pytorch-search 0.0.1
Jina is geared towards building search-as-a-service systems for any kind of data in just minutes.
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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<img src="https://github.com/jina-ai/jina/blob/master/.github/logo-only.gif?raw=true" alt="Jina banner" width="200px">
</p>

<p align="center">
<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>


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<!-- start elevator-pitch -->

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

<!-- end elevator-pitch -->

🌌 **All data type** - 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, distributing, streaming, paralleling, sharding, async scheduling with REST/gRPC/WebSocket.

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

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



## Run Quick Demo

- [👗 Fashion image search](./.github/pages/hello-world.md#-fashion-image-search): `pip install --pre && jina hello fashion`
- [🤖 QA chatbot](./.github/pages/hello-world.md#-covid-19-chatbot): `pip install --pre "jina[chatbot]" && jina hello chatbot`
- [📰 Multimodal search](./.github/pages/hello-world.md#-multimodal-document-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. [Why 2.0?](.github/2.0/1vs2.md)

```console
$ pip install --pre jina
$ jina -v
2.0.0rcN
```

#### via Docker

```console
$ docker run jinaai/jina:master -v
2.0.0rcN
```

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

| <br><sub><sup>x86/64,arm64,v6,v7,Apple M1</sup></sub> | On Linux/macOS & Python 3.7/3.8/3.9 | Docker Users|
| --- | --- | --- |
| Standard | `pip install --pre jina` | `docker run jinaai/jina:master` |
| <sub><a href="https://api.jina.ai/daemon/">Daemon</a></sub> | <sub>`pip install --pre "jina[daemon]"`</sub> | <sub>`docker run --network=host jinaai/jina:master-daemon`</sub> |
| <sub>With Extras</sub> | <sub>`pip install --pre "jina[devel]"`</sub> | <sub>`docker run jinaai/jina:master-devel`</sub> |

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.

<a href="https://colab.research.google.com/github/jina-ai/jupyter-notebooks/blob/main/2.0-readme-38l.ipynb"><img align="right" src="https://colab.research.google.com/assets/colab-badge.svg?raw=true"/></a>

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>

![Get started system diagram](.github/2.0/simple-arch.svg)

```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):
        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, score=d) for idx, d in enumerate(dist)]
            query.matches.sort(key=lambda m: m.score.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:

```python
from jina import Client, Document

def print_matches(req):  # the callback function invoked when task is done
    for idx, d in enumerate(req.docs[0].matches[:3]):  # print top-3 matches
        print(f'[{idx}]{d.score.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:  

```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)
- ⚙️ `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)
    - [`Flow`-as-a-Service](.github/2.0/cookbooks/Flow.md#flow-as-a-service)
- 🧼 [Write clean code in Jina](.github/2.0/cookbooks/CleanCode.md)
- 📓 [Developer References](https://docs2.jina.ai)
- 😎 [3 Reasons to use Jina 2.0](.github/2.0/1vs2.md)

## 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 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.

- [Contributing guidelines](CONTRIBUTING.md)
- [Code of conduct](https://github.com/jina-ai/jina/blob/master/.github/CODE_OF_CONDUCT.md) - play nicely with the Jina
  community
- [Good first issues](https://github.com/jina-ai/jina/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)
- [Release cycles and development stages](RELEASE.md)

<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-155-orange.svg?style=flat-square)](#contributors-)
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<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
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