Implement vector search and embedding storage using Azure Database for PostgreSQL Flexible Server with pgvector.
| # | Topic | Status | Description |
|---|---|---|---|
| 01 | PostgreSQL Fundamentals | Complete | Flexible Server, configuration, connectivity |
| 02 | pgvector Embeddings | Placeholder | Extension setup, embedding storage, indexing |
| 03 | Vector Search Optimization | Placeholder | HNSW/IVFFlat indexes, query tuning, hybrid search |
PostgreSQL with pgvector is entirely new to AI-200 (not in AZ-204). It represents the AI-200 philosophy that developers need to understand vector databases as a core building block for AI applications, specifically for RAG (Retrieval-Augmented Generation) patterns.
- Basic SQL knowledge
- Understanding of embeddings and vector similarity concepts (covered in Azure OpenAI learning paths)