AI for Postgres Training Course

Introduction to AI in PostgreSQL

• Overview of AI and data-centric systems
• Practical AI use cases in PostgreSQL environments
• Architectural factors for managing AI workloads

Environment Setup

• Installation of PostgreSQL and configuration of pgvector
• Setting up Python for AI integrations
• Linking PostgreSQL with local and cloud-based LLMs

AI Extensions and Vector Databases

• Comprehending vector embeddings within PostgreSQL
• Leveraging pgvector for similarity searches and semantic queries
• Comparing AI extensions against external vector stores

LLM Integration with PostgreSQL

• Connecting PostgreSQL to OpenAI, Deepseek, Qwen, and Mistral Small
• Creating AI query pipelines
• Efficient storage and retrieval of embeddings

Creating Intelligent Query Systems

• Translating natural language to SQL using LLMs
• Automating query generation and optimization processes
• AI-assisted database search and summarization features

Optimizing PostgreSQL for AI Workloads

• Indexing techniques for embeddings
• Performance tuning and caching for AI queries
• Scaling PostgreSQL using distributed and cloud architectures

Security and Governance in AI-Enabled Databases

• Considerations for data privacy and compliance
• Managing API keys and access controls
• Auditing AI interactions and maintaining query logs

Case Studies and Enterprise Applications

• Developing AI-powered recommendation systems with PostgreSQL
• Implementing enterprise search and analytics using embeddings
• Automation and predictive modeling within PostgreSQL

Summary and Future Steps