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. Cloud-hosted services like OpenAI has text-embedding-ada-002 provide high-quality vector encodings. 

But it comes with API keys, network latency, and per-token usage costs. In contrast, LocalEmbeddingService does all the computation within hosted process, no GPUs, no outbound requests, no model files to manage.