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What is Jina?

Jina is the easiest way to build neural search in the cloud. It provides an one-stop solution for multi-/cross-modality search and has long-term support from a full-time, venture-backed team.

Jina Search

The open-source core of Jina AI

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Jina Dashboard

Monitor and manage Jina Flows

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Jina Hub

Build and share custom Pods & Flows

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Jina Box

Our light-weight, highly customizable JavaScript-based front-end.

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WHAT IS NEURAL SEARCH?

In short, 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 donโ€™t have to write every little rule, saving them time and headaches, and the system trains itself to get better as it goes along.

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WHY JINA?

Time Saver

Bootstrap an AI-powered search system in just a few minutes with cookiecutter.

Plug & Play

With Jina Hub, you can extend Jina with simple Python scripts or Docker images optimized for your search domain.

First-Class AI models

Jina is a new design pattern for neural search systems with first-class support for state-of-the-art AI models like Faiss, Annoy, Onnx, and more.

Production Ready

Cloud-native features out-of-the-box, like containerization, microservice, distributing, scaling, sharding, async IO, REST, gRPC.

JINA 101: FIRST THINGS TO KNOW

Get a high-level overview of the different members of the Jina family and how they work together to deliver an end-to-end modular search system.

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JINA "HELLO, WORLD!" ๐Ÿ‘‹๐ŸŒ

As a starter, you can try out our "Hello, World" - a simple demo of image neural search for Fashion-MNIST. No extra dependencies needed, just run:

jina hello-world

...or even easier for Docker users, no install required:

docker run -v "$(pwd)/j:/j" jinaai/jina hello-world --workdir /j && open j/hello-world.html

replace "open" with "xdg-open" on Linux

The Docker image downloads the Fashion-MNIST training and test dataset and tells Jina to index 60,000 images from the training set. Then it randomly samples images from the test set as queries and asks Jina to retrieve relevant results.

GET STARTED

LINUX/MACOS
DOCKER
RASPBERRY PI