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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

Option 2: Combining single tools into one agent

In this leg of our journey toward multimodality, we will leverage different tools as plug-ins to our STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent. Our goal is to build a copilot agent that will help us generate reviews about YouTube videos, as well as post those reviews on our social media with a nice description and related picture. In all of that, we want to make little or no effort, so we need our agent to perform the following steps:

  1. Search and transcribe a YouTube video based on our input.
  2. Based on the transcription, generate a review with a length and style defined by the user query.
  3. Generate an image related to the video and the review.

We will call our copilot GPTuber. In the following subsections, we will examine each tool and then put them all together.

YouTube tools and Whisper

The first step of our agent will be to search and transcribe the YouTube video based on our input. To...

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