jina-search 0.0.0
Jina is the cloud-native neural search framework for any kind of data
License: Apache 2.0
Keywords: jina cloud-native neural-search query search index elastic neural-network encoding embedding serving docker container image video audio deep-learning

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<b>Cloud-Native <ins>Neural Search</ins><sup><a href=".github/2.0/neural-search.md">[?]</a></sup> Framework for <i>Any</i> Kind of Data</b>
</p>


<p align=center>
<a href="https://pypi.org/project/jina/"><img src="https://github.com/jina-ai/jina/blob/master/.github/badges/python-badge.svg?raw=true" alt="Python 3.7 3.8 3.9" title="Jina supports Python 3.7 and above"></a>
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<!-- start elevator-pitch -->

Jina<sup><a href=".github/pronounce-jina.mp3">`🔊`</a></sup> allows you to build search-as-a-service powered by deep learning in just minutes.

<!-- end elevator-pitch -->

🌌 **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

- [👗 Fashion image search](./.github/pages/hello-world.md#-fashion-image-search): `jina hello fashion`
- [🤖 QA chatbot](./.github/pages/hello-world.md#-covid-19-chatbot): `pip install "jina[demo]" && jina hello chatbot`
- [📰 Multimodal search](./.github/pages/hello-world.md#-multimodal-document-search): `pip install "jina[demo]" && jina hello multimodal`
- 🍴 Fork the source of a demo to your folder: `jina hello fork fashion ../my-proj/`

## Install

- via PyPI: `pip install -U jina`
- via Docker: `docker run jinaai/jina:latest`

<details>
<summary>More installation options</summary>

| On x86/64, arm64/v6/v7 | Linux/macOS with Python 3.7/3.8/3.9 | Docker Users |
| --- | --- | --- |
| Minimum <br>(no HTTP, WebSocket, Docker support) | `JINA_PIP_INSTALL_CORE=1 pip install jina` | `docker run jinaai/jina:latest` |
| Minimum but more performant <br>(use `uvloop` & `lz4`) | `JINA_PIP_INSTALL_PERF=1 pip install jina` | `docker run jinaai/jina:latest-perf` |
| With <a href="https://api.jina.ai/daemon/">Daemon</a> | `pip install "jina[daemon]"` | [Run JinaD](.github/2.0/cookbooks/Daemon.md#run) |
| Full development dependencies | `pip install "jina[devel]"` | `docker run jinaai/jina:latest-devel` |
| Pre-release<br>(all tags above can be added)| <sub>`pip install --pre jina` | `docker run jinaai/jina:master` |


Version identifiers [are explained here](https://github.com/jina-ai/jina/blob/master/RELEASE.md). Jina can run
on [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install-win10). We welcome the community
to help us with [native Windows support](https://github.com/jina-ai/jina/issues/1252).

</details>

## Get Started

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

- [📄 **Document**](.github/2.0/cookbooks/Document.md) is the basic data type in Jina;
- [⚙️ **Executor**](.github/2.0/cookbooks/Executor.md) is how Jina processes Documents;
- [🔀 **Flow**](.github/2.0/cookbooks/Flow.md) is how Jina streamlines and distributes Executors.

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

<sup>💡 Preliminaries: <a href="https://en.wikipedia.org/wiki/Word_embedding">character embedding</a>, <a href="https://computersciencewiki.org/index.php/Max-pooling_/_Pooling">pooling</a>, <a href="https://en.wikipedia.org/wiki/Euclidean_distance">Euclidean distance</a></sup>

<img src="https://github.com/jina-ai/jina/blob/master/.github/2.0/simple-arch.svg" alt="The architecture of a simple neural search system powered by Jina">

<!-- README-SERVER-START -->
```python
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
```
<!-- README-SERVER-END -->

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

```json
{"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 <kbd>Execute</kbd> button!

<p align="center">
<img src="https://github.com/jina-ai/jina/blob/master/.github/swagger-ui-prettyprint1.gif?raw=true" alt="Jina Swagger UI extension on visualizing neural search results" width="85%">
</p>

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


<!-- README-CLIENT-START -->
```python
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)
```

<!-- README-CLIENT-END -->

, which prints the following results:

