LangChain with LangGraph by Aravind choudhary

LangChain with LangGraph is a practical and comprehensive guide to building modern Generative AI applications and intelligent workflows using LangChain and LangGraph. The book starts with the fundamentals of Generative AI and Large Language Models (LLMs) and gradually introduces the core concepts of LangChain, including models, prompts, output parsers, chains, runnables, document loaders, text splitters, embeddings, vector stores, retrievers, agents, and tools. You will then learn how LangGraph extends these capabilities by providing a graph-based approach for creating stateful, multi-step, and controllable AI workflows. The book covers graphs, nodes, edges, state management, conditional routing, loops, tool execution, human-in-the-loop workflows, and AI agents. With practical examples and real-world projects, this book helps you build applications such as RAG-based assistants, document analysis systems, research assistants, chatbots, tool-enabled agents, and intelligent automation workflows. What You Will Learn Fundamentals of Artificial Intelligence and Generative AI Large Language Models (LLMs) Transformer architecture Tokens, context windows, and model parameters Text generation and conversational AI Prompt Engineering Zero-shot, one-shot, and few-shot prompting Structured output and prompt templates Introduction to LangChain LangChain architecture and components LLMs and Chat Models Prompt Templates Output Parsers Chains and sequential workflows Runnables and LCEL Document Loaders Text Splitting and Chunking Embeddings and semantic similarity Vector Databases and Vector Stores Retrievers and Semantic Search Retrieval-Augmented Generation (RAG) Building PDF and document question-answering systems LangChain Agents Tools and Custom Tools Function Calling and Tool Calling API and database integration Web search integration Conversational memory Introduction to LangGraph LangGraph architecture Graphs, Nodes, and Edges State management Conditional routing Loops and iterative workflows ToolNode and tool execution Stateful AI applications Building AI workflows with LangGraph AI agents with LangGraph Planning and task decomposition Human-in-the-loop workflows Error handling and recovery Workflow evaluation and monitoring LangSmith integration Multi-step AI workflows Multi-agent workflow concepts External API and database integration Hugging Face and LLM API integration Hallucination reduction Prompt injection and AI security Cost and performance optimization Deployment of LangChain and LangGraph applications