Python库 | jina-0.9.28.dev6.tar.gz

<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"> An easier way to build neural search in the cloud </p> <p align=center> <a href="#license"><img src="https://github.com/jina-ai/jina/blob/master/.github/badges/license-badge.svg?raw=true" alt="Jina" title="Jina is licensed under Apache-2.0"></a> <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://github.com/jina-ai/jina/actions?query=workflow%3ACI"><img src="https://github.com/jina-ai/jina/workflows/CI/badge.svg" alt="CI"></a> <a href="https://github.com/jina-ai/jina/actions?query=workflow%3ACD"><img src="https://github.com/jina-ai/jina/workflows/CD/badge.svg?branch=master" alt="CD"></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> </p> Jina is a deep learning-powered search framework for building <strong>cross-/multi-modal search systems</strong> (e.g. text, images, video, audio) in the cloud. ⏱️ **Time Saver** - *The* design pattern of neural search systems, from zero to a production-ready system in minutes. 🌌 **Universal Search** - Large-scale indexing and querying of unstructured data: video, image, long/short text, music, source code, etc. 🧠 **First-Class AI Models** - First-class support for [state-of-the-art AI models](https://docs.jina.ai/chapters/all_exec.html). ☁️ **Cloud Ready** - Decentralized architecture with cloud-native features out-of-the-box: containerization, microservice, scaling, sharding, async IO, REST, gRPC, WebSocket. 🧩 **Plug & Play** - Easily usable and extendable with a Pythonic interface. ❤️ **Made with Love** - Lean dependencies (only 6!) & tip-top, never compromises on quality, maintained by a [passionate full-time, venture-backed team](https://jina.ai). --- <p align="center"> <a href="http://docs.jina.ai">Docs</a> • <a href="#jina-hello-world-">Hello World</a> • <a href="#get-started">Quick Start</a> • <a href="#learn">Learn</a> • <a href="https://github.com/jina-ai/examples">Examples</a> • <a href="#contributing">Contribute</a> • <a href="https://jobs.jina.ai">Jobs</a> • <a href="http://jina.ai">Website</a> • <a href="http://slack.jina.ai">Slack</a> </p> ## Installation | 📦<br>_{^{x86/64,arm/v6,v7,[v8 (Apple M1)](https://github.com/jina-ai/jina/issues/1781)}} | On Linux/macOS & Python 3.7/3.8/[3.9](https://github.com/jina-ai/jina/issues/1801) | Docker Users| | --- | --- | --- | | Standard | `pip install -U jina` | `docker run jinaai/jina:latest` | | _Daemon | _{`pip install -U "jina[daemon]"`} | _{`docker run --network=host jinaai/jina:latest-daemon`} | | _{With Extras} | _{`pip install -U "jina[devel]"`} | _{`docker run jinaai/jina:latest-devel`} | | _{Dev/Pre-Release} | _{`pip install --pre jina`} | _{`docker run jinaai/jina:master`} | Version identifiers [are explained here](https://github.com/jina-ai/jina/blob/master/RELEASE.md). To install Jina with extra dependencies [please refer to the docs](https://docs.jina.ai/chapters/install/via-pip.html). 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). ## Jina "Hello, World!" 👋🌍 Just starting out? Try Jina's "Hello, World" - a simple image neural search demo for [Fashion-MNIST](https://hanxiao.io/2018/09/28/Fashion-MNIST-Year-In-Review/). No extra dependencies needed, simply run: ```bash jina hello-world ``` ...or even easier for Docker users, **no install required**: ```bash docker run -v "$(pwd)/j:/j" jinaai/jina hello-world --workdir /j && open j/hello-world.html # replace "open" with "xdg-open" on Linux ``` <details> <summary>Click here to see console output</summary> <p align="center"> <img src="https://github.com/jina-ai/jina/blob/master/.github/images/hello-world-demo.png?raw=true" alt="hello world console output"> </p> </details> This downloads the Fashion-MNIST training and test dataset and tells Jina to index 60,000 images from the training set. Then it randomly samples images from the test set as queries and asks Jina to retrieve relevant results. The whole process takes about 1 minute, and after running opens a webpage and shows results: <p align="center"> <img src="https://github.com/jina-ai/jina/blob/master/.github/images/hello-world.gif?raw=true" alt="Jina banner" width="80%"> </p> Intrigued? Play with different options: ```bash jina hello-world --help ``` ## Get Started | | | | --- |---| | 🐣 | [Create](#create) • [Visualize](#visualize) • [Feed Data](#feed-data) • [Fetch Result](#fetch-result) • [Construct Document](#construct-document) • [Add Logic](#add-logic) • [Inter & Intra Parallelism](#inter--intra-parallelism) • [Decentralize](#decentralized-flow) • [Asynchronous](#asynchronous-flow) | | 🚀 | [Customize Encoder](#customize-encoder) • [Test Encoder](#test-encoder-in-flow) • [Parallelism & Batching](#parallelism--batching) • [Add Data Indexer](#add-data-indexer) • [Compose Flow from YAML](#compose-flow-from-yaml) • [Search](#search) • [Evaluation](#evaluation) • [REST Interface](#rest-interface) | #### Create <a href="https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-create-flow.ipynb"><img align="right" src="https://github.com/jina-ai/jina/blob/master/.github/badges/run-badge.svg?raw=true"/></a> Jina provides a high-level [Flow API](https://101.jina.ai) to simplify building search/index workflows. To create a new Flow: ```python from jina import Flow f = Flow().add() ``` This creates a simple Flow with one [Pod](https://101.jina.ai). You can chain multiple `.add()`s in a single Flow. #### Visualize <a href="https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-visualize-a-flow.ipynb"><img align="right" src="https://github.com/jina-ai/jina/blob/master/.github/badges/run-badge.svg?raw=true"/></a> To visualize the Flow, simply chain it with `.plot('my-flow.svg')`. If you are using a Jupyter notebook, the Flow object will be automatically displayed inline *without* `plot`: <img src="https://github.com/jina-ai/jina/blob/master/.github/simple-flow0.svg?raw=true"/> `Gateway` is the entrypoint of the Flow. #### Feed Data <a href="https://mybinder.org/v2/gh/jina-ai/jupyter-notebooks/main?filepath=basic-feed-data.ipynb"><img align="right" src="https://github.com/jina-ai/jina/blob/master/.github/badges/run-badge.svg?raw=true"/></a> To use a Flow, open it via `with` context manager, like you would open a file in Python. Now let's create some empty document and index it: ```python from jina import Document with Flow().add() as f: f.index((Document() for _ in range(10))) ``` Flow supports CRUD operations: `index`, `search`, `update`, `delete`. Besides, it also provides sugary syntax on common data type such as files, text, and `ndarray`. ```python with f: f.index_ndarray(numpy.random.random([4,2]), on_done=print) # index ndarray data, document sliced on first dimension f.index_lines(['hello world!', 'goodbye world!']) # index textual data, each element is a document f.index_files(['/tmp