book

Learning LangChain

by Mayo Oshin, Nuno Campos

February 2025

Beginner to intermediate

296 pages

6h 39m

English

O'Reilly Media, Inc.

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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

Overview

If you're looking to build production-ready AI applications that can reason and retrieve external data for context-awareness, you'll need to master--;a popular development framework and platform for building, running, and managing agentic applications. LangChain is used by several leading companies, including Zapier, Replit, Databricks, and many more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI.

Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book shows you step-by-step how to build a production-ready AI agent that uses your data.

Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external up-to-date data
Develop and deploy AI applications that interact intelligently and contextually with users
Make use of the powerful agent architecture with LangGraph
Integrate and manage third-party APIs and tools to extend the functionality of your AI applications
Monitor, test, and evaluate your AI applications to improve performance
Understand the foundations of LLM app development and how they can be used with LangChain