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Neural Search - From Prototype to Production with Jina

Neural Search - From Prototype to Production with Jina

By : Bo Wang, Jina AI, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán
4.5 (6)
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Neural Search - From Prototype to Production with Jina

Neural Search - From Prototype to Production with Jina

4.5 (6)
By: Bo Wang, Jina AI, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán

Overview of this book

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.
Table of Contents (13 chapters)
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1
Part 1: Introduction to Neural Search Fundamentals
5
Part 2: Introduction to Jina Fundamentals
8
Part 3: How to Use Jina for Neural Search

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “To interact with the multi-modal application in the web browser via the UI, you can use the index.html HTML file provided in the static folder.”

A block of code is set as follows:

  - name: keyValueIndexer
    uses:
      jtype: KeyValueIndexer
      metas:
        workspace: ${{ ENV.HW_WORKDIR }}
        py_modules:
          - ${{ ENV.PY_MODULE }}
    needs: segment
  - name: joinAll
    needs: [textIndexer, imageIndexer, keyValueIndexer]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

  - name: keyValueIndexer
    uses:
      jtype: KeyValueIndexer
      metas:
        workspace: ${{ ENV.HW_WORKDIR }}
        py_modules:
          - ${{ ENV.PY_MODULE }}
    needs: segment
  - name: joinAll
    needs: [textIndexer, imageIndexer, keyValueIndexer]

Any command-line input or output is written as follows:

<jina.types.arrays.document.DocumentArray length=3 at 5701440528>
 
{'id': '6a79982a-b6b0-11eb-8a66-1e008a366d49', 'tags': {'id': 3.0}},
{'id': '6a799744-b6b0-11eb-8a66-1e008a366d49', 'tags': {'id': 2.0}},
{'id': '6a799190-b6b0-11eb-8a66-1e008a366d49', 'tags': {'id': 1.0}}

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Select System info from the Administration panel.”

Tips or Important Notes

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