AI for Postgres Training Course

Foundations of AI in PostgreSQL

• Overview of AI and data-centric systems
• Practical AI applications within PostgreSQL ecosystems
• Architectural requirements for AI workloads

Environment Configuration

• Installing PostgreSQL and setting up pgvector
• Preparing Python environments for AI integration
• Linking PostgreSQL to local and cloud-hosted LLMs

AI Extensions and Vector Storage

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

Connecting LLMs to PostgreSQL

• Integrating PostgreSQL with OpenAI, Deepseek, Qwen, and Mistral Small
• Structuring AI query pipelines
• Efficient management of embedding storage and retrieval

Developing Intelligent Query Systems

• Converting natural language to SQL via LLMs
• Streamlining query generation and optimization
• AI-supported database search and summarization

Enhancing PostgreSQL for AI

• Indexing techniques for embeddings
• Tuning performance and caching for AI-related queries
• Scaling PostgreSQL through distributed and cloud models

Security and Governance in AI Databases

• Addressing data privacy and regulatory compliance
• Controlling API keys and access permissions
• Monitoring AI interactions and query logs

Real-World Applications and Case Studies

• Implementing AI-based recommendation systems with PostgreSQL
• Advanced search and analytics using embeddings
• Automation and predictive modeling inside PostgreSQL

Wrap-up and Future Steps