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Want to build a search system backed by deep learning? You've come to the right place!

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

⏱️ Time Saver - Bootstrapping an AI-powered search system with Jina takes just minutes, and saves engineers months of time!

🧠 First-class AI models - Jina is a new design pattern for neural search systems, offering first-class support for state-of-the-art AI models.

🌌 Universal Search - Large-scale indexing and querying data of any kind on multiple platforms. Video, image, long/short text, music, source code, you name it!

🚀 Production Ready - Cloud-native features come out-of-the-box, e.g. containerization, microservice, distributing, scaling, sharding, async IO, REST, gRPC.

🧩 Plug & Play - Extend Jina with simple Python scripts or Docker images optimized for your search domain. Check out Jina Hub for more extensions.

Contents

• Install
• Jina "Hello, World!" 👋🌍
• Build your own Project
• Tutorials
• Documentation
• Contributors
• Contributing
• Community
• Join Us
• Roadmap
• License

Install

Install from PyPi

On Linux/MacOS with Python >= 3.7:

pip install jina

To install Jina with extra dependencies, or install on Raspberry Pi please refer to the documentation.

...or Run in a Docker Container

We provide a universal Docker image (only 80MB!) that supports multiple architectures (including x64, x86, arm-64/v7/v6). Simply run:

docker run jinaai/jina --help

Jina "Hello, World!" 👋🌍

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. The whole process takes about 1 minute, and eventually opens a webpage and shows results like this:

The implementation behind it is as simple as can be:

from jina.flow import Flow

f = (Flow()
        .add(uses='encoder.yml', parallel=2)
        .add(uses='indexer.yml', shards=2, 
             separated_workspace=True))

with f:
    f.index(fashion_mnist, batch_size=1024)

!Flow
pods:
  encode:
    uses: encoder.yml
    parallel: 2
  index:
    uses: indexer.yml
    shards: 2
    separated_workspace: true

All the big words you can name: computer vision, neural IR, microservice, message queue, elastic, replicas & shards. They all happened in just one minute!

Adding Parallelism and Sharding

from jina.flow import Flow

f = (Flow().add(uses='encoder.yml', parallel=2)
           .add(uses='indexer.yml', shards=2, separated_workspace=True))

Distributing the Flow

from jina.flow import Flow

f = Flow().add(uses='encoder.yml', host='192.168.0.99')

Using a Docker Container

from jina.flow import Flow

f = (Flow().add(uses='jinahub/cnn-encode:0.1')
           .add(uses='jinahub/faiss-index:0.2', host='192.168.0.99'))

Concatenating Embeddings

from jina.flow import Flow

f = (Flow().add(name='eb1', uses='BiTImageEncoder')
           .add(name='eb2', uses='KerasImageEncoder', needs='gateway')
           .join(needs=['eb1', 'eb2'], uses='_concat'))

Enabling Network Queries

from jina.flow import Flow

f = Flow(port_expose=45678, rest_api=True)

with f:
    f.block()

Intrigued? Play with different options:

jina hello-world --help

Be sure to continue with our Jina 101 Guide - to understand all key concepts of Jina in 3 minutes!

Build your own Project

pip install cookiecutter && cookiecutter gh:jina-ai/cookiecutter-jina

With Cookiecutter you can easily create a Jina project from templates with one terminal command. This creates a Python entrypoint, YAML configs and a Dockerfile. You can start from there.

Tutorials

Jina 101: First Thing to Learn About Jina   English • 日本語 • français • Português • Deutsch • Русский язык • 中文 • عربية

Documentation

The best way to learn Jina in depth is to read our documentation. Documentation is built on every push, merge, and release of the master branch.

• Jina command line interface arguments explained
• Jina Python API interface
• Jina YAML syntax for Executor, Driver and Flow
• Jina Protobuf schema
• Environment variables used in Jina
• ... and more

Are you a "Doc"-star? Join us! We welcome all kinds of improvements on the documentation.

Documentation for older versions is archived here.

Contributors

Contributing

We welcome all kinds of contributions from the open-source community, individuals and partners. We owe our success to your active involvement.

• Contributing guidelines
• Release cycles and development stages

Community

• Slack channel - a communication platform for developers to discuss Jina
• Community newsletter - subscribe to the latest updates, releases and event news of Jina
• LinkedIn - get to know Jina AI as a company and find job opportunities
• - follow us and interact with using hashtag #JinaSearch
• Company - know more about our company and how we are fully committed to open-source!

Join Us

Jina is an open-source project. We are hiring full-stack developers, evangelists, and PMs to build the next neural search ecosystem in open source.

Roadmap

GitHub milestones lay out the path to Jina's future improvements.

We are looking for partnerships to build a Open Governance model (e.g. Technical Steering Committee) around Jina, to enable a healthy open-source ecosystem and developer-friendly culture. If you are interested, contact us at [email protected].

License

Copyright (c) 2020 Jina AI Limited. All rights reserved.

Jina is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.