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Using AI Features in Azure HorizonDB (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 Azure HorizonDB
What the database is responsible for
What the database is not responsible for
Misconceptions to avoid
Positioning Azure HorizonDB 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
Vector distance comparison operators
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
Traditional PostgreSQL vector indexes
Introduction to DiskANN in HorizonDB
Creating a DiskANN index
Accuracy versus performance trade-offs
CPU and memory impact of vector queries
Monitoring and diagnosing vector query performance
Quiz 5
Module introduction
Why HorizonDB abstracts REST API complexity
Understanding the azure_ai schema
Security and managed identities
Cost and throttling implications of outbound calls
Operational risks and failure modes
Quiz 6
Module introduction
Synchronous vs asynchronous calls
Retrieving text embeddings via azure_openai.create_embeddings
Invoking chat models directly from SQL
Handling changes in embedding models and versioning strategies
Re-embedding strategies at scale
Quiz 7
Module introduction
Challenges with external ETL for AI workflows
pg_durable execution engine
Defining a pipeline with sources, steps, and sinks
Handling automatic chunking and embedding in SQL
Managing pipeline state and recovery
Monitoring and troubleshooting persistent workflows
Quiz 8
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
Quiz 9
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
Quiz 10
Module introduction
Why it matters to say no
Scenarios where HorizonDB is the wrong tool
When a specialized vector database makes more sense
Cost, complexity, and mainteance trade-offs
Decision checklist for architects and DBAs
Quiz 11
Module introduction
Data API Builder and PostgreSQL
Architecture and configuration for HorzionDB
Exposing tables and other objects via REST and GraphQL
Authentication and security
Quiz 12
Module introduction
AI agents and external tools
SQL Server MCP vs PostgreSQL MCP implementation
MCP architecture for HorizonDB
Example agent workflow querying pgvector data
Quiz 13
Clean up
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 Azure HorizonDB
Module introduction
AI features in Azure HorizonDB
What the database is responsible for
What the database is not responsible for
Misconceptions to avoid
Positioning Azure HorizonDB 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
Vector distance comparison operators
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
Traditional PostgreSQL vector indexes
Introduction to DiskANN in HorizonDB
Creating a DiskANN index
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
Why HorizonDB abstracts REST API complexity
Understanding the azure_ai schema
Security and managed identities
Cost and throttling implications of outbound calls
Operational risks and failure modes
Quiz 6
Module 7: Calling AI services from HorizonDB
Module introduction
Synchronous vs asynchronous calls
Retrieving text embeddings via azure_openai.create_embeddings
Invoking chat models directly from SQL
Handling changes in embedding models and versioning strategies
Re-embedding strategies at scale
Quiz 7
Module 8: Durable AI pipelines
Module introduction
Challenges with external ETL for AI workflows
pg_durable execution engine
Defining a pipeline with sources, steps, and sinks
Handling automatic chunking and embedding in SQL
Managing pipeline state and recovery
Monitoring and troubleshooting persistent workflows
Quiz 8
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
Quiz 9
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
Quiz 10
Module 11: When NOT to use AI features in HorizonDB
Module introduction
Why it matters to say no
Scenarios where HorizonDB is the wrong tool
When a specialized vector database makes more sense
Cost, complexity, and mainteance trade-offs
Decision checklist for architects and DBAs
Quiz 11
Module 12: Using Data API Builder with HorizonDB
Module introduction
Data API Builder and PostgreSQL
Architecture and configuration for HorzionDB
Exposing tables and other objects via REST and GraphQL
Authentication and security
Quiz 12
Module 13: Using HorizonDB with AI Agents via MCP
Module introduction
AI agents and external tools
SQL Server MCP vs PostgreSQL MCP implementation
MCP architecture for HorizonDB
Example agent workflow querying pgvector data
Quiz 13
Module 14: Next steps
Clean up
Summary and further steps
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