A Fully Self‑Contained Text Embedding Service in C#

Modern semantic search, retrieval-augmented generation (RAG) pipelines, and large-scale recommendation models heavily rely on embeddings — transformations of natural language text into dense numeric representations called vectors. These embeddings position semantically related text in nearby regions of vector space. It enables similarity computation through distant metrices such as Cosine similarity or Euclidean distance.