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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

Using OpenAI models instead of local ones

In this chapter, we used different models. Some of these models were running locally, and the one from OpenAI was used via API calls. We can utilize OpenAI models in all recipes. The simplest way to do it is to initialize the LLM using the following snippet. Using OpenAI models does not require any GPU and all recipes can be simply executed by using the OpenAI model as a service:

import getpass
from langchain_openai import ChatOpenAI
os.environ["OPENAI_API_KEY"] = getpass.getpass()
llm = ChatOpenAI(model="gpt-4o-mini")

This completes our chapter on generative AI and LLMs. We have just scratched the surface of what is possible via generative AI; we hope that the examples presented in this chapter help illuminate the capabilities of LLMs and their relation to generative AI. We recommend exploring the LangChain site for updates and new tools and agents for their use cases and applying them in production scenarios following...

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