Semantic Search

Semantic search retrieves information by meaning rather than literal keyword matching. Each document and query is converted into a high-dimensional vector (an embedding) by a neural model, and results are ranked by how close those vectors sit in the embedding space. Because conceptually related text lands near each other geometrically, a search for "firmware that lowers power draw" can surface a document about "undervolting tuning profiles" even when none of the query words appear in it.

How it works

An embedding model maps each chunk of text to a fixed-length vector where semantic similarity is preserved as geometric proximity. At query time the system embeds the query and finds the nearest vectors, usually scored with cosine similarity, which measures the directional alignment between two vectors regardless of their magnitude. Unlike sparse keyword methods such as BM25 or TF-IDF, dense embeddings capture nuance and synonyms, so closely related ideas are retrieved even without shared words.

Scaling and sovereignty

When a corpus grows past what fits comfortably in memory, exact comparison becomes costly, so vectors are stored in an index that supports approximate nearest-neighbor (ANN) search, returning the closest matches within milliseconds across millions of entries. Semantic search is the retrieval backbone of most retrieval-augmented generation pipelines. For a sovereign operator, the appeal is that the embedding model, the vector index, and the documents can all run on local hardware, keeping a private knowledge base entirely off third-party servers.

D-Central treats semantic search as a building block for self-hosted reference tooling. It pairs naturally with a reranking step that re-scores the top candidates for precision, and the vectors themselves come from a chosen multimodal model or text embedding model.