Multimodal Embeddings

In this guide, we show you how to use the embed endpoint to embed a series of images. This guide uses a simple dataset of graphs to illustrate how semantic search can be done over images with Cohere. To see an end-to-end example of retrieval, check out this notebook.

Introduction to Multimodal Embeddings

Information is often represented in multiple modalities. A document, for instance, may contain text, images, and graphs, while a product can be described through images, its title, and a written description. This combination of elements often leads to a comprehensive semantic understanding of the subject matter. Traditional embedding models have been limited to a single modality, and even multimodal embedding models often suffer from degradation in text-to-text or text-to-image retrieval tasks. The embed-v3.0 series of models, however, is fully multimodal, enabling it to embed both images and text effectively. We have achieved state-of-the-art performance without compromising text-to-text retrieval capabilities.

How to use Multimodal Embeddings

1. Prepare your Image for Embeddings

The Embed API takes in images with the following file formats: png, jpeg,Webp, and gif. The images must then be formatted as a Data URL.

1
# Import the necessary packages
2
import os
3
import base64
4
5
# Defining the function to convert an image to a base 64 Data URL
6
def image_to_base64_data_url(image_path):
7
  _, file_extension = os.path.splitext(image_path)
8
  file_type=(file_extension[1:])
9
  
10
  with open(image_path, "rb") as f:
11
    enc_img = base64.b64encode(f.read()).decode('utf-8')
12
    enc_img = f"data:image/{file_type};base64,{enc_img}"
13
  return enc_img
14
15
image_path='<YOUR IMAGE PATH>'
16
processed_image=image_to_base64_data_url(image_path)

2. Call the Embed Endpoint

1
# Import the necessary packages
2
import cohere
3
co = cohere.Client(api_key="<YOUR API KEY>")
4
5
co.embed(
6
    model='embed-english-v3.0',
7
    images=[processed_image],
8
    input_type='image'
9
)

Sample Output

Below is a sample of what the output would look like if you passed in a jpeg with original dimensions of 1080x1350 with a standard bit-depth of 24.

1
{
2
    'id': '0d9bb922-f15f-4b8b-9a2f-72577324528f', 
3
    'texts': [], 
4
    'images': [{'width': 1080, 'height': 1350, 'format': 'jpeg', 'bit_depth': 24}], 
5
    'embeddings': {'float': [[-0.035369873, 0.040740967, 0.008262634, -0.008766174, .....]]}, 
6
    'meta': {
7
    'api_version': {'version': '1'},
8
    'billed_units': {'images': 1}
9
    }, 
10
    'response_type': 'embeddings_by_type'
11
}