A free 64-chapter mastery path

AI-Native Vector Databases with PostgreSQL & pgvector

From Zero to Production RAG, Search, and Agent Memory

This beginner-first pgvector tutorial takes you from your first PostgreSQL table and three-dimensional vector to evaluated hybrid search, citation-first RAG, durable agent memory, security, operations, and scale. Every major concept includes runnable evidence, AI pair-work prompts, production failure modes, and verification contracts.

Start from zeroExplore all chapters
Chapters
64
Learning parts
10
Capstones
3
AI-Native Vector Databases with PostgreSQL and pgvector book cover
One system of record for structured truth, metadata, full-text search, vectors, transactions, permissions, and recoverable AI retrieval.

What will you be able to build and operate?

You will learn the entire lifecycle: represent meaning, ingest versioned sources, retrieve authorized evidence, evaluate quality, build AI products, and keep the database secure, observable, affordable, and recoverable as models and data change.

  • PostgreSQL, SQL, vector mathematics, embeddings, and every pgvector operator
  • Exact search, HNSW, IVFFlat, filtered ANN, iterative scans, and query plans
  • Versioned extraction, chunking, embedding jobs, reprocessing, and deletion
  • Full-text plus vector hybrid search, reranking, diversity, and golden datasets
  • Citation-first RAG, prompt-injection boundaries, conversation and agent memory
  • RLS, privacy, backups, PITR, replication, SLOs, incidents, and zero-downtime releases
  • Quantization, partitioning, two-stage retrieval, sharding, capacity, and cost
  • Semantic search, governed RAG, and approval-gated agent memory capstones

What is inside the complete pgvector tutorial?

The sequence removes hidden prerequisites, establishes exact quality before approximate speed, and does not call a system production-ready until security, deletion, recovery, observability, and measurable user outcomes are present.

PART 01

Start Here

  1. 00About This Book: Your Zero-to-Expert Path
  2. 01What Is an AI-Native Vector Database?
  3. 02Relational Databases and PostgreSQL from First Principles
  4. 03Install PostgreSQL, pgvector, Python, and Docker
  5. 04Your First Vector Table and Similarity Query
PART 02

Vectors and Embeddings

  1. 01Vectors Without Intimidating Mathematics
  2. 02Embeddings: Models, Tokens, Dimensions, and Meaning
  3. 03L2, Cosine, Inner Product, L1, Hamming, and Jaccard Distance
  4. 04Normalization, Dimensions, Precision, and Model Compatibility
  5. 05Choose and Evaluate an Embedding Model
PART 03

PostgreSQL and pgvector Essentials

  1. 01SQL Essentials for AI Engineers
  2. 02Schema Design for Documents, Chunks, Metadata, and Embeddings
  3. 03pgvector Data Types: vector, halfvec, bit, and sparsevec
  4. 04Exact Nearest-Neighbor Search
  5. 05Filters, Joins, Transactions, and Referential Integrity
  6. 06Insert, Upsert, Update, Delete, and Bulk COPY
  7. 07Connect from Python and TypeScript Safely
PART 04

Build the Ingestion System

  1. 01Document Extraction, Normalization, and Provenance
  2. 02Chunking by Structure and Meaning
  3. 03Idempotent Embedding Pipelines and Job State
  4. 04Batch APIs, Rate Limits, Retries, and Backpressure
  5. 05Change Detection, Re-embedding, Tombstones, and Deletion
  6. 06Embedding-Model Migrations Without Downtime
  7. 07Multilingual, Multimodal, and Multi-Vector Records
PART 05

Retrieval Engineering

  1. 01Exact Search as the Quality Baseline
  2. 02HNSW Indexes from Intuition to Tuning
  3. 03IVFFlat Indexes from Training to Tuning
  4. 04Filtered Approximate Search and Iterative Scans
  5. 05PostgreSQL Full-Text Search for Lexical Retrieval
  6. 06Hybrid Search with Reciprocal Rank Fusion
  7. 07Reranking, Query Rewriting, and Multi-Query Retrieval
  8. 08Metadata Filters, Freshness, Diversity, and Business Rules
  9. 09Build a Golden Dataset and Evaluate Retrieval
PART 06

