AI for Postgres Training Course

Introduction to AI in Postgres

• Overview of AI and data-driven system architectures
• Practical AI use cases within Postgres environments
• Key architectural considerations for AI workloads

Environment Setup

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

AI Extensions and Vector Databases

• Exploring vector embeddings within Postgres
• Leveraging pgvector for similarity search and semantic querying
• Comparing AI extensions against external vector store solutions

Integrating LLMs with Postgres

• Connecting Postgres to OpenAI, Deepseek, Qwen, and Mistral Small
• Designing efficient AI query pipelines
• Optimizing the storage and retrieval of embeddings

Developing Intelligent Query Systems

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

Optimizing Postgres for AI Workloads

• Developing indexing strategies for embeddings
• Performance tuning and caching techniques for AI queries
• Scaling Postgres using distributed and cloud-native architectures

Security and Governance in AI-Enabled Databases

• Navigating data privacy and compliance requirements
• Managing API keys and robust access controls
• Auditing AI interactions and maintaining query logs

Case Studies and Enterprise Applications

• Building AI-powered recommendation systems with Postgres
• Enhancing enterprise search and analytics using embeddings
• Implementing automation and predictive modeling within Postgres

Summary and Next Steps