Embeddings are essential for vector search, a method of searching for similar items in large datasets using vector representations. In vector search, both the query and the items in the dataset are converted into embeddings, which are then compared using distance metrics like cosine similarity or Euclidean distance. The idea is that items with similar embeddings are likely to be relevant to the query, even if they don't share the exact same words or features.
For example, in a product search system, both the user's query and the product descriptions are converted into embeddings. The system then searches for products whose embeddings are closest to the query embedding, ensuring that the results are relevant based on the semantic meaning rather than exact keyword matching. Vector search is used extensively in applications like image search, document retrieval, and recommendation systems, where traditional keyword-based methods are less effective.
Embeddings make vector search highly efficient, as they enable fast and accurate similarity comparisons. By using embeddings, search systems can handle complex and high-dimensional data, offering users more relevant and meaningful search results. This approach is commonly used in AI-powered search engines, content-based filtering, and knowledge base systems.