AI guides for WordPress & WooCommerce

Leveraging Artificial Intelligence to enhance SEO strategies

Artificial Intelligence has experienced exponential growth across various fields associated with website performance, whether aimed at enhancing user experience or accelerating website search capabilities. Moreover, AI proves highly beneficial in enhancing website SEO to achieve superior search engine rankings. Enhanced SEO holds paramount importance for ecommerce websites as it significantly influences sales. Therefore, leveraging artificial intelligence isn’t merely beneficial; it’s imperative for maintaining competitiveness. This article delves into how AI is reshaping the future of SEO on WordPress and how site owners can harness these technologies to gain a competitive advantage. Artificial Intelligence tools and techniques for WordPress SEO Numerous AI tools exist to enhance website SEO, each focusing on different aspects of optimization. This article will explore a couple of these tools.

Image search for WordPress & Woocommerce

Image search is becoming more and more common with the advent of AI. It is very useful for many purposes but one of the most important is e-commerce. Using image search, users can upload images to find products that closely resemble what they are looking for, yielding far better conversion rates than text-based search ever would. So what if you wanted to add image search to Woocommerce, the world’s leading solution when it comes to e-commerce platforms. Learn all there is to know about WordPress search by reading our guide. How does image search work ? You can’t add image search to your WordPress or Woocommerce using the default search. The default WordPress search uses basic SQL to search for matching items

WordPress search : Ultimate Guide for 2024

WordPress is the most widely used CMS and single handedly powers 40% of the web. If you haven’t tried it already, it is intuitive, powerful and if you use one of many hosting providers out there, you can easily set up a working website in minutes. But once you made all of your content, how do you ensure that your users can find it. For that, you need a good site search. Many types of search exist : keyword search, AI search, even personalized search. WordPress provides one by default, but it is incredibly slow and ineffective. This guide will go over the benefits of a fast and relevant search and how you could implement third party search engines easily into any WordPress or Woocommerce

Algolia recommendations guide

Algolia is one of the leading providers of search and recommendation solutions, with a significant market share in the industry. So considering using Algolia to power your website’s search & recommendations is a no brainer. Why use AI recommendations ? AI recommendation models are trained on user interactions or clicks to recommend the most relevant products or items for each user. You can usually find them in e-commerce websites, at the bottom of products detailed view or in the homepage. But they can also be found on streaming websites (movie and music) or services, news and many other industries. Algolia AI recommendations in Gymshark Recommendations are useful since they can increase user engagement and drive conversions. Since users are continously

Integrate Woocommerce with Algolia

Algolia is the most popular cloud search & recommendations provider. You could want to integrate it into your Woocommerce or WordPress website. Why use Algolia ? Search Algolia provides search of all kinds : simple keyword search, AI/vector search & personalized search. Keyword search is available in the free tier. Keyword search works by matching the keywords in a search query to the ones present in the indexed items/documents. It can be simple and limited without any configuration but Algolia offers multiple features to fine-tune it. For keyword search, you could define synonyms which is useful for expert websites. For example, if you have a medical website, you could need to set “acute myocardial infarction” as a synonym for heart attack.

How to add a recommendation engine to your Woocommerce ?

One of the best technologies to boost sales for e-commerce today are recommendation systems. But how could you add a recommendation engine to your woocommerce website? You’re at the right place. First we will define what recommendation engines are, then we can establish strategies and solutions to add recommendation engines to Woocommerce. What is a recommendation engine? A recommendation engine, also called a recommender system, will suggest (or recommend) items that it predicts users will be inclined to view or buy. These predictions are based on patterns of users’ past behaviors and preferences. This technology is based on personalization and is closely related to personalized search. What are the benefits of a recommendation engine for Woocommerce? Recommendations greatly boosts sales

Recombee vs Algolia pricing

Algolia and Recombee both offer similar functionalities : search and recommendations. Which solution you should go for would depend on your use case : Overall Overall, Recombee offers a better price to performance ratio compared to Algolia. It’s free tier includes personalized search and recommendations with almost all features available (models optimized for general purposes, e-commerce, streaming, ads, etc…) and very generous usage limits (100 000 recommendation requests per month, etc…) which is more than enough for most (smaller) websites. Algolia also works very well (you can check out case studies for both Algolia and Recombee) but is a more costly solution, good for bigger websites. If you want to learn more about Recombee, you should check out our detailed review.

