A practical walkthrough of implementing vector similarity search in .NET using PostgreSQL with the pgvector extension. The tutorial covers setting up a Postgres container with pgvector pre-installed via .NET Aspire, running an Ollama embedding model (Qwen3 0.6B), generating 1024-dimension embeddings for ~180 blog articles, storing them in Postgres, and performing cosine-distance semantic search queries. The implementation uses Dapper for database access, Microsoft.Extensions.AI for the embedding generator abstraction, and HNSW indexing for efficient approximate nearest neighbor search. Practical search examples demonstrate retrieval of semantically relevant articles for queries about distributed messaging patterns, CQRS, and software architecture.