Jina is the cloud-native neural search solution powered by the state-of-the-art AI and deep learning
Project description
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Website • Docs • Examples • Newsletter • Hub (beta) • Dashboard (beta) • Twitter • We are Hiring
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
- Contributing
- Community
- Open Governance
- Join Us
- 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 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
Click 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 as simple as can be:
Python API | or use YAML spec | 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
|
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')
.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 JinaEnglish • 日本語 • français • Português • Deutsch • Русский язык • 中文 • عربية |
Tutorials | Level |
---|---|
My First Jina AppGet started in your Jina journey by building a basic text search app and learning as you go |
🐣 |
Use Flow API to Compose Your Search WorkflowOrchestrate Pods to work together: sequentially and in parallel; locally and remotely |
🐣 |
Input and Output Functions in JinaUse Jina's input and output functions |
🐣 |
Use Dashboard to Get Insight of Jina WorkflowMonitor workflows and get insights with Jina's dashboard |
🐣 |
From BERT-as-Service to X-as-ServiceExtract feature vector data using any deep learning representation |
🐣 |
Build a NLP Semantic Search SystemSearch South Park scripts and practice with Flows and Pods |
🐣 |
Build a Flower Image Search SystemSearch images, define your own executors, and run them in Docker |
🐣 |
Video Semantic Search in Scale with Prefetching and ShardingImprove performance using prefetching and sharding |
🕊 |
Distribute Your Workflow RemotelyRun Jina on remote instances and distribute your workflow |
🕊 |
Extend Jina by Implementing Your Own ExecutorImplement your own ideas as Jina plugins |
🕊 |
Run Jina Pod via Docker ContainerSolve complex dependencies easily with Docker containers |
🕊 |
Google's Big Transfer Model in (Poké-)ProductionSearch Pokemon with SOTA visual representation! |
🚀 |
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.
Contributing
We welcome all kinds of contributions from the open-source community, individuals and partners. We owe our success to your active involvement.
Contributors ✨
Community
- Slack workspace - a communication platform for developers to discuss Jina
- 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 us and interact with using hashtag
#JinaSearch
- Company - know more about our company and how we are fully committed to open-source!
Open Governance
GitHub milestones lay out the path to Jina's future improvements.
As a part of open governance model, we host the Engineering All Hands of Jina AI in public. This Zoom meeting recurs monthly on the second Tuesday, 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.
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.
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.
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