AI for Postgres Training Course

Introduction to AI in Postgres

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

Setting Up the Environment

• Installing PostgreSQL and configuring the pgvector extension
• Preparing the Python environment for AI integrations
• Establishing connections between Postgres and local or cloud-based LLMs

AI Extensions and Vector Databases

• Comprehending vector embeddings within Postgres
• Leveraging pgvector for similarity search and semantic querying
• 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
• Implementing AI-assisted database search and summarization

Optimizing Postgres for AI Workloads

• Developing 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

• Addressing data privacy and compliance requirements
• 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
• Implementing enterprise search and analytics using embeddings
• Enhancing automation and predictive modeling within Postgres

Summary and Next Steps