An easier way to build neural search in the cloud
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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) in the cloud. Jina is supported long-term by a full-time, venture-backed team.
⏱️ Time Saver - Bootstrap an AI-powered system in just a few minutes.
🧠 First-Class AI Models - The design pattern for neural search systems, with first-class support for state-of-the-art AI models.
🌌 Universal Search - Large-scale indexing and querying of any kind of data on multiple platforms: video, image, long/short text, music, source code, etc.
🚀 Cloud Ready - Decentralized architecture with cloud-native features out-of-the-box: containerization, microservice, scaling, sharding, async IO, REST, gRPC.
🧩 Plug & Play - Easily extendable with Pythonic interface.
Website • Docs • Examples • Hub • Dashboard • Jinabox • Twitter • We are Hiring
- Get Started
- Jina "Hello, World!" 👋🌍
- Tutorials
- Documentation
- Contributing
- Community
- Open Governance
- Join Us
- License
On Linux/MacOS with Python 3.7/3.8:
pip install jinaTo install Jina with extra dependencies, or install on Raspberry Pi please refer to the documentation.
We provide a universal Docker image that supports multiple architectures (including x64, x86, arm-64/v7/v6). No need to install anything, simply run:
docker run jinaai/jina --helpAs 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 LinuxClick here to see console output
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:
| Python API | or use YAML | or use Dashboard |
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 |
|
Explore sharding, containerization, concatenating embeddings, and more
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()Intrigued? Play with different options:
jina hello-world --helppip install jina[devel]
jina hub new --type appYou 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.
|
English • 日本語 • Français • Português • Deutsch • Русский язык • 中文 • عربية |
| Level | Tutorials |
|---|---|
| Search South Park scripts and practice with Flows and Pods | |
| Using cookiecutter for bootstrap a jina app | |
| Spice up the Hello-World with Query Language | |
| Split documents in order to search on a finegrained level | |
| Search cross modal to get images from captions and vice versa | |
| Improve performance using prefetching and sharding |
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.
- Code of conduct - play nicely with the Jina community
- 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.
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.