Recombee - Visually similar recommendations

Recommend visually similar products using Recombee

Recombee is an AI recommender that offers many models for personalized search and recommendations. Some of them are “similar”, “popular” or “personal” (based on the users preferences). But one that is severly underrated is the “visually similar” model which uses AI to recommend similar items not based on their content but on the appearance of their featured image. Our Recombee visually similar recommendations demo (Woocommerce) If you are interested in viewing how Recombee visually similar recommendations perform, you could check our Recombee & Woocommerce demo (view all our demos). You can find them in a product detailed view. Why use visually similar recommendations ? When browsing for products, the first thing we notice are the thumbnails. Recommending items with similar

Recombee recommendations for WordPress

Recombee is a personalization & recommendations engine that is proven to improve conversion rates greatly. But what if you wanted to implement it into your WordPress website ? Well you’re at the right place. This will explain why you wold want to add Recombee to your WordPress website but also how you could go it. Why integrate Recombee into WordPress? Integrating Recombee into WordPress offers countless benefits : Firstly, it enhances user experience by delivering personalized recommendations tailored to individual preferences, thereby increasing user engagement and satisfaction. Recombee is also a very versatile recommendation engine. It can be used for e-commerce, movie or video streaming and blog or news websites. This is because it offers countless user events (interactions) and

Build a recommendation system using Recombee and PHP

Recombee is a recommender that offers personalized search & recommendations for e-commerce, media, and other industries. It uses machine learning algorithms to analyze user behavior, preferences, and interactions in order to provide relevant and accurate suggestions. With Recombee, businesses can improve their customer experience by offering personalized product recommendations based on each individual’s browsing history, purchase history, and interests. This helps increase customer engagement and satisfaction, ultimately leading to higher sales and revenue. If you wish to learn more about Recombee, you can check out our full review. In this guide, we will learn how to build a recommendation system using Recombee and PHP. Getting started with Recombee and initializing the project Create a Recombee account at the following : https://oauth.recombee.com/

Integrate Recombee into Woocommerce website

Integrate Woocommerce with Recombee

Woocommerce is the #1 solution for e-commerce globally. This WordPress plugin provides a powerful and customizable platform for online businesses to sell their products and services. With its user-friendly interface and easy integration with various payment gateways, Woocommerce has become the go-to choice for many entrepreneurs. One of the key features of Woocommerce is its flexibility. It offers a wide range of extensions and themes that allow you to customize your store according to your specific needs. Whether you want to add new functionalities or enhance the design of your website, there is an extension or theme available for almost any requirement. One of these features you could consider adding is recommendations. Why equip Woocommerce with personalized search & recommendations? Woocommerce already offers users

ReQL – How to create business rules for Recombee (with examples)

Recombee is a personalized search & recommendations provider. One of it’s major advantages is that Recombee gives the user the option of modifying the queries from the backend using scenarios and business rules. For example, if you have a Woocommerce website with WPSolr & Recombee, you can hide certain specific items without needing to access the WordPress website, only the Recombee dashboard. You can then construct these business rules using ReQL, a Recombee exclusive Query Language that allows total freedom. You can learn more about it from the official Recombee documentation. How to create business rules Create business rule Click on the ‘Business Rules’ tab in the Recombee dashboard. Click on the ‘+ Create Rule’ button. Set the title in the

AI Recommendations for movie streaming services

Movie streaming platforms utilize advanced recommendation algorithms to enhance content discovery for viewers, making the viewing experience personalized and enjoyable. By providing tailored suggestions based on individual preferences, these platforms boost user retention rates significantly. Personalized recommendations reduce search time, increase user satisfaction, and improve the overall browsing experience, increasing the likelihood of continued subscription. Improve content discovery Movie streaming platforms offer a vast array of content for viewers to choose from, catering to diverse preferences and interests. By utilizing personalization, these platforms enhance content discovery significantly, making the viewing experience more personalized and enjoyable. As a result, the platform’s catalogue appears more extensive and appealing to users, showcasing a wide variety of entertainment options to explore. Acquiring the rights to

