AI for Postgres Training Course

Introduction to AI in Postgres

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

Setting Up the Environment

• Installing PostgreSQL and configuring 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.
• Utilising 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 optimisation.
• AI-assisted database search and summarisation.

Optimising 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 using Postgres.
• Enterprise search and analytics with embeddings.
• Automation and predictive modelling within Postgres.

Summary and Next Steps