How to Generate Image Embeddings Using Python

In the era of visual data, extracting meaningful information from images has become critical for tasks like image search, recommendation systems, clustering, and computer vision. One powerful approach to this is image embeddings, transforming images into numerical vectors that capture their semantic content.

In this article, we'll explore how to generate image embeddings using the Eden AI API, a unified API platform that simplifies access to multiple AI providers. We’ll walk through each step to implement this in Python, from getting your API key to interpreting the output of a real request.

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What Are Image Embeddings

Image embeddings convert images into dense vector representations. These vectors preserve semantic similarity — meaning that visually or contextually similar images will have similar embeddings. This makes them invaluable in:

• Image similarity search
• Clustering images
• Training AI models for image classification
• Content-based recommendation engines

Instead of dealing with raw pixel data, embeddings allow algorithms to work with more meaningful features.

Eden AI provides access to multiple image embedding models (from providers like Google, AWS, etc.) through a single, unified API, saving you the hassle of integrating with multiple services individually.

You can test, switch, and compare providers without rewriting your code.

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How to Generate Image Embeddings

Get Access to Eden AI

1. Sign Up for an Eden AI Account: To begin using the Image Embeddings API, create an account on Eden AI. After registration, you’ll receive an API key that unlocks access to many AI services.

2. Access Image Technologies: After logging in, navigate to the image section of the platform.

3. Select Image Embeddings: Choose the Image Embeddings feature.

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Test Models Live on Eden AI

Before integrating in code, you can test embedding models directly on the Eden AI platform to compare providers and understand outputs.

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Implementing Image Embeddings in Python

Install the requests Module

We’ll use Python’s requests module to make HTTP calls. Install it using pip if it’s not already installed:

pip install requests

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Prepare the Code

Here's a complete example of how to generate image embeddings using Eden AI:

import requests

response = requests.post(
    "https://api.edenai.run/v2/image/embeddings/",
    json={
        "providers": ["google"],
        "representation": "document",
        "file_url": "https://example.com/image.jpg"  # Replace with your image URL
    },
    headers={
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer <your_api_key>"  # Replace with your Eden AI token
    }
)

print(response.json())

‍Explanation of Code Sections

• Endpoint: The URL https://api.edenai.run/v2/image/embeddings/ targets the image embedding API.
• Providers: Choose the embedding model provider. Eden AI allows you to test multiple (e.g., "google", "amazon", etc.).
• Representation: "document" refers to how the image is encoded. This may vary based on provider.
  
• file_url: The image you want to generate embeddings for. (Alternatively, you can upload base64 images.)
• Headers: Include your API key for authentication.

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Interpreting the Output

Here’s an example output (simplified):

{
  "google": {
    "embeddings": [0.021, -0.456, ..., 0.879],
    "status": "success",
    "provider": "google"
  }
}

Explanation:

• embeddings: A numerical array representing the image.
• status: Indicates whether the request was successful.
• provider: The API provider that generated the embeddings.

You can store, analyze, or compare these vectors for downstream tasks.

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Why Eden AI is the Best Tool for Image Embeddings

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Eden AI offers several advantages for Image Embeddings.

Access to multiple providers‍

With Eden AI, you can choose from a variety of providers, giving you great flexibility.

Ease of use‍

Eden AI’s API is designed to be simple and intuitive, making it easy for developers to integrate many AI services into their applications with minimal effort.

Scalability‍

Whether you’re working on small projects or large-scale applications, Eden AI is built to scale with your needs, making it suitable for a wide range of use cases.

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Conclusion

We’ve walked through the process of generating image embeddings using Eden AI, from obtaining your API key to interpreting the response.

Eden AI simplifies working with multiple providers and helps you integrate powerful AI features in just a few lines of Python code.

Whether you're building an AI-powered search engine or clustering millions of images, image embeddings are foundational, and with Eden AI, you can implement them quickly and flexibly.