AI for Postgres Training Course

Getting Started with AI in Postgres

• Overview of AI and data-centric systems
• Practical AI applications in Postgres environments
• Architectural factors for AI-driven tasks

Preparing the Environment

• Installing PostgreSQL and configuring pgvector
• Setting up Python for AI connections
• Linking Postgres with local and cloud-based LLMs

AI Extensions and Vector Storage

• Concepts behind vector embeddings in Postgres
• Leveraging pgvector for semantic searches and similarity checks
• Comparing AI extensions with external vector databases

Combining LLMs with Postgres

• Connecting Postgres to OpenAI, Deepseek, Qwen, and Mistral Small
• Structuring AI query workflows
• Efficient storage and retrieval of embeddings

Creating Smart Query Systems

• Translating natural language to SQL via LLMs
• Automating query creation and refinement
• AI-enhanced database searching and content summarization

Optimizing Postgres for AI Performance

• Indexing approaches for embeddings
• Tuning performance and caching for AI queries
• Scaling Postgres using distributed and cloud frameworks

Security and Governance in AI-Powered Databases

• Data privacy and regulatory compliance aspects
• Managing API keys and access permissions
• Monitoring AI interactions and query logs

Real-World Examples and Business Applications

• Postgres-based recommendation engines
• Enterprise search and analytics utilizing embeddings
• Automation and predictive analytics within Postgres

Recap and Future Directions