By using Redis backend inside DocArray, building a real-time product recommendation system via vector similarity search is easy and straightforward. Follow along to learn the essential steps and how it works.

Twenty years ago, “hybrid” was a term used only by botanists and chemists. Today, hybrid is booming… even in search. Many search systems are rolling out hybrid search schemes with the latest AI. But is "hybrid search" really more than a buzzword?

Build and deploy a knowledge base search app to Jina AI Cloud in under 50 lines of code and 10 minutes. Here is how you do it.

Today, businesses and developers are keen to use cloud for deep learning. Especially with the GPU cloud instances, you pay as you go. It is much more cost-efficient comparing to having an expensive metal machine in the office. But let's switch the role now. Say you are the

In part 3, let me summarize all lessons learned: pitfalls and perils of building a PDF search engine

In our previous post we looked at how (and how not) to break down PDF files into usable chunks so we could build a search engine for them. In this article, we continue our journey.

I know several folks already building PDF search engines powered by AI, so I figured I’d give it a stab too. How hard could it possibly be? Part I

Sharding is a piece of cake, or is it? Thankfully, Jina framework supports sharding out of the box. Users can process their data between multiple machines to get faster performance.

Jina AI Finetuner can bring performance improvements of up to 63% to pre-trained CLIP models. Here is how we did that.

Use Jina, Whisper and Stable Diffusion to build a cloud-native application for generating images using your speech.