AI for Postgres Training Course

Foundations of AI in Postgres

• Overview of AI and data-centric systems
• Practical AI use cases in Postgres environments
• Architectural considerations for AI workloads

Environment Preparation

• Installation of PostgreSQL and configuration of pgvector
• Python setup for AI integrations
• Establishing connections between Postgres and local or cloud-based LLMs

AI Extensions and Vector Data Management

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

LLM Integration with Postgres

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

Creating Intelligent Query Systems

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

Performance Optimization for AI in Postgres

• Indexing techniques for embeddings
• Performance tuning and caching strategies for AI queries
• Scalability through distributed and cloud architectures

Security and Governance for AI-Enabled Databases

• Data privacy and regulatory compliance
• Managing API keys and access controls
• Auditing AI interactions and query logs

Case Studies and Enterprise Applications

• Building AI-powered recommendation systems using Postgres
• Enhancing enterprise search and analytics with embeddings
• Automation and predictive modeling within the Postgres ecosystem

Conclusion and Future Directions