Vector search leverages machine learning (ML) to capture the meaning and context of unstructured data, including text and images, transforming it into a numeric representation. Frequently used for semantic search, vector search finds similar data using approximate nearing neighbor (ANN) algorithms. Compared to traditional keyword search, vector search yields more relevant results and executes faster.
How often have you looked for something, but you're not sure what it's called? You may know what it does or have a description. But without the keywords, you're left searching.
Vector search overcomes this limitation, allowing you to search by what you mean. It can quickly deliver answers to queries based on context. That's because vector embeddings capture synonyms and associations — essentially the meaning behind your search. You can enhance the search experience by combining vector search with filtering and aggregations to optimize relevance by implementing a hybrid search and combining it with traditional scoring.
Vector search engines — known as vector databases, semantic, or cosine search — find the nearest neighbors to a given (vectorized) query.
Where traditional search relies on mentions of keywords, lexical similarity, and the frequency of word occurrences, vector search engines use distances in the embedding space to represent similarity. Finding related data becomes searching for nearest neighbors of your query.
How to get started
You don't have to move mountains to implement vector search and apply NLP models. Elastic Search Platform covers all the bases for you: import pretrained models from public repositories, apply them on the fly while ingesting your data, and use model predictions to improve your search experience and automated system monitoring.