Recombee Review : The most versatile AI recommender

Recombee is an AI-powered recommender that offers both recommendations & personalized search. Whether it’s recommending products, articles, or movies, Recombee leverages advanced algorithms and machine learning to deliver personalized recommendations & search that keep users engaged and satisfied. With its easy integration and customizable features, Recombee empowers businesses to optimize content discovery, increase user retention, and drive conversions effortlessly. Highly-performing recommendations Recombee prides itself in increasing conversion rates and in turn revenue tenfold. You can verify this by reading their case studies. Using the case studies, you can observe that Recombee improves website performance in most situations whether it is general purposes, e-commerce, streaming, news, etc… Here are the main types of recommendations : 1. Item recommendations

Top 5 Recommendation Plugins for Woocommerce & WordPress

Recommendations are an emerging technology today, often used in e-commerce or video streaming. If you wish to equip your WordPress or Woocommerce website with recommendations, there are quite a few plugins available. However, not all of them are created equal. Some may be limited in functionality, while others may lack compatibility with certain themes or other plugins. Here are the top 5 : 1. WPSolr WPSolr is a complete search and recommendations WordPress plugin that integrates countless search engines & recommenders into your WordPress website. Apache Solr, Elasticsearch, Opensearch, Weaviate, Algolia, Recombee, Google Retail are all compatible (check out our engine features comparison). It is versatile and can be used for multiple purposes : blog website, e-commerce website, news website,

Overview of different filtering algorithms (for personalization)

In the past, websites aimed to boost click-through and conversion rates by improving search features. Now, a new technology called recommender systems has emerged. These systems work subtly in the background, requiring little user interaction. They personalize recommendations, closely linking with personalized search to tailor the user experience. But how do these recommendation systems work ? They use algorithms to filter the results that will be returned to the user. These algorithms are : content-based filtering, collaborative filtering, hybrid filtering and knowledge-based filtering. Diagram explaining how collaborative filtering works Collaborative filtering We will start of with the most popular type of filtering algorithm today : collaborative filtering. Collaborative filtering makes use of machine learning to quickly filter (recommend) the most relevant items

Photo of a person looking through a magnifyer.

What is Retrieval-Augmented Recommendation (RAR)?

I bet that the name Retrieval-augmented recommendation reminds you of something. That’s right! It’s a similar process to RAG (retrieval-augmented generation) but with a twist. Instead of using a search engine to retrieve the items and then feeding them to a generative AI, you retrieve the items using a search engine and then re-rank them using a recommender (personalization). This way you get the best of both worlds : any search engine of your choice but with personalization. This is powerful since you can basically build your own personalized search. Why? At WPSolr (WordPress search and recommendations plugin), we came up with this new method of search because one of our clients needed a personalized vector (AI) search for their e-commerce website

What is personalization ?

Personalization has become impossible to ignore in today’s digital landscape, permeating various aspects of our online experiences. As consumers increasingly expect personalized experiences, businesses across industries are investing in personalization technology to enhance user engagement, drive conversions, and foster customer loyalty in today’s highly competitive digital market. What is personalization? Personalization can be used whenever content is delivered to the user. It works by tracking user events that trigger whenever the user interacts with elements of interest in the website (page views, added to cart, added to favourites, etc…). This data is then used to train a model that comprehensively learns about users, enabling it to provide personalized recommendations tailored to each individual’s interests. What are some uses of personalization

What is personalized search?

Search has evolved over the years, first starting with traditional keyword search, then moving on to Vector (AI) databases. But nowadays, a new type of search is being popularized : personalized search. As it’s name implies, this technique consists of personalizing the search (keyword or vector) using stored events (or interactions) for each user. It is closely related to the other popular personalization system called recommendations. What is personalization ? Personalization consists of customizing the search results based on the user’s preferences. This is done by collecting user events, that are triggered whenever the user interacts with or does an action on the website, and using this to tailor the search results to the user’s preferences and needs. The personalization model

AI vs non-AI recommenders

Recommenders are intelligent systems designed to analyze user preferences, behaviors, and interactions to provide personalized recommendations. These systems utilize various algorithms and techniques to suggest relevant content, products, or services to users, aiming to enhance their browsing or shopping experience. By leveraging data such as purchase history, browsing patterns, and demographic information, recommenders offer tailored suggestions that align with individual user preferences, increasing engagement, satisfaction, and ultimately driving conversions. But there are two different types of recommenders : AI vs non-AI recommender. Non-AI recommenders This type of recommendation suggests content based on explicit user or programmatically entered data and metadata. It is fairly limited since it can only make use of two filtering algorithms : content-based filtering and knowledge based filtering. This

