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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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Jina Jina Jina Jina Docs We are hiring tweet button Python 3.7 3.8 PyPI Docker Docker Image Version (latest semver) CI CD Release Cycle Release CD API Schema codecov

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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

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
Click here to see console output

hello world 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:

Jina banner

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

Flow in Dashboard

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 Concept Illustration Book, Copyright by Jina AI Limited   

Jina 101: First Thing to Learn About Jina

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TutorialsLevel

Use Flow API to Compose Your Search Workflow

Orchestrate Pods to work together: sequentially and in parallel; locally and remotely

🐣

Input and Output Functions in Jina

Use Jina's input and output functions

🐣

Use Dashboard to Get Insight of Jina Workflow

Monitor workflows and get insights with Jina's dashboard

🐣

From BERT-as-Service to X-as-Service

Extract feature vector data using any deep learning representation

🐣

Build a NLP Semantic Search System

Search South Park scripts and practice with Flows and Pods

🐣

Build a Flower Image Search System

Search images, define your own executors, and run them in Docker

🐣

Video Semantic Search in Scale with Prefetching and Sharding

Increase performance using prefetching and sharding

🕊

Revisit "Hello, World!" in a Client-Server Architecture

Run a Flow remotely and connect from a local client

🕊

Distribute Your Workflow Remotely

Run Jina on remote instances and distribute your workflow

🕊

Extend Jina by Implementing Your Own Executor

Implement your own ideas as Jina plugins

🕊

Run Jina Pod via Docker Container

Solve complex dependencies easily with Docker containers

🕊

Google's Big Transfer Model in (Poké-)Production

Search Pokemon with SOTA visual representation!

🚀

Share Your Extension with the World

Share your extensions with engineers around the globe on Jina Hub

🚀

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.

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.

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
  • Twitter Follow - 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 hello@jina.ai.

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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0.4.4

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