AI for Postgres Training Course

Introduction to AI in Postgres

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

Setting Up the Environment

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

AI Extensions and Vector Databases

• Comprehending vector embeddings in Postgres
• Utilizing pgvector for similarity search and semantic queries
• Comparing AI extensions with external vector stores

Integrating LLMs with Postgres

• Connecting Postgres to OpenAI, Deepseek, Qwen, and Mistral Small
• Designing efficient AI query pipelines
• Efficiently storing and retrieving 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 embedding data
• Performance tuning and caching 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 access controls
• Auditing AI interactions and query logs

Case Studies and Enterprise Use Cases

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

Summary and Next Steps