WordPress & WooCommerce AI tools - blog

(Following Part 4) The coding of query filters and query aggregations (facets) for Vespa is now complete. One can define facets and filters with advanced

(Following Part 3) The coding of document indexing operations for Vespa is now complete. One can create a new document, update a document, and delete

(Following Part 2) The coding of index CRUD operations for Vespa is now complete. One can create a new index, update an index, and remove

https://aiquarious.com/ #vectorsearch #promptengineering #finetuning #largelanguagemodels

(Following Part 1) A major prerequisite of WPSOLR is to modify an index schema automatically when a user adds/update fields from the plugin admin screens.

How to map your WooCommerce search to a Vespa architecture? Vespa is a highly modular big data engine, with many concepts: tenant, application package, zone, instance,

After training LLMs on the whole world, prompting them for zero-shot, finetuning them on specialized domains, what is the next stage? The goal is clear:

1. Just prompt #ChatGPT to fix the query with almost any kind of typos, and it will do it with ease. Prompt: “”” Fix the typo

The answer is yes, of course. It has all the features required for an e-commerce search, inverted index and vectors included. But being so flexible

Recommenders are built on user breadcrumbs: what a user visited, clicked, added to a basket, bought … Could we use #ChatGPT generative power to recommend the next

— The problem — Let’s face it, we’re exposed to recommendations all day long. And they are often pretty dull and easy to discard. And

As a developer without real experience with ML, fine-tuning or distilling a SBERT model is a huge leap. We’ve all heard about learn to rank

E-Commerce search is built from keywords, filters (for stock availability for instance), and aggregations (for attributes facets). Keywords search is usually tuned with analysers (stemming,

LLMs give you the flexibility of natural languages to express your query and format your answers. But this comes with the same flaws as human

BEIR benchmarks are great as absolute landmarks for theoretical research. But measuring improvements over an existing search is much more dramatic for production systems. (one

With the rise of AI, all e-commerce shops should get a great faceted search, augmented with a great semantic search. e-commerce shops need both, as

If you’re a shop owner, you know how the front-end and back-end searches are different. On the front-end, vector search is a must. It prevents

Often, an image is worth a thousand words. When you are a retailer, search are often disappointing because images in results does not match the

Search by keywords is very good when your catalog contains the keywords. But many times, search by keywords cannot retrieve your products. With AI searching

What is most frustrating that showing a potential customer no results, just for a typo or a misspelling? Well, our search AI is able to

Your WordPress or WooCommerce shop is visible worldwide. Why should your visitors forced to speak English? Choose a multilingual AI model like Cohere, or one among tens at Hugging Face,

But of course, it entirely depends on the data. No solution is absolute. This is why it is so important to be able to compare

Nobody can escape the hype: a client of WPSOLR was considering switching to a “#chatgpt” search. But how does it compare to the current search?

(This is a follow up of Part 3 /comparing-classic-search-vector-search-and-hybrid-search-is-it-even-possible-part-3/)   Multilingual search is a huge topic nowadays, especially for WooCommerce in a globalised market.  

(This is a follow up of Part 2 /comparing-classic-search-vector-search-and-hybrid-search-is-it-even-possible-part-2/) An incredible and understated effect of AI search is typo tolerance. LLM are very strong in

Description: – Hybrid search (BM25 sparse search & dense vector search) – WooCommerce with the Flatsome theme are hosted on Cloudways – WPSOLR plugin is installed and configured

It’s Christmas almost every day with Weaviate and WPSOLR 🙂 Release documentation: /forums/topic/release-22-9/ You can try several engines live at https://tmp-bopa1934-odns.wpsolr.com, in the header search boxes: –

People are already aware, thanks to #ChatGPT, of what AI can do in general. People also know that their competitors are willing to use it

(This is a follow up of Part 1)   Indexing several indexes, from several search engines is already difficult. But you also need to do

It looks impossible at first sight. You will have to: – Install each search engine server – Create each index with the right schema –

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

The problem: SBERT models https://www.sbert.net/ are trained to build a similarity embedding between a query and a passage. The problem is that queries and passages are

Demo link: https://demo-woocommerce-flatsome-cloudways-2k-openai.wpsolr.com/shop/   Description: – WooCommerce with the Flatsome theme are hosted on Cloudways – WPSOLR plugin is installed and configured – Weaviate is installed on a Google Cloud

– Classical search – Anything around BM25 statistical scoring. Including #elasticsearch , Apache #solr, Algolia, and WPSOLR https://tmp-bopa1934-odns.wpsolr.com. – Classical search AI augmented – Still the classical engines, but with

You do not have to wonder or spend weeks prototyping anymore. With WPSOLR ‘s SeMI Technologies Weaviate integration, you can just: – Install docker Weaviate locally with

And a big motivation to add a thin layer of fine-tuning (labeling?) to #wpsolr‘s WooCommerce SeMI Technologies search integration ! It’s pretty straightforward with OpenAI

Modules already supported, with full WooCommerce integration (suggestions, facets, filters, pagination, sort, …): – Weaviate Transformers module – Weaviate CLIP module – Weaviate Hugging Face

(And they are also the two OSS vector search databases supporting aggregation, which make them to my opinion the best contenders for e-commerce vector search)

SeMI Technologies released Weaviate v 1.17.0 with a new hybrid search, and an alpha setting to set how much search is pure dense/vector or pure

— When LLMs suck – LLMs (large Language Models) do not rank well on recent or specialised corpus, as they are trained on oldish and

Vector databases are lacking Pay as You Go OSS embeddings… – Cannot start? – You’ve carefully selected your favourite vector database. You’re ready to start,

– What? – Who is using search nowadays when recommenders secretly choose for us in the background? Personally, I do confess not having used the

Checkout the picture, and compare the 17th of December to the 18th of December. Both figures are issued from the same automated tests on WPSOLR’s

We’re proud to announce that WPSOLR 22.8 just release the new SeMI Technologies Weaviate module for Cohere‘s multilingual-22-12 model. Cohere multi-language embeddings: https://docs.cohere.ai/docs/multilingual-language-models WPSOLR 22.8:

Get instantly cheaper and better embeddings for WooCommerce WPSOLR & Weaviate: /feature-weaviate/ Weaviate & OpenAI embeddings: https://weaviate.io/developers/weaviate/current/retriever-vectorizer-modules/text2vec-openai.html #wpsolr #search #woocommerce #weaviate #openai #embeddings #vectorsearch