book

Learning LangChain

Name: Learning LangChain
ISBN: 9781098167288

by Mayo Oshin, Nuno Campos

February 2025

Beginner to intermediate

296 pages

6h 39m

English

O'Reilly Media, Inc.

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Includes

Quizzes

Preface
Brief Primer on LLMsInstruction-Tuned LLMsDialogue-Tuned LLMsFine-Tuned LLMsBrief Primer on PromptingZero-Shot PromptingChain-of-ThoughtRetrieval-Augmented GenerationTool CallingFew-Shot PromptingLangChain and Why It’s ImportantWhat to Expect from This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. LLM Fundamentals with LangChain
Getting Set Up with LangChainUsing LLMs in LangChainMaking LLM Prompts ReusableGetting Specific Formats out of LLMsJSON OutputOther Machine-Readable Formats with Output ParsersAssembling the Many Pieces of an LLM ApplicationUsing the Runnable InterfaceImperative CompositionDeclarative CompositionSummary
2. RAG Part I: Indexing Your Data
The Goal: Picking Relevant Context for LLMsEmbeddings: Converting Text to NumbersEmbeddings Before LLMsLLM-Based EmbeddingsSemantic Embeddings ExplainedConverting Your Documents into TextSplitting Your Text into ChunksGenerating Text EmbeddingsStoring Embeddings in a Vector StoreGetting Set Up with PGVectorWorking with Vector StoresTracking Changes to Your DocumentsIndexing OptimizationMultiVectorRetrieverRAPTOR: Recursive Abstractive Processing for Tree-Organized RetrievalColBERT: Optimizing EmbeddingsSummary
3. RAG Part II: Chatting with Your Data
Introducing Retrieval-Augmented GenerationRetrieving Relevant DocumentsGenerating LLM Predictions Using Relevant DocumentsQuery TransformationRewrite-Retrieve-ReadMulti-Query RetrievalRAG-FusionHypothetical Document EmbeddingsQuery RoutingLogical RoutingSemantic RoutingQuery ConstructionText-to-Metadata FilterText-to-SQLSummary
4. Using LangGraph to Add Memory to Your Chatbot
Building a Chatbot Memory SystemIntroducing LangGraphCreating a StateGraphAdding Memory to StateGraphModifying Chat HistoryTrimming MessagesFiltering MessagesMerging Consecutive MessagesSummary
5. Cognitive Architectures with LangGraph
Architecture #1: LLM CallArchitecture #2: ChainArchitecture #3: RouterSummary
6. Agent Architecture
The Plan-Do LoopBuilding a LangGraph AgentAlways Calling a Tool FirstDealing with Many ToolsSummary
7. Agents II
ReflectionSubgraphs in LangGraphCalling a Subgraph DirectlyCalling a Subgraph with a FunctionMulti-Agent ArchitecturesSupervisor ArchitectureSummary
8. Patterns to Make the Most of LLMs
Structured OutputIntermediate OutputStreaming LLM Output Token-by-TokenHuman-in-the-Loop ModalitiesMultitasking LLMsSummary
9. Deployment: Launching Your AI Application into Production
PrerequisitesInstall DependenciesLarge Language ModelVector StoreBackend APICreate a LangSmith AccountUnderstanding the LangGraph Platform APIData ModelsFeaturesDeploying Your AI Application on LangGraph PlatformCreate a LangGraph API ConfigTest Your LangGraph App LocallyDeploy from the LangSmith UILaunch LangGraph StudioSecuritySummary

10. Testing: Evaluation, Monitoring, and Continuous Improvement
Testing Techniques Across the LLM App Development CycleThe Design Stage: Self-Corrective RAGThe Preproduction StageCreating DatasetsDefining Your Evaluation CriteriaRegression TestingEvaluating an Agent’s End-to-End PerformanceProductionTracingCollect Feedback in ProductionClassification and TaggingMonitoring and Fixing ErrorsSummary
11. Building with LLMs
Interactive ChatbotsCollaborative Editing with LLMsAmbient ComputingSummary
Index
About the Authors

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Learning LangChain

by Mayo Oshin, Nuno Campos

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