LangGraph combines the power of graph-based structures with large language models to create dynamic, intelligent workflows for advanced AI applications. This book provides a comprehensive guide to leveraging LangGraph for building sophisticated language-driven systems, bridging graph theory and natural language processing.- Who This Book Is For: This book is written for AI developers, data engineers, NLP researchers, and software architects who want to leverage LangGraph to build sophisticated, language-driven workflows. ...
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LangGraph combines the power of graph-based structures with large language models to create dynamic, intelligent workflows for advanced AI applications. This book provides a comprehensive guide to leveraging LangGraph for building sophisticated language-driven systems, bridging graph theory and natural language processing.- Who This Book Is For: This book is written for AI developers, data engineers, NLP researchers, and software architects who want to leverage LangGraph to build sophisticated, language-driven workflows. It's ideal for professionals working on advanced AI applications, such as knowledge graphs, conversational agents, or automated reasoning systems, and for those exploring the intersection of graph theory and natural language processing. Whether you're building recommendation engines or intelligent search systems, this book provides the expertise needed to succeed. - What's Inside the Book: Across 15 in-depth chapters, LangGraph: Engineering Dynamic Language-Driven Workflows offers a comprehensive guide to building graph-based AI workflows with LangGraph. The book starts with the fundamentals of graph theory and its application to language models, introducing LangGraph's architecture and its integration with frameworks like LangChain and LLMs such as Llama and GPT. It covers the design of dynamic workflows for tasks like question-answering, semantic search, and recommendation systems. Readers will explore practical projects, such as constructing knowledge graphs for enterprise data or building conversational agents with enhanced reasoning capabilities. The book includes Python-based code examples, integrations with Neo4j and GraphQL, and strategies for optimizing NLP pipelines for scalability and performance. Advanced topics include handling complex graph traversals, embedding techniques, and real-time processing. - What You Will Learn: o Understand the principles of graph-based AI and its synergy with language models. o Design and implement LangGraph workflows for advanced NLP applications. o Integrate LLMs with graph structures to enhance reasoning and context awareness. o Build scalable knowledge graphs and conversational agents for real-world use cases. o Optimize NLP pipelines for performance, scalability, and real-time processing.
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Add this copy of LangGraph: Engineering Dynamic Language-Driven to cart. $17.70, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
