{"product_id":"pgvector-for-beginners-a-practical-introduction-to-vector-search-semantic-retrieval-and-ai-features-with-postgresql-9798277748374","title":"pgvector for Beginners: A Practical Introduction to Vector Search, Semantic Retrieval, and AI Features with PostgreSQL","description":"\u003cp\u003e • Author(s): Alira Vexel\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Languages - SQL\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBuild real AI-powered applications using nothing more than PostgreSQL and the pgvector extension.\u003c\/b\u003e\u003cbr\u003eThis hands-on beginner's guide shows you how to turn Postgres into a full vector database-capable of semantic search, similarity ranking, document retrieval, and complete Retrieval-Augmented Generation (RAG) systems powered by modern AI models.\u003c\/p\u003e\u003cp\u003eDesigned for developers, data engineers, analysts, and beginners entering the world of AI search, this book provides a practical, real-world introduction to vector embeddings, semantic search techniques, indexing, cloud deployment, and building usable end-to-end applications using Python, LangChain, and LlamaIndex. No prior experience with vector databases or machine learning is required.\u003c\/p\u003e\u003cp\u003eYou will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eInstall and configure PostgreSQL + pgvector on Windows, macOS, Linux, Docker, Supabase, Neon, and AWS\u003c\/li\u003e\n\u003cli\u003eUnderstand embeddings, similarity metrics, chunking, and semantic retrieval\u003c\/li\u003e\n\u003cli\u003eGenerate embeddings using OpenAI, Cohere, and HuggingFace models\u003c\/li\u003e\n\u003cli\u003eStore and query vectors using Postgres tables with HNSW and IVFFlat indexes\u003c\/li\u003e\n\u003cli\u003eBuild fast and accurate semantic search engines with SQL\u003c\/li\u003e\n\u003cli\u003eCombine keyword search (BM25) and vector search for hybrid retrieval\u003c\/li\u003e\n\u003cli\u003eConstruct complete RAG pipelines using LangChain and LlamaIndex\u003c\/li\u003e\n\u003cli\u003eBuild a fully functional \"Chat with Your Documents\" AI application\u003c\/li\u003e\n\u003cli\u003eDeploy everything to the cloud and tune for performance, cost, and scalability\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe book includes \u003cb\u003estep-by-step practice labs\u003c\/b\u003e that guide you through the entire workflow: \u003cbr\u003efrom ingestion → embeddings → vector storage → semantic search → RAG → deployment.\u003cbr\u003eYou will build multiple hands-on projects, culminating in a complete production-ready AI semantic search system deployed on the cloud.\u003c\/p\u003e\u003cb\u003eWhat makes this book different\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eBeginner-friendly\u003c\/b\u003e yet technically accurate\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eUp-to-date for 2025\u003c\/b\u003e, covering the latest pgvector, PostgreSQL, and AI ecosystem tools\u003c\/li\u003e\n\u003cli\u003eEntirely \u003cb\u003epractical, project-driven\u003c\/b\u003e, and focused on real results\u003c\/li\u003e\n\u003cli\u003eUses only \u003cb\u003efree or low-cost tools\u003c\/b\u003e where possible\u003c\/li\u003e\n\u003cli\u003eBuilds a full AI application from scratch-no shortcuts, no magic\u003c\/li\u003e\n\u003cli\u003eCovers indexing, optimization, and troubleshooting so you understand how things work internally\u003c\/li\u003e\n\u003cli\u003eSuitable for both local learning and real production environments\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWho is this book for\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eDevelopers and data engineers learning vector search for the first time\u003c\/li\u003e\n\u003cli\u003ePostgreSQL users wanting to add semantic capabilities to existing systems\u003c\/li\u003e\n\u003cli\u003eTeams building internal knowledge bases, customer-support search, or AI chatbots\u003c\/li\u003e\n\u003cli\u003eStudents, analysts, and AI beginners who need practical, clear explanations\u003c\/li\u003e\n\u003cli\u003eAnyone interested in turning traditional Postgres into a modern AI-powered vector database\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eBy the end of this book, you will be able to: \u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eTransform raw documents, text files, or product catalogs into structured embeddings\u003c\/li\u003e\n\u003cli\u003eBuild scalable semantic search features directly inside PostgreSQL\u003c\/li\u003e\n\u003cli\u003eTune indexes, manage large datasets, and optimize performance\u003c\/li\u003e\n\u003cli\u003eIntegrate advanced AI models to generate context-aware answers\u003c\/li\u003e\n\u003cli\u003eDeploy a full vector-enabled search and RAG system to the cloud\u003c\/li\u003e\n\u003cli\u003eConfidently extend your application into multimodal search (PDFs, images, audio)\u003c\/li\u003e\n\u003cli\u003eMaintain, secure, and operate a production-grade AI application\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you're building your first AI search feature or deploying a real RAG system for your organization, \u003cb\u003ethis book gives you everything you need to get started with pgvector-and to do it the right way.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eUnlock the power of semantic search and AI with the tools you already know: PostgreSQL, SQL, and Python.\u003c\/b\u003e\u003cbr\u003eStart building intelligent applications today.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":46861702070423,"sku":"9798277748374","price":2666.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798277748374.webp?v=1769963144","url":"https:\/\/atlanticbooks.com\/products\/pgvector-for-beginners-a-practical-introduction-to-vector-search-semantic-retrieval-and-ai-features-with-postgresql-9798277748374","provider":"Atlantic Books","version":"1.0","type":"link"}