Cloud-Native Neural Search? Framework for Any Kind of Data
Jina๐ is a neural search framework that empowers anyone to build SOTA and scalable deep learning search applications in minutes.
๐ All data types - Scalable indexing, querying, understanding of any data: video, image, long/short text, music, source code, PDF, etc.
โฑ๏ธ Save time - The design pattern of neural search systems, from zero to a production-ready system in minutes.
๐ฉ๏ธ Fast & cloud-native - Distributed architecture from day one, scalable & cloud-native by design: enjoy containerizing, streaming, paralleling, sharding, async scheduling, HTTP/gRPC/WebSocket protocols.
๐ฑ 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
- ๐ Fashion image search:
jina hello fashion - ๐ค QA chatbot:
pip install "jina[demo]" && jina hello chatbot - ๐ฐ Multimodal search:
pip install "jina[demo]" && jina hello multimodal - ๐ด Fork the source of a demo to your folder:
jina hello fork fashion ../my-proj/
Document, Executor, and Flow are three fundamental concepts in Jina.
- ๐ Document is the basic data type in Jina;
- โ๏ธ Executor is how Jina processes Documents;
- ๐ Flow is how Jina streamlines and distributes Executors.
Leveraging these three components, let's build an app that find lines from a code snippet that are most similar to the query.
๐ก Preliminaries: character embedding, pooling, Euclidean distance ๐ Read our docs for details
1๏ธโฃ Copy-paste the minimum example below and run it:
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')
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 request2๏ธโฃ 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!
3๏ธโฃ 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(resp.docs[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
c.post('/search', Document(text='request(on=something)'), on_done=print_matches)This 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"
๐ Doesn't work? Our bad! Please report it here.
- 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
Jina is backed by Jina AI. We are actively hiring AI engineers, solution engineers to build the next neural search ecosystem in opensource.
We welcome all kinds of contributions from the open-source community, individuals and partners. We owe our success to your active involvement.
