9789378545689 - vector databases and rag with python: build intelligent search and retrieval systems using embeddings and llms (english edition) de dua, rajdeep (6 resultados)
- Tapa blanda
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 40,00
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 42,33
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
- Tapa blanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 38,25
Envío por EUR 5,87Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
- Tapa blanda
- Impresión bajo demanda
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 42,70
Envío por EUR 43,23Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. As large language models continue to transform how we build intelligent systems, the ability to integrate proprietary data through vector search and RAG has become essential for creating accurate, contextually-aware applications that go beyond the limitations of pre-trained models.This compr…ehensive guide takes you from foundational concepts to production-ready implementations of vector databases and RAG systems. Starting with vector semantics and embeddings, you will learn to generate vector representations using neural networks, BERT, and OpenAI models. The book covers popular vector databases including Weaviate and Milvus, teaching you how to implement efficient search algorithms like k-nearest neighbors and hierarchical navigable small worlds. You will build complete RAG pipelines, explore advanced techniques like GraphRAG, and master evaluation frameworks using LlamaIndex. Each chapter includes hands-on Python examples with practical code implementations that demonstrate real-world applications.By the end of this book, you will have mastered the skills needed to design, build, and evaluate production-grade vector search systems and RAG applications. You will be equipped to enhance LLM applications with private data, implement semantic search at scale, troubleshoot retrieval issues, and solve real-world information retrieval challenges using cutting-edge AI techniques with confidence.WHAT YOU WILL LEARN Generate embeddings using neural networks, BERT, and OpenAI models. Implement vector search algorithms including KNN and HNSW. Develop GraphRAG systems for structured knowledge representation. Evaluate and optimize RAG applications using LlamaIndex frameworks. Design scalable vector database architectures for production environments. Integrate vector search with LLMs for intelligent retrieval. WHO THIS BOOK IS FORThis book is designed for data scientists, machine learning engineers, and software developers who want to build intelligent search and retrieval systems using modern AI techniques. It is ideal for professionals working with large language models who need to integrate private data, implement semantic search capabilities, or build production-ready RAG applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 64,27
Envío por EUR 31,93Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. As large language models continue to transform how we build intelligent systems, the ability to integrate proprietary data through vector search and RAG has become essential for creating accurate, contextually-aware applications that go beyond the limitations of pre-trained models.This compr…ehensive guide takes you from foundational concepts to production-ready implementations of vector databases and RAG systems. Starting with vector semantics and embeddings, you will learn to generate vector representations using neural networks, BERT, and OpenAI models. The book covers popular vector databases including Weaviate and Milvus, teaching you how to implement efficient search algorithms like k-nearest neighbors and hierarchical navigable small worlds. You will build complete RAG pipelines, explore advanced techniques like GraphRAG, and master evaluation frameworks using LlamaIndex. Each chapter includes hands-on Python examples with practical code implementations that demonstrate real-world applications.By the end of this book, you will have mastered the skills needed to design, build, and evaluate production-grade vector search systems and RAG applications. You will be equipped to enhance LLM applications with private data, implement semantic search at scale, troubleshoot retrieval issues, and solve real-world information retrieval challenges using cutting-edge AI techniques with confidence.WHAT YOU WILL LEARN Generate embeddings using neural networks, BERT, and OpenAI models. Implement vector search algorithms including KNN and HNSW. Develop GraphRAG systems for structured knowledge representation. Evaluate and optimize RAG applications using LlamaIndex frameworks. Design scalable vector database architectures for production environments. Integrate vector search with LLMs for intelligent retrieval. WHO THIS BOOK IS FORThis book is designed for data scientists, machine learning engineers, and software developers who want to build intelligent search and retrieval systems using modern AI techniques. It is ideal for professionals working with large language models who need to integrate private data, implement semantic search capabilities, or build production-ready RAG applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 48,30
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Vector Databases and RAG with Python | Build intelligent search and retrieval systems using embeddings and LLMs (English Edition) | Rajdeep Dua | Taschenbuch | Englisch | 2026 | BPB Publications | EAN 9789378545689 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gp…sr[at]libri[dot]de | Anbieter: preigu Print on Demand.
