Generative AI with LangChain : build large language model (LLM) apps with Python, ChatGPT and other LLMs / Ben Auffarth.

Format:

Book

Author/Creator:

Auffarth, Ben, author.

Language:

English

Subjects (All):

Application software--Development.

Application software.

Artificial intelligence.

Computer programming.

Application program interfaces (Computer software).

Physical Description:

1 online resource

Place of Publication:

Birmingham, UK : Packt Publishing Ltd., 2023.

Summary:

Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants, integrating with web searches, and code execution. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LLMs' capabilities and work around their inherent weaknesses Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book Description ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity. What you will learn Understand LLMs, their strengths and limitations Grasp generative AI fundamentals and industry trends Create LLM apps with LangChain like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena. Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Chapter 1: What Is Generative AI?

Introducing generative AI

What are generative models?

Why now?

Understanding LLMs

What is a GPT?

Other LLMs

Major players

How do GPT models work?

Pre-training

Tokenization

Scaling

Conditioning

How to try out these models

What are text-to-image models?

What can AI do in other domains?

Summary

Questions

Chapter 2: LangChain for LLM Apps

Going beyond stochastic parrots

What are the limitations of LLMs?

How can we mitigate LLM limitations?

What is an LLM app?

What is LangChain?

Exploring key components of LangChain

What are chains?

What are agents?

What is memory?

What are tools?

How does LangChain work?

Comparing LangChain with other frameworks

Chapter 3: Getting Started with LangChain

How to set up the dependencies for this book

pip

Poetry

Conda

Docker

Exploring API model integrations

Fake LLM

OpenAI

Hugging Face

Google Cloud Platform

Jina AI

Replicate

Others

Azure

Anthropic

Exploring local models

Hugging Face Transformers

llama.cpp

GPT4All

Building an application for customer service

Chapter 4: Building Capable Assistants

Mitigating hallucinations through fact-checking

Summarizing information

Basic prompting

Prompt templates

Chain of density

Map-Reduce pipelines

Monitoring token usage

Extracting information from documents

Answering questions with tools

Information retrieval with tools

Building a visual interface

Exploring reasoning strategies

Questions

Chapter 5: Building a Chatbot like ChatGPT

What is a chatbot?

Understanding retrieval and vectors

Embeddings

Vector storage

Vector indexing

Vector libraries

Vector databases

Loading and retrieving in LangChain

Document loaders

Retrievers in LangChain

kNN retriever

PubMed retriever

Custom retrievers

Implementing a chatbot

Document loader

Memory

Conversation buffers

Remembering conversation summaries

Storing knowledge graphs

Combining several memory mechanisms

Long-term persistence

Moderating responses

Summary

Questions

Chapter 6: Developing Software with Generative AI

Software development and AI

Code LLMs

Writing code with LLMs

StarCoder

StarChat

Llama 2

Small local model

Automating software development

Chapter 7: LLMs for Data Science

The impact of generative models on data science

Automated data science

Data collection

Visualization and EDA

Preprocessing and feature extraction

AutoML

Using agents to answer data science questions

Data exploration with LLMs

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1415721028