Multimodal Embedding 001

Google multimodal embedding model generating shared vector spaces for text, images, and video search.

Parameters

Multimodal Embed

Context Length

About this model

Multimodal Embedding 001 projects text and images into a shared vector space, enabling cross-modal search (text-to-image search) and visual similarity retrieval.

Capabilities

Text and image embeddingsCross-modal searchVisual similarity

Use Cases

Visual search
Multimodal RAG
Image catalog indexing

Model Details

Provider: Google
Model ID: multimodal-embedding-001
Parameters: Multimodal Embed
Context Length: 2K tokens
Category: embedding

API Usage

Use the DOS API to integrate Multimodal Embedding 001 into your applications. Our API is compatible with OpenAI's client libraries for easy migration.

Model ID

multimodal-embedding-001

Python

python

from dos import DOS

client = DOS()

response = client.chat.completions.create(
    model="multimodal-embedding-001",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

cURL

bash

curl https://api.dos.ai/v1/chat/completions \
  -H "Authorization: Bearer $DOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "multimodal-embedding-001",
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Node.js

javascript

import DOS from 'dos-ai';

const client = new DOS();

const response = await client.chat.completions.create({
  model: "multimodal-embedding-001",
  messages: [
    { role: "user", content: "Hello, how are you?" }
  ]
});

console.log(response.choices[0].message.content);

View full API reference

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