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Idioma: Inglés
Publicado por Manning Publications 9/2/2025, 2025
ISBN 10: 1633436268 ISBN 13: 9781633436268
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EUR 48,10
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Añadir al carritoPaperback or Softback. Condición: New. Essential Graphrag: Knowledge Graph-Enhanced Rag. Book.
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Publicado por Manning Publications, US, 2025
ISBN 10: 1633436268 ISBN 13: 9781633436268
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
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Añadir al carritoPaperback. Condición: New. 1st. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step.
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Idioma: Inglés
Publicado por Manning Publications, New York, 2025
ISBN 10: 1633436268 ISBN 13: 9781633436268
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 54,37
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Añadir al carritoPaperback. Condición: new. Paperback. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step. Upgrade your RAG applications with the power of knowledge graphs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Manning Publications, US, 2025
ISBN 10: 1633436268 ISBN 13: 9781633436268
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
Original o primera edición
EUR 55,34
Cantidad disponible: 4 disponibles
Añadir al carritoPaperback. Condición: New. 1st. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step.
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Añadir al carritoCondición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Añadir al carritoCondición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 2 working days.
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Añadir al carritoCondición: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Idioma: Inglés
Publicado por Manning Publications, New York, 2025
ISBN 10: 1633436268 ISBN 13: 9781633436268
Librería: CitiRetail, Stevenage, Reino Unido
EUR 56,70
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Añadir al carritoPaperback. Condición: new. Paperback. Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction. Knowledge graph basics: Model context data for instant, precise retrieval. Vector similarity search toolkit: Surface only the most relevant passages, cut noise. Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production. Cypher and Python templates: Drop-in code accelerates prototypes to deployable services. Evaluation framework: Measure accuracy, latency, and traceability with confidence. Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base. Essential GraphRAG by graph experts Tomaz Bratanic and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects. Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results. Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model. For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step. Upgrade your RAG applications with the power of knowledge graphs. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.