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ChatGPT for Conversational AI and Chatbots

You're reading from   ChatGPT for Conversational AI and Chatbots Learn how to automate conversations with the latest large language model technologies

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781805129530
Length 250 pages
Edition 1st Edition
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Author (1):
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Adrian Thompson Adrian Thompson
Author Profile Icon Adrian Thompson
Adrian Thompson
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Table of Contents (15) Chapters Close

Preface 1. Part 1: Foundations of Conversational AI FREE CHAPTER
2. Chapter 1: An Introduction to Chatbots, Conversational AI, and ChatGPT 3. Chapter 2: Using ChatGPT with Conversation Design 4. Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
5. Chapter 3: ChatGPT Mastery – Unlocking Its Full Potential 6. Chapter 4: Prompt Engineering with ChatGPT 7. Chapter 5: Getting Started with LangChain 8. Chapter 6: Advanced Debugging, Monitoring, and Retrieval with LangChain 9. Part 3: Building and Enhancing ChatGPT-Powered Applications
10. Chapter 7: Vector Stores as Knowledge Bases for Retrieval-augmented Generation 11. Chapter 8: Creating Your Own LangChain Chatbot Example 12. Chapter 9: The Future of Conversational AI with LLMs 13. Index 14. Other Books You May Enjoy

Understanding LangChain Expression Language

We’ve covered the core LangChain components. Now, we turn our focus to a pivotal concept that can be used in the creation and execution of these components: LangChain Expression Language (LCEL). Let’s delve deeper into what LCEL is and understand its significance in the LangChain framework.

What is LCEL?

The concept of chains is that they are the key building blocks of LangChain. A chain is a collection of blocks of functionality and in ’its simplest form, a chain would be made up of a prompt, a call to an LLM, and the processing of the result. We’ve seen these functional blocks in the previous section. At their most complex, chains can be made up of hundreds of steps.

LCEL provides a declarative syntax and orchestration framework that was created so users could easily create chains of functionality with a standardized way of defining and invocation to make it as easy as possible to run as well as create...

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