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WebsiteDocsExamplesNewsletterHub (beta)Dashboard (beta)TwitterWe are Hiring

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

Jina is cloud-native neural search, powered by the state-of-the-art AI and deep learning. It has long-term supported from a full-time, venture-backed team.

🌌 Universal Search - Jina enables large-scale indexing and querying of any kind on multiple platforms and architectures. Whether you're searching for images, video clips, audio snippets, long legal documents, or short tweets, Jina can handle them all.

🚀 High Performance & State-of-the-Art - Jina aims for AI-in-production. You can easily scale out your VideoBERT, Xception, word tokenizer, image segmenter, and database to handle billions of data points. Features like async, replicas, and sharding come out-of-the-box.

🐣 System Engineering Made Easy - Jina offers a one-stop solution that frees you from handcrafting and gluing packages, libraries and databases. With the most intuitive API and dashboard, building a cloud-native search system can be done in minutes.

🧩 Powerful Extensions, Simple Integration - Want a new AI model for Jina? Just write a Python script or build a Docker image. Plugging in new algorithms has never been so simple. Check out Jina Hub (beta) and find more extensions from the community for different use-cases.

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

Table of Contents


Install from PyPi

On Linux/MacOS with Python >= 3.7, simply run:

pip install jina

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

...or Run with 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 are invited to try Jina's "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 Fashion-MNIST training and test data 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 it'll eventually open a webpage and show results like this:

Jina banner

As for the implementation behind it? It's as simple as can be:

Python API index.yml Flow in Dashboard
from jina.flow import Flow

f = Flow.load_config('index.yml')

with f:
    yaml_path: helloworld.crafter.yml
    replicas: $REPLICAS
    read_only: true
    yaml_path: helloworld.indexer.doc.yml
    yaml_path: helloworld.encoder.yml
    needs: chunk_seg
    replicas: $REPLICAS
    yaml_path: helloworld.indexer.chunk.yml
    replicas: $SHARDS
    separated_workspace: true
    yaml_path: _merge
    needs: [doc_idx, chunk_idx]
    read_only: 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!

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!

Getting Started

Start a project from the template

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.


Jina 101 Concept Illustration Book, Copyright by Jina AI Limited   

Jina 101: First Thing to Learn About Jina

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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



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? Affirmative? 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. Without your active involvement, Jina won't be successful.


  • 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!


GitHub milestones lay out the path to the 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 in participating, contact us at


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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