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Jina is the cloud-native neural search framework for any kind of data

Project description

Jina logo: Jina is a cloud-native neural search framework

Cloud-Native <ins>Neural Search</ins>[?] Framework for Any Kind of Data

Python 3.7 3.8 3.9 PyPI Docker Image Version (latest semver) codecov

Jina🔊 allows you to build search-as-a-service powered by deep learning in just minutes.

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

🌩️ Fast & cloud-native - Distributed architecture from day one, scalable & cloud-native by design: enjoy containerizing, streaming, paralleling, sharding, async scheduling, HTTP/gRPC/WebSocket protocol.

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

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

Run Quick Demo

Install

  • via PyPI: pip install -U jina
  • via Docker: docker run jinaai/jina:latest
More installation options
On x86/64, arm64/v6/v7 Linux/macOS with Python 3.7/3.8/3.9 Docker Users
Minimum
(no HTTP, WebSocket, Docker support)
JINA_PIP_INSTALL_CORE=1 pip install jina docker run jinaai/jina:latest
Minimum but more performant
(use uvloop & lz4)
JINA_PIP_INSTALL_PERF=1 pip install jina docker run jinaai/jina:latest-perf
With Daemon pip install "jina[daemon]" Run JinaD
Full development dependencies pip install "jina[devel]" docker run jinaai/jina:latest-devel
Pre-release
(all tags above can be added)
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, and Flow are the three fundamental concepts in Jina.

1️⃣ Copy-paste the minimum example below and run it:

💡 Preliminaries: character embedding, pooling, Euclidean distance

The architecture of a simple neural search system powered by Jina
import numpy as np
from jina import Document, DocumentArray, Executor, Flow, requests

class CharEmbed(Executor):  # a simple character embedding with mean-pooling
    offset = 32  # letter `a`
    dim = 127 - offset + 1  # last pos reserved for `UNK`
    char_embd = np.eye(dim) * 1  # one-hot embedding for all chars

    @requests
    def foo(self, docs: DocumentArray, **kwargs):
        for d in docs:
            r_emb = [ord(c) - self.offset if self.offset <= ord(c) <= 127 else (self.dim - 1) for c in d.text]
            d.embedding = self.char_embd[r_emb, :].mean(axis=0)  # average pooling

class Indexer(Executor):
    _docs = DocumentArray()  # for storing all documents in memory

    @requests(on='/index')
    def foo(self, docs: DocumentArray, **kwargs):
        self._docs.extend(docs)  # extend stored `docs`

    @requests(on='/search')
    def bar(self, docs: DocumentArray, **kwargs):
         docs.match(self._docs, metric='euclidean', limit=20)

f = Flow(port_expose=12345, protocol='http', cors=True).add(uses=CharEmbed, parallel=2).add(uses=Indexer)  # build a Flow, with 2 parallel CharEmbed, tho unnecessary
with f:
    f.post('/index', (Document(text=t.strip()) for t in open(__file__) if t.strip()))  # index all lines of _this_ file
    f.block()  # block for listening request

2️⃣ Open http://localhost:12345/docs (an extended Swagger UI) in your browser, click /search tab and input:

{"data": [{"text": "@requests(on=something)"}]}

That means, we want to find lines from the above code snippet that are most similar to @request(on=something). Now click Execute button!

Jina Swagger UI extension on visualizing neural search results

3️⃣ Not a GUI person? Let's do it in Python then! Keep the above server running and start a simple client:

from jina import Client, Document
from jina.types.request import Response


def print_matches(resp: Response):  # the callback function invoked when task is done
    for idx, d in enumerate(resp.docs[0].matches[:3]):  # print top-3 matches
        print(f'[{idx}]{d.scores["euclidean"].value:2f}: "{d.text}"')


c = Client(protocol='http', port_expose=12345)  # connect to localhost:12345
c.post('/search', Document(text='request(on=something)'), on_done=print_matches)

, which prints the following results:

         Client@1608[S]:connected to the gateway at localhost:12345!
[0]0.168526: "@requests(on='/index')"
[1]0.181676: "@requests(on='/search')"
[2]0.192049: "query.matches = [Document(self._docs[int(idx)], copy=True, score=d) for idx, d in enumerate(dist)]"

😔 Doesn't work? Our bad! Please report it here.

Read Tutorials

Support

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.

All Contributors

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