Vector Search | Clarifai Docs
Leverage vector search capabilities to find similar items in a dataset
Vector search is a technique that enables searching and retrieving unstructured data — like text, images, and videos — based on meaning rather than exact matches.
At its core are vector embeddings — numerical representations of data that capture their semantic or visual essence. These embeddings allow data to be mathematically processed and compared, making it possible to find similar items even when they don’t share exact words or features.
Unlike traditional keyword-based search, which relies on exact or fuzzy text matches, vector search compares the distances between embeddings to find results that are contextually or visually similar. This allows for much more intelligent and intuitive retrieval.
Note
When you post inputs to our platform, either via the UI or API, your app's base workflow automatically indexes those inputs using the outputs from your models. This index powers vector search capabilities — so you can search using concepts, annotations, or advanced parameters to find inputs that align with the meanings you're interested in.
Powered by a Vector Database
Our vector search engine uses deep learning embedding models to first analyze the visual features of each input, such as color, shape, and texture. This process, known as feature extraction, generates a corresponding vector representation for each piece of unstructured data.
The embedding models then index these vector representations and store them in our vector database (also called a vector store or a semantic search engine).
When a user performs a search, their query is also converted into a vector representation. The vector DB then searches for the vector representations that are most similar to the query vector representation. The results are then displayed to the user.
By using our vector search as a service, you can get more relevant search results, faster search times, and scalable performance.
Types of Vector Search
Rank — Leverage your model’s understanding of concepts to rank search results by relevance. This can be based on how confident the model is that a specific concept (like “cat” or “tree”) is present in an input, or how similar one input is to another. This is especially powerful for semantic and visual similarity use cases.
Filter — Use filters to narrow down search results and focus only on the data that matters to you. For example, you might want to see only inputs that a specific collaborator has labeled with the word “dog,” or filter by metadata, such as limiting your results to inputs captured in a certain geographic region.
'AND' — Create more precise queries by combining multiple conditions. This makes your searches even more targeted. For instance, you might look for all inputs within a specific geographic area that also contain the concept of a “weapon,” or find annotations assigned to a particular user like “Joe.”
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