AI for Postgres Training Course

Introduction to AI in Postgres

• Exploration of AI and data-centric system architectures
• Practical applications of AI within Postgres environments
• Architectural considerations specific to AI workloads

Environment Setup

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

AI Extensions and Vector Databases

• Comprehending vector embeddings within the Postgres context
• Leveraging pgvector for similarity searches and semantic queries
• Evaluating AI extensions against external vector stores

Integrating LLMs with Postgres

• Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small
• Designing effective AI query pipelines
• Efficiently storing and retrieving embeddings

Building Intelligent Query Systems

• Translating natural language to SQL via LLMs
• Automating the generation and optimisation of queries
• Utilising AI for database search and summarisation tasks

Optimising Postgres for AI Workloads

• Indexing techniques for embedding data
• Performance tuning and caching strategies for AI queries
• Scaling Postgres using distributed and cloud-based architectures

Security and Governance in AI-Enabled Databases

• Addressing data privacy and compliance requirements
• Overseeing API keys and access controls
• Auditing AI interactions and maintaining query logs

Case Studies and Enterprise Use Cases

• Creating AI-powered recommendation systems with Postgres
• Implementing enterprise search and analytics using embeddings
• Automation and predictive modelling within the Postgres framework

Summary and Next Steps