Image Embedding API

• API Endpoint Access URL
• Description
• HTTP Methods
• HTTP Parameters
• HTTP Response
• Code Samples
• Similar API Endpoints

API Endpoint Access URL

https://api.pixlab.io/imgembed

Description

The Image Embedding API (img-embed) converts an image into a dense vector representation that you can compare, index, and search. It gives developers a practical way to build visual similarity search, product matching, duplicate detection, recommendation systems, and image understanding workflows over standard HTTP requests.

These embeddings are useful when you need fast visual retrieval across ecommerce catalogs, media libraries, marketplaces, moderation datasets, or internal asset systems. Because the output captures high-level visual features instead of raw pixels, it works well for near-duplicate matching, related image discovery, and downstream classification or ranking pipelines.

You can submit images in either of these ways:

• A direct file upload with multipart/form-data
• A public image URL passed to img

The API returns a JSON response containing the generated image embedding vector as a fixed-length float32[] array, ready for indexing, retrieval, and analysis.

All embeddings are compatible with ANN libraries such as FAISS, HNSWlib, and scikit-learn, making it straightforward to plug this endpoint into production search, recommendation, or catalog analysis stacks.

Use Cases

• Visual search - Find related photos, product shots, or design assets in large image libraries
• Duplicate detection - Identify near-identical images across catalogs, uploads, or archives
• Visual clustering - Group images by style, theme, or subject for tagging and organization
• Moderation workflows - Compare uploads against flagged reference sets or policy datasets
• Reverse image lookup - Match an input image against an indexed gallery using ANN search

Integration & Developer Notes

The img-embed endpoint can be integrated from any stack over HTTP. It fits cleanly into retrieval systems, vector databases, recommendation services, and ML preprocessing pipelines.

You can start by getting an API key from the PixLab Console ↗. For working integration examples in Python, JavaScript, and PHP, refer to the code samples below ↓.

Technical Summary

The img-embed API endpoint is part of PixLab's Vision Platform and is built for teams that need reliable image vectorization without running their own embedding infrastructure. Use it to power catalog search, asset organization, recommendation features, or downstream computer vision pipelines through a simple REST interface.

HTTP Methods

GET, POST

HTTP Parameters

HTTP Response

application/json

This endpoint returns a lightweight JSON response that includes the generated embedding vector, request token usage, and any error details when a call fails.

Image Embedding Response Format

{
  "status": 200,
  "embedding": [2.8765, 1.9870, 4.9076],
  "total_tokens": 1024
}

Code Samples

import requests
import json

# Generate image embedding vector for a given image using the PixLab image embedding API
# Refer to: https://pixlab.io/endpoints/img-embed for the official documentation.
#
# Convert images into numerical vectors for efficient image classification, similarity search, and so on.

# Target Image URL we want to generate embedding for
# Change to any link or switch to POST if you want to upload your image directly.
imgUrl = 'https://pixlab.io/assets/images/nature31.jpg' 

key = 'PIXLAB_API_KEY' # Get your API key from https://console.pixlab.io/

# Make the API call; Switch to POST if you want to upload your image directly.
req = requests.get('https://api.pixlab.io/imgembed',params={
  'img': imgUrl,
  'key': key,
  'dimension': 1024, # Output vector dimension
})
reply = req.json()
if reply['status'] != 200:
	print (reply['error'])
else:
  embedding = reply['embedding']
  print(f"Image embedding vector: {embedding}")

// Generate image embedding vector for a given image using the PixLab image embedding API
// Refer to: https://pixlab.io/endpoints/img-embed for the official documentation.
//
// Convert images into numerical vectors for efficient image classification, similarity search, and so on.

// Target Image we want to generate embedding for
// Change to any link or switch to POST if you want to upload your image directly.
const imgUrl = 'https://pixlab.io/assets/images/nature31.jpg';

const key = 'PIXLAB_API_KEY'; // Get your API key from https://console.pixlab.io/

// Make the API call; Switch to POST if you want to upload your image directly.
fetch('https://api.pixlab.io/imgembed', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    img: imgUrl,
    key: key,
    dimension: 1024 // Output vector dimension
  })
})
.then(response => response.json())
.then(reply => {
  if (reply.status !== 200) {
    console.log(reply.error);
  } else {
    const embedding = reply.embedding;
    console.log(`Image embedding vector: ${embedding}`);
  }
})
.catch(error => {
  console.error('Error:', error);
});

<?php
# Generate image embedding vector for a given image using the PixLab image embedding API
# Refer to: https://pixlab.io/endpoints/img-embed for the official documentation.
#
# Convert images into numerical vectors for efficient image classification, similarity search, and so on.

# Target Image we want to generate embedding for
# Change to any link or switch to POST if you want to upload your image directly.
$img = 'https://pixlab.io/assets/images/nature31.jpg';

$key = 'PIXLAB_API_KEY'; // Get your API key from https://console.pixlab.io/

# Make the API call; Switch to POST if you want to upload your image directly.
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://api.pixlab.io/imgembed?img=' . urlencode($img) . '&key=' . $key . '&dimension=1024');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
$reply = json_decode(curl_exec($ch), true);
curl_close($ch);

if ($reply['status'] != 200) {
    echo $reply['error'];
} else {
    $embedding = $reply['embedding'];
    echo "Image embedding vector: " . json_encode($embedding);
}