-provider" dark /> </Tab> </Tabs>

Provider Instance

You can import the default provider instance jina from jina-ai-provider:

import { jina } from 'jina-ai-provider';

If you need a customized setup, you can import createJina from jina-ai-provider and create a provider instance with your settings:

import { createJina } from 'jina-ai-provider';

const customJina = createJina({
  // custom settings
});

You can use the following optional settings to customize the Jina provider instance:

• baseURL string

  The base URL of the Jina API. The default prefix is https://api.jina.ai/v1.

• apiKey string

  API key that is being sent using the Authorization header. It defaults to the JINA_API_KEY environment variable.

• headers Record<string,string>

  Custom headers to include in the requests.

• fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

  Custom fetch implementation. Defaults to the global fetch function. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing.

Text Embedding Models

You can create models that call the Jina text embeddings API using the .embeddingModel() factory method.

import { jina } from 'jina-ai-provider';

const .embeddingModel = jina.embeddingModel('jina-embeddings-v3');

You can use Jina embedding models to generate embeddings with the embed or embedMany function:

import { jina } from 'jina-ai-provider';
import { embedMany } from 'ai';

const .embeddingModel = jina.embeddingModel('jina-embeddings-v3');

export const generateEmbeddings = async (
  value: string,
): Promise<Array<{ embedding: number[]; content: string }>> => {
  const chunks = value.split('\n');

  const { embeddings } = await embedMany({
    model: .embeddingModel,
    values: chunks,
    providerOptions: {
      jina: {
        inputType: 'retrieval.passage',
      },
    },
  });

  return embeddings.map((embedding, index) => ({
    content: chunks[index]!,
    embedding,
  }));
};

Multimodal Embedding

You can create models that call the Jina multimodal (text + image) embeddings API using the .multiModalEmbeddingModel() factory method.

import { jina, type MultimodalEmbeddingInput } from 'jina-ai-provider';
import { embedMany } from 'ai';

const multimodalModel = jina.multiModalEmbeddingModel('jina-clip-v2');

export const generateMultimodalEmbeddings = async () => {
  const values: MultimodalEmbeddingInput[] = [
    { text: 'A beautiful sunset over the beach' },
    { image: 'https://i.ibb.co/r5w8hG8/beach2.jpg' },
  ];

  const { embeddings } = await embedMany<MultimodalEmbeddingInput>({
    model: multimodalModel,
    values,
  });

  return embeddings.map((embedding, index) => ({
    content: values[index]!,
    embedding,
  }));
};

Provider Options

Pass Jina embedding options via providerOptions.jina. The following options are supported:

• inputType 'text-matching' | 'retrieval.query' | 'retrieval.passage' | 'separation' | 'classification'

  Intended downstream application to help the model produce better embeddings. Defaults to 'retrieval.passage'.

  • 'retrieval.query': input is a search query.
  • 'retrieval.passage': input is a document/passage.
  • 'text-matching': for semantic textual similarity tasks.
  • 'classification': for classification tasks.
  • 'separation': for clustering tasks.

• outputDimension number

  Number of dimensions for the output embeddings. See model documentation for valid ranges.

  • jina-embeddings-v3: min 32, max 1024.
  • jina-clip-v2: min 64, max 1024.
  • jina-clip-v1: fixed 768.

• embeddingType 'float' | 'binary' | 'ubinary' | 'base64'

  Data type for the returned embeddings.

• normalized boolean

  Whether to L2-normalize embeddings. Defaults to true.

• truncate boolean

  Whether to truncate inputs beyond the model context limit instead of erroring. Defaults to false.

• lateChunking boolean

  Split long inputs into 1024-token chunks automatically. Only for text embedding models.

Model Capabilities

Supported Input Formats

Text Embeddings

• Array of strings, for example: const strings = ['text1', 'text2']

Multimodal Embeddings

• Text objects: const text = [{ text: 'Your text here' }]
• Image objects: const image = [{ image: 'https://example.com/image.jpg' }] or Base64 data URLs
• Mixed arrays: const mixed = [{ text: 'object text' }, { image: 'image-url' }, { image: 'data:image/jpeg;base64,...' }]