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

Cloud-Native Neural Search[?] Framework for Any Kind of Data

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

Jina๐Ÿ”Š is a neural search framework that empowers anyone to build SOTA & scalable deep learning search applications in minutes.

๐ŸŒŒ All data types - Large-scale indexing and querying of any kind of unstructured data: video, image, long/short text, music, source code, PDF, etc.

๐ŸŒฉ๏ธ Fast & cloud-native - Distributed architecture from day one, scalable & cloud-native by design: enjoy containerizing, streaming, paralleling, sharding, async scheduling, HTTP/gRPC/WebSocket protocol.

โฑ๏ธ Save time - The design pattern of neural search systems, from zero to a production-ready system in minutes.

๐Ÿฑ Own your stack - Keep end-to-end stack ownership of your solution, avoid integration pitfalls you get with fragmented, multi-vendor, generic legacy tools.


  • via PyPI: pip install jina
  • via Conda: conda install jina -c conda-forge
  • via Docker: docker run jinaai/jina:latest
  • More install options


Run Quick Demo

Build Your First Jina App

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

Leveraging these three components, let's build an app that find lines from a code snippet that are most similar to the query.

๐Ÿ’ก Preliminaries: character embedding, pooling, Euclidean distance

1๏ธโƒฃ Copy-paste the minimum example below and run it:

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

    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

    def foo(self, docs: DocumentArray, **kwargs):
        self._docs.extend(docs)  # extend stored `docs`

    def bar(self, docs: DocumentArray, **kwargs):
         docs.match(self._docs, metric='euclidean')

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:'/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

2๏ธโƒฃ 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

3๏ธโƒฃ Not a GUI fan? 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([0].matches[:3]):  # print top-3 matches
        print(f'[{idx}]{d.scores["euclidean"].value:2f}: "{d.text}"')

c = Client(protocol='http', port=12345)  # connect to localhost:12345'/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]0.218218: "from jina import Document, DocumentArray, Executor, Flow, requests"

๐Ÿ˜” Doesn't work? Our bad! Please report it here.


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.


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

All Contributors