RAG and Agentic Applications

  1. 01The Complete RAG Request Lifecycle
  2. 02Build a Citation-First RAG API
  3. 03Context Assembly, Token Budgets, and Prompt-Injection Boundaries
  4. 04Conversation State, Summaries, and Semantic Memory
  5. 05Agent Memory, Tool Retrieval, and Human Approval
  6. 06Recommendations and Similarity Features Beyond RAG
  7. 07Caching, Streaming, Fallbacks, and Graceful Degradation
PART 07

Production PostgreSQL

  1. 01Measure Query Plans, Latency, Recall, and Throughput
  2. 02Memory, Maintenance, Vacuum, and Index Builds
  3. 03Connections, Pooling, Transactions, and Concurrency
  4. 04Row-Level Security and Multi-Tenant Isolation
  5. 05Encryption, Secrets, Privacy, Retention, and Audit
  6. 06Backups, Point-in-Time Recovery, Replication, and High Availability
  7. 07Observability, SLOs, Incident Response, and Quality Drift
  8. 08Zero-Downtime Migrations, Releases, and Rollback
PART 08

Scale and Advanced Techniques

  1. 01Partition Vectors by Tenant, Time, Language, or Model
  2. 02Half Precision, Binary Quantization, Sparse Vectors, and Subvectors
  3. 03Two-Stage Retrieval and Expression Indexes
  4. 04Vertical Scaling, Read Replicas, Sharding, and Distributed PostgreSQL
  5. 05Capacity Planning and Cost Modeling
  6. 06When to Choose pgvector Versus a Specialist Vector Database
PART 09

Deployment and Integration

  1. 01A Reproducible Docker Development Environment
  2. 02Deploy pgvector on Managed PostgreSQL
  3. 03ORMs, LangChain, LlamaIndex, and Abstraction Boundaries
  4. 04CI/CD, Migrations, Fixtures, and Retrieval Regression Tests
PART 10

Capstones and Reference

  1. 01Beginner Capstone: Semantic Product Search
  2. 02AI Capstone: Governed Citation-First RAG
  3. 03Agent Capstone: Durable Memory with Approval Gates
  4. 04Production Launch Checklist and Architecture Review
  5. 05Troubleshooting Playbook and SQL Cookbook
  6. 06Glossary, Learning Map, and Next Steps

How does the book make AI a verified engineering partner?

Every chapter includes an AI pair-work prompt, immediately followed by a verification contract. The assistant can explain, critique, or draft a small artifact; PostgreSQL output, query plans, tests, retrieval labels, denial cases, and recovery drills decide whether that artifact is correct.

Continue the AI-native learning system

Pair this book with Digital FTEs: Engineering and the AI-Native Azure book. For a real enterprise implementation, explore AI Native Consulting and Forward Deployed Engineering.

Frequently asked questions

Can I learn pgvector without knowing PostgreSQL or vector mathematics?
Yes. The book begins with relational database vocabulary, a reproducible local installation, one three-dimensional vector, and the SQL needed for retrieval. Mathematics is introduced through small worked examples before real embeddings, indexes, application code, evaluation, security, and operations.
What makes this pgvector book AI-native?
Every database concept begins from an AI product need. The curriculum treats hybrid retrieval, reranking, RAG, citations, agent memory, evaluation, prompt-injection boundaries, human approval, model migrations, and observable quality as core system concerns rather than optional framework features.
Does the book cover HNSW, IVFFlat, and hybrid search?
Yes. It first establishes exact-search quality, then teaches HNSW and IVFFlat construction and tuning, filtered approximate search, iterative scans, PostgreSQL full-text search, Reciprocal Rank Fusion, reranking, query rewriting, metadata rules, and golden-dataset evaluation.
Can pgvector support production RAG and agent memory?
Yes, when the measured workload fits PostgreSQL and the surrounding design handles authorization, provenance, updates, deletion, evaluation, backups, observability, and scaling. Dedicated chapters and capstones build citation-first RAG and consent-aware agent memory with approval gates.
When should I use a specialist vector database instead?
The book includes a vendor-neutral decision framework. Test a specialist system when required scale, geographic distribution, filtered ANN behavior, ingestion, managed operations, or vector-native features exceed your proven pgvector design. Compare both with the same workload, labels, failures, recovery goals, and cost horizon.

Start with one vector. Finish with operational proof.

Begin without prerequisites and keep every lab local. When your team needs a secure production retrieval architecture, evaluate the workload and failure boundaries before committing to infrastructure.

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