Generative AI with LangChain, RAG, and MCP: Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with Python - Brossura
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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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Generative AI with LangChain, RAG, and MCP (Paperback)
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Paperback. Condizione: new. Paperback. Generative AI with LangChain, RAG, and MCP: Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with PythonStop 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 systemsinternal knowledge assistantscustomer support RAG agentstool-connected AI workflowsLangGraph-based agent systemsMCP servers, clients, and integrationsproduction-ready AI applications with logging, validation, and security controlsThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798180221759
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Generative AI with LangChain, RAG, and MCP: Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with Python
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Generative AI with LangChain, RAG, and MCP (Paperback)
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Paperback. Condizione: new. Paperback. Generative AI with LangChain, RAG, and MCP: Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with PythonStop 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 systemsinternal knowledge assistantscustomer support RAG agentstool-connected AI workflowsLangGraph-based agent systemsMCP servers, clients, and integrationsproduction-ready AI applications with logging, validation, and security controlsThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798180221759
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Generative AI with LangChain, RAG, and MCP : Build Production-Ready AI Agents, Retrieval Systems, and Tool-Connected LLM Applications with Python
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