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Using AI Features in PostgreSQL (In development - coming soon)
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Who is this course for?
Who is Greg?
What will I learn in this course?
Module introduction
AI features in PostgreSQL
What the database is responsible for
What the database is not responsible for
What the model service is responsible for
Extensions vs PostgreSQL core
Misconceptions to avoid
Positioning PostgreSQL correctly
Quiz 1
Module introduction
What is an AI model?
What embeddings are and why they exist
How vector similarity differs from relational comparison
Typical AI scenarios that involve vectors
Why vectors work well for these scenarios
When vectors are inappropriate
Where embeddings models are hosted
Common embeddings models
Quiz 2
Module introduction
Vector data type basics
pgvector extension basics
Declaring vector columns and variables
How vector values are represented
Practical table pattern for embeddings
Dimensionality is enforced
Dimensionality changes are migration events
Storage characteristics and limitations of pgvector
Quiz 3
Module introduction
What similarity search means in PostgreSQL
Writing similarity queries in SQL
Exact similarity (KNN) vs approximate search (ANN)
Combining vector similarity with relational predicates
Common query mistakes and inefficiencies
Quiz 4
Module introduction
Why vector indexing exists
HNSW vector indexes
IVFFlat vector indexes
Accuracy versus performance trade-offs
CPU and memory impact of vector queries
Monitoring and diagnosing vector query performance
Quiz 5
Module introduction
Module introduction
Module introduction
Module introduction
What RAG means in practical terms
Executing similarity search in an AI workflow
In-database re-ranking
Passing retrieved data back to the app tier or to LLMs
Module introduction
Protecting sensitive data used in AI workflows
Applying PostgreSQL RLS to vector searches
Auditing AI-related queries
Governance boundaries in AI-enabled systems
Module introduction
Module introduction
Module introduction
Summary and further steps
Module 0: Getting started
Who is this course for?
Preview
Who is Greg?
Preview
What will I learn in this course?
Preview
Module 1: AI in the context of PostgreSQL
Module introduction
AI features in PostgreSQL
What the database is responsible for
What the database is not responsible for
What the model service is responsible for
Extensions vs PostgreSQL core
Misconceptions to avoid
Positioning PostgreSQL correctly
Quiz 1
Module 2: Vector data and embeddings fundamentals
Module introduction
What is an AI model?
What embeddings are and why they exist
How vector similarity differs from relational comparison
Typical AI scenarios that involve vectors
Why vectors work well for these scenarios
When vectors are inappropriate
Where embeddings models are hosted
Common embeddings models
Quiz 2
Module 3: Vector data types in PostgreSQL
Module introduction
Vector data type basics
pgvector extension basics
Declaring vector columns and variables
How vector values are represented
Practical table pattern for embeddings
Dimensionality is enforced
Dimensionality changes are migration events
Storage characteristics and limitations of pgvector
Quiz 3
Module 4: Querying vector data
Module introduction
What similarity search means in PostgreSQL
Writing similarity queries in SQL
Exact similarity (KNN) vs approximate search (ANN)
Combining vector similarity with relational predicates
Common query mistakes and inefficiencies
Quiz 4
Module 5: Vector indexing and performance
Module introduction
Why vector indexing exists
HNSW vector indexes
IVFFlat vector indexes
Accuracy versus performance trade-offs
CPU and memory impact of vector queries
Monitoring and diagnosing vector query performance
Quiz 5
Module 6: Integrating External AI Services
Module introduction
Module 7: Calling AI services from PostgreSQL
Module introduction
Module 8: Creating AI Pipelines
Module introduction
Module 9: Retrieval-augmented query patterns
Module introduction
What RAG means in practical terms
Executing similarity search in an AI workflow
In-database re-ranking
Passing retrieved data back to the app tier or to LLMs
Module 10: Security, governance, and operational concerns
Module introduction
Protecting sensitive data used in AI workflows
Applying PostgreSQL RLS to vector searches
Auditing AI-related queries
Governance boundaries in AI-enabled systems
Module 11: When NOT to use AI features in PostgreSQL
Module introduction
Module 12: Using Data API Builder with PostgreSQL
Module introduction
Module 13: Using PostgreSQL with AI Agents via MCP
Module introduction
Module 14: Next steps
Summary and further steps
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