Metadata-Version: 2.1
Name: jina
Version: 2.0.0rc5.dev73
Summary: Jina is geared towards building search-as-a-service systems for any kind of data in just minutes.
Home-page: https://github.com/jina-ai/jina/
Author: Jina Dev Team
Author-email: dev-team@jina.ai
License: Apache 2.0
Download-URL: https://github.com/jina-ai/jina/tags
Project-URL: Documentation, https://docs2.jina.ai
Project-URL: Source, https://github.com/jina-ai/jina/
Project-URL: Tracker, https://github.com/jina-ai/jina/issues
Description: <p align="center">
        <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>
        
        
        <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>
        <a href="https://hub.docker.com/r/jinaai/jina/tags"><img src="https://img.shields.io/docker/v/jinaai/jina?color=%23099cec&amp;label=Docker&amp;logo=docker&amp;logoColor=white&amp;sort=semver" alt="Docker Image Version (latest semver)"></a>
        <a href="https://pepy.tech/project/jina"><img src="https://pepy.tech/badge/jina/month"></a>
        <a href="https://codecov.io/gh/jina-ai/jina"><img src="https://codecov.io/gh/jina-ai/jina/branch/master/graph/badge.svg" alt="codecov"></a>
        <a href="https://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-600%2B-blueviolet"></a>
        </p>
        
        Jina allows you to build deep learning-powered search-as-a-service in just minutes.
        
        🌌 **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-)
        <!-- ALL-CONTRIBUTORS-BADGE:END -->
        
        <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
        <!-- prettier-ignore-start -->
        <!-- markdownlint-disable -->
        
        
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Keywords: jina cloud-native neural-search query search index elastic neural-network encoding embedding serving docker container image video audio deep-learning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Unix Shell
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Database :: Database Engines/Servers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Internet :: WWW/HTTP :: Indexing/Search
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
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