What is hybrid search?
Two ways of matching, run together. Keyword search finds the exact words; vector search finds the meaning around them. The combination beats either one alone.
Hybrid search runs keyword search and vector search side by side and merges their results. Keyword matching catches exact terms, names and rare identifiers that meaning-based methods can drift past; vector matching catches paraphrases and different wording that keyword matching misses entirely. Because the two fail in opposite places, their combination covers far more of what a real question can look like, and both halves run comfortably on a personal machine.
What each method misses.
Plain keyword search is literal. Ask it for "soil nitrogen retention" and it will not return a passage that says "nutrient holding capacity of earth", even though the passage is exactly what you wanted. Embedding-based search, the engine behind semantic search, fixes exactly that by matching meaning instead of characters. But it has the mirror-image weakness: a passage containing the one rare identifier you actually need can rank below several vague topical neighbors, because the meaning space does not care much about a single token. Each method's strength is the other's gap.
Running both, merging results.
The hybrid part is the merge. Both methods rank the library independently, and a fusion step combines the two lists into one, usually by rank position rather than raw score, since the scores come from incompatible scales. A passage that both methods rank highly is the strongest candidate, and a passage only keyword search found, or only vector search found, still gets its chance. That merged list is what feeds retrieval-augmented generation, so the model reads the passages two different kinds of matching agreed on.
Why it runs locally.
Nothing in the method demands infrastructure. Keyword search is an inverted index, vector search is comparing stored vectors by distance, and a research-sized library of thousands of documents is small enough that a plain database does both on a laptop. The embeddings come from a local model, so the whole pipeline, both searches and the fusion, completes without a network request. It is one of those techniques that sounds like a data-center feature and turns out to be arithmetic a personal machine handles easily. What feeds both searches is its own topic: what a retrieval query is covers how the question you typed becomes the text these searches run.