Master the Core Concepts and Tools Behind Vector Databases and AI Retrieval Systems In a rapidly evolving AI landscape, knowing how to store, search, and retrieve vector embeddings is no longer optional. Whether you're building RAG pipelines, designing search infrastructure, or integrating with modern LLM frameworks, this book gives you a clear and practical understanding of vector databases from the ground up. Many developers struggle to implement scalable vector search because most resources are either too academic or ...
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Master the Core Concepts and Tools Behind Vector Databases and AI Retrieval Systems In a rapidly evolving AI landscape, knowing how to store, search, and retrieve vector embeddings is no longer optional. Whether you're building RAG pipelines, designing search infrastructure, or integrating with modern LLM frameworks, this book gives you a clear and practical understanding of vector databases from the ground up. Many developers struggle to implement scalable vector search because most resources are either too academic or too shallow. This book bridges that gap. It walks you through the mathematical foundations, shows you how real systems are built, and gives you the tools to reason about tradeoffs across performance, cost, and design. What You Will Learn: Key concepts: cosine similarity, vector norms, top-k retrieval When and how to use FAISS, Milvus, Weaviate, and Pinecone Understanding indexes like IVF, HNSW, PQ, DiskANN How RAG systems work and how retrieval affects output quality Evaluating embeddings and retrievers with MRR, NDCG, Recall Multilingual search, memory systems, and semantic filters Security, tenancy, real-time updates, and production scaling End-to-end design of indexing pipelines and RAG workflows Includes Practical Add-Ons: Cheat Sheet for quick reference Flashcards to test your understanding Key Takeaways and Application Tips Common Mistakes to avoid Full Index for quick lookups Includes Code and Math Equations: This is a hands-on technical book. You'll find working code in Python, SQL, and Bash, along with clearly formatted mathematical equations that explain scoring functions, distances, and evaluation metrics. Every concept is grounded in practical logic and tied to real-world use. Grab your copy today and build your foundation in modern vector search and AI retrieval.
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Add this copy of Vector Database Fundamentals for Developers: Core to cart. $16.09, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.