Metadata-Version: 2.1
Name: jina
Version: 2.4.11.dev8
Summary: Jina is the cloud-native neural search framework for any kind of data
Home-page: https://github.com/jina-ai/jina/
Author: Jina AI
Author-email: hello@jina.ai
License: Apache 2.0
Download-URL: https://github.com/jina-ai/jina/tags
Project-URL: Documentation, https://docs.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">
        <!--startmsg-->
        
        <!--endmsg-->
        </p>
        <p align="center">
        <a href="https://jina.ai/"><img src="https://github.com/jina-ai/jina/blob/master/.github/logo-only.gif?raw=true" alt="Jina logo: Jina is a cloud-native neural search framework" width="200px"></a>
        </p>
        
        <p align="center">
        <b>Cloud-Native Neural Search<sup><a href="https://docs.jina.ai/get-started/neural-search/">?</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://pypi.org/project/jina/"><img src="https://img.shields.io/pypi/v/jina?color=%23099cec&amp;label=PyPI&amp;logo=pypi&amp;logoColor=white" alt="PyPI"></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-2.0k%2B-blueviolet?logo=slack&amp;logoColor=white"></a>
        </p>
        
        <!-- start elevator-pitch -->
        
        Jina is a neural search framework that empowers anyone to build
        SOTA and scalable deep learning search applications in minutes.
        
        🌌 **All data types** - Scalable indexing, querying, understanding of any data: video, image, long/short text, music,
        source code, PDF, etc.
        
        ⏱️ **Save time** - *The* design pattern of neural search systems, from zero to a production-ready system in minutes.
        
        🌩️ **Fast & cloud-native** - Distributed architecture from day one, scalable & cloud-native by design: enjoy
        containerization, streaming, sharding, replication, async scheduling, HTTP/gRPC/WebSocket protocols.
        
        🍱 **Own your stack** - Keep end-to-end stack ownership of your solution, avoid integration pitfalls you get with
        fragmented, multi-vendor, generic legacy tools.
        
        <!-- end elevator-pitch -->
        
        ## Install
        
        - via PyPI: `pip install jina`
        - via Conda: `conda install jina -c conda-forge`
        - via Docker: `docker run jinaai/jina:latest`
        - [More install options](https://docs.jina.ai/get-started/install/)
        
        ## [Documentation](https://docs.jina.ai)
        
        ## Run Quick Demo
        
        - [👗 Fashion image search](https://docs.jina.ai/get-started/hello-world/fashion/): `jina hello fashion`
        - [🤖 QA chatbot](https://docs.jina.ai/get-started/hello-world/covid-19-chatbot/): `pip install "jina[demo]" && jina hello chatbot`
        - [📰 Multimodal search](https://docs.jina.ai/get-started/hello-world/multimodal/): `pip install "jina[demo]" && jina hello multimodal`
        - 🍴 Fork the source of a demo to your folder: `jina hello fork fashion ../my-proj/`
        
        ## Build Your First Jina App
        
        Document, Executor, and Flow are three fundamental concepts in Jina.
        
        - [📄 **Document**](https://docs.jina.ai/fundamentals/document/) is the basic data type in Jina;
        - [⚙️ **Executor**](https://docs.jina.ai/fundamentals/executor/) is how Jina processes Documents;
        - [🔀 **Flow**](https://docs.jina.ai/fundamentals/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.**
        
        <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>
        <sup><a href="https://docs.jina.ai/tutorials/fuzzy-grep/">📗 Read our docs for details</a></sup>
        
        1️⃣ Copy-paste the minimum example below and run it:
        
        <img src="https://github.com/jina-ai/jina/blob/master/.github/2.0/simple-arch.svg?raw=true" 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')
        
        
        f = Flow(port_expose=12345, protocol='http', cors=True).add(uses=CharEmbed, replicas=2).add(
            uses=Indexer)  # build a Flow, with 2 replica 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/swaggerui.gif?raw=true" alt="Jina Swagger UI extension on visualizing neural search results" width="85%">
        </p>
        
        3️⃣ Not a GUI fan? 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=12345)  # connect to localhost:12345
        c.post('/search', Document(text='request(on=something)'), on_done=print_matches)
        ```
        
        <!-- README-CLIENT-END -->
        
        This 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]0.218218: "from jina import Document, DocumentArray, Executor, Flow, requests"
        ```
        
        <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>
        
        <!-- start support-pitch -->
        
        ## 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 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)/[Meetup
              group](https://www.meetup.com/jina-community-meetup/))
              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) and licensed under [Apache-2.0](./LICENSE).
        [We are actively hiring](https://jobs.jina.ai) AI engineers, solution engineers to build the next neural search
        ecosystem in open source.
        
        <!-- end support-pitch -->
        
        ## 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)
        
        <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
        [![All Contributors](https://img.shields.io/badge/all_contributors-200-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
Provides-Extra: scipy
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Provides-Extra: standard
Provides-Extra: daemon
Provides-Extra: demo
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Provides-Extra: uvicorn[standard]
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Provides-Extra: torch
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Provides-Extra: cicd
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Provides-Extra: Pillow
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Provides-Extra: pytest-lazy-fixture
Provides-Extra: datasets
Provides-Extra: av
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Provides-Extra: paddlepaddle
Provides-Extra: all
