AI for Postgres Training Course

Introduction to AI in Postgres

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

Environment Setup

• Installation of PostgreSQL and configuration of pgvector
• Setting up Python for AI integrations
• Connecting Postgres to local and cloud-based LLMs

AI Extensions and Vector Databases

• Understanding vector embeddings in Postgres
• Leveraging pgvector for similarity search and semantic queries
• Benchmarking AI extensions against external vector stores

Integrating LLMs with Postgres

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

Building Intelligent Query Systems

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

Optimizing Postgres for AI Workloads

• Indexing strategies for embeddings
• Performance tuning and caching for AI queries
• Scaling Postgres with distributed and cloud architectures

Security and Governance in AI-Enabled Databases

• Data privacy and compliance considerations
• Managing API keys and access control
• Auditing AI interactions and query logs

Case Studies and Enterprise Use Cases

• AI-powered recommendation systems with Postgres
• Enterprise search and analytics with embeddings
• Automation and predictive modeling within Postgres

Summary and Next Steps