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A new use case showing Weaviate in action for the Royal Netherlands Standardization Institute (NEN)

Before you can offer a product or service in any country, you have to make sure that your product complies with the rules and regulations that apply in that country. These standards ensure that products comply with national and international quality, safety, and reliability standards.

Semantic search and Q&A through 34k of complex standardization documents with Weaviate. In less then 50 milliseconds

One of the main challenges is finding your way through this large and complex collection of unstructured documents. There are many standards and standardization publications. …

New to the world of containers? Here’s an introduction — so you can get started with Weaviate quickly.

Weaviate attracts different users with various backgrounds. Some have been working with containers for years, but we understand that not everyone has. Inspired by a few recent questions and comments about Docker on the Weaviate Slack, I’ve set out to write an article to provide a better background on Docker and containers in general. After reading this article, your most common questions about these technologies should be answered and there should be nothing in the way of building amazing use cases with Weaviate.

In this short overview, we will

  • look at what “Docker” and “Docker Compose” is,
  • why Weaviate relies…

Taxonomies, ontologies, and schemas. How do they relate to Weaviate?

Because Weaviate has a graph-like data model, people often ask questions about how Weaviate deals with taxonomies, ontologies, and schemas. And to make things even more complicated, Weaviate is adding terminology like vectorizers and contextionary to the mix.

Confusing? fret not! It’s actually quite simple…

Taxonomies, ontologies, and schemas

  1. A taxonomy has a hierarchy (e.g., an elephant is of the order Proboscidea, which is of the class Mammalia and of the kingdom Animalia)
  2. An ontology distinguishes concepts and their relationships (an elephant with the name Alice that lives in a zoo that is located in Amsterdam).
  3. Ontologies focus more on the semantic relationships whereas…

In the v1.0 release of Weaviate (docs — Github) we introduced the concept of modules. Weaviate modules are used to extend the vector search engine with vectorizers or functionality that can be used to query your dataset. With the release of Weaviate v1.2, we have introduced the use of transformers (DistilBERT, BERT, RoBERTa, Sentence-BERT, etc) to vectorize and semantically search through your data.

Weaviate v1.2 Transformers introduction video

What are transformers?

A transformer (e.g., BERT) is a deep learning model that is used for NLP-tasks. Within Weaviate the transformer module can be used to vectorize and query your data.

By selecting the text-module…

A complete implementation guide to creating a React.js app styled with Material UI that integrates with a Weaviate dataset


In this article, we will be using React hooks to build our React app, style it with Material UI and learn how to integrate it with Weaviate neural search. This article will go through the steps of starting a React.js app from scratch, building the UI necessary to interact with Weaviate, and display results in the app’s interface.

But before we get into it, let me give you a brief introduction about what Weaviate is and some of its use cases.

What is Weaviate?

Weaviate is a cloud-native, modular, real-time vector search engine. It is extremely powerful and it makes a lot easier…

How the vector search engine Weaviate overcomes the limitations of popular Approximate Nearest Neighbor (ANN) libraries.

Written by Etienne Dilocker — Co-Founder & CTO at SeMI Technologies

The article has been originally published on

In this article we will cover:

  • how ANN models enable fast & large-scale vector searches
  • where popular ANN libraries fall short
  • what Weaviate is and how it can bring your vector search needs to production
  • a glimpse of how Weaviate works under the hood

With the rising popularity of machine learning models, the demand for vector similarity search solutions has also increased dramatically. Machine learning models typically output vectors and common search queries involve finding the closest set of related vectors…

Everybody who works with data in any way shape or form knows that one of the most important challenges is searching for the correct answers to your questions. There is a whole set of excellent (open source) search engines available but there is one thing that they can’t do, search and related data based on context.

Written by Bob van Luijt — Co-Founder & CEO at SeMI Technologies

Weaviate is an open-source, GraphQL-based, search graph based on a build in embedding mechanism.

Before we get started, some further reading while exploring Weaviate.

Getting Started with Weaviate

Let look at the following data object that one might store in a search engine:

"title": "African bush elephant",
"photoUrl": ""

You can retrieve the data object from…

Any kind of data storage architecture needs an API. You want your users, and their applications, to access and interact with their data. And no matter how complicated the database architecture itself is, you want this interaction to happen as intuitively as possible.

Written by Laura Ham — Community Solution Engineer at SeMI Technologies

Choosing a good API, its design and development, is a crucial but time-consuming process, especially if you want to develop one in an ongoing software development project.

Weaviate uses the API query language GraphQL. GraphQL enables efficient development and provides high user experience (UX) for data interaction.

In this article we explain how the use of GraphQL leverages the UX of Weaviate, and how we approach the design of this API.

A vector search for the masses needs an intuitive interface

Written by Etienne Dilocker – Co-Founder & CTO at SeMI Technologies

Vector-Searching enables a large spectrum of use cases which are impossible with traditional full-text search engines. A pure full-text search tries to match occurrences of terms in a set of documents. At the most, simple distances — such as Levenshtein distances between words — can be computed.

Weaviate on the other hand — a vector search engine — ranks each object by how close it is to another. As a vector searches, Weaviate isn’t limited to text objects. Vector searches also work with music, images, video clips, voice recordings…

Weaviate is an open-source search engine with a build-in NLP model called the Contextionary. What makes Weaviate unique, is that it stores data in a vector space rather than a traditional row-column or graph structure, allowing you to search through data based on its meaning rather than keywords alone.

Written by Bob van Luijt – Co-Founder & CEO at SeMI Technologies

In this article, I want to share the history of Weaviate, how the concept was born, and where we are heading towards in the near future.

A World of Wonders called Natural Language Processing

Somewhere early 2015 I was introduced to the concept of word embeddings through the publication of an article that contained a machine-learning algorithm to turn individual words into embeddings called GloVe.

# Example of an embedding
squarepants 0.27442 -0.25977 -0.17438 0.18573 0.6309 0.77326 -0.50925 -1.8926 0.72604 0.54436 -0.2705 1.1534 0.20972 1.2629 1.2796 -0.12663 0.58185 0.4805 -0.51054 0.026454 0.20253 0.32844 0.72568 1.23 0.90203…

SeMI Technologies

SeMI’s Weaviate is a next generation search engine platform that understands your data.

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