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

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Spis treści

LangChain for Life Sciences and Healthcare. Innovation Through LLMs and Generative AI Agents eBook -- spis treści

• Preface
  • How to Read This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 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
      • Molecules
      • Substances
      • Materials
      • Genes
    • 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
      • Visual 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
      • Incomplete and unfinished answers
      • One small change in user query, one giant shift in the results
      • Decomposing complex questions
      • Stepping back
    • Routing to Correct Database/Index
    • Query Construction
      • Querying tables and SQL/NoSQL/graph databases
      • Requesting APIs
      • Challenges with HTML, code, and PDFs
    • Data Retrieval
      • Incorrect document ranking
      • Incomplete and low-quality information
    • Data Augmentation and Response Generation
      • Relevant context missing
      • Data overflow
      • Output Format Management
    • RAG Variations: Self-RAG, Tree-RAG, CAG, Agentic RAG
      • 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
• 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
      • Data security
      • Data privacy
      • 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
      • LlamaIndex
      • Haystack
      • AdalFlow
    • Low-Code/No-Code Platforms
      • Flowise
      • Langflow
      • n8n
    • LLM Observability and Debugging Tools
      • LangSmith
    • Langfuse
    • AI Agent and Workflow Frameworks, and Specialized LLM Tools
      • Semantic Kernel
      • txtai
      • Griptape
      • Transformers Agent
    • Multi-Agent Frameworks
      • LangGraph
      • CrewAI
      • AutoGen
  • Summary
• Index