PostgreSQL for AI Master SQL, pgvector & RAG Search 2026Published 7/2026
Created by Sachin Kumar
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 43 Lectures ( 8h 21m ) |
Size: 8 GB
Go from SQL basics to advanced vector operations. Build production RAG pipelines with pgvector, HNSW, & Python.⚡ Master advanced PostgreSQL database administration, data modeling, complex CTEs, and relational window functions for predictive analytics.
⚡ Implement the pgvector extension to seamlessly store, calibrate, and query high-dimensional machine learning embeddings directly in SQL.
⚡ Build and deploy production-grade Retrieval-Augmented Generation (RAG) pipelines using Python, LangChain, and LlamaIndex architectures.
⚡ Optimize high-speed similarity engines using Approximate Nearest Neighbor (ANN) indexing frameworks like HNSW and IVF for scalable matching.
⚡ Engineer high-performance hybrid search systems combining traditional relational SQL filtering with semantic vector search capabilities.
⚡ Secure enterprise AI data using Row-Level Security (RLS) policies and implement precise PII data privacy controls in modern databases.
⚡ Scale read-heavy AI workloads across production environments using strategic table partitioning, read replicas, and cloud deployment techniques.
⚡ Diagnose performance bottlenecks and automate vector index maintenance using pg_stat_statements and automated database vacuuming routines.
Requirements❗ Basic SQL Knowledge: Familiarity with writing simple database queries (such as standard SELECT, WHERE, and JOIN statements) is helpful but not mandatory.
❗ Fundamental Python Skills: A basic understanding of Python syntax will help you comfortably follow along during the hands-on AI integration and LangChain labs.
❗ No Prior AI or Vector Experience Required: We teach the math and mechanics behind high-dimensional embeddings, pgvector, and HNSW indexes completely from scratch.
❗ A Computer (Windows, Mac, or Linux): You will need a standard laptop or desktop computer to install Docker or PostgreSQL and build your local development environment.
DescriptionMaster the database engine powering the modern AI revolution.Every major LLM application requires a reliable, blazing-fast backend to store, filter, and search high-dimensional embeddings. While many developers scramble to adopt complex, standalone vector databases, industry leaders are turning to the most trusted relational database on earth:
PostgreSQL.
This course is a comprehensive blueprint for building production-grade, AI-native backends using
PostgreSQL and pgvector. Whether you are starting from scratch with relational database fundamentals or you are a seasoned performance engineer looking to master advanced vector geometry, this course bridges the gap between traditional relational database management systems (RDBMS) and modern AI infrastructure.
The Curriculum Snapshot
SectionCore FocusKey Technologies & Concepts1 & 2: Database FoundationsSQL Mastery & OptimizationB-Trees, GIN, BRIN, CTEs, Window Functions, Optimizer Execution Plans
3 & 4: The Vector RevolutionHigh-Dimensional Searchpgvector, Cosine/L2 Distance, ANN, IVF vs. HNSW Indexing
5: Production RAG PipelinesLLM Architecture PatternsDocument Chunking, LangChain, LlamaIndex, Metadata Filtering
6: Scaling & OperationsEnterprise DeploymentPartitioning, Row-Level Security, Read Replicas, Cloud Deployment
What You Will Master In This Course
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Advanced pgvector Engineering: Install, calibrate, and optimize the pgvector extension to store and query high-dimensional machine learning embeddings.
✨
Blazing-Fast Similarity Searches: Demystify the math behind Cosine, L2, and Inner Product distances. Implement
HNSW (Hierarchical Navigable Small World) and IVF indexes to accelerate vector matching at scale.
✨
Production RAG Architectures: Build complete Retrieval-Augmented Generation loops from scratch. Master document chunking pipelines and seamlessly integrate Postgres with
LangChain and LlamaIndex.
✨
The Hybrid Search Edge: Combine traditional SQL relational filters (CTEs, Window Functions, JSONB data) with semantic vector search for precise context retrieval.
✨
Enterprise Operations & Security: Lock down your data using Row-Level Security (RLS), manage massive datasets with table partitioning, and scale read-heavy AI workloads with replicas.
Hands-On Labs (20+ Practical Chapters)
You will not just watch theory videos. You will write code across
20 dedicated practical labs, including
✨ Setting up a professional database development environment.
✨ Generating and orchestrating text embeddings using Python and the OpenAI API.
✨ Engineering hybrid search engines that combine keyword filters with semantic vector weights.
✨ Automating index maintenance, health checks, and database vacuuming routines under high AI data loads.
Who Is This Course For?
✨
Backend Developers & Software Engineers who want to build AI-powered apps without the overhead of learning a brand-new niche database system.
✨
Data Engineers & AI Architects tasked with designing reliable, scalable infrastructure for LLMs and RAG pipelines.
✨
SQL Performance Engineers who want to future-proof their skills and transition into AI-native database administration.
Stop duct-taping temporary data solutions together. Harness the 25-year battle-tested power of PostgreSQL to scale your intelligence layers.
Enroll today and build the future of AI data infrastructure!Who this course is for⭐ Backend & Software Engineers: Developers looking to build AI-powered apps, LLM backends, or semantic search features without the overhead of learning a niche, standalone vector database.
⭐ Data Engineers & AI Architects: Professionals tasked with designing, optimizing, and maintaining scalable data pipelines, document chunking workflows, and enterprise-grade RAG architectures.
⭐ SQL Developers & Database Administrators (DBAs): Traditional database performance engineers wanting to future-proof their skills and master modern AI-native PostgreSQL extensions like pgvector.
⭐ Full-Stack Developers & Tech Leads: Creators and technical decision-makers who want to leverage a 25-year battle-tested RDBMS to handle hybrid relational data and high-dimensional machine learning embeddings under one roof.
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