What is RAG and how does it work

Nowadays, text generation is making a big wave thanks to LLMs (Large Language Models). Trained on large amounts of publicly (or sometimes privately) accessible data, these models can complete various language related tasks such as conversation (chatbot), question answering and even advising. They impact many sectors like writing, coding and marketing. But what about search? Well you’re at the right place because that is what RAG (Retrieval Augmented Generation) is about. What does RAG do ? RAG, as it’s name implies, combines both search and AI-based text generation. It has become a very trendy topic recently since it is capable of delivering the same capabilities as LLMs while remaining a more reliable source of information. This is because, when integrated into a RAG

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7 Best User Events to Track for Training AI Recommenders

AI recommenders (and personalization in general) are used to suggest pertinent content to your website’s visitors. They work by being trained using data that is passively being sent by visitors (called user events) so the AI can learn about their behaviours and needs. Some of the most popular recommenders are Algolia, Recombee and Google Retail. They are a great way of increasing CTR. Here are some of the 7 best events to train your AI recommenders : Detailed view of an item (clicked) This user event is sent whenever a user views any page or product on your website. They are valuable for recommenders since they can give insights into what the user might be interested by and is one of

Picture of a dashboard on laptop with "Recommendations for e-commerce" written under.

How Product Recommendation Systems Boost your E-commerce Sales

AI Recommendation systems play a crucial role in enhancing the shopping experience for e-commerce customers. E-commerce platforms (example: Woocommerce) can provide personalized product recommendations tailored to each individual shopper’s preferences, browsing history, and purchasing behavior. They are a major driver for achieving higher conversion rates and selling more products. Here are the reasons why it could help your bottom-line : Your own dedicated salesman in a digital landscape Trust your recommender to recommend the most relevant content to potential customers. Like an ordinary salesman would, the recommender system will try to learn more about visitors. Instead of blindly suggesting random items, the recommender will collect user events/interactions (page views, add to cart, etc…) and use this data to recommend the

Image of CTR skyrocketing using AI recommendation systems

What are AI-powered Recommendation systems : A 2024 complete beginner’s guide

In the past, you used to increase your website’s click-through/conversion rate by adding a high performance and relevant search experience. But recently, a new type of technology has emerged : recommender systems (or recommenders). Recommendation systems often operate more subtly, with users interacting minimally with the recommendations and not fully registering their presence. They rely on personalization and are closely related to personalized search. Some of the most prominent recommenders on the market are Recombee, Amazon Personalize, Algolia and Google retail. Why choose a recommendation system? Example of the Amazon homepage full of recommendations to guide the user Recommendation systems offer an effortless browsing experience Forcing your users to search for the products or articles they want adds unnecessary steps to a

How to use Weaviate with any Huggingface vectorization model

For more info about Weaviate, check out our documentation. If you’ve ever wanted to use Weaviate but were worried that you couldn’t use the most efficient or relevant vectorization model you want, I have just the thing for you. In this notebook/guide, I have detailed the different steps and code needed to setup Weaviate with any Huggingface vectorization model. TLDR Choose from a wide selection of Huggingface models using the official rankings page. Create your own transformers inference container to be used by Weaviate to vectorize the data. Learn how to add your chosen Huggingface model.. Startup the containers and create the class that will use your new vectorizer model. Send the data to the Weaviate that will now be automatically vectorized by the custom model.

Podcast with Connor Shorten (Weaviate) Patrice Bourgougnon (WPSOLR)

A very friendly talk about Weaviate, #WordPress, WooCommerce, and … the meaning of life (almost)… weaviate.com wpsolr.com

A new WooCommerce demo with Weaviate and sentence transformers

— The embedding model — The demo uses the MiniLM-L6-v2 embeddings model https://lnkd.in/eCwAzH_h, installed on a self-hosted Weaviate Kubernetes cluster. This model is considered to have the best performance vs quality for all sentence transformer models. — Bigger models are better — Notice that much bigger models (GPU(s) required?) are now trusting the top of the MTEB leaderboard for the retrieval task https://lnkd.in/efmNJyTP — Indexing time — Also notice that indexing takes quite some time (around 1 per second) on a (single :)) CPU. — Quality — Quality looks inferior to the same demos with PaLM2, OpenAI or Cohere embeddings. For instance, check out the position of a mattress for keywords “something to sleep on”: – MiniLM-L6-v2 (not on first page !): https://lnkd.in/eUQnVBXV – OpenAI (1st position): https://lnkd.in/eVdYpC-P – PaLM2 (1st position): https://lnkd.in/e4FFVcUj – Cohere (2nd position): https://lnkd.in/eb3yCw-C –

