What is semantic search?

How semantic search works

A semantic search system turns text (and sometimes images or audio) into embeddings: lists of numbers that place each piece of content at a point in a high-dimensional space. The model that produces them is trained so that things with similar meaning land near each other. “Car won't start” and “the engine died this morning” end up close together even though they share no words.

When you type a query, it gets turned into an embedding too. The search engine then looks for the stored items whose embeddings are nearest to it and returns them ranked by similarity. Many systems follow this with a second, slower pass, called reranking, that reads the query and each top candidate together to put the best match first.

An example

Say you have a folder of recorded lectures and you remember a professor explaining why prices rise when money supply grows, but not the words she used. A keyword search for “inflation” fails if she never said that word. A semantic search for “why printing money makes things more expensive” can still surface the right passage, because the meaning matches.

The same approach works for pictures. A model trained on images and captions puts a photo of a red umbrella near the words “red umbrella,” so typing a description can find a video frame or photo that was never tagged. That is the main reason semantic search matters for media: most of what is in a video library was never written down anywhere, and meaning-based search is the only practical way to reach it without watching everything again.

Limits worth knowing

Semantic search is not magic, and it trades some precision for flexibility:

• It can return things that are related but not what you wanted, such as a passage about deflation when you asked about inflation.
• It is weaker on exact identifiers: part numbers, order IDs, rare names. For those, plain keyword matching often wins, which is why many tools combine the two. See keyword vs semantic search.
• Results depend on the embedding model. A model trained mostly on English web text may do poorly on other languages or specialist jargon.
• It only searches what was converted to text or vectors. If a recording was never transcribed, there is nothing for semantic search to match.

In MediaFind

MediaFind builds on-device embeddings for your transcripts, frames and photos, so you can type a description like “the part where she explains the pricing change” and get ranked moments with exact timestamps. Visual search works the same way for what's on screen, and it sits alongside word, on-screen text and speaker search rather than replacing them. Nothing is sent to a server to compute the embeddings.

To see it applied to video files specifically, read how to search inside video files.

Frequently asked questions

Is semantic search the same as AI search?

It is one kind. Semantic search ranks existing content by meaning. Tools that also write an answer on top of the results usually combine semantic search with a language model, which is called retrieval-augmented generation.

Does semantic search work on video?

Yes, once the video has been turned into something searchable: a transcript for speech, and image embeddings of frames for what's shown on screen.

Why does semantic search sometimes miss an exact name?

Embeddings capture general meaning, and a rare name or code carries little meaning on its own. Combining semantic search with keyword matching covers that gap.