Generative AI with LangChain, RAG, and MCP: Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with Python - Tapa blanda

Sinopsis

Generative AI with LangChain, RAG, and MCP: Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with Python

Stop building AI demos that fall apart when real users, real documents, and real workflows enter the picture.

Many developers can make a chatbot respond. Far fewer can build a generative AI application that retrieves the right context, uses tools safely, handles errors, returns grounded answers, and runs behind a clean production-ready structure.

Generative AI with LangChain, RAG, and MCP gives you a practical path for building modern AI applications with Python, LangChain, Retrieval-Augmented Generation, LangGraph, and the Model Context Protocol. Instead of treating these technologies as separate buzzwords, this book shows how they work together inside useful AI systems.

You will learn how to set up a clean Python AI project, connect language models, prepare documents for RAG, create embeddings, use vector stores, build retrievers, return source-grounded answers, evaluate retrieval quality, debug failures, design agent workflows, connect tools with MCP, and prepare applications for deployment with FastAPI and Docker.

Inside, you will gain practical skills for building:

• document question-answering systems
• internal knowledge assistants
• customer support RAG agents
• tool-connected AI workflows
• LangGraph-based agent systems
• MCP servers, clients, and integrations
• production-ready AI applications with logging, validation, and security controls

This book is for Python developers, software engineers, AI builders, technical founders, students, and self-taught programmers who want to move beyond simple prompts and build AI systems that are structured, maintainable, and useful.

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