Algolia’s impact on user engagement metrics

Introduction Algolia is a powerful search-as-a-service platform that has revolutionized the way websites and applications implement search functionality. Its impressive search capabilities combined with its developer-friendly API have made it a favorite among developers and businesses alike, and it has had a significant impact on user engagement metrics. User engagement metrics, such as average time on site, bounce rate, and conversion rate, are critical indicators of a website or application’s success. Algolia’s fast and accurate search results greatly enhance the user experience, leading to increased user engagement and ultimately improved metrics. Algolia’s Impact on User Engagement Metrics Algolia’s key features, such as instant search results, typo tolerance, and relevance ranking, contribute to higher user engagement on websites and applications. Let’s take a closer look at

Elasticsearch and the world of machine learning

Introduction Elasticsearch is a powerful, open-source search engine built on top of the Apache Lucene library. It is commonly used to store, search, and analyze large volumes of data. The world of machine learning, on the other hand, is concerned with creating algorithms that can learn from and make predictions or take actions based on data. Combining Elasticsearch and machine learning can be a game-changer in terms of the insights and actions that can be derived from data. In this post, we will explore some ways in which Elasticsearch can be leveraged for machine learning tasks and how the two technologies can complement each other. Using Elasticsearch for Machine Learning Elasticsearch provides several features and functionalities that can be beneficial for various machine learning tasks.

How Weaviate can be used to enhance personalized recommendations

Introduction Personalized recommendations have become an integral part of the digital experience, whether it is for e-commerce platforms, music streaming services, or social media platforms. These recommendations help users discover new products, services, or content that aligns with their interests and preferences. Weaviate, an open-source vector search engine, offers a powerful solution to enhance personalized recommendations by leveraging its semantic search capabilities. In this post, we will explore how Weaviate can be used and integrated to provide personalized recommendations. Additionally, we will discuss how WPSOLR can complement Weaviate and further improve the recommendation engine. Enhancing Recommendations with Weaviate Weaviate is a vector search engine powered by machine learning, which enables semantic search and knowledge graph integration. By indexing and organizing data into vectors, Weaviate

Using Weaviate to improve your WooCommerce store’s user experience

Introduction In today’s competitive e-commerce landscape, having a seamless and personalized user experience can make all the difference for your WooCommerce store. By integrating intelligent search capabilities into your store, you can improve customer satisfaction, increase conversions, and drive revenue. One powerful tool that can help you achieve this is Weaviate, an open-source knowledge graph system. In this post, we will explore how you can leverage Weaviate to enhance your WooCommerce store’s user experience. Weaviate and the PHP Client Weaviate allows you to build and utilize a semantic knowledge graph, which organizes data based on its meaning, relationships, and context. By leveraging the power of natural language processing, Weaviate enables advanced search capabilities, recommendation systems, and content tagging. To integrate Weaviate into your WooCommerce store,

Weaviate vs other search engines: a comparison

Introduction When it comes to search engines, we all know the giants like Google, Bing, and Yahoo. However, there are also many other search engines out there, each with their own unique features and capabilities. One such search engine that has been gaining attention recently is Weaviate. Weaviate is an open-source search engine with a focus on “contextual search.” This means that it takes into account the relationships between different pieces of data in order to provide more relevant search results. In this post, we’ll be comparing Weaviate to some of the more traditional search engines and exploring its unique features. Weaviate vs. other search engines First, let’s take a look at some of the features that Weaviate offers that set it apart from other

How to get started with Weaviate in WooCommerce

Introduction: Weaviate is an open-source vector search engine that allows you to build smart search applications. If you are using WooCommerce to power your online store, you can enhance your search capabilities by integrating Weaviate. In this post, we will show you how to get started with Weaviate in WooCommerce. How to Get Started with Weaviate in WooCommerce: To get started with Weaviate in WooCommerce, you will need to install the Weaviate PHP client. You can install it using Composer, which is a dependency manager for PHP. Once you have installed the Weaviate PHP client, you will need to create a Weaviate instance and an index. You can do this using the following code: // Include the Weaviate PHP client require_once 'vendor/autoload.php'; use Weaviate\Client\Configuration; use

What is Weaviate and how does it work?