```text
         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)]"
```
<sup>😔 Doesn't work? Our bad! <a href="https://github.com/jina-ai/jina/issues/new?assignees=&labels=kind%2Fbug&template=---found-a-bug-and-i-solved-it.md&title=">Please report it here.</a></sup>


## Read Tutorials

- 🧠 [What is "Neural Search"?](.github/2.0/neural-search.md)
- 📄 `Document` & `DocumentArray`: the basic data type in Jina.
    - [Minimum Working Example](.github/2.0/cookbooks/Document.md#minimum-working-example)
    - [`Document` API](.github/2.0/cookbooks/Document.md#document-api)
    - [`DocumentArray` API](.github/2.0/cookbooks/Document.md#documentarray-api)
    - [`DocumentArrayMemmap` API](.github/2.0/cookbooks/Document.md#documentarraymemmap-api)
- ⚙️ `Executor`: how Jina processes Documents.
    - [Minimum working example](.github/2.0/cookbooks/Executor.md#minimum-working-example)
    - [`Executor` API](.github/2.0/cookbooks/Executor.md#executor-api)
    - [`Executor` Built-in Features](.github/2.0/cookbooks/Executor.md#executor-built-in-features)
    - [Use Tensorflow, Pytorch, Pytorch Lightning, Fastai, Mindspore, PaddlePaddle, Scikit-learn in `Executor`](.github/2.0/cookbooks/Executor.md#executors-in-action)
- 🔀 `Flow`: how Jina streamlines and distributes Executors.
    - [Minimum Working Example](.github/2.0/cookbooks/Flow.md#minimum-working-example)
    - [`Flow` API](.github/2.0/cookbooks/Flow.md#flow-api)
- 🤹 Serving Jina as a Service
    - [Minimum Working Example](.github/2.0/cookbooks/Serving.md#minimum-working-example)
    - [`Flow`-as-a-Service](.github/2.0/cookbooks/Serving.md#flow-as-a-service)
    - [Deliver OAS3.0 Friendly API](.github/2.0/cookbooks/Serving.md#extend-http-interface)
- 👹️ `JinaD`: create & manage remote Jina Executors & Flows.
  - [Minimum Working Example](.github/2.0/cookbooks/Daemon.md#minimum-working-example)
  - [JinaD Server & Client](.github/2.0/cookbooks/Daemon.md#setup-jinad-server)
  - [Create Remote Executors](.github/2.0/cookbooks/Daemon.md#create-a-remote-executor)
  - [Create Remote Flows](.github/2.0/cookbooks/Daemon.md#create-remote-flows)
- 📓 [Developer Reference](https://docs.jina.ai)
- 🧼 [Clean & Efficient Coding in Jina](.github/2.0/cookbooks/CleanCode.md)
- 🚶‍ Walkthroughs
  - [How to Build Hello World Chatbot](https://jina.ai/blog/tutorial)
  - [Create Your Own Executor](https://jina.ai/blog/tutorial-executors)

    
## Support

- Join our [Slack community](https://slack.jina.ai) to chat to our engineers about your use cases, questions, and
  support queries.
- Join our [Engineering All Hands](https://youtube.com/playlist?list=PL3UBBWOUVhFYRUa_gpYYKBqEAkO4sxmne) 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](https://calendar.google.com/calendar/embed?src=c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com&ctz=Europe%2FBerlin)/[.ical](https://calendar.google.com/calendar/ical/c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com/public/basic.ics))
      and [live stream on YouTube](https://youtube.com/c/jina-ai)
- Subscribe to the latest video tutorials on our [YouTube channel](https://youtube.com/c/jina-ai)

## Join Us

Jina is backed by [Jina AI](https://jina.ai). [We are actively hiring](https://jobs.jina.ai) 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.

- [Release cycles and development stages](RELEASE.md)
- [Contributing guidelines](CONTRIBUTING.md)
- [Code of conduct](https://github.com/jina-ai/jina/blob/master/.github/CODE_OF_CONDUCT.md)
  community
- [Get yourself some swags](https://jina.ai/blog/swag)

<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-172-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
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