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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 2. LLMs for AI-Powered Applications FREE CHAPTER 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

Summary

In this chapter, we approached the end-to-end implementation of a conversational application, leveraging LangChain’s modules and progressively adding layers of complexity. We started with a plain vanilla chatbot with no memory, to then move towards more complex system with the ability to keep trace of past interaction. We’ve also seen how to add non-parametric knowledge to our application with external tools, making it more “agentic” so that it is able to determine which tool to use, depending on user’s query. Finally, we introduced Streamlit as front-end framework to build the webapp for our Globebotter.In next chapter, we will focus on a more specific domain where LLMs are adding values and demonstrating emerging behaviours, that is the one of recommendation systems.

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