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.
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. …
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
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…
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.
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.
Weaviate is a cloud-native, modular, real-time vector search engine. It is extremely powerful and it makes a lot easier…
The article has been originally published on DB-Engines.com.
In this article we will cover:
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…
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.
Let look at the following data object that one might store in a search engine:
"title": "African bush elephant",
You can retrieve the data object from…
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.
In this article we explain how the use of GraphQL leverages the UX of Weaviate, and how we approach the design of this API.
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…
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.
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’s Weaviate is a next generation search engine platform that understands your data.