Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Vector Databases and RAG with Python: Build intelligent search and retrieval systems using embeddings and LLMs (English Edition)

Vector Databases and RAG with Python: Build intelligent search and retrieval systems using embeddings and LLMs (English Edition)

Dua, Rajdeep

Paperback

DatabasesGeneral ComputersProgramming

ISBN10: 9378545688
ISBN13: 9789378545689
Publisher: Bpb Publications
Published: Jul 6 2026
Pages: 336
Weight: 1.27
Height: 0.70 Width: 7.50 Depth: 9.25
Language: English

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 comprehensive 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.

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