Mastering LangChain and LangGraph by Ankur Kulshreshtha - 9798868829451
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Mastering LangChain and LangGraphBuild RAG and Agentic AI Applications with LLMs, MCP, and LangChain Agents\nAuthor(s): Ankur Kulshreshtha\nFormat: Paperback\nPublisher: APress, United States\nImprint: APress\nISBN-13: 9798868829451, 979-8868829451\nSynopsis\nMastering LangChain and LangGraph is a comprehensive, hands?on guide for developers, data scientists, and AI practitioners looking to build robust, production?ready applications using large language models, retrieval?augmented generation (RAG), and agentic systems.\n\nThe book begins by establishing a clear foundation, introducing the LLM ecosystem and core concepts such as RAG and AI agents, before guiding readers into the LangChain framework and its practical abstractions. Readers will explore essential building blocks including chat models, prompt templates, and structured output generation, followed by in?depth coverage of document loaders, text splitters, embeddings, vector stores, and retrieverskey components for creating .
Mastering LangChain and LangGraph by Ankur Kulshreshtha - 9798868829451
Mastering LangChain and LangGraphBuild RAG and Agentic AI Applications with LLMs, MCP, and LangChain Agents\nAuthor(s): Ankur Kulshreshtha\nFormat: Paperback\nPublisher: APress, United States\nImprint: APress\nISBN-13: 9798868829451, 979-8868829451\nSynopsis\nMastering LangChain and LangGraph is a comprehensive, hands?on guide for developers, data scientists, and AI practitioners looking to build robust, production?ready applications using large language models, retrieval?augmented generation (RAG), and agentic systems.\n\nThe book begins by establishing a clear foundation, introducing the LLM ecosystem and core concepts such as RAG and AI agents, before guiding readers into the LangChain framework and its practical abstractions. Readers will explore essential building blocks including chat models, prompt templates, and structured output generation, followed by in?depth coverage of document loaders, text splitters, embeddings, vector stores, and retrieverskey components for creating .
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