Embedding and semantic search

An embedding is a way of turning a piece of data into a list of numbers, a vector, that captures its meaning. The trick is that the numbers are arranged so that similar things end up near each other in the resulting space. Two photos of the same kind of scene, or two sentences that express the same idea, will have embeddings that sit close together, while unrelated items will be far apart. This lets a computer measure how similar two things are just by measuring the distance between their vectors, even for messy, unstructured data like images and text.

Semantic search is the natural application of this idea. Instead of matching exact words or relying on manually assigned tags, semantic search embeds a query and then finds the items whose embeddings are closest to it, returning results that are similar in meaning. Ask for "a dog running on a beach" and semantic search can surface matching images even if none of them were ever labeled with those words. This shift from matching symbols to matching meaning is what makes it possible to search and organize large collections of data by what the data actually contains.

Embeddings are produced by models trained so that related inputs map to nearby vectors and unrelated inputs map to distant ones. Once data is embedded, each item becomes a point in a high-dimensional space, and similarity is measured by how close two points are. Semantic search embeds the query the same way and then looks for the nearest points, often using specialized indexes that can find nearest neighbors quickly even across millions of items. Because a shared embedding space can be built across modalities, it is possible to search images with text, or find examples similar to a given image, which is what makes embeddings so useful for exploring multimodal data.

Embeddings and semantic search turn a large, unlabeled pile of data into something you can actually navigate by meaning, which is transformative for working with real-world datasets. For physical AI, where datasets are enormous and mostly unlabeled, this is a key enabler of curation, letting practitioners find similar scenes, surface rare cases, and understand what their data contains. Anyone trying to make sense of large multimodal collections benefits, because searching by content is often the only practical way to find the specific examples that matter.

An embedding is a numerical vector that represents a piece of data in a way that captures its meaning, arranged so that similar items have nearby vectors. It lets a computer reason about similarity for unstructured data like images and text by comparing distances in the vector space.

Keyword search matches exact words or tags, so it misses relevant results that use different wording or were never labeled. Semantic search matches meaning by comparing embeddings, so it can find conceptually similar items even when the exact words do not appear.

Because similar items sit close in embedding space, you can find scenes like a known example, surface unusual or rare cases, and organize data by content. This makes it far easier to explore large, mostly unlabeled datasets and to target the examples worth labeling or investigating.

Multimodal data, Data curation, Longtail investigation, Distribution shift / domain shift, Vision-Language Model (VLM).