AI search guides for WordPress & WooCommerce

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

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.

Vespa error : “feed: got status 507”

When trying to deploy your Vespa application and index your documents, you could receive the following error : feed: got status 507 ({"pathId":"/document/v1/mynamespace/music/docid/hardwired-to-self-destruct","id":"id:mynamespace:music::hardwired-to-self-destruct","message":"[UNKNOWN(251009) @ tcp/a9a1e3304702:19112/default]: ReturnCode(NO_SPACE, External feed is blocked due to resource exhaustion: in content cluster 'music': disk on node 0 [a9a1e3304702] is 93.9% full (the configured limit is 75.0%). See https://docs.vespa.ai/en/operations/feed-block.html) "}) for put id:mynamespace:music::hardwired-to-self-destruct: not retryable This is because the disk space is almost full (in this case 93.9%). When the nodes reach a certain limit, the nodes block external write operations. By default Vespa sets it’s disk space limit at 0.75 (75%). You can increase it by adding the following lines to the ‘services.xml’ file in your Vespa application package, more precisely inside the ‘<content>’ tag :

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.

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

How Weaviate is enhancing customer support with conversational AI

Introduction Customer support is a crucial aspect of any business, as it directly impacts customer satisfaction and loyalty. With the advancements in technology, businesses are now leveraging conversational AI to enhance their customer support services. Weaviate, an open-source knowledge graph tool, is playing a significant role in revolutionizing customer support through its robust conversational AI capabilities. Weaviate and Conversational AI Weaviate is a powerful, scalable, and flexible knowledge graph tool that allows businesses to analyze and understand their unstructured data effectively. It is specifically designed to provide machine learning capabilities and natural language processing (NLP) functionalities. These features enable Weaviate to process and understand human language, making it an ideal choice for conversational AI applications. With Weaviate, customer support can be revolutionized through the

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.

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,

Multi-modal WooCommerce search: Elasticsearch orders + Weaviate Cohere products + Weaviate CLIP media.

— Current solution — Just finished upgrading a WooCommerce client, who already had a front-end products multi-lingual Weaviate + Cohere search, and also a 200,000 orders/coupons Elasticsearch admin search. — Needs — The client’s team had troubles searching in thousands of unlabelled media images. — New solution — After upgrading the Weaviate Kubernetes cluster to enable the CLIP module, we indexed the thousands of images. This was relatively fast, on CPUs. — Results — Now the team can search images from their content, rather than from titles and captions. This was done thanks to the WPSOLR “views”, each setup with a dedicated index. WPSOLR + Weaviate + Cohere + CLIP: https://wpsolr.com #search #clip #cohere #weaviate #woocommerce

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