Learning LangChain: Building AI and LLM Applications with LangChain and LangGraph

PrefaceBrief 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 UsAcknowledgments1. LLM Fundamentals with LangChainGetting 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 CompositionSummary2. RAG Part I: Indexing Your DataThe 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 EmbeddingsSummary3. RAG Part II: Chatting with Your DataIntroducing 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-SQLSummary4. Using LangGraph to Add Memory to Your ChatbotBuilding a Chatbot Memory SystemIntroducing LangGraphCreating a StateGraphAdding Memory to StateGraphModifying Chat HistoryTrimming MessagesFiltering MessagesMerging Consecutive MessagesSummary5. Cognitive Architectures with LangGraphArchitecture #1: LLM CallArchitecture #2: ChainArchitecture #3: RouterSummary6. Agent ArchitectureThe Plan-Do LoopBuilding a LangGraph AgentAlways Calling a Tool FirstDealing with Many ToolsSummary7. Agents IIReflectionSubgraphs in LangGraphCalling a Subgraph DirectlyCalling a Subgraph with a FunctionMulti-Agent ArchitecturesSupervisor ArchitectureSummary8. Patterns to Make the Most of LLMsStructured OutputIntermediate OutputStreaming LLM Output Token-by-TokenHuman-in-the-Loop ModalitiesMultitasking LLMsSummary9. Deployment: Launching Your AI Application into ProductionPrerequisitesInstall 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 StudioSecuritySummary10. Testing: Evaluation, Monitoring, and Continuous ImprovementTesting 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 ErrorsSummary11. Building with LLMsInteractive ChatbotsCollaborative Editing with LLMsAmbient ComputingSummaryIndexAbout the Authors