Cloud-Native Neural Search[?] Framework for Any Kind of Data
Jina allows you to build deep learning-powered search-as-a-service in just minutes.
Run Quick Demo
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👗 Fashion image search:pip install --pre && jina hello fashion -
🤖 QA chatbot:pip install --pre "jina[chatbot]" && jina hello chatbot -
📰 Multimodal search:pip install --pre "jina[multimodal]" && jina hello multimodal -
🍴 Fork the source of a demo to your folder:jina hello fork fashion ../my-proj/
Install
2.0 is in pre-release, add --pre to install it.
$ 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,arm64,v6,v7,Apple M1 |
On Linux/macOS & Python 3.7/3.8/3.9 | Docker Users |
|---|---|---|
| Standard | pip install --pre jina |
docker run jinaai/jina:master |
| Daemon | pip install --pre "jina[daemon]" |
docker run --network=host jinaai/jina:master-daemon |
| With Extras | pip install --pre "jina[devel]" |
docker run jinaai/jina:master-devel |
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.
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📄 Document is the basic data type in Jina; -
⚙️ Executor is how Jina processes Documents; -
🔀 Flow is how Jina streamlines and distributes Executors.
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):
q = np.stack(docs.get_attributes('embedding')) # get all embeddings from query docs
d = np.stack(self._docs.get_attributes('embedding')) # get all embeddings from stored docs
euclidean_dist = np.linalg.norm(q[:, None, :] - d[None, :, :], axis=-1) # pairwise euclidean distance
for dist, query in zip(euclidean_dist, docs): # add & sort match
query.matches = [Document(self._docs[int(idx)], copy=True, scores={'euclid': d}) for idx, d in enumerate(dist)]
query.matches.sort(key=lambda m: m.scores['euclid'].value) # sort matches by their values
f = Flow(port_expose=12345).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
Keep the above 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["euclid"].value:2f}: "{d.text}"')
c = Client(host='localhost', port_expose=12345) # connect to localhost:12345
c.post('/search', Document(text='request(on=something)'), on_done=print_matches)
It finds the lines most similar to "request(on=something)" from the server code snippet and prints the following:
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)]"
Read Tutorials
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🧠 What is "Neural Search"? -
📄 Document&DocumentArray: the basic data type in Jina. -
⚙️ Executor: how Jina processes Documents. -
🔀 Flow: how Jina streamlines and distributes Executors. -
🤹 Serving Jina -
🧼 Write clean code in Jina -
📓 Developer References -
😎 3 Reasons to use Jina 2.0
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 (see our public events calendar/.ical) 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
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