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Weaviate guides for WordPress & WooCommerce

Add Weaviate AI search to your Wordpress

How to (Easily) Configure Weaviate (AI search) on WordPress

What if you wanted to have an AI search for your WordPress websites ? Thanks to WPSolr and Weaviate, you can set it up.   What is Weaviate ? Weaviate is an open-source vector database. It is free to download (and install) and you can use it as an AI search engine to upgrade your WordPress search. AI search offers multiple benefits and uses : Semantic search : This type of search uses a vector database and works by matching meanings and contexts instead of literal words. For example, “something to sleep on” would return beds even if the results don’t contain the word “sleep”. The advantages of this search is that it needs less fine-tuning than keyword search and it is multilingual. Image search

Wordpress image search

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

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

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

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 –

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

Using Weaviate to personalize the shopping experience in WooCommerce

Introduction In today’s ultra-competitive e-commerce landscape, delivering personalized shopping experiences to customers is crucial for success. Personalization allows online retailers to tailor their offerings to individual preferences, increasing customer satisfaction and driving conversions. One powerful tool that can help achieve this is Weaviate, an open-source knowledge graph that acts as a vector search engine. By incorporating Weaviate into your WooCommerce store, you can harness the power of AI and machine learning to provide personalized product recommendations for each customer. Getting Started with Weaviate To begin personalizing the shopping experience in WooCommerce using Weaviate, you’ll first need to set up and configure Weaviate. Here’s a step-by-step guide: 1. Install Weaviate: You can download Weaviate from the official website and follow the installation instructions provided. 2. Set

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,

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

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

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  

Neon AI on a keyboard

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