Hello everyone, I am Brother Tao. The content of this article comes from Brother Tao talking about Python. Please mark the originality when reprinting.

Today I will share with you an incredible Python library – jina

Github address: https://github.com/jina-ai/jina

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Python Jina is an open source tool for building large-scale, distributed and high-performance search systems. This article will introduce how to install, main features, basic functions, advanced functions, practical application scenarios and summary.

Install

First, you need to install the Python Jina library.

It can be installed through the pip command:

pip install jina

characteristic

• Distributed search : Supports the construction of distributed search systems to achieve efficient search and query functions.
• Elastic scaling : It has the ability to elastically scale and can cope with search needs of different scales and loads.
• Multi-modal search : Supports search and query in multiple modalities (text, image, video, etc.).
• Custom process : Search pipelines can be flexibly built through custom processes to meet different search needs.

basic skills

The basic functions of the Python Jina library cover core functions such as creating processes, loading data, indexing, and querying. These functions will be described in more detail below and sample codes will be provided.

1. Create a flow

In Python Jina, Flow is the core object for building search processes. It is responsible for managing components and handling data flow.

The steps to create a Flow are as follows:

from jina import Flow

# Create a simple Flow 
f = Flow().add(uses= 'config.yml' )

In this example, a simple Flow is created and the configuration file ‘config.yml’ used is specified. The configuration file can define various components and their parameters, such as the used Encoder, Indexer, etc.

2. Load data

Loading data is an important step in a search system. Python Jina provides flexible ways to load data and pass it to Flow.

The sample code is as follows:

with f:
    f.index(input_fn= 'data.json' )

In this example, indexthe method is used to load data into Flow for indexing. The parameter input_fn='data.json'specifies the data file to load. The format and source of input data can be modified according to actual conditions.

3. Process search requests

Once the data is indexed, Flow can be used to handle search requests.

The sample code is as follows:

with f:
    response = f.search(inputs=[query_data], top_k= 5 )
     for result in response:
         print (result)

In this example, searchmethods are used to handle search requests. The parameters inputs=[query_data]specify the input data for the search query and top_k=5specify the number of results returned. The number of query data and returned results can be adjusted according to actual needs.

Advanced Features

The advanced features of the Python Jina library include custom components, optimized search processes, and distributed deployment.

1. Custom components

Python Jina allows users to customize various components, such as Encoder, Indexer and Evaluator, to meet specific needs.

The sample code is as follows:

from jina import Executor, DocumentArray

class  MyCustomEncoder ( Executor ):
     def  encode ( self, data: DocumentArray, *args, **kwargs ):
         # Custom encoding logic 
        for doc in data:
            doc.embedding = ...   # Add embedding vector 
        return data

In this example, a component named Encoder is customized MyCustomEncoderand its encodemethods are implemented to define its own encoding logic.

2. Optimize the search process

By optimizing the search process, the performance and efficiency of the search system can be improved. Python Jina provides various optimization strategies and techniques, such as using GPU acceleration, asynchronous processing, and parallel computing.

The sample code is as follows:

from jina import Flow

# Use GPU acceleration 
f = Flow().add(uses= 'config.yml' , device= 'gpu' )

#Asynchronous processing 
with f:
    f.index(inputs= 'data.json' , batch_size= 64 , asynchronous= True )

In this example, device='gpu'GPU acceleration is used by specifying it in Flow, and asynchronous=Trueasynchronous processing is implemented by setting it.

3. Distributed deployment

Python Jina supports distributed deployment and can process data and query requests in parallel on multiple machines to improve the scalability and fault tolerance of the system.

The sample code is as follows:

from jina import Flow

# Create a distributed process 
f = Flow().add(uses= 'config.yml' , replicas= 2 )

# Run the process on multiple nodes 
with f:
    f.index(inputs= 'data.json' , routing= 'broadcast' )

In this example, the settings replicas=2are used to create a distributed process containing two copies, and the settings are routing='broadcast'used to implement broadcast data processing.

Practical application scenarios

The practical application scenarios of the Python Jina library are very wide, including text search, image retrieval, speech recognition, recommendation systems and other fields.

1. Text search

Python Jina can be used to build powerful text search engines that support fast and efficient searches of large-scale text data.

The sample code is as follows:

from jina import Flow

# Create a process and index text data 
f = Flow().add(uses= 'config.yml' )
 with f:
    f.index(inputs= 'text_data.txt' )

# Search text data 
with f:
    response = f.search(inputs= 'query.txt' )
     print (response)

In this example, a text search engine is built using Python Jina, which first indexes the text data, then searches the query text and obtains the search results.

2. Image retrieval

Python Jina can also be used for image retrieval tasks, which can process large-scale image data and implement fast and accurate image search functions.

The sample code is as follows:

from jina import Flow

# Create a flow and index image data 
f = Flow().add(uses= 'config.yml' )
 with f:
    f.index(inputs= 'image_data/' )
    
# Search image data 
with f:
    response = f.search(inputs= 'query_image.jpg' )
     print (response)

In this example, an image retrieval system is built using Python Jina, which first indexes the image data, then searches the query image and obtains the search results.

3. Voice recognition

Python Jina can also be used in the field of speech recognition, which can process speech data and achieve accurate speech recognition functions.

The sample code is as follows:

from jina import Flow

# Create a process and index voice data 
f = Flow().add(uses= 'config.yml' )
 with f:
    f.index(inputs= 'audio_data/' )
    
# Recognize voice data 
with f:
    response = f.search(inputs= 'query_audio.wav' )
     print (response)

In this example, a speech recognition system is built using Python Jina. The speech data is first indexed, and then the query speech is recognized and the recognition results are obtained.

Summarize

Python Jina is a powerful library suitable for a variety of practical application scenarios, including text search, image retrieval, speech recognition, etc. It provides flexible interfaces and rich functions, capable of processing large-scale data and achieving efficient and accurate search and recognition tasks. Through sample code, you can see the advantages of Python Jina in building search engines, image retrieval systems, and speech recognition systems. Overall, Python Jina provides developers with a convenient and powerful way to process and analyze various types of data, making it an ideal choice for implementing search and identification functions.