标签归档:cloud-native

Jina-面向任何类别数据的云原生神经搜索框架

云-本地神经搜索[?]适用于以下方面的框架任何数据类型

Jina 允许您在短短几分钟内构建以深度学习为动力的搜索即服务

🌌所有数据类型-大规模索引和查询任何类型的非结构化数据:视频、图像、长/短文本、音乐、源代码、PDF等

🌩️FAST和本机云-从第一天开始的分布式架构,可扩展且设计为本地云:享受集装箱化、流式处理、并行、分片、异步调度、HTTP/GRPC/WebSocket协议

⏱️节省时间这个神经搜索系统的设计模式,从零到生产准备就绪的系统只需几分钟

🍱拥有您的堆栈-保持解决方案的端到端堆栈所有权,避免使用零散的、多供应商的通用旧式工具带来的集成陷阱

运行快速演示

安装

  • 通过PyPI:pip install -U "jina[standard]"
  • 通过Docker:docker run jinaai/jina:latest
更多安装选项
x86/64、arm64、v6、v7 Linux/MacOS和Python 3.7/3.8/3.9 Docker用户
最低要求
(不支持HTTP、WebSocket、Docker)
pip install jina docker run jinaai/jina:latest
Daemon pip install "jina[daemon]" docker run --network=host jinaai/jina:latest-daemon
使用附加服务 pip install "jina[devel]" docker run jinaai/jina:latest-devel

版本标识符are explained here吉娜可以继续奔跑Windows Subsystem for Linux我们欢迎社会各界帮助我们native Windows support

开始使用

文档、执行者和流是JINA中的三个基本概念

1个️⃣复制-粘贴下面的最小示例并运行它:

💡预赛:character embeddingpoolingEuclidean distance

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, scores={'euclid': d}) for idx, d in enumerate(dist)]
            query.matches.sort(key=lambda m: m.scores['euclid'].value)  # sort matches by their values

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

2个️⃣打开http://localhost:12345/docs(扩展的Swagger UI)在浏览器中,单击/搜索制表符和输入:

{"data": [{"text": "@requests(on=something)"}]}

也就是说,我们希望从上面的代码片段中找到与以下内容最相似的行@request(on=something)现在单击执行巴顿!

3个️⃣不是图形用户界面的人?那就让我们用Python来做吧!保持上述服务器运行,并启动一个简单的客户端:

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["euclid"].value:2f}: "{d.text}"')


c = Client(protocol='http', port_expose=12345)  # connect to localhost:12345
c.post('/search', Document(text='request(on=something)'), on_done=print_matches)

,它打印以下结果:

         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)]"

😔不管用吗?我们的错!Please report it here.

阅读教程

支持

加入我们吧

吉娜的后盾是Jina AIWe are actively hiring全栈开发人员、解决方案工程师在开源领域构建下一个神经搜索生态系统

贡献

我们欢迎来自开源社区、个人和合作伙伴的各种贡献。我们的成功归功于你的积极参与