AI for Postgres Training Course

Introduction to AI in Postgres

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

Environment Setup

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

AI Extensions and Vector Databases

• Concepts behind vector embeddings in Postgres
• Utilizing pgvector for similarity searches and semantic querying
• Comparing the performance of AI extensions against external vector stores

Integrating LLMs with Postgres

• Linking Postgres with OpenAI, Deepseek, Qwen, and Mistral Small
• Designing efficient AI query pipelines
• Best practices for storing and retrieving embeddings

Building Intelligent Query Systems

• Translating natural language to SQL via LLMs
• Automating the generation and optimization of queries
• Implementing AI-assisted database search and summarization features

Optimizing Postgres for AI Workloads

• Effective indexing strategies for embeddings
• Tuning performance and applying caching techniques for AI queries
• Scaling Postgres using distributed and cloud-native architectures

Security and Governance in AI-Enabled Databases

• Addressing data privacy and compliance requirements
• Managing API keys and implementing strict access control
• Auditing AI interactions and monitoring query logs

Case Studies and Enterprise Applications

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

Summary and Next Steps