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.

Chunking: the unglamorous variable that decides quality

Before anything is embedded, documents must be split into chunks, and this step quietly determines how well the whole system works. Chunks that are too large dilute their embedding across many topics, so nothing matches strongly; chunks that are too small strip away the context that makes a passage meaningful. Practical systems chunk along natural boundaries — headings, paragraphs, list items — often with modest overlap between adjacent chunks so that an idea split across a boundary still survives in at least one piece. Attaching metadata (source document, section title, date) to each chunk pays off later, both for filtering and for showing the user where an answer came from.

Hybrid search and reranking

Dense embeddings have a known blind spot: exact identifiers. A part number, an error code, or a rare proper noun may embed poorly even though a keyword match would be trivial. Production systems therefore run hybrid retrieval — a dense vector search and a sparse BM25 search in parallel, with the two result lists fused — getting the semantic recall of embeddings plus the literal precision of keywords. A reranking model then re-scores the top few dozen candidates with a slower, more accurate comparison against the full query. This retrieve-then-rerank pattern is cheap insurance: the first stage optimizes recall, the second optimizes precision.

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 — graph-based structures like HNSW are the common choice — returning the closest matches within milliseconds across millions of entries, trading a sliver of exactness for orders-of-magnitude speed. 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: your manuals, notes, and archives become searchable by meaning without a single query leaving the building. One discipline matters when self-hosting: queries and documents must be embedded by the same model, so changing embedding models later means re-indexing the whole corpus — pick deliberately. And evaluate with your own queries rather than trusting benchmark scores: build a small set of real questions with known correct passages, and measure whether the right chunk appears in the top results. Ten honest test queries reveal more about a retrieval setup than any leaderboard.

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.