jina 2.0.0rc2.dev24

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

Cloud-Native Neural Search Framework for Any Kind of Data

Powered by deep learning and cloud-native techniques, Jina is geared towards building search systems for any kind of data in just minutes.

🌌 Search anything - Large-scale indexing and querying of unstructured data: video, image, long/short text, music, source code, PDF, etc.

⏱️ Save time - The design pattern of neural search systems, from zero to a production-ready system in minutes.

🍱 Own your stack - Keep an end-to-end stack ownership of your solution, avoid the integration pitfalls with fragmented, multi-vendor, generic legacy tools.

🌩️ Fast & cloud-ready - Decentralized architecture from day one. Scalable & cloud-native by design: enjoy containerizing, distributing, sharding, async, REST/gRPC/WebSocket.

Installation

2.0 is still in pre-release, add --pre to install it. Why 2.0?

$ pip install --pre jina
$ jina -v
2.0.0rcN

via Docker

$ docker run jinaai/jina:master -v
2.0.0rcN

📦 More installation options

x86/64,arm/v6,v7,v8 (Apple M1)	On Linux/macOS & Python 3.7/3.8/3.9	Docker Users
Standard	pip install --pre jina	docker run jinaai/jina:2.0.0rc
Daemon	pip install --pre "jina[daemon]"	docker run --network=host jinaai/jina:latest-daemon
With Extras	pip install --pre "jina[devel]"	docker run jinaai/jina:latest-devel
Dev/Pre-Release	pip install --pre jina	docker run jinaai/jina:master

Version identifiers are explained here. Jina can run on Windows Subsystem for Linux. We welcome the community to help us with native Windows support.

Get Started

Document, Executor, Flow are three fundamental concepts in Jina.

• 📄 Document is the basic data type in Jina;
• ⚙️ Executor is how Jina processes Documents;
• 🔀 Flow is how Jina streamlines and distributes Executors.

Copy-paste the minimum example below and run it:

from jina import Document, Executor, Flow, requests


class MyExecutor(Executor):

    @requests
    def foo(self, docs, parameters, **kwargs):
        print(f'{parameters["p1"]} - {docs[0]}')


f = Flow().add(uses=MyExecutor)

with f:
    f.post(on='/bar', inputs=Document(), parameters={'p1': 'hello'}, on_done=print)

Run Quick Demo

• 👗 Fashion image search: jina hello fashion
• 🤖 QA chatbot: pip install "jina[chatbot]" && jina hello chatbot
• 📰 Multimodal search: pip install "jina[multimodal]" && jina hello multimodal

Fork Demo & Build Your Own

Copy the source code of a hello world to your own directory and start from there:

$ jina hello fork fashion ../my-proj/ 

Read Tutorials

• 📄 Document & DocumentArray: the basic data type in Jina.
  • Minimum working example
  • Document API
  • DocumentArray API
• ⚙️ Executor: how Jina processes Documents.
  • Minimum working example
  • Executor API
  • Executor Built-in Features
  • Migration from 1.x to 2.0 in Practice
• 🔀 Flow: how Jina streamlines and distributes Executors.
  • Minimum working example
  • Flow API
• 🧼 Write clean code in Jina

Support

• Join our Slack community to chat to our engineers about your use cases, questions, and support queries.
• Join our Engineering All Hands meet-up to discuss your use case and learn Jina's new features.
  • When? The second Tuesday of every month
  • Where? Zoom (calendar link/.ics) and live stream on YouTube)
• Subscribe to the latest video tutorials on our YouTube channel.

Join Us

Jina is backed by Jina AI. We are actively hiring full-stack developers, solution engineers to build the next neural search ecosystem in open source.

Contributing

We welcome all kinds of contributions from the open-source community, individuals and partners. We owe our success to your active involvement.

• Contributing guidelines
• Code of conduct - play nicely with the Jina community
• Good first issues
• Release cycles and development stages
• Upcoming features - what's being planned, what we're thinking about.

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