PyPI 官网下载 | jina-2.0.22.dev13.tar.gz

<p align="center"> <!-- <a href="https://www.meetup.com/jina-community-meetup/events/279360997/"><img src="https://github.com/jina-ai/jina/blob/master/.github/images/meetup.svg"></a> --> <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 <ins>Neural Search</ins>^{<a href=".github/2.0/neural-search.md">[?]</a>} 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-1.2K%2B-blueviolet?logo=slack&amp;logoColor=white"></a> </p> <!-- start elevator-pitch --> Jina^{<a href=".github/pronounce-jina.mp3">`🔊`</a>} 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: ^{💡 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>} <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=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]0.218218: "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="