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 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 JinaEnglish • 日本語 • français • Português • Deutsch • Русский язык • 中文 • عربية |
Tutorials | Level |
---|---|
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 ShardingIncrease performance using prefetching and sharding |
🕊 |
Revisit "Hello, World!" in a Client-Server ArchitectureRun a Flow remotely and connect from a local client |
🕊 |
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! |
🚀 |
Share Your Extension with the WorldShare 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.
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 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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