Search
4 results for “vector-search”
Search results
Embeddings & Vector Search
How embeddings turn text into vectors that cosine similarity can compare mathematically, why shortening an embedding's dimensions trades some accuracy for real storage and speed savings, and when semantic search actually beats keyword search.
What does an embedding actually represent, and what does the distance between two embeddings measure?
An embedding is a vector, a list of floating-point numbers, produced by a model that maps a piece of text into that vector space such that texts with similar meaning end up positioned close together. The distance between two embeddings, typically measured with cosine similarity, quantifies how related the two original texts are: a small distance (high cosine similarity) means the model judged them semantically close, even if they don't share any of the same words, and a large distance means they're unrelated. This is the property that makes embeddings useful for search and clustering: comparison happens on meaning, encoded numerically, rather than on literal text overlap.
Why would you shorten an embedding from 1536 dimensions to 256, and what do you give up by doing it?
Fewer dimensions means less storage per vector and faster similarity computation at query time, which matters directly at scale, a vector database holding millions of embeddings pays that storage and compute cost on every one of them. Modern embedding models are specifically trained to support this trade-off gracefully: a newer model shortened to 256 dimensions can still outperform an older, unshortened model at 1536 dimensions, so shortening isn't simply "less accurate," it's a deliberate trade against a specific model's own accuracy ceiling. What you give up is some of that ceiling, the shortened embedding is measurably less precise than the same model's full-length embedding, which is why the right dimension count depends on how much accuracy a specific use case can actually trade away.
When does semantic search (via embeddings) actually outperform traditional keyword search, and when might keyword search still win?
Semantic search wins when a query and the relevant document use different words for the same idea, "car won't start" matching a document about "vehicle fails to ignite", since embeddings compare meaning rather than literal tokens, which keyword search cannot do at all. Keyword search still wins, or at least remains necessary, for exact-match needs, an error code, a product SKU, a specific proper noun, where the literal string matters and a semantically "close" but textually different result is actually the wrong answer. This is why production search systems commonly combine both (hybrid search) rather than treating embeddings as a strict replacement for keyword matching.