Graphrag engineering series (8 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Zustand: Neu. Neuware.

Taschenbuch. Zustand: Neu. Neuware.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Paperback. Zustand: new. Paperback. Building a GraphRAG prototype is one thing. Making it fast, reliable, scalable, and production-ready is another.As Retrieval-Augmented Generation evolves beyond basic vector similarity, knowledge graphs are becoming a powerful foundation for AI systems that need to understand relationships, navigate complex information, and reason across connected data. But moving GraphRAG from experimentation into production introduces an entirely new set of challenges: retrieval quality, graph growth, latency, evaluation, cost, security, observability, and scalability.Advanced GraphRAG with Python takes you beyond the fundamentals and into the engineering decisions required to design, optimize, and operate sophisticated GraphRAG systems in real-world environments.Written for developers, AI engineers, machine learning practitioners, and technical architects ready to move past basic implementations, this hands-on guide explores advanced retrieval architectures, graph reasoning strategies, production optimization techniques, and scalable system designs all with practical Python implementations.What You'll LearnDesign advanced GraphRAG architectures for complex and large-scale applications.Engineer scalable knowledge graphs capable of evolving alongside continuously changing data.Combine graph, vector, semantic, and keyword retrieval into powerful hybrid retrieval pipelines.Implement multi-hop retrieval and graph-based reasoning across interconnected information.Apply community detection, hierarchical graph organization, and summarization to improve knowledge discovery.Develop intelligent query planning, routing, and context-selection strategies.Optimize indexing, traversal, caching, and retrieval pipelines for lower latency and higher throughput.Evaluate GraphRAG systems using meaningful retrieval, generation, and end-to-end quality metrics.Reduce hallucinations and improve answer grounding through structured knowledge and evidence-aware retrieval.Build incremental and streaming pipelines that keep knowledge graphs synchronized with changing information.Control LLM, embedding, storage, and infrastructure costs without sacrificing retrieval quality.Design GraphRAG architectures for AI agents, tool-using systems, and multi-step reasoning workflows.Implement monitoring, tracing, logging, and observability for production GraphRAG applications.Address authentication, authorization, privacy, security, governance, and enterprise data access.Deploy resilient GraphRAG services capable of scaling from individual applications to enterprise AI platforms.Go Beyond Building GraphRAG Engineer ItThroughout the book, you'll explore practical architectures and implementation patterns for solving the problems that appear when GraphRAG meets real-world workloads.You'll learn how to make informed engineering trade-offs between retrieval accuracy, latency, cost, complexity, and scalability while developing systems that can be monitored, evaluated, maintained, and continuously improved.Rather than depending entirely on a single framework or vendor, the book emphasizes transferable engineering principles while demonstrating how modern Python tools, graph technologies, retrieval frameworks, vector search, large language models, and production infrastructure can work together.From advanced hybrid retrieval and multi-hop reasoning to distributed processing, agentic workflows, evaluation, optimization, and production deployment, Advanced GraphRAG with Python provides a practical roadmap for taking knowledge graph-powered AI beyond prototypes. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Zustand: new. Paperback. Large language models have transformed how we build intelligent applications, but traditional Retrieval-Augmented Generation (RAG) systems still struggle with complex relationships, multi-hop reasoning, and fragmented context. GraphRAG changes that by combining the semantic understanding of LLMs with the structured power of knowledge graphs, enabling AI systems that retrieve information more accurately, reason across connected data, and generate responses grounded in meaningful relationships rather than isolated documents.GraphRAG with Python is a practical, hands-on guide that takes you from the fundamentals of knowledge graphs to designing and deploying production-ready GraphRAG applications. Whether you're an AI engineer, machine learning practitioner, data scientist, software developer, or technical architect, this book provides the concepts, tools, and implementation techniques needed to build intelligent retrieval systems that go beyond traditional vector search.Rather than focusing on a single framework, you'll develop a solid understanding of the principles behind GraphRAG and learn how to implement them using Python and today's leading AI ecosystem, ensuring your skills remain valuable as the technology evolves.Inside this book, you'll learn how to: Understand the architecture and principles behind GraphRAG and how it extends traditional Retrieval-Augmented Generation.Build knowledge graphs from structured and unstructured data using modern Python libraries and LLM-powered extraction techniques.Design entity extraction, relationship extraction, ontology modeling, and graph enrichment pipelines.Implement GraphRAG workflows with Microsoft GraphRAG, Neo4j, LangChain, LlamaIndex, and other leading open-source tools.Combine graph, vector, and keyword retrieval into hybrid search systems for improved accuracy and context awareness.Apply graph algorithms, community detection, graph traversal, and multi-hop reasoning to solve complex retrieval problems.Integrate GraphRAG with AI agents and tool-calling workflows for advanced enterprise applications.Evaluate retrieval quality, optimize performance, reduce hallucinations, and improve response reliability.Scale GraphRAG systems using production-ready architectures, monitoring, caching, indexing, and deployment best practices.Build complete, real-world projects for enterprise search, question answering, research assistants, document intelligence, legal analysis, financial knowledge management, healthcare, and recommendation systems.Throughout the book, you'll build practical Python projects while learning the engineering principles behind robust GraphRAG systems. Every chapter combines clear explanations with diagrams, reusable code, implementation patterns, and best practices that you can immediately apply to your own AI applications.By the end of this book, you'll be able to design, implement, optimize, and deploy GraphRAG solutions capable of delivering more accurate retrieval, richer contextual understanding, and more trustworthy AI-powered experiences.Whether you're modernizing enterprise search, building AI assistants, creating intelligent knowledge management platforms, or exploring the next generation of Retrieval-Augmented Generation, GraphRAG with Python provides the practical foundation you need to build smarter, relationship-aware AI systems with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…