Introduction In today’s digital era, data is the king, and as the volume of data grows, it becomes increasingly challenging to extract useful information from it. Weaviate is an open-source vector search engine that solves this problem by providing fast and efficient searches, allowing you to find and extract data easily. This post dives into what Weaviate is, how it works, and how it can help you. What is Weaviate? Weaviate is an open-source, decentralized, and cloud-native vector search engine that allows you to add vector-based search functionality to your application. It uses artificial intelligence and machine learning to enable fast and efficient searches that traditional databases cannot match. It is built to handle millions/billions of vectors. Weaviate allows you to store and search objects

Managing Multilingual WooCommerce Stores with Weaviate

Introduction In today’s globalized world, businesses are expanding their reach beyond borders, catering to customers from different linguistic backgrounds. This has created a need for multilingual support in e-commerce platforms like WooCommerce. Managing a multilingual WooCommerce store can be a complex task, but with the help of powerful tools like Weaviate, it becomes much easier. Weaviate is an open-source knowledge graph that can be used to build intelligent applications. In this post, we will explore how to manage multilingual WooCommerce stores using Weaviate and provide some example code using the PHP client. Managing Multilingual WooCommerce Stores with Weaviate 1. Setting up Weaviate: The first step is to set up Weaviate on your server. You can follow the official documentation to install and configure Weaviate

Optimizing WooCommerce Product Filtering with Weaviate

Introduction WooCommerce is a popular e-commerce platform for WordPress that allows businesses to set up online stores and sell products. One important aspect of any e-commerce store is product filtering, which enables customers to narrow down their search and find the products they are looking for quickly and efficiently. However, the default filtering options provided by WooCommerce may not always meet the specific requirements of a business. In this post, we will explore how you can optimize WooCommerce product filtering using Weaviate, an open-source, vector-based search engine, and introduce a PHP client library to facilitate integration. Optimizing WooCommerce Product Filtering with Weaviate Weaviate is an excellent tool for enhancing product filtering in WooCommerce due to its powerful search capabilities and flexible schema design. By

Exploring the Role of AI in Enhancing Search Engine Efficiency

Introduction Search engines have become an integral part of our daily lives, assisting us in finding relevant information from the vast sea of data available on the internet. Over the years, advancements in artificial intelligence (AI) have played a crucial role in enhancing search engine efficiency. AI-powered techniques, such as LLMs (large language models), vector search, and advanced algorithms, have revolutionized the way search engines retrieve and rank information. In this article, we will explore the role of AI in enhancing search engine efficiency and highlight some cutting-edge technologies like Weaviate, Pinecone, Vespa, Elasticsearch, Solr, and Algolia. Additionally, we will discuss how AI has rendered previous search tricks unnecessary and has opened up new possibilities for multi-language search capabilities. Enhancing Search Efficiency with AI

A WooCommerce vector search live demo with Weaviate & CLIP (text & image) embeddings

Description: – WooCommerce with the Flatsome theme are hosted on Cloudways – WPSOLR plugin is installed and configured – Weaviate is installed on a Google Cloud Kubernetes cluster https://weaviate.io/developers/weaviate/installation/kubernetes/ – The data vectorization is performed by a CLIP model https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/multi2vec-clip/ – Search, filters, facets, sorting, and pagination are performed by query/data similarity within the Weaviate database Demo link: https://demo-woocommerce-flatsome-cloudways-2k-clip.wpsolr.com/shop/ WPSOLR: https://wpsolr.com #wpsolr #weaviate #woocommerce #vectorsearch #vectordatabase #clipmodel

GPT embeddings with Vector search for WordPress

GPT is the most widely used AI model today. But what if you wanted to use these same vectors (or embeddings) for your AI search? You could use OpenAI’s (or “GPT”) embedding models to generate embeddings and then store these GPT embeddings in a vector database solution like Weaviate, which offers a straightforward method for integrating OpenAI vectorizers. This allows you to efficiently incorporate GPT embeddings into your AI search engine or vector database. You can choose between three models : text-embedding-3-small, text-embedding-3-large and ada v2. This guide will explain how you could add this AI search with GPT embeddings to your WordPress (or even Woocommerce) website. Why use GPT embeddings ? OpenAI provides the most widely used model today so why