فهرست مطالب

• Preface
• Chapter 1. LLM Fundamentals with LangChain
• Getting Set Up with LangChain
• Using LLMs in LangChain
• Making LLM Prompts Reusable
• Getting Specific Formats out of LLMs
• Assembling the Many Pieces of an LLM Application
• Summary
• Chapter 2. RAG Part I: Indexing Your Data
• The Goal: Picking Relevant Context for LLMs
• Embeddings: Converting Text to Numbers
• Converting Your Documents into Text
• Splitting Your Text into Chunks
• Generating Text Embeddings
• Storing Embeddings in a Vector Store
• Tracking Changes to Your Documents
• Indexing Optimization
• Summary
• Chapter 3. RAG Part II: Chatting with Your Data
• Introducing Retrieval-Augmented Generation
• Query Transformation
• Query Routing
• Query Construction
• Summary
• Chapter 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
• Summary
• Chapter 5. Cognitive Architectures with LangGraph
• Architecture #1: LLM Call
• Architecture #2: Chain
• Architecture #3: Router
• Summary
• Chapter 6. Agent Architecture
• The Plan-Do Loop
• Building a LangGraph Agent
• Always Calling a Tool First
• Dealing with Many Tools
• Summary
• Chapter 7. Agents II
• Reflection
• Subgraphs in LangGraph
• Multi-Agent Architectures
• Summary
• Chapter 8. Patterns to Make the Most of LLMs
• Structured Output
• Summary
• Chapter 9. Deployment: Launching Your AI Application into Production
• Prerequisites
• Understanding the LangGraph Platform API
• Deploying Your AI Application on LangGraph Platform
• Security
• Summary
• Chapter 10. Testing: Evaluation, Monitoring, and Continuous Improvement
• Testing Techniques Across the LLM App Development Cycle
• The Design Stage: Self-Corrective RAG
• The Preproduction Stage
• Production
• Summary
• Chapter 11. Building with LLMs
• Interactive Chatbots
• Collaborative Editing with LLMs
• Ambient Computing
• Summary
• Index
• About the Authors