AI for Postgres Training Course

Introduction to AI within Postgres

• Overview of AI and data-centric systems.
• Exploring AI use cases in Postgres environments.
• Architectural considerations for AI workloads.

Environment Setup

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

AI Extensions and Vector Databases

• Comprehending vector embeddings within Postgres.
• Utilizing pgvector for semantic queries and similarity search.
• Benchmarking AI extensions against external vector stores.

LLM Integration with Postgres

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

Constructing Intelligent Query Systems

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

Optimizing Postgres for AI Workloads

• Developing indexing strategies for embeddings.
• Tuning performance and implementing caching for AI queries.
• Scaling Postgres via distributed and cloud architectures.

Security and Governance in AI-Enabled Databases

• Addressing data privacy and compliance requirements.
• Managing access control and API keys securely.
• Auditing query logs and AI interactions.

Case Studies and Enterprise Applications

• Implementing AI-driven recommendation systems using Postgres.
• Enhancing enterprise search and analytics through embeddings.
• Applying predictive modeling and automation within Postgres.

Summary and Next Steps