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.
