LangChain with LangGraph

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