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Jina is an AI-powered search framework, empowering developers to create cross-/multi-modal search systems (e.g. text, images, video, audio) on the cloud. Jina is long-term supported by a full-time, venture-backed team.

⏱️ Time Saver - Bootstrap an AI-powered system in just a few minutes.

🧠 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.

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

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

🧩 Plug & Play - With Jina Hub, easily extend Jina with simple Python scripts or Docker images optimized for your search domain.

• Get Started
• Jina "Hello, World!" 👋🌍
• Tutorials
• Documentation
• Contributing
• Community
• Open Governance
• Join Us
• License

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.

⚠️ Currently, Jina doesn't support Windows. See here for more information.

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

docker run jinaai/jina --help

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 simple:

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

from jina.flow import Flow

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

from jina.flow import Flow

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

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'))

from jina.flow import Flow

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

from jina.flow import Flow

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

with f:
    f.block()

jina hello-world --help

pip install jina[devel]
jina hub new --type app

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.

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

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.

• Use Flow API to Compose Your Search Workflow
• Input and Output Functions in Jina
• Use Dashboard to Get Insight of Jina Workflow
• Distribute Your Workflow Remotely
• Run Jina Pods via Docker Container

• Command line interface arguments
• Python API interface
• YAML syntax for Executor, Driver and Flow
• Protobuf schema
• Environment variables
• ... 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.

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

• Slack workspace - join #general on our Slack to meet the team and ask questions
• YouTube channel - subscribe to the latest video tutorials, release demos, webinars and presentations.
• LinkedIn - get to know Jina AI as a company and find job opportunities
• - follow and interact with us using hashtag #JinaSearch
• Company - know more about our company and how we are fully committed to open-source.

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

As part of our open governance model, we host Jina's Engineering All Hands in public. This Zoom meeting recurs monthly on the second Tuesday of each month, at 14:00-15:30 (CET). Everyone can join in via the following calendar invite.

• Add to Google Calendar
• Download .ics

The meeting will also be live-streamed and later published to our YouTube channel.

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.

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.