AI for Postgres Training Course

Introduction to AI in Postgres

• Overview of AI and data-centric systems
• Applications of AI within Postgres environments
• Architectural considerations for AI workloads

Environment Setup

• Installation of PostgreSQL and pgvector configuration
• Setting up Python for AI integration
• Linking Postgres to local and cloud-based LLMs

AI Extensions and Vector Databases

• Concepts of vector embeddings in Postgres
• Applying pgvector for similarity search and semantic queries
• Comparing AI extensions with external vector stores

LLM Integration with Postgres

• Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small
• Designing AI query pipelines
• Efficient storage and retrieval of embeddings

Constructing Intelligent Query Systems

• Converting natural language to SQL via LLMs
• Automating query creation and optimization
• AI-assisted database searching and summarization

Postgres Optimization for AI Workloads

• Indexing approaches for embeddings
• Performance tuning and caching for AI queries
• Scaling Postgres using distributed and cloud architectures

Security and Governance in AI-Enabled Databases

• Considerations for data privacy and compliance
• Handling API keys and access control
• Auditing AI interactions and query logs

Case Studies and Enterprise Applications

• AI-driven recommendation systems with Postgres
• Enterprise search and analytics utilizing embeddings
• Automation and predictive modeling within Postgres

Summary and Future Steps