Generative AI with LangChain

Generative AI with LangChain is a practical and comprehensive guide to building modern AI applications using Large Language Models (LLMs) and the LangChain framework. The book takes you from the fundamentals of Generative AI and prompt engineering to advanced concepts such as Retrieval-Augmented Generation (RAG), embeddings, vector databases, agents, tools, memory, and AI-powered applications.

You will learn how to integrate LLMs with documents, databases, APIs, web search, and external tools to create intelligent applications. The book also introduces LangChain's modern architecture, including models, prompts, output parsers, runnable workflows, document loaders, text splitters, retrievers, vector stores, agents, and tool calling.

With practical examples and real-world projects, this book helps students, developers, and AI enthusiasts understand not only how Generative AI works but also how to build useful applications with LangChain and Python.

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 and prompt design
Zero-shot, one-shot, and few-shot prompting
Role-based prompting and structured output
Working with LLM and Chat Model APIs
Introduction to LangChain and its architecture
LangChain models and chat models
Prompt templates and reusable prompts
Output parsers and structured responses
Chains and sequential workflows
Runnables and LCEL concepts
Document loaders and document processing
Text splitting and chunking strategies
Embeddings and semantic similarity
Vector databases and vector stores
Retrievers and semantic search
Retrieval-Augmented Generation (RAG)
Building document question-answering systems
PDF and text-based AI applications
LangChain agents and agent architecture
Tools and custom tools
Function calling and tool calling
Connecting AI applications with APIs and databases
Web search and external information retrieval
Memory and conversational applications
Building intelligent chatbots
Hugging Face and other LLM providers
Working with OpenAI-compatible and other model APIs
Error handling and debugging
LangSmith for tracing, monitoring, and evaluation
Introduction to LangGraph for advanced workflows
AI agents and Agentic AI concepts
Security, hallucination, prompt injection, and responsible AI
Cost and performance optimization
Deploying Generative AI applications
Real-world Generative AI projects
Real-World Projects
AI Chatbot
PDF Question-Answering System
Document Analysis Assistant
RAG-Based Knowledge Assistant
AI Research Assistant
Web Search AI Assistant
Database Question-Answering System
AI Content Generation Application
Customer Support AI Assistant
LangChain Agent with Tools
AI Data Analysis Assistant
Intelligent Document Processing System