PrestaShop Semantic Search Powered By AI {User-Guide}

PrestaShop Semantic Search Powered By AI: Provide users with more accurate and relevant search results by integrating Semantic Search Powered By AI into your PrestaShop store.

Semantic search uses Natural Language Processing(NLP) and Artificial Intelligence for search.

Based on the query typed by a user in the search bar, semantic search understand the context of the search to deliver the most relevant results.

Customers can filter the search results based on various product fields including title, price, attributes, features, brands, categories, etc.

Offer a quick, easy, and unique search experience for your customers by allowing them to search the products via image.

• Allows customers to search the products using search queries.
• Customers can search for the products using images.
• Allows users to filter search results based on title, price, attributes, features, brands, tags, and categories.
• Set number of matching products to display in the search results.
• Set vector distance for search accuracy.
• Synchronize all or specific products data to Chroma database as per requirement.

The following minimum system requirements are needed for this extension.

• Python Version – 3.10
• RAM (12 GB)
• Space (20 GB)
• Server key and cert files (for Flask API)
• Docker
• Three ports (5000, 5001, and 8000)
• API Key (If you would like to use Hosted Platforms Gemini, OpenAI, etc. for creating embeddings) – Optional

• Firstly, go to the module manager in the back office and click on ‘upload a module’.
• After that, drag and drop the module file or select the file from the system.
• In this way, the module installation will be successful.

General Settings

ChromaDb endpoint – Admin needs to enter the ChromaDB Endpoint.

Server Endpoint – Enter the Server Endpoint address.

Note:

1. ChromaDB has been used here as a vector database.
2. A model is required to perform the search results on the website. So, we need to install this on your server. Please contact us for installation.

Once saved, the admin can edit configuration.

While updating the general settings, a popup appears on the page in which it is mentioned that on updating the current configuration, the existing embeddings will be lost.

Database Settings

Under Database Settings, the admin can:

• Database name
• Tenant name
• Collection name

Once saved, the admin can edit configuration.

While updating the database settings, a popup appears on the page where you can view that on updating the current configuration, the existing embeddings will be lost.

Product Fields

Under this tab, the admin can configure the fields that he wants consider for product search on the front end.

The admin needs to regenerate embeddings if he does any modifications in the settings.

Search Settings

Under Search tab, the admin can:

• Allow customers to search products through image.
• Set collection name for image.
• Configure the number of matching products that will displaye in the search results.
• Set vector distance for search results. A lesser distance provides highly accurate data and a higher distance yields less accurate data.

Sync Data

Select action for embedding

The admin can choose to synchronize all or specific products data to Chroma database.

To embed the specific product, search the product in ‘Select products’ field.

Click on ‘Start adding embeddings’ button to embed the product.

Once the admin has successfully configured the module settings, the customers can use semantic search by AI on the front end.

Customers can enter a phrase in the search bar or upload an image by clicking on the camera icon to get relevant search results.

Suppose a customer is looking for boots on the store. They may use the search query “mug under $ 300.

The search query includes mug that comes under “Accessories” category and “Price”.

Based on the search query, the products will display as shown in the below screenshot.

Attribute-Based Search

As selected in the back office, the customers can find the products using the attributes in the search queries.

For example, a customer uses the attribute “black” in the search bar.

Now, suppose a customer is looking for black cotton clothes under $ 100.

This search query includes “black” which is an attribute and price.

Relevant search results based on the search query are displayed on store.

Search via Image

A customer can upload the desired image to search for the related products by clicking on the ‘camera’ icon.

Suppose a customer uploads an images for boots.

In the above screenshot, you can see based on image uploaded, relevant products are displayed.

This was all about PrestaShopSemantic Search Powered By AI module. I hope this document will help you in checking the functionality of the module in a better way.

Also, do let us know about your views in the comments.

For any kind of technical assistance or query, please raise a ticket or send us a mail at [email protected]

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