AI for Postgres Training Course

Foundations of AI in Postgres

• An introduction to AI and data-centric systems.
• Practical AI use cases within Postgres environments.
• Architectural strategies for supporting AI workloads.

Environment Setup

• Installing PostgreSQL and configuring pgvector.
• Preparing Python environments for AI integrations.
• Linking Postgres with local and cloud-based LLMs.

AI Extensions and Vector Storage

• Conceptualizing vector embeddings within Postgres.
• Leveraging pgvector for semantic queries and similarity searches.
• Comparing AI extensions against external vector databases.

LLM Integration

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

Creating Intelligent Query Systems

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

Optimizing Postgres for AI

• Developing indexing strategies for embeddings.
• Tuning performance and caching for AI-specific queries.
• Scaling Postgres using distributed and cloud architectures.

Security and Governance

• Navigating data privacy and compliance requirements.
• Managing API keys and controlling access securely.
• Auditing AI interactions and maintaining query logs.

Case Studies and Enterprise Applications

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

Conclusion and Next Steps