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Neural Search - From Prototype to Production with Jina

You're reading from  Neural Search - From Prototype to Production with Jina

Product type Book
Published in Oct 2022
Publisher Packt
ISBN-13 9781801816823
Pages 188 pages
Edition 1st Edition
Languages
Authors (6):
Jina AI Jina AI
Profile icon Jina AI
Bo Wang Bo Wang
Profile icon Bo Wang
Cristian Mitroi Cristian Mitroi
Profile icon Cristian Mitroi
Feng Wang Feng Wang
Profile icon Feng Wang
Shubham Saboo Shubham Saboo
Profile icon Shubham Saboo
Susana Guzmán Susana Guzmán
Profile icon Susana Guzmán
View More author details

Table of Contents (13) Chapters

Preface Part 1: Introduction to Neural Search Fundamentals
Chapter 1: Neural Networks for Neural Search Chapter 2: Introducing Foundations of Vector Representation Chapter 3: System Design and Engineering Challenges Part 2: Introduction to Jina Fundamentals
Chapter 4: Learning Jina’s Basics Chapter 5: Multiple Search Modalities Part 3: How to Use Jina for Neural Search
Chapter 6: Building Practical Examples with Jina Chapter 7: Exploring Advanced Use Cases of Jina Index Other Books You May Enjoy

Working with multimodal search

Multimodal search is another pre-built example that comes with the Jina installation, which you can run directly from the comfort of your command line without even getting into the code.

This example uses Kaggle’s public people image dataset (https://www.kaggle.com/ahmadahmadzada/images2000), which consists of 2,000 image-caption pairs. The data type here is a multimodal document containing multiple data types such as a PDF document that contains text and images together. Jina lets you build the search for multimodal data types with the same ease and comfort:

  1. To run this example from the command line, you need to install the following dependencies:
    • pip install transformers
    • pip install torch
    • pip install torchvision
    • pip install “jina[demo]”
  2. Once all the dependencies are installed, simply type the following command to launch the application:
    jina hello multimodal
  3. After typing this command, you will see the following text...
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