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Spis treści

Learning LangChain. Building AI and LLM Applications with LangChain and LangGraph eBook -- spis treści

• Preface
  • Brief Primer on LLMs
    • Instruction-Tuned LLMs
    • Dialogue-Tuned LLMs
    • Fine-Tuned LLMs
  • Brief Primer on Prompting
    • Zero-Shot Prompting
    • Chain-of-Thought
    • Retrieval-Augmented Generation
    • Tool Calling
    • Few-Shot Prompting
  • LangChain and Why Its Important
  • What to Expect from This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. LLM Fundamentals with LangChain
  • Getting Set Up with LangChain
  • Using LLMs in LangChain
  • Making LLM Prompts Reusable
  • Getting Specific Formats out of LLMs
    • JSON Output
    • Other Machine-Readable Formats with Output Parsers
  • Assembling the Many Pieces of an LLM Application
    • Using the Runnable Interface
    • Imperative Composition
    • Declarative Composition
  • Summary
• 2. RAG Part I: Indexing Your Data
  • The Goal: Picking Relevant Context for LLMs
  • Embeddings: Converting Text to Numbers
    • Embeddings Before LLMs
    • LLM-Based Embeddings
    • Semantic Embeddings Explained
  • Converting Your Documents into Text
  • Splitting Your Text into Chunks
  • Generating Text Embeddings
  • Storing Embeddings in a Vector Store
    • Getting Set Up with PGVector
    • Working with Vector Stores
  • Tracking Changes to Your Documents
  • Indexing Optimization
    • MultiVectorRetriever
    • RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
    • ColBERT: Optimizing Embeddings
  • Summary
• 3. RAG Part II: Chatting with Your Data
  • Introducing Retrieval-Augmented Generation
    • Retrieving Relevant Documents
    • Generating LLM Predictions Using Relevant Documents
  • Query Transformation
    • Rewrite-Retrieve-Read
    • Multi-Query Retrieval
    • RAG-Fusion
    • Hypothetical Document Embeddings
  • Query Routing
    • Logical Routing
    • Semantic Routing
  • Query Construction
    • Text-to-Metadata Filter
    • Text-to-SQL
  • Summary
• 4. Using LangGraph to Add Memory to Your Chatbot
  • Building a Chatbot Memory System
  • Introducing LangGraph
  • Creating a StateGraph
  • Adding Memory to StateGraph
  • Modifying Chat History
    • Trimming Messages
    • Filtering Messages
    • Merging Consecutive Messages
  • Summary
• 5. Cognitive Architectures with LangGraph
  • Architecture #1: LLM Call
  • Architecture #2: Chain
  • Architecture #3: Router
  • Summary
• 6. Agent Architecture
  • The Plan-Do Loop
  • Building a LangGraph Agent
  • Always Calling a Tool First
  • Dealing with Many Tools
  • Summary
• 7. Agents II
  • Reflection
  • Subgraphs in LangGraph
    • Calling a Subgraph Directly
    • Calling a Subgraph with a Function
  • Multi-Agent Architectures
    • Supervisor Architecture
  • Summary
• 8. Patterns to Make the Most of LLMs
  • Structured Output
    • Intermediate Output
    • Streaming LLM Output Token-by-Token
    • Human-in-the-Loop Modalities
      • Resume
      • Restart
      • Edit state
      • Fork
    • Multitasking LLMs
      • Refuse concurrent inputs
      • Handle independently
      • Queue concurrent inputs
      • Interrupt
      • Fork and merge
  • Summary
• 9. Deployment: Launching Your AI Application into Production
  • Prerequisites
    • Install Dependencies
    • Large Language Model
    • Vector Store
    • Backend API
    • Create a LangSmith Account
  • Understanding the LangGraph Platform API
    • Data Models
      • Assistants
      • Threads
      • Runs
      • Cron jobs
    • Features
      • Streaming
      • Human-in-the-loop
      • Double texting
      • Stateless runs
      • Webhooks
  • Deploying Your AI Application on LangGraph Platform
    • Create a LangGraph API Config
    • Test Your LangGraph App Locally
    • Deploy from the LangSmith UI
      • Deployment details
      • Development type
      • Environment variables
    • Launch LangGraph Studio
  • Security
  • Summary
• 10. Testing: Evaluation, Monitoring, and Continuous Improvement
  • Testing Techniques Across the LLM App Development Cycle
  • The Design Stage: Self-Corrective RAG
  • The Preproduction Stage
    • Creating Datasets
    • Defining Your Evaluation Criteria
      • Improving LLM-as-a-judge evaluators performance
      • Pairwise evaluation
    • Regression Testing
    • Evaluating an Agents End-to-End Performance
      • Testing an agents final response
      • Testing a single step of an agent
      • Testing an agents trajectory
  • Production
    • Tracing
    • Collect Feedback in Production
    • Classification and Tagging
    • Monitoring and Fixing Errors
  • Summary
• 11. Building with LLMs
  • Interactive Chatbots
  • Collaborative Editing with LLMs
  • Ambient Computing
  • Summary
• Index