book

Learning LangChain

by Mayo Oshin, Nuno Campos

February 2025

Beginner to intermediate

296 pages

6h 39m

English

O'Reilly Media, Inc.

Book available

Read now

Unlock full access

Includes

Includes Quizzes

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
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
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
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
Building a Chatbot Memory SystemIntroducing LangGraphCreating a StateGraphAdding Memory to StateGraphModifying Chat HistoryTrimming MessagesFiltering MessagesMerging Consecutive MessagesSummary
Architecture #1: LLM CallArchitecture #2: ChainArchitecture #3: RouterSummary
The Plan-Do LoopBuilding a LangGraph AgentAlways Calling a Tool FirstDealing with Many ToolsSummary
ReflectionSubgraphs in LangGraphCalling a Subgraph DirectlyCalling a Subgraph with a FunctionMulti-Agent ArchitecturesSupervisor ArchitectureSummary
Structured OutputIntermediate OutputStreaming LLM Output Token-by-TokenHuman-in-the-Loop ModalitiesMultitasking LLMsSummary
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

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
Interactive ChatbotsCollaborative Editing with LLMsAmbient ComputingSummary

Content preview from Learning LangChain

About the Authors

Mayo Oshin is a tech entrepreneur, AI advisor, and angel investor. Mayo was an early developer contributor and advocate for the open source LangChain library and an early pioneer in the popular AI “chat” with data movement (5+ million people reached through his thought-leadership ideas so far).

Mayo has consulted with, advised, and trained hundreds of engineers and product managers at various leading institutions, including Amazon, LinkedIn, Evercore, Visa, and BCG.

You can learn more about him at his website, mayooshin.com, and get his latest ideas on AI and technology on X.

Nuno Campos is a founding software engineer at LangChain, Inc. Nuno has a decade of experience as a Python and JavaScript software engineer, architect and open source maintainer. He has worked for various tech startups in software engineering and data science roles. He holds an MSc in Finance.