This is an introduction to the background and content of vald-demo repository that was recently released.

Why did we create this repository?

Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is a newly made OSS project and is still not popular yet. Now, we are working on growing the user community and attract more contribution to it. This post and the publishing of the notebook are part of those efforts.

Currently, Vald is designed with many new technologies to allow greater flexibility and to meet the various demands of users. On the other hand, high flexibility is often accompanied…


Case study of logo designs and design assets for OSS.

With many OSSs available, a visual identity including logos is important to give originality to OSS created by each individual, and as icons to symbolize abstract functions of OSS. Today, we’ll show you how to make a visual identity for OSS, taking Vald as an example.

This post has the following 3 topics.

  • Logo design
  • Making visual guideline
  • Creation and use of design assets

Vald provides high-scalable, and high-speed vector approximate neighbours search features to users. However, there are many usages of the vector search. It is not easy to imagine how users can use it in their services. It is easy to use for teams that already know, but it is a challenge to use it for team just starting their investigation.

In this post, we would like to introduce an example of the usage of Vald in a service called JAPAN SEARCH provided by the National Diet Library in Japan. The following article was provided by the National Diet Library and introduced…


We decided to focus on developing a more simple and fast Vald as a Cloud-Native ANN search engine. Along with this decision, we also chose to deprecate some internal components that do not fit the way our going.

This change will make Vald more usable and more maintainable for users.

These components will be removed in the next minor release(v1.2.0).

[Vald Meta]

  • Saving metadata including UUID and vector into user-defined external Databases.

[Vald Meta Gateway]

  • Forwarding metadata to Vald Meta.

[Vald Backup Manager]

  • Saving the set of UUID, vector, Agent Pod IPs into the user-defined external Database.

[Vald Backup Gateway]


How to deploy Vald on your k3d within 5 minutes

Photo by Sebastian Unrau on Unsplash

In this post, we’ll show a quick and easy way to perform a similarity search using Vald.

Before we start, we need to install 4 tools.

If you have Homebrew installed, you can install them by the following command.

$ brew install helm
$ brew install jq
$ brew install k3d
$ brew install kubectl

Create a k3d cluster by executing the following command. It creates a cluster with 4 nodes (1 server + 3 agents).

$ k3d cluster create vald --api-port…


As you know, in recent decades, information technology has been extremely evolved. The technology evolution such as Smart Phone, 5G Network, Cloud Computing, has been affecting our lifestyle. We can not only search for what you’d like to know but also buy anything on the Internet, sharing and saving images, videos, memories, and more digital content. Along with those changes, the demand for searching with object data has become stronger. The most popular search technology is the k-NN (k nearest neighbor). It is a very simple, fast, and high-precision algorithm. However, considering using a vectorized object data, the dimension of…


Nowadays, the need for information retrieval is increasingly based on text and various types of data, including text, images, video, and audio. These technologies are getting more and more advanced every year. The types of data and ways to interpret them are also diversifying. Currently, these technologies are helpful in a wide range of areas, such as recommendation search, which includes the user’s behaviour in context, and image search, which uses image feature vector to perform similar searches. As these technologies and contents develop, the scale of these technologies is increasing every year. It is not easy to search these…

A highly scalable distributed fast approximate nearest neighbor dense vector search engine.

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