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Building LLM Powered  Applications

You're reading from   Building LLM Powered Applications Create intelligent apps and agents with large language models

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
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Author (1):
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Large Language Models FREE CHAPTER 2. LLMs for AI-Powered Applications 3. Choosing an LLM for Your Application 4. Prompt Engineering 5. Embedding LLMs within Your Applications 6. Building Conversational Applications 7. Search and Recommendation Engines with LLMs 8. Using LLMs with Structured Data 9. Working with Code 10. Building Multimodal Applications with LLMs 11. Fine-Tuning Large Language Models 12. Responsible AI 13. Emerging Trends and Innovations 14. Other Books You May Enjoy
15. Index

8 LLMs on structured data

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In this chapter, we are going to cover yet another great capabilities of Large Language Models: the ability to handle structured, tabular data. We will see how, thanks to plug-ins and an agentic approach, we can use LLMs as a natural language interface between us and our structured data, reducing the gap between the business user and the structured information.During this chapter, we will cover the following topics:

  • Introduction to the main structured data systems
  • Using tools and plug-ins to connect LLMs to tabular data
  • Building a Database copilot with LangChain

By the end of this chapter, you will be able to build your own natural language interface towards your data estate, also combining unstructured with structured sources.

Technical requirements

  • Hugging Face account and user access token.
  • OpenAI account and user access token.
  • Python 3.7.1 or later version.
  • Python packages....
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