jina 0.3.1

Jina is the cloud-native neural search solution powered by the state-of-the-art AI and deep learning

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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
• Jina "Hello, World!" 👋🌍
• Getting Started
• Documentation
• Contributing
• Community
• Roadmap
• License

Install

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

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:

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: f.index(input_fn)	!Flow pods: chunk_seg: yaml_path: helloworld.crafter.yml replicas: $REPLICAS read_only: true doc_idx: yaml_path: helloworld.indexer.doc.yml encode: yaml_path: helloworld.encoder.yml needs: chunk_seg replicas: $REPLICAS chunk_idx: yaml_path: helloworld.indexer.chunk.yml replicas: $SHARDS separated_workspace: true join_all: yaml_path: _merge needs: [doc_idx, chunk_idx] read_only: true

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.

Tutorials

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

Tutorials	Level
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.

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

• Contributing guidelines
• Release cycles and development stages

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
• - follow us and interact with using hashtag #JinaSearch
• Company - know more about our company and how we are fully committed to open-source!

Roadmap

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 [e-mail].

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.