Understanding neural search framework: Jina AI

#python #beginners #productivity #programming

Search is a big business and is getting bigger each day. Just a few years down the line, searching meant typing text in a search bar. With advancement it now allows us to search for audio, video, images, and many more. Just before the turn of the millennium, there were just 3.5 million Google searches per day. Today that figure is around 5 billion searches per day.

Trust me or not Google search is dying. If you have ever googled about recipes or even some blogs recently, I don’t need to tell you that Google search results have gone to shit. You would have already noticed that the first few non-ad results are SEO-optimized sites filled with affiliate links and ads. If you don't believe me give a read on what happened in Norway 🇳🇴 Here comes the need for a better search engine.

Just a bit about Symbolic Search ⚜️

Symbolic search works better when you want your search to be fully customized. Google search is a general-purpose search engine, which cannot be used just everywhere. If you have a team of developers who has the skill and time to jot down each parameter Symbolic search can be taken to heights. Say, you want to build a blog site, where an author pays the team to show the blog on top so you might rank according to the need and the money flow goes well.

The hell lot of customization in indexing products, searching users, implementing filters, and sorting along with more comes with a high cost. You Have to Explain Every. Little. Thing and moreover Text is fragile.

Therefore, If you are someone who plans to create the next search engine with more accurate results with fewer ads or affiliate links OR You are someone who wants to add search in your application be it in production or development you need to know this. According to reports, Google started using deep neural networks in the search engine in 2016. If you want to disrupt the industry with your build, I suggest you check out Jina. Let's get started.

What is a neural search? 🤔

In short, a neural search is a new approach to retrieving information. Instead of telling a machine a set of rules to understand what data is what, neural search does the same thing with a pre-trained neural network. This means developers don’t have to write every little rule, saving them time and headaches, and the system trains itself to get better as it goes along.

Where can it be fruitful? 🧐

In the early internet days, it was really amazing for firms to build search engines that use text as the parameter and correspondingly pull and rank URLs from the web. With the recent advancements in Deep Learning, a neural search system can go beyond simple text search. Let me take you through a not so techy situation.

You are sitting in a fancy restaurant and hear some beats which you like now you want to search for beats and songs which are similar to what you have heard neural search comes into play. The very next moment you like the shinny tree upfront and you want to have it in your house, just click a photo and search it, you get the aptest results from it.

Starting from getting most similar furniture, garments, songs, memes it can be used by any business and I can bet it is the future for search!

How I came to know about Jina? 💥

Before starting with what is Jina AI, I thought sharing this would make this more interesting. Remember the time when Money Heist was releasing their final season and some companies gave holidays to employees to bid goodbye to the amazing series? Jina was the first startup to do so, according to my knowledge.

I was amazed by the company culture and went straight to their career portal without knowing about their product, but at a point in time, they were not hiring interns. After visiting their GitHub I thought it to be an alternative to Elastic until I used it in my side project.

I wanted to build a small search engine for one of my software, with having 0 knowledge of AI I used K-means clustering to build a basic AI model. After resolving a hell of lot of bugs with integrations and model training I was waiting with an inefficient text search bar 🙂. Then was the time I went straight to elastic and then Solr but at that point of time I wanted something different, thus went for Jina AI and boom. The quick implementation, cloud-native approach, and accuracy made me a fanboy! This blog is just an appreciation post for the amazing open-source project.

What is Jina? 🚀

Jina AI is a cloud-native neural search framework for any kind of Data

Jina is an approach to neural search. It’s cloud-native, so it can be deployed in containers, and it offers anything-to-anything search. Text-to-text, image-to-image, video-to-video, or whatever else you can feed it. It empowers anyone to build SOTA and scalable neural search applications in minutes. Jina runs on 3 fundamental concepts Document, Executor and Flow.

What makes Jina different from other players

Local & Cloud friendly 🌥: From the very beginning it had distributed architecture, scalable and cloud-native. Ultimately, which gave the same DX on local, Docker Compose, and Kubernetes.
Server, Scale, and Share 🚀: It helps you to scale your applications to meet availability and throughput requirements. Serves a local project with HTTP, WebSockets or gRPC endpoints in just minutes.
Time Saver ⏱: Let's you quickly build solutions for indexing, querying, understanding multi or cross-modal documents. The document is the basic data type that Jina operates with. Video, image, text, audio, source code and PDFs are the types of document.

Own Stack 🍱: Jina empowers to keep end-to-end ownership of your solution. Avoid integration pitfalls you get with fragmented, multi-vendor, generic legacy tools. Move over, Jina has out-of-the-box integrations with cloud-native ecosystems.

Outro 💚

There’s no better way to test-drive Jina than by diving in and playing with it. Jina's team provides pre-trained Docker images and jinabox.js, an easy-to-use front-end for searching text, images, audio, or video. The purpose of the blog is to create awareness about Jina and similar neural search frameworks. To learn further it is recommended to go through the Jina's GitHub and Jina's blogs. In case you have some questions regarding the article or want to discuss something under the sun feel free to connect with me on LinkedIn 💕