Building LLM Apps: Create Intelligent Apps and Agents with Large Language Models
by 作者: Valentina Alto (Author)
Publisher Finelybook 出版社: Packt Publishing – ebooks Account
Publication Date 出版日期: 2024-undefined-Jun.
Language 语言: English
Pages 页数: 334 pages
ISBN-10 书号: 1835462316
ISBN-13 书号: 9781835462317
Book Description
Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications
Key Features
- Embed LLMs into real-world applications
- Use LangChain to orchestrate LLMs and their components within applications
- Grasp basic and advanced techniques of prompt engineering
Book Description
Building LLM Apps delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer. Ultimately paving the way for the emergence of Large Foundation Models (LFMs) that extend the boundaries of AI capabilities.
The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain. We guide readers through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio.
Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.
What you will learn
- Core components of LLMs’ architecture, including encoder-decoders blocks, embedding and so on
- Get well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM
- Use AI orchestrators like LangChain, and Streamlit as frontend
- Get familiar with LLMs components such as memory, prompts and tools
- Learn non-parametric knowledge, embeddings and vector databases
- Understand the implications of LFMs for AI research, and industry applications
- Customize your LLMs with fine tuning
- Learn the ethical implications of LLM-powered applications
Who this book is for
Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics.
We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.
Table of
Contents
- Introduction to LLMs
- LLMs for AI-powered applications
- Choosing an LLM for your app
- Embedding LLMs within apps
- Building Conversational apps
- Developing search and recommendation engines
- Generative Text apps
- LLMs on structured data
- Generating Code and Structured Outputs
- Building multi-modal Agents
- Fine-tuning LLMs
- Responsible AI
- Emerging Trends and Innovations
About the Author
After completing her bachelor’s degree in finance, Valentina Alto pursued a master’s degree in data science in 2021. She began her professional career at Microsoft as an Azure Solution Specialist, and since 2022, she has been primarily focused on working with Data & AI solutions in the Manufacturing and Pharmaceutical industries. Valentina collaborates closely with system integrators on customer projects, with a particular emphasis on deploying cloud architectures that incorporate modern data platforms, data mesh frameworks, and applications of Machine Learning and Artificial Intelligence.
Alongside her academic journey, she has been actively writing technical articles on Statistics, Machine Learning, Deep Learning, and AI for various publications, driven by her passion for AI and Python programming.
