LangChain and Generative AI with LangGraph

LangChain and Generative AI with LangGraph is a practical and comprehensive guide to building modern Generative AI applications using Large Language Models, LangChain, and LangGraph. The book takes you from the fundamentals of Generative AI and LLMs to advanced AI workflows, Retrieval-Augmented Generation (RAG), tool calling, memory, and intelligent AI agents.

You will learn how LangChain simplifies the development of LLM-powered applications and how LangGraph can be used to build stateful, controllable, and multi-step AI workflows. The book explains important concepts such as prompts, models, chains, runnables, document loaders, text splitters, embeddings, vector databases, retrievers, agents, tools, and structured outputs.

The book also introduces LangGraph concepts including graphs, nodes, edges, state management, conditional routing, loops, tool execution, human-in-the-loop workflows, and multi-agent systems. Practical examples and real-world projects help you apply these concepts to build intelligent AI applications.

What You Will Learn
Fundamentals of Artificial Intelligence and Generative AI
Large Language Models (LLMs) and Transformer architecture
Tokens, context windows, parameters, and model behavior
Text generation and conversational AI
Prompt Engineering
Zero-shot, one-shot, and few-shot prompting
Role-based prompting and structured output
LLM and Chat Model integration
Introduction to LangChain and its architecture
LangChain models and chat models
Prompt templates
Output parsers and structured responses
Chains and sequential workflows
Runnables and LCEL concepts
Document loaders and document processing
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
Memory and conversational applications
Introduction to LangGraph
LangGraph architecture
Graphs, nodes, and edges
State management
Conditional routing
Loops and iterative workflows
ToolNode and tool execution
Human-in-the-loop workflows
Building stateful AI applications
AI agents with LangGraph
Agentic AI fundamentals
Planning and task decomposition
Multi-agent systems
Supervisor and worker agent architectures
Agent communication and collaboration
Error handling and recovery
Evaluation and monitoring
LangSmith integration
Hugging Face and LLM APIs
Hallucination reduction and responsible AI
Prompt injection and AI security
Cost and performance optimization
Deployment of Generative AI applicatio