Neural Search – From Prototype to Production with Jina: Build deep learning–powered search systems that you can deploy and manage with ease

by Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzman

Length: 188 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-10-14
ISBN-10: 1801816824
ISBN-13: 9781801816823
Sales Rank: #92796 (See Top 100 Books)

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Implement neural search systems on the cloud by leveraging Jina design patterns

Key Features

Identify the different search techniques and discover applications of neural search
Gain a solid understanding of vector representation and apply your knowledge in neural search
Unlock deeper levels of knowledge of Jina for neural search

Book Description

Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search.

Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning–powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you’ll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine.

By the end of this deep learning book, you’ll be able to make the most of Jina’s neural search design patterns to build an end-to-end search solution for any modality.

What you will learn

Understand how neural search and legacy search work
Grasp the machine learning and math fundamentals needed for neural search
Get to grips with the foundation of vector representation
Explore the basic components of Jina
Analyze search systems with different modalities
Uncover the capabilities of Jina with the help of practical examples

Who this book is for

If you are a machine learning, deep learning, or artificial intelligence engineer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python engineers who are interested in building a search system of any kind using state-of-the-art deep learning techniques.

Neural Search – From Prototype to Production with Jina
Contributors
About the authors
About the reviewer
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
Part 1: Introduction to Neural Search Fundamentals
Chapter 1: Neural Networks for Neural Search
    Technical requirements
    Legacy search versus neural search
        Exploring various data types and search scenarios
        How does the traditional search system work?
        Pros and cons of the traditional search system
    Machine learning for search
        Understanding machine learning and artificial intelligence
        Machine learning and learning-to-rank
    Practical applications powered by neural search
    New terms learned in this chapter
    Summary
Chapter 2: Introducing Foundations of Vector Representation
    Technical requirements
    Introducing vectors in ML
        Using vectors to represent data
    Measuring similarity between two vectors
        Metrics beyond cosine similarity
    Local and distributed representations
        Local vector representation
        Distributed vector representation
    Summary
    Further reading
Chapter 3: System Design and Engineering Challenges
    Technical requirements
    Introducing indexing and querying
        Indexing
        Querying
    Evaluating a neural search system
    Engineering challenges of building a neural search system
    Summary
Part 2: Introduction to Jina Fundamentals
Chapter 4: Learning Jina’s Basics
    Technical requirements
    Exploring Jina
    Documents
        Document attributes
    DocumentArray
        Constructing a DocumentArray
    Executors
        Creating an Executor
    Flow
        Creating a Flow
        Adding Executors to a Flow
    Summary
Chapter 5: Multiple Search Modalities
    Technical requirements
    Introducing multimodal documents
        Text document
        Image document
        Audio document
        Multimodal document
    How to encode multimodal documents
        Encoding text documents
        Encoding image documents
        Encoding audio documents
    Cross-modal and multimodal searches
        Cross-modal search
        Multimodal search
    Summary
Part 3: How to Use Jina for Neural Search
Chapter 6: Building Practical Examples with Jina
    Technical requirements
    Getting started with the Q/A chatbot
        Navigating through the code
    Understanding fashion image search
        Navigating through the code
    Working with multimodal search
        Navigating through the code
    Summary
Chapter 7: Exploring Advanced Use Cases of Jina
    Technical requirements
    Introducing multi-level granularity
        Navigating through the code
        app.py
        index.yml
        query.yml
        Installing and running the example
    Cross-modal search with images with text
        app.py
        flow-index.yml
        query.yml
        Installing and running the example
    Concurrent querying and indexing data
        app.py
        flow.yml
        Installing and running the example
    Summary
Index
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