Название: LangChain for Life Sciences and Healthcare: Innovation Through LLMs and Generative AI Agents
Автор: Ivan Reznikov
Издательство: O’Reilly Media, Inc.
Год: 2025
Страниц: 474
Язык: английский
Формат: epub
Размер: 17.7 MB

Feeling overwhelmed by the volume of data in your research? Sifting through massive amounts of data to find useful insights is becoming increasingly difficult in drug discovery, genetics, and healthcare. Enter the era of generative AI with LangChain, whose groundbreaking tools are changing the way life scientists and researchers operate.

In this groundbreaking book, Dr. Ivan Reznikov teaches you to harness the power of AI to elevate your research capabilities. Divided into two parts, the first is essential for any specialist, covering the transition from traditional statistics to generative AI, the fundamentals of large language models, and the practical uses of LangChain. The second part is designed for life science professionals who want to create AI applications for biology, chemistry, drug development, and more. By the end, you will:

- Learn how to easily create and integrate LangChain applications into research
- Discover how to substantially accelerate your experimental and data analysis operations
- Explore cutting-edge AI solutions designed to address complex research problems
- Gain the skills and knowledge to advance your career in AI-enhanced life sciences

The book is separated into two parts. Part I is dedicated to setting the stage:
• Chapter 1 surveys the modern generative AI landscape.
• Chapter 2 discusses how large language models work.
• Chapter 3 teaches how to use LangChain components.
• Chapter 4 describes what hallucinations are, when to use them, and how to avoid them.
• Chapter 5 discovers ways in which LangChain applications can speed up general research with a debate machine and showcases simple LangGraph teams.

Part II is dedicated to research, life science domains, and building commercial applications:
• Chapter 6 examines fine-tuned chemical models and teaches how to build multifunctional chemistry AI assistants.
• Chapter 7 develops research teams with an AlphaFold team member, DNA generation agents, and many more, followed by fine-tuning a DeepSeek reasoning model on biological data.
• Chapter 8 explores how to generate molecules with preset characteristics using variational autoencoders and looks into merging graph technologies with generative AI.
• Chapter 9 builds a powerful LangGraph team of different AI superagents, responsible for speech-to-text, retrieving table data, generating reports, and performing hypothesis reasoning.
• Chapter 10 concludes the book by discussing guardrails and best practices regarding data privacy, security, and compliance, looking into some alternatives such as LlamaIndex, CrewAI, and AutoGen, and creating live-time production generative AI applications with LangChain and its add-ons.

All code is Python, unless mentioned otherwise, typically executed in a code editor or an integrated development environment (IDE).