Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Building LLM Powered  Applications

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

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
Arrow right icon
View More author details
Toc

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

Join our book community on Discord

https://packt.link/EarlyAccessCommunity

Qr code Description automatically generated

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....
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime