AI for Postgres Training Course

Introduction to AI in Postgres

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

Environment Setup

• Installing PostgreSQL and configuring pgvector
• Setting up Python for seamless AI integrations
• Connecting Postgres to both local and cloud-based LLMs

AI Extensions and Vector Databases

• Understanding vector embeddings within 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 efficient AI query pipelines
• Optimizing the storage and retrieval of embeddings

Building Intelligent Query Systems

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

Optimizing Postgres for AI Workloads

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

Security and Governance in AI-Enabled Databases

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

Case Studies and Enterprise Applications

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

Summary and Future Steps