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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
Languages
Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Generating a SQL query using human-defined requirements

In this recipe, we will learn how to use an LLM to infer the schema of a database and generate SQL queries based on human input. The human input would be a simple question. The LLM will use the schema information along with the human question to generate the correct SQL query. Also, we will connect to a database that has populated data, execute the generated SQL query, and present the answer in a human-readable format. Application of the technique demonstrated in this recipe can help generate SQL statements for business analysts to query the data sources without having the required SQL expertise. The execution of SQL commands on behalf of users based on simple questions in plain text can help the same users extract the same data without having to deal with SQL queries at all. Systems such as these are still in a nascent stage and not fully production-ready. Our goal here is to demonstrate the basic building blocks of how to make...

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