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Category: "Vector Embeddings and Reranking"

Vector Embeddings and Reranking

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  1. Vector Search Inside Out
    Vector Search Inside Out
    Ritesh Modi

    HNSW powers almost every vector database, and few of the engineers who rely on it have seen inside it. Vector Search Inside Out builds it from first principles in 92 step-by-step figures, then tunes it on 1.18 million real vectors. You'll know exactly what M, efConstruction and ef do, and how to set them for your own data.

  2. EmbeddingGemma 2
    EmbeddingGemma 2
    A Technical Guide to Google's Embedding Model for Semantic Search and RAG
    Steve Publications

    EmbeddingGemma 2 offers a hands-on look at Google's embedding model and how to put it to work. From semantic search and RAG to code retrieval and multimodal applications, this guide covers the architecture, trade-offs and practical implementation details you need to build reliable systems and make the right choices for your stack.

  3. The Art and Science of RAG
    The Art and Science of RAG
    Understanding, Designing, and Deploying Retrieval-Augmented Generation Systems
    Steve Publications

    Unlock the full potential of large language models with RAG. This book takes you from core concepts to production-ready systems, exploring everything from embeddings and search to evaluation and security. With clear explanations, runnable code and practical insights, you'll learn how to build reliable AI systems grounded in the knowledge that matters.

  4. Embeddings and Reranking in Retrieval-Augmented Generation
    Embeddings and Reranking in Retrieval-Augmented Generation
    Building Production-Grade Search Systems with Dense Retrieval and Neural Reranking
    Steve Publications

    Build reliable, production-ready search systems with embeddings and neural reranking. Learn how to turn semantic meaning into relevant results, refine retrieved content and deliver better context for AI-generated answers. With hands-on Python examples and practical architectures, this book takes you from core concepts to real-world RAG systems.