Plan du cours

Introduction to AI in Postgres

  • Overview of AI and data-driven systems
  • AI use cases within Postgres environments
  • Architecture 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
  • Using pgvector for similarity search and semantic queries
  • Benchmarking AI extensions vs. external vector stores

Integrating LLMs with Postgres

  • Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small
  • Designing AI query pipelines
  • Storing and retrieving embeddings efficiently

Building Intelligent Query Systems

  • 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 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 with Postgres
  • Enterprise search and analytics with embeddings
  • Automation and predictive modeling within Postgres

Summary and Next Steps

Pré requis

  • An understanding of SQL and relational database concepts
  • Experience with Postgres administration or development
  • Basic familiarity with AI and machine learning principles

Audience

  • Database administrators who wish to integrate AI into Postgres
  • Data engineers building AI-powered database pipelines
  • Developers and architects designing intelligent data-driven applications
 21 Heures

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