- 面向生命科学和医疗保健的LangChain(影印版)(英文版)
- - 作者：(美)伊万·雷兹尼科夫|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576627800
  - 出版日期：2026/07/01
  - 页数：393
- 售价：63.2

内容大纲
伊万·雷兹尼科夫博士撰写的这本开创性著作，专为希望运用生成式AI技术颠覆传统科研模式、提升研究能力的科学家、研究人员及医疗从业者量身打造。全书分为两部分：前半部分面向所有领域专家，内容涵盖从（传统统计学向生成式AI的过渡）、LLM（大语言模型）基础、AI智能体，以及LangChain和LangGraph的实战应用；后半部分面向生命科学专业人士，指导构建适用于化学、生物学、药物研发、医疗健康等领域的AI应用与多智能体系统。
作者介绍
伊万·雷兹尼科夫拥有化学博士学位，在计算建模、数据科学、生成式AI领域造诣深厚。他积累了十余年经验，将生命科学、医疗健康、数据科学无缝融合。凭借这一独特背景，他写出了本书，为释放LangChain在科研与医疗场景中的潜力提供了无与伦比的洞见与知识。
目录
Preface
Part I. Generative AI, Understanding Large Language Models, and LangChain
  1. From Statistics to Generative AI in Life Sciences
    Introduction
    Application of Generative AI in Life Sciences
    Audio and Visual
    Text
    Scientific Components
    Research Studies
    Drawbacks of Generative AI in Science
    Summary
  2. Introducing Large Language Models
    Embedding Models
    Chat and Large Language Models
    Tokens
    Text and Sequence Generation
    Decoding Strategies
    All Sorts of Language Models
    Large Language Model Limitations
    Summary
  3. Introducing LangChain
    Indexes
    Indexing
    Vector Search
    Vector Stores
    Chains
    The LangChain Expression Language
    LangGraph
    Prompts
    Memory
    Tools
    Agents
    Creating Apps with LangChain
    Summary
  4. Hallucinations and RAG Systems
    Hallucinations, Their Causes, and Consequences
    Hallucinations and Possible Solutions
    Retrieval-Augmented Generation
    Indexing and Data Preparation
    Query Translation and Understanding
    Routing to Correct Database/Index
    Query Construction
    Data Retrieval
    Data Augmentation and Response Generation
    RAG Variations: Self-RAG, Tree-RAG, CAG, Agentic RAG
    Evaluating RAGs
    The Advantages of Hallucinating
    Summary
  5. Building Personal Assistants
    Building Assistants with Chains

    Building Assistants with Agents
    Building Assistants with Multiple Agents
    Model Context Protocol
    Summary
Part II. Building AI Agents and Assistants Using LangChain and LangGraph
  6. LangChain for Chemistry
    Generative AI in Chemistry
    Text-Based
    Code-Generative
    Chemistry-Generative
    Creating Applications with External Packages
    ChemCrow and CACTUS
    LLMs
    LCEL Chains
    Custom LangChain Agent
    RDKit Custom Agents
    Using Chemistry-Based LLMs
    Using Text-Based LLMs in Chemical Applications
    Summary
  7. LangChain for Biology
    LLMs in Biology
    Biological LangGraph Application
    Creating Biological Tools
    Fine-Tuning Large Language and Reasoning Models
    Introduction to Large Reasoning Models
    Summary
  8. LangChain for Drug Discovery
    In Silico Drug Discovery
    Small Molecule Generation
    Autoencoders
    Knowledge Graphs
    Neo4j Vectors
    Summary
  9. LangChain for Medicine and Healthcare
    Generative AI for Healthcare
    Creating a Generative Healthcare Application
    Brainstorming Assistant
    Advanced Brainstorming Scenario
    Integrating Speech-to-Text
    RAG over SQL
    Summarization
    Report Generation
    Multi-Team Applications
    Adopting AI in Healthcare and Medicine
    Summary
  10. LangChain for Enterprise
    Guardrails, Enterprise Best Practices, and Policies
    Data Security, Privacy, and Compliance
    Prompt Injection
    Fallbacks

    Off-Topic Questions
    Preventing the Generation of Harmful Content and Toxicity
    Evaluating LLMs and Generative AI Applications
    LangChain and LangGraph Alternatives and Add-Ons
    Data Integration and Retrieval Frameworks
    Low-Code/No-Code Platforms
    LLM Observability and Debugging Tools
    Langfuse
    AI Agent and Workflow Frameworks, and Specialized LLM Tools
    Multi-Agent Frameworks
    Summary
Index

内容大纲

作者介绍

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