Guide for Magento 2 AI Image Search

• The admin can set the number of results for the matched product.
• Allows the user to select the Pre-Configured Model or MobileNetV3 for processing and generating image embeddings.
• The admin can set the AI model dimension based on the model’s embedding dimension to define the vector size used for storing image embeddings.
• Admin can set the minimum image score for the lowest confidence score for accepting an image match.
• The admin can set HNSW Index M Value to define how many connections each node will have higher values mean better accuracy.
• The admin can set HNSW Index EF Construction Quality to control the neighbor search list size higher values improve recall.
• Allows the users to search the products using the related images of the product.
• Customers can search using the search bar and advanced search on the front end.
• Allows users to search products using the GraphQL product API.
• It provides the accurate result or the relevant result by matching the image.
• This module supports jpg, jpeg, png, and gif only.

The following minimum system requirements are needed for this extension,

• Python Version – 3.10
• RAM (4 GB)
• Space (8 GB)
• Server key and cert files (for Flask API)
• Docker (Optional)
• One port (8000)

Note: The minimum system requirements may vary based on the data.

To install AI module, we need SSH access. You can also check the AI Models Server Installation Guide for reference.

The installation is quite simple just like the standard Magento 2 extensions.

#Download Module

Firstly, you need to log in to the Webkul Store, go to My Account>My Purchased Products section, verify, and then download and extract the contents of this zip folder on the system.

#Upload Folder

Once the module zip extracts, follow path src>app and then copy the app folder into the Magento 2 root directory on the server as shown below:

# Run Commands

You need to run the following commands:

php bin/magento setup:upgrade

php bin/magento setup:di:compile

php bin/magento setup:static-content:deploy

php bin/magento indexer:reindex

php bin/magento cache:flush

# Additional Commands

You need to run the following commands to create the embeddings:

Create/update existing product image embeddings via terminal.

php bin/magento generate:image:embeddings

Create/update selected product image embeddings via the terminal.

php bin/magento generate:image:embeddings -p 1,2,3

For translating the module language, navigate through the app/code/Webkul/AIImageSearch/i18n and edit the en_US.csv file.

Thereafter, rename the CSV as “en_SA.csv” and translate all right side content after the comma in the Arabic language. After editing the CSV, save it.

Now, upload it to the path app/code/Webkul/AIImageSearch/i18n where the installation of Magento 2 is on the server.

The Magento 2 AI Image Search will be translated into the Arabic Language. It supports both RTL and LTR languages.

The user can edit the CSV like the image below.

After the successful installation of the module, the admin will navigate the Stores->Configuration->AI Image Search Configuration

You can also access the initial configuration by navigating the AI Image Search->General Configuration.

AI Image Search Settings:

1. Enable AI Image Search: Allows the admin to enable/disable the module.

2. Delete Vector Storage Image Collection Index: Remove existing image embeddings from the database.

3. AI Model Type: Select the AI model –

• Pre-configured: Shows Server Endpoint and API Key fields (hides Run Embedding Profiler and Pending Products Embedding Profiler).
• MobileNetV3: Shows Run Embedding Profiler and Pending Products Embedding Profiler buttons.

Note:

• If Pre-configured is selected, the system uses external API details.
• When MobileNetV3 is selected, embeddings are generated through the locally configured MobileNetV3 model.

4. Server Endpoint (visible only when Pre-configured model is selected): Enter your Server Endpoint.

5. API Key (visible only when Pre-configured model is selected): Enter the secure API key to connect with the AI image search service.

6. Run Embedding Profiler (visible only for MobileNetV3): Generate embeddings for all product images.

7. Pending Products Embedding Profiler (visible only for MobileNetV3): Generate embeddings for new or edited product images.

8. AI Model Dimension: Set the feature vector size (e.g., 576).

9. Number Of Results: This setting lets the admin set the number of products(results) displayed on the front end after matching the image.

10. Minimum Image Score: Admin can set the minimum accuracy score required for an image match.

11. HNSW Index M Value (Number of links per node): Admin can set how many links each node will have in the AI image search index.

12. HNSW Index EF Construction Quality: Admin can set how many options are checked while building the index. It controls the quality of the HNSW graph.

Note – If you change the HNSW M Value or EF Construction Quality, you need to delete the Elasticsearch/Opensearch image collection index and re-save the embeddings for the changes to take effect.

Now, click on the save Config to save the configuration.

Run Embedding Profiler:

The MobileNetV3 Embedding Profiler shows the process of generating image embeddings using the MobileNetV3 model.

It loads the model, validates the vector storage connection, and processes all product images.

The progress bar displays real-time status, and users should avoid closing or refreshing the window until the process is complete.

After the successful configuration of the module, on the front end, a camera icon will be shown in the search bar.

The customers will upload the image to search for the related product by clicking the camera icon.

After uploading the image, the customer can also make changes to the image and now click on the save changes as per the below snapshot.

Now, the accurate product or the relevant products related to the uploaded image will be displayed.

Also, the name of the uploaded image will be displayed in the search bar as shown in the below snapshot.

Also, customers can use the advanced search option available in the footer section.

After that, customers can search by filter available or image in the advanced search form.

When customers use the AI image search by clicking on “Browse Image,” they can search directly with an image.

After uploading the image, the customer can click on Save Changes. The product will then appear based on the image search results.

That’s all for the Magento 2 AI Image Search Extension. You may also check our top-quality Magento AI 2 Extensions.

Boost your store’s functionality with Magento 2 AI Content Generator extension for automatic, smart content creation and SEO optimization.

If you still have any issues feel free to add a ticket and let us know your views to make the module better at  webkul.uvdesk.com.