Overview

Build real AI-powered applications using nothing more than PostgreSQL and the pgvector extension. This 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. Designed 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. You will learn how to: Install and configure PostgreSQL + pgvector on Windows, macOS, Linux, Docker, Supabase, Neon, and AWS Understand embeddings, similarity metrics, chunking, and semantic retrieval Generate embeddings using OpenAI, Cohere, and HuggingFace models Store and query vectors using Postgres tables with HNSW and IVFFlat indexes Build fast and accurate semantic search engines with SQL Combine keyword search (BM25) and vector search for hybrid retrieval Construct complete RAG pipelines using LangChain and LlamaIndex Build a fully functional ""Chat with Your Documents"" AI application Deploy everything to the cloud and tune for performance, cost, and scalability The book includes step-by-step practice labs that guide you through the entire workflow: from ingestion → embeddings → vector storage → semantic search → RAG → deployment. You will build multiple hands-on projects, culminating in a complete production-ready AI semantic search system deployed on the cloud. What makes this book different Beginner-friendly yet technically accurate Up-to-date for 2025, covering the latest pgvector, PostgreSQL, and AI ecosystem tools Entirely practical, project-driven, and focused on real results Uses only free or low-cost tools where possible Builds a full AI application from scratch-no shortcuts, no magic Covers indexing, optimization, and troubleshooting so you understand how things work internally Suitable for both local learning and real production environments Who is this book for Developers and data engineers learning vector search for the first time PostgreSQL users wanting to add semantic capabilities to existing systems Teams building internal knowledge bases, customer-support search, or AI chatbots Students, analysts, and AI beginners who need practical, clear explanations Anyone interested in turning traditional Postgres into a modern AI-powered vector database By the end of this book, you will be able to: Transform raw documents, text files, or product catalogs into structured embeddings Build scalable semantic search features directly inside PostgreSQL Tune indexes, manage large datasets, and optimize performance Integrate advanced AI models to generate context-aware answers Deploy a full vector-enabled search and RAG system to the cloud Confidently extend your application into multimodal search (PDFs, images, audio) Maintain, secure, and operate a production-grade AI application Whether you're building your first AI search feature or deploying a real RAG system for your organization, this book gives you everything you need to get started with pgvector-and to do it the right way. Unlock the power of semantic search and AI with the tools you already know: PostgreSQL, SQL, and Python. Start building intelligent applications today.

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